SlideShare une entreprise Scribd logo
1  sur  50
Télécharger pour lire hors ligne
12+ Image Quality
Attributes that Impact
Computer Vision
Max Henkart
Imaging Optics & Camera Engineer, Founder
Commonlands LLC
• Intro
• Types of image quality
• Review image quality metrics and the impact on CV
• Summary
Overview
2
© 2022 Commonlands LLC
Illumination & Object
Characteristics
Lens and Alignment
Sensor & Image
Capture
Electronics
Digital Image
Processing
Computer Vision
Analytics,
Localization,
Prediction, etc.
Image quality and computer vision require
experts from multiple industries
3
Optical + Mech Engineering Electrical + Firmware + Image Quality Engineering Software Engineering
© 2022 Commonlands LLC
Image quality in the field is fundamental to
embedded computer vision performance
4
Su, et. al. “One Pixel Attack for Fooling Deep
Neural Networks”
© 2022 Commonlands LLC
Image quality in the field is fundamental to
embedded computer vision performance
5
Su, et. al. “One Pixel Attack for Fooling Deep
Neural Networks”
“Nonetheless, we show some examples of situations
where nearly imperceptible image modifications can
result in dramatic perception changes.
Even in applications without malicious people trying
to trick your system, the natural world may be
adversarial enough.”
Pezzementi, et. al “Putting Image Manipulations in Context: Robustness
Testing for Safe Perception”
© 2022 Commonlands LLC
Objective
• Independent of preference
• Measurements with image quality test
charts
Subjective
• Dependent on preference
• Measurements through focus groups and
other user feedback methods
Two types of purpose-based image quality metrics
are required to fully characterize an image
6
“Too Grainy”
“Too Blurry”
“Not Colorful”
“SNR = 70db”
“eSFR=20%@100lp/mm”
“∆E=2”
?
?
© 2022 Commonlands LLC
Computational-Based
• Inputs are related to human visual
cognition such as structure and color.
• Includes content-aware image processing.
• Directly related to image saliency in
embedded computer vision.
“Subjective Image Quality”
Engineering-Based
• Inputs are related to test charts, camera
hardware, and image processing.
• Independent of scene content and human
visual quality assessment.
“Objective Image Quality”
Objective and subjective were coined before
modern embedded vision systems
7
Reference: Zwanenberg, et. al., 2020, “Edge Detection Techniques for
Quantifying Spatial Imaging System Performance and Image Quality”
© 2022 Commonlands LLC
1. Exposure + Motion Blur
2. Dynamic Range + Artifacts
3. Noise
4. Color
5. Shading
6. Resolution
7. Distortion
8. Texture Blur
9. Stray Light
10.Fringing + Blooming
11.Blemish
12.Dead Pixels
8
Let’s investigate how objective image quality
metrics could impact your computer vision
© 2022 Commonlands LLC
Related KPI
• Exposure Value (EV)
Reference: ISO12232
Exposure index combines the scene, lens, exposure
length, gain, and image processing
9
Blasinski, etc. al. “Optimizing Image Acquisition Systems for Autonomous Driving”
© 2022 Commonlands LLC
Motion blur is created by long exposure and/or
imperfect high-dynamic range recombination
10
Pei, et. al. “Effects of Image Degradations to CNN-based Image Classification”
Angular shift
Blur Length
© 2022 Commonlands LLC
1. Exposure + Motion Blur
2. Dynamic Range + Artifacts
3. Noise
4. Color
5. Shading
6. Resolution
7. Distortion
8. Texture Blur
9. Stray Light
10.Fringing + Blooming
11.Blemish
12.Dead Pixels
Let’s investigate how objective image quality
metrics could impact your computer vision
11
© 2022 Commonlands LLC
Related KPI
• Dynamic Range (dB)
Reference: ISO21550
Camera dynamic range is the ratio of maximum to
minimum signal, before saturation occurs
12
Hasinoff, et. al. “Burst photography for high dynamic range and low-light
imaging on mobile cameras”
© 2022 Commonlands LLC
Even modern HDR techniques can introduce other
image quality artifacts
13
Robidoux, et. al. “End-to-end High Dynamic Range Camera Pipeline Optimization”
© 2022 Commonlands LLC
1. Exposure + Motion Blur
2. Dynamic Range + Artifacts
3. Noise
4. Color
5. Shading
6. Resolution
7. Distortion
8. Texture Blur
9. Stray Light
10.Fringing + Blooming
11.Blemish
12.Dead Pixels
14
Let’s investigate how objective image quality
metrics could impact your computer vision
© 2022 Commonlands LLC
Related KPIs
• Signal-to-Noise Ratios (multiple types)
• Noise Power Spectrum (Frequency)
Reference: ISO15739
Noise is split into single pixel (temporal /random)
and multi-pixel (spatial/pattern)
15
Dodge, et. al. “Understanding How Image Quality Affects Deep Neural Networks”
© 2022 Commonlands LLC
1. Exposure + Motion Blur
2. Dynamic Range + Artifacts
3. Noise
4. Color
5. Shading
6. Resolution
7. Distortion
8. Texture Blur
9. Stray Light
10.Fringing + Blooming
11.Blemish
12.Dead Pixels
16
Let’s investigate how objective image quality
metrics could impact your computer vision
© 2022 Commonlands LLC
Related KPI
• ∆E (Color Accuracy)
Reference: ISO17321
Color can impact edge contrast when using multiple
channels and auto-white balance
17
Afifi+Brown. “ What Else Can Fool Deep Learning? Addressing Color Constancy
Errors on Deep Neural Network Performance
© 2022 Commonlands LLC
Related KPI
• Contrast Detection Probability
Reference: ISO12232
Dynamic range and color are closely related to tone
mapping which impacts perception at every scale
18
Yeganeh, et. al. “Objective Quality Assessment of Tone-Mapped Images”
© 2022 Commonlands LLC
1. Exposure + Motion Blur
2. Dynamic Range + Artifacts
3. Noise
4. Color
5. Shading
6. Resolution
7. Distortion
8. Texture Blur
9. Stray Light
10.Fringing + Blooming
11.Blemish
12.Dead Pixels
19
Let’s investigate how objective image quality
metrics could impact your computer vision
© 2022 Commonlands LLC
Related KPIs
• Luminance Non-uniformity
• Lightness non-uniformity
Reference: ISO17957
Luminance shading changes accuracy from center to
edge of the field of view
20
Marc Levoy, ICCV 2015, “Extreme imaging using cell phones”
© 2022 Commonlands LLC
Shading can include a radial color shift, impacting
CV in different parts of the field
21
© 2022 Commonlands LLC
1. Exposure + Motion Blur
2. Dynamic Range + Artifacts
3. Noise
4. Color
5. Shading
6. Resolution
7. Distortion
8. Texture Blur
9. Stray Light
10.Fringing + Blooming
11.Blemish
12.Dead Pixels
22
Let’s investigate how objective image quality
metrics could impact your computer vision
© 2022 Commonlands LLC
Resolution comes in many flavors
23
# Pixels
Resolution
Pixel
Size
Scene Field of View,
Memory/Speed, On-
board Functionality
“Pixel Resolution”
Image Quality
Across an Edge in
Object Space
“Sharpness”
Optical
Resolution
(MTF)
System
Resolution
(SFR)
Angular
Resolution
(deg/px)
© 2022 Commonlands LLC
All types of resolution jointly impact the
performance of embedded vision systems
24
Xu, et. al. “3D Human Shape and Pose from a Single Low-Resolution Image with
Self-Supervised Learning”
SOTA=State of the art as of Q1’20: “SPIN”
© 2022 Commonlands LLC
Related KPIs
• Edge SFR (eSFR), sinusoidal SFR (sSFR)
• Lens MTF
• Contrast Sensitivity Function (CSF)
• Contrast Detection Probability (CDP)
Reference: ISO12233, IEEE P2020
The Spatial Frequency Response (SFR) and contrast
sensitivity are a corollary to “blur”
25
Vasiljevic, et. Al. “Examining the Impact of Blur on Recognition by
Convolutional Networks”
© 2022 Commonlands LLC
SFR can also characterize 10+ artifacts resulting from
image compression quality
26
Zheng, et. Al “ Improving the Robustness of Deep Neural Networks via Stability
Training”
Example of Artifacts
• Aliasing
• Ringing
• Blocking
Reference: E. Allen, Thesis:
“Image Quality Evaluation in
Lossy Compressed Imaged”
© 2022 Commonlands LLC
Related KPIs
• # Pixels per °
• # Pixels per unit distance across an object
Angular resolution defines the # of pixels each
object has for feature extraction
27
Dollár, et. al. “Pedestrian Detection: An Evaluation of the State of the Art”
© 2022 Commonlands LLC
1. Exposure + Motion Blur
2. Dynamic Range + Artifacts
3. Noise
4. Color
5. Shading
6. Resolution
7. Distortion
8. Texture Blur
9. Stray Light
10.Fringing + Blooming
11.Blemish
12.Dead Pixels
28
Let’s investigate how objective image quality
metrics could impact your computer vision
© 2022 Commonlands LLC
Related KPI
• % Distortion (Optical, TV, SMIA TV)
Reference: ISO17850
Distortion is the change in angular resolution
(magnification) across field
29
Pei, et. al. “Effects of Image Degradations to CNN-based Image Classification”
Negative FΘ Rectilinear
Angular resolution
and perspective
distortion at
45° Off Axis
© 2022 Commonlands LLC
Related KPI
• % Distortion (Optical, TV, SMIA TV)
Reference: ISO17850
Distortion is the change in angular resolution
(magnification) across field
30
Negative FΘ Rectilinear
Angular resolution and perspective distortion at 45° Off Axis
© 2022 Commonlands LLC
Related KPI
• % Distortion (Optical, TV, SMIA TV)
Reference: ISO17850
Distortion is the change in angular resolution
(magnification) across field
31
Li, et.al. 2020, “ULSD: Unified Line Segment Detection across Pinhole, Fisheye,
and Spherical Cameras”
Rectilinear
Spherical/Fisheye
© 2022 Commonlands LLC
1. Exposure + Motion Blur
2. Dynamic Range + Artifacts
3. Noise
4. Color
5. Shading
6. Resolution
7. Distortion
8. Texture Blur
9. Stray Light
10.Fringing + Blooming
11.Blemish
12.Dead Pixels
32
Let’s investigate how objective image quality
metrics could impact your computer vision
© 2022 Commonlands LLC
Related KPI
• Texture SFR
Reference: ISO19567
Texture SFR (and loss) results from noise reduction
algorithms that filter high frequencies
33
Chen, et. Al. “Texture Construction Edge Detection Algorithm”
© 2022 Commonlands LLC
1. Exposure + Motion Blur
2. Dynamic Range + Artifacts
3. Noise
4. Color
5. Shading
6. Resolution
7. Distortion
8. Texture Blur
9. Stray Light
10.Fringing + Blooming
11.Blemish
12.Dead Pixels
34
Let’s investigate how objective image quality
metrics could impact your computer vision
© 2022 Commonlands LLC
Related KPIs
• Glare spread function
• Contrast detection probability
Reference: ISO18844
Stray light from lenses create regions of low contrast
and low detection probability
35
IEEE P2020 Whitepaper
© 2022 Commonlands LLC
1. Exposure + Motion Blur
2. Dynamic Range + Artifacts
3. Noise
4. Color
5. Shading
6. Resolution
7. Distortion
8. Texture Blur
9. Stray Light
10.Fringing + Blooming
11.Blemish
12.Dead Pixels
36
Let’s investigate how objective image quality
metrics could impact your computer vision
© 2022 Commonlands LLC
Chromatic aberration from lenses can result in
artifacts around high contrast edges
37
Related KPIs
• Chromatic Displacement
Reference: ISO19084
Kang, “Automatic Removal of Chromatic Aberration from a Single Image”
© 2022 Commonlands LLC
There are many types of color fringing, some result
from blooming/cross-talk in sensor and tuning
38
Original images
© 2022 Commonlands LLC
1. Exposure + Motion Blur
2. Dynamic Range + Artifacts
3. Noise
4. Color
5. Shading
6. Resolution
7. Distortion
8. Texture Blur
9. Stray Light
10.Fringing + Blooming
11.Blemish
12.Dead Pixels
39
Let’s investigate how objective image quality
metrics could impact your computer vision
© 2022 Commonlands LLC
Image blemishes occur when dust /dirt / moisture are
on the sensor or in / on the lens
40
Related KPIs
• # and size of blemishes
Reference:
https://www.imatest.com/docs/blemish/
Michal Uricar “Let’s Get Dirty: GAN Based Data Augmentation
for Camera Lens Soiling Detection in Autonomous Driving.”
© 2022 Commonlands LLC
1. Exposure + Motion Blur
2. Dynamic Range + Artifacts
3. Noise
4. Color
5. Shading
6. Resolution
7. Distortion
8. Texture Blur
9. Stray Light
10.Fringing + Blooming
11.Blemish
12.Dead Pixels
41
Let’s investigate how objective image quality
metrics could impact your computer vision
© 2022 Commonlands LLC
Dead pixels brings us full circle, as a real-world
adversarial attack if no correction is performed
42
Su, et. al. “One Pixel Attack for Fooling Deep
Neural Networks”
© 2022 Commonlands LLC
Dead pixels brings us full circle, as a real-world
adversarial attack if no correction is performed
43
Su, et. al. “One Pixel Attack for Fooling Deep
Neural Networks”
“Nonetheless, we show some examples of situations
where nearly imperceptible image modifications can
result in dramatic perception changes.
Even in applications without malicious people trying to
trick your system, the natural world,
[your camera hardware, and your image
processing pipeline] may be adversarial enough.”
Pezzementi, et. al “Putting Image Manipulations in Context: Robustness Testing
for Safe Perception”
© 2022 Commonlands LLC
Where do I learn more about how image quality
influences computer vision?
Visit our website for the slides, the papers cited in this talk, plus related resources:
-> Contact me at max.henkart@commonlands.com if working on a camera HW project or looking for lenses
Resources through the Alliance
• Felix Heide, Embedded Vision Summit 2018:
• https://www.edge-ai-vision.com/2018/08/understanding-real-world-imaging-challenges-for-adas-
and-autonomous-vision-systems-ieee-p2020-a-presentation-from-algolux/
Notable examples included on our reference page:
IEEE P2020 Automotive Image Quality (Computer Vision) White Paper
Electronic Imaging (January 2023) and Imaging.org
University of Westminster & Nvidia’s Collaboration on Image Quality Metrics
44
https://commonlands.com/summit2022
© 2022 Commonlands LLC
Backup Slides
Contrast loss impacts both human visual perception
and CNN-based methods.
46
Geirhos, et. al. “Comparing deep neural networks against humans: object
recognition when the signal gets weaker”
© 2022 Commonlands LLC
Pezzementi, et. al. “Putting Image
Manipulations in Context: Robustness
Testing for Safe Perception”
• ADR= Average Detection Rate
Quantitative Degradation of Agricultural Outdoor
Detection Based on Image Quality
47
© 2022 Commonlands LLC
Karahan, et. al. “How Image
Degradations Affect Deep CNN-
based Face Recognition?”
Quantitative Degradation of Facial Recognition
Networks Based on Image Quality
48
© 2022 Commonlands LLC
Roy, et. Al. “Effects of
Degradations on Deep Neural
Network Architectures”
Quantitative Degradation of A Variety of Images and
Datasets
49
© 2022 Commonlands LLC
Example of CV impact
• Must select line detection and/or
dewarping methods carefully as camera
to camera variations can throw off
Hough transfroms and RANSAC
• Fewer pixels for detection tasks at
edges of negative FΘ lenses
KPI
• % Distortion (Optical, TV, SMIA TV)
Reference: ISO17850
6) Distortion is the change in angular resolution
(magnification) across field
50
Negative F-Θ
Rectilinear
Two 90° HFoV lenses on IMX477.
Left 10% of image is shown.
Original Images
© 2022 Commonlands LLC

Contenu connexe

Similaire à “12+ Image Quality Attributes that Impact Computer Vision,” a Presentation from Commonlands

A Review On Single Image Depth Prediction with Wavelet Decomposition
A Review On Single Image Depth Prediction with Wavelet DecompositionA Review On Single Image Depth Prediction with Wavelet Decomposition
A Review On Single Image Depth Prediction with Wavelet DecompositionIRJET Journal
 
Pacs cg ani_ve_ip
Pacs  cg ani_ve_ipPacs  cg ani_ve_ip
Pacs cg ani_ve_ipSuma Dawn
 
Introduction talk to Computer Vision
Introduction talk to Computer Vision Introduction talk to Computer Vision
Introduction talk to Computer Vision Chen Sagiv
 
Face Recognition & Detection Using Image Processing
Face Recognition & Detection Using Image ProcessingFace Recognition & Detection Using Image Processing
Face Recognition & Detection Using Image Processingpaperpublications3
 
Camera Calibration Market
Camera Calibration MarketCamera Calibration Market
Camera Calibration MarketGuy Martin
 
Object Based Image Analysis
Object Based Image Analysis Object Based Image Analysis
Object Based Image Analysis Kabir Uddin
 
Introduction to 3D Computer Vision and Differentiable Rendering
Introduction to 3D Computer Vision and Differentiable RenderingIntroduction to 3D Computer Vision and Differentiable Rendering
Introduction to 3D Computer Vision and Differentiable RenderingPreferred Networks
 
screenlessppt-141002031247-phpapp02.pptx
screenlessppt-141002031247-phpapp02.pptxscreenlessppt-141002031247-phpapp02.pptx
screenlessppt-141002031247-phpapp02.pptx22X047SHRISANJAYM
 
Face Recognition & Detection Using Image Processing
Face Recognition & Detection Using Image ProcessingFace Recognition & Detection Using Image Processing
Face Recognition & Detection Using Image Processingpaperpublications3
 
Quality assessment for online iris
Quality assessment for online irisQuality assessment for online iris
Quality assessment for online iriscsandit
 
Deep learning for understanding faces
Deep learning for understanding facesDeep learning for understanding faces
Deep learning for understanding facessieubebu
 
“Applying the Right Deep Learning Model with the Right Data for Your Applicat...
“Applying the Right Deep Learning Model with the Right Data for Your Applicat...“Applying the Right Deep Learning Model with the Right Data for Your Applicat...
“Applying the Right Deep Learning Model with the Right Data for Your Applicat...Edge AI and Vision Alliance
 
Image super resolution using Generative Adversarial Network.
Image super resolution using Generative Adversarial Network.Image super resolution using Generative Adversarial Network.
Image super resolution using Generative Adversarial Network.IRJET Journal
 
Computer Vision Performance and Image Quality Metrics : A Reciprocal Relation
Computer Vision Performance and Image Quality Metrics : A Reciprocal Relation Computer Vision Performance and Image Quality Metrics : A Reciprocal Relation
Computer Vision Performance and Image Quality Metrics : A Reciprocal Relation cscpconf
 
COMPUTER VISION PERFORMANCE AND IMAGE QUALITY METRICS: A RECIPROCAL RELATION
COMPUTER VISION PERFORMANCE AND IMAGE QUALITY METRICS: A RECIPROCAL RELATION COMPUTER VISION PERFORMANCE AND IMAGE QUALITY METRICS: A RECIPROCAL RELATION
COMPUTER VISION PERFORMANCE AND IMAGE QUALITY METRICS: A RECIPROCAL RELATION csandit
 
“Selecting the Right Camera for Your Embedded Computer Vision Project,” a Pre...
“Selecting the Right Camera for Your Embedded Computer Vision Project,” a Pre...“Selecting the Right Camera for Your Embedded Computer Vision Project,” a Pre...
“Selecting the Right Camera for Your Embedded Computer Vision Project,” a Pre...Edge AI and Vision Alliance
 
Rendering Algorithms.pptx
Rendering Algorithms.pptxRendering Algorithms.pptx
Rendering Algorithms.pptxSherinRappai
 
Virtual retinal display ppt
Virtual retinal display pptVirtual retinal display ppt
Virtual retinal display pptHina Saxena
 
“Next-generation Computer Vision Methods for Automated Navigation of Unmanned...
“Next-generation Computer Vision Methods for Automated Navigation of Unmanned...“Next-generation Computer Vision Methods for Automated Navigation of Unmanned...
“Next-generation Computer Vision Methods for Automated Navigation of Unmanned...Edge AI and Vision Alliance
 

Similaire à “12+ Image Quality Attributes that Impact Computer Vision,” a Presentation from Commonlands (20)

A Review On Single Image Depth Prediction with Wavelet Decomposition
A Review On Single Image Depth Prediction with Wavelet DecompositionA Review On Single Image Depth Prediction with Wavelet Decomposition
A Review On Single Image Depth Prediction with Wavelet Decomposition
 
Pacs cg ani_ve_ip
Pacs  cg ani_ve_ipPacs  cg ani_ve_ip
Pacs cg ani_ve_ip
 
Introduction talk to Computer Vision
Introduction talk to Computer Vision Introduction talk to Computer Vision
Introduction talk to Computer Vision
 
Face Recognition & Detection Using Image Processing
Face Recognition & Detection Using Image ProcessingFace Recognition & Detection Using Image Processing
Face Recognition & Detection Using Image Processing
 
Camera Calibration Market
Camera Calibration MarketCamera Calibration Market
Camera Calibration Market
 
Object Based Image Analysis
Object Based Image Analysis Object Based Image Analysis
Object Based Image Analysis
 
Introduction to 3D Computer Vision and Differentiable Rendering
Introduction to 3D Computer Vision and Differentiable RenderingIntroduction to 3D Computer Vision and Differentiable Rendering
Introduction to 3D Computer Vision and Differentiable Rendering
 
screenlessppt-141002031247-phpapp02.pptx
screenlessppt-141002031247-phpapp02.pptxscreenlessppt-141002031247-phpapp02.pptx
screenlessppt-141002031247-phpapp02.pptx
 
Face Recognition & Detection Using Image Processing
Face Recognition & Detection Using Image ProcessingFace Recognition & Detection Using Image Processing
Face Recognition & Detection Using Image Processing
 
Quality assessment for online iris
Quality assessment for online irisQuality assessment for online iris
Quality assessment for online iris
 
Deep learning for understanding faces
Deep learning for understanding facesDeep learning for understanding faces
Deep learning for understanding faces
 
AR/SLAM for end-users
AR/SLAM for end-usersAR/SLAM for end-users
AR/SLAM for end-users
 
“Applying the Right Deep Learning Model with the Right Data for Your Applicat...
“Applying the Right Deep Learning Model with the Right Data for Your Applicat...“Applying the Right Deep Learning Model with the Right Data for Your Applicat...
“Applying the Right Deep Learning Model with the Right Data for Your Applicat...
 
Image super resolution using Generative Adversarial Network.
Image super resolution using Generative Adversarial Network.Image super resolution using Generative Adversarial Network.
Image super resolution using Generative Adversarial Network.
 
Computer Vision Performance and Image Quality Metrics : A Reciprocal Relation
Computer Vision Performance and Image Quality Metrics : A Reciprocal Relation Computer Vision Performance and Image Quality Metrics : A Reciprocal Relation
Computer Vision Performance and Image Quality Metrics : A Reciprocal Relation
 
COMPUTER VISION PERFORMANCE AND IMAGE QUALITY METRICS: A RECIPROCAL RELATION
COMPUTER VISION PERFORMANCE AND IMAGE QUALITY METRICS: A RECIPROCAL RELATION COMPUTER VISION PERFORMANCE AND IMAGE QUALITY METRICS: A RECIPROCAL RELATION
COMPUTER VISION PERFORMANCE AND IMAGE QUALITY METRICS: A RECIPROCAL RELATION
 
“Selecting the Right Camera for Your Embedded Computer Vision Project,” a Pre...
“Selecting the Right Camera for Your Embedded Computer Vision Project,” a Pre...“Selecting the Right Camera for Your Embedded Computer Vision Project,” a Pre...
“Selecting the Right Camera for Your Embedded Computer Vision Project,” a Pre...
 
Rendering Algorithms.pptx
Rendering Algorithms.pptxRendering Algorithms.pptx
Rendering Algorithms.pptx
 
Virtual retinal display ppt
Virtual retinal display pptVirtual retinal display ppt
Virtual retinal display ppt
 
“Next-generation Computer Vision Methods for Automated Navigation of Unmanned...
“Next-generation Computer Vision Methods for Automated Navigation of Unmanned...“Next-generation Computer Vision Methods for Automated Navigation of Unmanned...
“Next-generation Computer Vision Methods for Automated Navigation of Unmanned...
 

Plus de Edge AI and Vision Alliance

“Learning Compact DNN Models for Embedded Vision,” a Presentation from the Un...
“Learning Compact DNN Models for Embedded Vision,” a Presentation from the Un...“Learning Compact DNN Models for Embedded Vision,” a Presentation from the Un...
“Learning Compact DNN Models for Embedded Vision,” a Presentation from the Un...Edge AI and Vision Alliance
 
“Introduction to Computer Vision with CNNs,” a Presentation from Mohammad Hag...
“Introduction to Computer Vision with CNNs,” a Presentation from Mohammad Hag...“Introduction to Computer Vision with CNNs,” a Presentation from Mohammad Hag...
“Introduction to Computer Vision with CNNs,” a Presentation from Mohammad Hag...Edge AI and Vision Alliance
 
“Selecting Tools for Developing, Monitoring and Maintaining ML Models,” a Pre...
“Selecting Tools for Developing, Monitoring and Maintaining ML Models,” a Pre...“Selecting Tools for Developing, Monitoring and Maintaining ML Models,” a Pre...
“Selecting Tools for Developing, Monitoring and Maintaining ML Models,” a Pre...Edge AI and Vision Alliance
 
“Building Accelerated GStreamer Applications for Video and Audio AI,” a Prese...
“Building Accelerated GStreamer Applications for Video and Audio AI,” a Prese...“Building Accelerated GStreamer Applications for Video and Audio AI,” a Prese...
“Building Accelerated GStreamer Applications for Video and Audio AI,” a Prese...Edge AI and Vision Alliance
 
“Understanding, Selecting and Optimizing Object Detectors for Edge Applicatio...
“Understanding, Selecting and Optimizing Object Detectors for Edge Applicatio...“Understanding, Selecting and Optimizing Object Detectors for Edge Applicatio...
“Understanding, Selecting and Optimizing Object Detectors for Edge Applicatio...Edge AI and Vision Alliance
 
“Introduction to Modern LiDAR for Machine Perception,” a Presentation from th...
“Introduction to Modern LiDAR for Machine Perception,” a Presentation from th...“Introduction to Modern LiDAR for Machine Perception,” a Presentation from th...
“Introduction to Modern LiDAR for Machine Perception,” a Presentation from th...Edge AI and Vision Alliance
 
“Vision-language Representations for Robotics,” a Presentation from the Unive...
“Vision-language Representations for Robotics,” a Presentation from the Unive...“Vision-language Representations for Robotics,” a Presentation from the Unive...
“Vision-language Representations for Robotics,” a Presentation from the Unive...Edge AI and Vision Alliance
 
“ADAS and AV Sensors: What’s Winning and Why?,” a Presentation from TechInsights
“ADAS and AV Sensors: What’s Winning and Why?,” a Presentation from TechInsights“ADAS and AV Sensors: What’s Winning and Why?,” a Presentation from TechInsights
“ADAS and AV Sensors: What’s Winning and Why?,” a Presentation from TechInsightsEdge AI and Vision Alliance
 
“Computer Vision in Sports: Scalable Solutions for Downmarkets,” a Presentati...
“Computer Vision in Sports: Scalable Solutions for Downmarkets,” a Presentati...“Computer Vision in Sports: Scalable Solutions for Downmarkets,” a Presentati...
“Computer Vision in Sports: Scalable Solutions for Downmarkets,” a Presentati...Edge AI and Vision Alliance
 
“Detecting Data Drift in Image Classification Neural Networks,” a Presentatio...
“Detecting Data Drift in Image Classification Neural Networks,” a Presentatio...“Detecting Data Drift in Image Classification Neural Networks,” a Presentatio...
“Detecting Data Drift in Image Classification Neural Networks,” a Presentatio...Edge AI and Vision Alliance
 
“Deep Neural Network Training: Diagnosing Problems and Implementing Solutions...
“Deep Neural Network Training: Diagnosing Problems and Implementing Solutions...“Deep Neural Network Training: Diagnosing Problems and Implementing Solutions...
“Deep Neural Network Training: Diagnosing Problems and Implementing Solutions...Edge AI and Vision Alliance
 
“AI Start-ups: The Perils of Fishing for Whales (War Stories from the Entrepr...
“AI Start-ups: The Perils of Fishing for Whales (War Stories from the Entrepr...“AI Start-ups: The Perils of Fishing for Whales (War Stories from the Entrepr...
“AI Start-ups: The Perils of Fishing for Whales (War Stories from the Entrepr...Edge AI and Vision Alliance
 
“A Computer Vision System for Autonomous Satellite Maneuvering,” a Presentati...
“A Computer Vision System for Autonomous Satellite Maneuvering,” a Presentati...“A Computer Vision System for Autonomous Satellite Maneuvering,” a Presentati...
“A Computer Vision System for Autonomous Satellite Maneuvering,” a Presentati...Edge AI and Vision Alliance
 
“Bias in Computer Vision—It’s Bigger Than Facial Recognition!,” a Presentatio...
“Bias in Computer Vision—It’s Bigger Than Facial Recognition!,” a Presentatio...“Bias in Computer Vision—It’s Bigger Than Facial Recognition!,” a Presentatio...
“Bias in Computer Vision—It’s Bigger Than Facial Recognition!,” a Presentatio...Edge AI and Vision Alliance
 
“Sensor Fusion Techniques for Accurate Perception of Objects in the Environme...
“Sensor Fusion Techniques for Accurate Perception of Objects in the Environme...“Sensor Fusion Techniques for Accurate Perception of Objects in the Environme...
“Sensor Fusion Techniques for Accurate Perception of Objects in the Environme...Edge AI and Vision Alliance
 
“Updating the Edge ML Development Process,” a Presentation from Samsara
“Updating the Edge ML Development Process,” a Presentation from Samsara“Updating the Edge ML Development Process,” a Presentation from Samsara
“Updating the Edge ML Development Process,” a Presentation from SamsaraEdge AI and Vision Alliance
 
“Combating Bias in Production Computer Vision Systems,” a Presentation from R...
“Combating Bias in Production Computer Vision Systems,” a Presentation from R...“Combating Bias in Production Computer Vision Systems,” a Presentation from R...
“Combating Bias in Production Computer Vision Systems,” a Presentation from R...Edge AI and Vision Alliance
 
“Developing an Embedded Vision AI-powered Fitness System,” a Presentation fro...
“Developing an Embedded Vision AI-powered Fitness System,” a Presentation fro...“Developing an Embedded Vision AI-powered Fitness System,” a Presentation fro...
“Developing an Embedded Vision AI-powered Fitness System,” a Presentation fro...Edge AI and Vision Alliance
 
“Navigating the Evolving Venture Capital Landscape for Edge AI Start-ups,” a ...
“Navigating the Evolving Venture Capital Landscape for Edge AI Start-ups,” a ...“Navigating the Evolving Venture Capital Landscape for Edge AI Start-ups,” a ...
“Navigating the Evolving Venture Capital Landscape for Edge AI Start-ups,” a ...Edge AI and Vision Alliance
 
“Advanced Presence Sensing: What It Means for the Smart Home,” a Presentation...
“Advanced Presence Sensing: What It Means for the Smart Home,” a Presentation...“Advanced Presence Sensing: What It Means for the Smart Home,” a Presentation...
“Advanced Presence Sensing: What It Means for the Smart Home,” a Presentation...Edge AI and Vision Alliance
 

Plus de Edge AI and Vision Alliance (20)

“Learning Compact DNN Models for Embedded Vision,” a Presentation from the Un...
“Learning Compact DNN Models for Embedded Vision,” a Presentation from the Un...“Learning Compact DNN Models for Embedded Vision,” a Presentation from the Un...
“Learning Compact DNN Models for Embedded Vision,” a Presentation from the Un...
 
“Introduction to Computer Vision with CNNs,” a Presentation from Mohammad Hag...
“Introduction to Computer Vision with CNNs,” a Presentation from Mohammad Hag...“Introduction to Computer Vision with CNNs,” a Presentation from Mohammad Hag...
“Introduction to Computer Vision with CNNs,” a Presentation from Mohammad Hag...
 
“Selecting Tools for Developing, Monitoring and Maintaining ML Models,” a Pre...
“Selecting Tools for Developing, Monitoring and Maintaining ML Models,” a Pre...“Selecting Tools for Developing, Monitoring and Maintaining ML Models,” a Pre...
“Selecting Tools for Developing, Monitoring and Maintaining ML Models,” a Pre...
 
“Building Accelerated GStreamer Applications for Video and Audio AI,” a Prese...
“Building Accelerated GStreamer Applications for Video and Audio AI,” a Prese...“Building Accelerated GStreamer Applications for Video and Audio AI,” a Prese...
“Building Accelerated GStreamer Applications for Video and Audio AI,” a Prese...
 
“Understanding, Selecting and Optimizing Object Detectors for Edge Applicatio...
“Understanding, Selecting and Optimizing Object Detectors for Edge Applicatio...“Understanding, Selecting and Optimizing Object Detectors for Edge Applicatio...
“Understanding, Selecting and Optimizing Object Detectors for Edge Applicatio...
 
“Introduction to Modern LiDAR for Machine Perception,” a Presentation from th...
“Introduction to Modern LiDAR for Machine Perception,” a Presentation from th...“Introduction to Modern LiDAR for Machine Perception,” a Presentation from th...
“Introduction to Modern LiDAR for Machine Perception,” a Presentation from th...
 
“Vision-language Representations for Robotics,” a Presentation from the Unive...
“Vision-language Representations for Robotics,” a Presentation from the Unive...“Vision-language Representations for Robotics,” a Presentation from the Unive...
“Vision-language Representations for Robotics,” a Presentation from the Unive...
 
“ADAS and AV Sensors: What’s Winning and Why?,” a Presentation from TechInsights
“ADAS and AV Sensors: What’s Winning and Why?,” a Presentation from TechInsights“ADAS and AV Sensors: What’s Winning and Why?,” a Presentation from TechInsights
“ADAS and AV Sensors: What’s Winning and Why?,” a Presentation from TechInsights
 
“Computer Vision in Sports: Scalable Solutions for Downmarkets,” a Presentati...
“Computer Vision in Sports: Scalable Solutions for Downmarkets,” a Presentati...“Computer Vision in Sports: Scalable Solutions for Downmarkets,” a Presentati...
“Computer Vision in Sports: Scalable Solutions for Downmarkets,” a Presentati...
 
“Detecting Data Drift in Image Classification Neural Networks,” a Presentatio...
“Detecting Data Drift in Image Classification Neural Networks,” a Presentatio...“Detecting Data Drift in Image Classification Neural Networks,” a Presentatio...
“Detecting Data Drift in Image Classification Neural Networks,” a Presentatio...
 
“Deep Neural Network Training: Diagnosing Problems and Implementing Solutions...
“Deep Neural Network Training: Diagnosing Problems and Implementing Solutions...“Deep Neural Network Training: Diagnosing Problems and Implementing Solutions...
“Deep Neural Network Training: Diagnosing Problems and Implementing Solutions...
 
“AI Start-ups: The Perils of Fishing for Whales (War Stories from the Entrepr...
“AI Start-ups: The Perils of Fishing for Whales (War Stories from the Entrepr...“AI Start-ups: The Perils of Fishing for Whales (War Stories from the Entrepr...
“AI Start-ups: The Perils of Fishing for Whales (War Stories from the Entrepr...
 
“A Computer Vision System for Autonomous Satellite Maneuvering,” a Presentati...
“A Computer Vision System for Autonomous Satellite Maneuvering,” a Presentati...“A Computer Vision System for Autonomous Satellite Maneuvering,” a Presentati...
“A Computer Vision System for Autonomous Satellite Maneuvering,” a Presentati...
 
“Bias in Computer Vision—It’s Bigger Than Facial Recognition!,” a Presentatio...
“Bias in Computer Vision—It’s Bigger Than Facial Recognition!,” a Presentatio...“Bias in Computer Vision—It’s Bigger Than Facial Recognition!,” a Presentatio...
“Bias in Computer Vision—It’s Bigger Than Facial Recognition!,” a Presentatio...
 
“Sensor Fusion Techniques for Accurate Perception of Objects in the Environme...
“Sensor Fusion Techniques for Accurate Perception of Objects in the Environme...“Sensor Fusion Techniques for Accurate Perception of Objects in the Environme...
“Sensor Fusion Techniques for Accurate Perception of Objects in the Environme...
 
“Updating the Edge ML Development Process,” a Presentation from Samsara
“Updating the Edge ML Development Process,” a Presentation from Samsara“Updating the Edge ML Development Process,” a Presentation from Samsara
“Updating the Edge ML Development Process,” a Presentation from Samsara
 
“Combating Bias in Production Computer Vision Systems,” a Presentation from R...
“Combating Bias in Production Computer Vision Systems,” a Presentation from R...“Combating Bias in Production Computer Vision Systems,” a Presentation from R...
“Combating Bias in Production Computer Vision Systems,” a Presentation from R...
 
“Developing an Embedded Vision AI-powered Fitness System,” a Presentation fro...
“Developing an Embedded Vision AI-powered Fitness System,” a Presentation fro...“Developing an Embedded Vision AI-powered Fitness System,” a Presentation fro...
“Developing an Embedded Vision AI-powered Fitness System,” a Presentation fro...
 
“Navigating the Evolving Venture Capital Landscape for Edge AI Start-ups,” a ...
“Navigating the Evolving Venture Capital Landscape for Edge AI Start-ups,” a ...“Navigating the Evolving Venture Capital Landscape for Edge AI Start-ups,” a ...
“Navigating the Evolving Venture Capital Landscape for Edge AI Start-ups,” a ...
 
“Advanced Presence Sensing: What It Means for the Smart Home,” a Presentation...
“Advanced Presence Sensing: What It Means for the Smart Home,” a Presentation...“Advanced Presence Sensing: What It Means for the Smart Home,” a Presentation...
“Advanced Presence Sensing: What It Means for the Smart Home,” a Presentation...
 

Dernier

New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
Modern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
Modern Roaming for Notes and Nomad – Cheaper Faster Better StrongerModern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
Modern Roaming for Notes and Nomad – Cheaper Faster Better Strongerpanagenda
 
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS:  6 Ways to Automate Your Data IntegrationBridging Between CAD & GIS:  6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integrationmarketing932765
 
Generative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdfGenerative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdfIngrid Airi González
 
Emixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native developmentEmixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native developmentPim van der Noll
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc
 
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...Alkin Tezuysal
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxLoriGlavin3
 
Moving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdfMoving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdfLoriGlavin3
 
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality AssuranceInflectra
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.Curtis Poe
 
React Native vs Ionic - The Best Mobile App Framework
React Native vs Ionic - The Best Mobile App FrameworkReact Native vs Ionic - The Best Mobile App Framework
React Native vs Ionic - The Best Mobile App FrameworkPixlogix Infotech
 
2024 April Patch Tuesday
2024 April Patch Tuesday2024 April Patch Tuesday
2024 April Patch TuesdayIvanti
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxLoriGlavin3
 
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxLoriGlavin3
 
Glenn Lazarus- Why Your Observability Strategy Needs Security Observability
Glenn Lazarus- Why Your Observability Strategy Needs Security ObservabilityGlenn Lazarus- Why Your Observability Strategy Needs Security Observability
Glenn Lazarus- Why Your Observability Strategy Needs Security Observabilityitnewsafrica
 
Connecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfConnecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfNeo4j
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxLoriGlavin3
 
Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Hiroshi SHIBATA
 

Dernier (20)

New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
Modern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
Modern Roaming for Notes and Nomad – Cheaper Faster Better StrongerModern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
Modern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
 
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS:  6 Ways to Automate Your Data IntegrationBridging Between CAD & GIS:  6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
 
Generative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdfGenerative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdf
 
Emixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native developmentEmixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native development
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
 
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
 
Moving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdfMoving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdf
 
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.
 
React Native vs Ionic - The Best Mobile App Framework
React Native vs Ionic - The Best Mobile App FrameworkReact Native vs Ionic - The Best Mobile App Framework
React Native vs Ionic - The Best Mobile App Framework
 
2024 April Patch Tuesday
2024 April Patch Tuesday2024 April Patch Tuesday
2024 April Patch Tuesday
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
 
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptx
 
Glenn Lazarus- Why Your Observability Strategy Needs Security Observability
Glenn Lazarus- Why Your Observability Strategy Needs Security ObservabilityGlenn Lazarus- Why Your Observability Strategy Needs Security Observability
Glenn Lazarus- Why Your Observability Strategy Needs Security Observability
 
Connecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfConnecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdf
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
 
Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024
 

“12+ Image Quality Attributes that Impact Computer Vision,” a Presentation from Commonlands

  • 1. 12+ Image Quality Attributes that Impact Computer Vision Max Henkart Imaging Optics & Camera Engineer, Founder Commonlands LLC
  • 2. • Intro • Types of image quality • Review image quality metrics and the impact on CV • Summary Overview 2 © 2022 Commonlands LLC
  • 3. Illumination & Object Characteristics Lens and Alignment Sensor & Image Capture Electronics Digital Image Processing Computer Vision Analytics, Localization, Prediction, etc. Image quality and computer vision require experts from multiple industries 3 Optical + Mech Engineering Electrical + Firmware + Image Quality Engineering Software Engineering © 2022 Commonlands LLC
  • 4. Image quality in the field is fundamental to embedded computer vision performance 4 Su, et. al. “One Pixel Attack for Fooling Deep Neural Networks” © 2022 Commonlands LLC
  • 5. Image quality in the field is fundamental to embedded computer vision performance 5 Su, et. al. “One Pixel Attack for Fooling Deep Neural Networks” “Nonetheless, we show some examples of situations where nearly imperceptible image modifications can result in dramatic perception changes. Even in applications without malicious people trying to trick your system, the natural world may be adversarial enough.” Pezzementi, et. al “Putting Image Manipulations in Context: Robustness Testing for Safe Perception” © 2022 Commonlands LLC
  • 6. Objective • Independent of preference • Measurements with image quality test charts Subjective • Dependent on preference • Measurements through focus groups and other user feedback methods Two types of purpose-based image quality metrics are required to fully characterize an image 6 “Too Grainy” “Too Blurry” “Not Colorful” “SNR = 70db” “eSFR=20%@100lp/mm” “∆E=2” ? ? © 2022 Commonlands LLC
  • 7. Computational-Based • Inputs are related to human visual cognition such as structure and color. • Includes content-aware image processing. • Directly related to image saliency in embedded computer vision. “Subjective Image Quality” Engineering-Based • Inputs are related to test charts, camera hardware, and image processing. • Independent of scene content and human visual quality assessment. “Objective Image Quality” Objective and subjective were coined before modern embedded vision systems 7 Reference: Zwanenberg, et. al., 2020, “Edge Detection Techniques for Quantifying Spatial Imaging System Performance and Image Quality” © 2022 Commonlands LLC
  • 8. 1. Exposure + Motion Blur 2. Dynamic Range + Artifacts 3. Noise 4. Color 5. Shading 6. Resolution 7. Distortion 8. Texture Blur 9. Stray Light 10.Fringing + Blooming 11.Blemish 12.Dead Pixels 8 Let’s investigate how objective image quality metrics could impact your computer vision © 2022 Commonlands LLC
  • 9. Related KPI • Exposure Value (EV) Reference: ISO12232 Exposure index combines the scene, lens, exposure length, gain, and image processing 9 Blasinski, etc. al. “Optimizing Image Acquisition Systems for Autonomous Driving” © 2022 Commonlands LLC
  • 10. Motion blur is created by long exposure and/or imperfect high-dynamic range recombination 10 Pei, et. al. “Effects of Image Degradations to CNN-based Image Classification” Angular shift Blur Length © 2022 Commonlands LLC
  • 11. 1. Exposure + Motion Blur 2. Dynamic Range + Artifacts 3. Noise 4. Color 5. Shading 6. Resolution 7. Distortion 8. Texture Blur 9. Stray Light 10.Fringing + Blooming 11.Blemish 12.Dead Pixels Let’s investigate how objective image quality metrics could impact your computer vision 11 © 2022 Commonlands LLC
  • 12. Related KPI • Dynamic Range (dB) Reference: ISO21550 Camera dynamic range is the ratio of maximum to minimum signal, before saturation occurs 12 Hasinoff, et. al. “Burst photography for high dynamic range and low-light imaging on mobile cameras” © 2022 Commonlands LLC
  • 13. Even modern HDR techniques can introduce other image quality artifacts 13 Robidoux, et. al. “End-to-end High Dynamic Range Camera Pipeline Optimization” © 2022 Commonlands LLC
  • 14. 1. Exposure + Motion Blur 2. Dynamic Range + Artifacts 3. Noise 4. Color 5. Shading 6. Resolution 7. Distortion 8. Texture Blur 9. Stray Light 10.Fringing + Blooming 11.Blemish 12.Dead Pixels 14 Let’s investigate how objective image quality metrics could impact your computer vision © 2022 Commonlands LLC
  • 15. Related KPIs • Signal-to-Noise Ratios (multiple types) • Noise Power Spectrum (Frequency) Reference: ISO15739 Noise is split into single pixel (temporal /random) and multi-pixel (spatial/pattern) 15 Dodge, et. al. “Understanding How Image Quality Affects Deep Neural Networks” © 2022 Commonlands LLC
  • 16. 1. Exposure + Motion Blur 2. Dynamic Range + Artifacts 3. Noise 4. Color 5. Shading 6. Resolution 7. Distortion 8. Texture Blur 9. Stray Light 10.Fringing + Blooming 11.Blemish 12.Dead Pixels 16 Let’s investigate how objective image quality metrics could impact your computer vision © 2022 Commonlands LLC
  • 17. Related KPI • ∆E (Color Accuracy) Reference: ISO17321 Color can impact edge contrast when using multiple channels and auto-white balance 17 Afifi+Brown. “ What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network Performance © 2022 Commonlands LLC
  • 18. Related KPI • Contrast Detection Probability Reference: ISO12232 Dynamic range and color are closely related to tone mapping which impacts perception at every scale 18 Yeganeh, et. al. “Objective Quality Assessment of Tone-Mapped Images” © 2022 Commonlands LLC
  • 19. 1. Exposure + Motion Blur 2. Dynamic Range + Artifacts 3. Noise 4. Color 5. Shading 6. Resolution 7. Distortion 8. Texture Blur 9. Stray Light 10.Fringing + Blooming 11.Blemish 12.Dead Pixels 19 Let’s investigate how objective image quality metrics could impact your computer vision © 2022 Commonlands LLC
  • 20. Related KPIs • Luminance Non-uniformity • Lightness non-uniformity Reference: ISO17957 Luminance shading changes accuracy from center to edge of the field of view 20 Marc Levoy, ICCV 2015, “Extreme imaging using cell phones” © 2022 Commonlands LLC
  • 21. Shading can include a radial color shift, impacting CV in different parts of the field 21 © 2022 Commonlands LLC
  • 22. 1. Exposure + Motion Blur 2. Dynamic Range + Artifacts 3. Noise 4. Color 5. Shading 6. Resolution 7. Distortion 8. Texture Blur 9. Stray Light 10.Fringing + Blooming 11.Blemish 12.Dead Pixels 22 Let’s investigate how objective image quality metrics could impact your computer vision © 2022 Commonlands LLC
  • 23. Resolution comes in many flavors 23 # Pixels Resolution Pixel Size Scene Field of View, Memory/Speed, On- board Functionality “Pixel Resolution” Image Quality Across an Edge in Object Space “Sharpness” Optical Resolution (MTF) System Resolution (SFR) Angular Resolution (deg/px) © 2022 Commonlands LLC
  • 24. All types of resolution jointly impact the performance of embedded vision systems 24 Xu, et. al. “3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning” SOTA=State of the art as of Q1’20: “SPIN” © 2022 Commonlands LLC
  • 25. Related KPIs • Edge SFR (eSFR), sinusoidal SFR (sSFR) • Lens MTF • Contrast Sensitivity Function (CSF) • Contrast Detection Probability (CDP) Reference: ISO12233, IEEE P2020 The Spatial Frequency Response (SFR) and contrast sensitivity are a corollary to “blur” 25 Vasiljevic, et. Al. “Examining the Impact of Blur on Recognition by Convolutional Networks” © 2022 Commonlands LLC
  • 26. SFR can also characterize 10+ artifacts resulting from image compression quality 26 Zheng, et. Al “ Improving the Robustness of Deep Neural Networks via Stability Training” Example of Artifacts • Aliasing • Ringing • Blocking Reference: E. Allen, Thesis: “Image Quality Evaluation in Lossy Compressed Imaged” © 2022 Commonlands LLC
  • 27. Related KPIs • # Pixels per ° • # Pixels per unit distance across an object Angular resolution defines the # of pixels each object has for feature extraction 27 Dollár, et. al. “Pedestrian Detection: An Evaluation of the State of the Art” © 2022 Commonlands LLC
  • 28. 1. Exposure + Motion Blur 2. Dynamic Range + Artifacts 3. Noise 4. Color 5. Shading 6. Resolution 7. Distortion 8. Texture Blur 9. Stray Light 10.Fringing + Blooming 11.Blemish 12.Dead Pixels 28 Let’s investigate how objective image quality metrics could impact your computer vision © 2022 Commonlands LLC
  • 29. Related KPI • % Distortion (Optical, TV, SMIA TV) Reference: ISO17850 Distortion is the change in angular resolution (magnification) across field 29 Pei, et. al. “Effects of Image Degradations to CNN-based Image Classification” Negative FΘ Rectilinear Angular resolution and perspective distortion at 45° Off Axis © 2022 Commonlands LLC
  • 30. Related KPI • % Distortion (Optical, TV, SMIA TV) Reference: ISO17850 Distortion is the change in angular resolution (magnification) across field 30 Negative FΘ Rectilinear Angular resolution and perspective distortion at 45° Off Axis © 2022 Commonlands LLC
  • 31. Related KPI • % Distortion (Optical, TV, SMIA TV) Reference: ISO17850 Distortion is the change in angular resolution (magnification) across field 31 Li, et.al. 2020, “ULSD: Unified Line Segment Detection across Pinhole, Fisheye, and Spherical Cameras” Rectilinear Spherical/Fisheye © 2022 Commonlands LLC
  • 32. 1. Exposure + Motion Blur 2. Dynamic Range + Artifacts 3. Noise 4. Color 5. Shading 6. Resolution 7. Distortion 8. Texture Blur 9. Stray Light 10.Fringing + Blooming 11.Blemish 12.Dead Pixels 32 Let’s investigate how objective image quality metrics could impact your computer vision © 2022 Commonlands LLC
  • 33. Related KPI • Texture SFR Reference: ISO19567 Texture SFR (and loss) results from noise reduction algorithms that filter high frequencies 33 Chen, et. Al. “Texture Construction Edge Detection Algorithm” © 2022 Commonlands LLC
  • 34. 1. Exposure + Motion Blur 2. Dynamic Range + Artifacts 3. Noise 4. Color 5. Shading 6. Resolution 7. Distortion 8. Texture Blur 9. Stray Light 10.Fringing + Blooming 11.Blemish 12.Dead Pixels 34 Let’s investigate how objective image quality metrics could impact your computer vision © 2022 Commonlands LLC
  • 35. Related KPIs • Glare spread function • Contrast detection probability Reference: ISO18844 Stray light from lenses create regions of low contrast and low detection probability 35 IEEE P2020 Whitepaper © 2022 Commonlands LLC
  • 36. 1. Exposure + Motion Blur 2. Dynamic Range + Artifacts 3. Noise 4. Color 5. Shading 6. Resolution 7. Distortion 8. Texture Blur 9. Stray Light 10.Fringing + Blooming 11.Blemish 12.Dead Pixels 36 Let’s investigate how objective image quality metrics could impact your computer vision © 2022 Commonlands LLC
  • 37. Chromatic aberration from lenses can result in artifacts around high contrast edges 37 Related KPIs • Chromatic Displacement Reference: ISO19084 Kang, “Automatic Removal of Chromatic Aberration from a Single Image” © 2022 Commonlands LLC
  • 38. There are many types of color fringing, some result from blooming/cross-talk in sensor and tuning 38 Original images © 2022 Commonlands LLC
  • 39. 1. Exposure + Motion Blur 2. Dynamic Range + Artifacts 3. Noise 4. Color 5. Shading 6. Resolution 7. Distortion 8. Texture Blur 9. Stray Light 10.Fringing + Blooming 11.Blemish 12.Dead Pixels 39 Let’s investigate how objective image quality metrics could impact your computer vision © 2022 Commonlands LLC
  • 40. Image blemishes occur when dust /dirt / moisture are on the sensor or in / on the lens 40 Related KPIs • # and size of blemishes Reference: https://www.imatest.com/docs/blemish/ Michal Uricar “Let’s Get Dirty: GAN Based Data Augmentation for Camera Lens Soiling Detection in Autonomous Driving.” © 2022 Commonlands LLC
  • 41. 1. Exposure + Motion Blur 2. Dynamic Range + Artifacts 3. Noise 4. Color 5. Shading 6. Resolution 7. Distortion 8. Texture Blur 9. Stray Light 10.Fringing + Blooming 11.Blemish 12.Dead Pixels 41 Let’s investigate how objective image quality metrics could impact your computer vision © 2022 Commonlands LLC
  • 42. Dead pixels brings us full circle, as a real-world adversarial attack if no correction is performed 42 Su, et. al. “One Pixel Attack for Fooling Deep Neural Networks” © 2022 Commonlands LLC
  • 43. Dead pixels brings us full circle, as a real-world adversarial attack if no correction is performed 43 Su, et. al. “One Pixel Attack for Fooling Deep Neural Networks” “Nonetheless, we show some examples of situations where nearly imperceptible image modifications can result in dramatic perception changes. Even in applications without malicious people trying to trick your system, the natural world, [your camera hardware, and your image processing pipeline] may be adversarial enough.” Pezzementi, et. al “Putting Image Manipulations in Context: Robustness Testing for Safe Perception” © 2022 Commonlands LLC
  • 44. Where do I learn more about how image quality influences computer vision? Visit our website for the slides, the papers cited in this talk, plus related resources: -> Contact me at max.henkart@commonlands.com if working on a camera HW project or looking for lenses Resources through the Alliance • Felix Heide, Embedded Vision Summit 2018: • https://www.edge-ai-vision.com/2018/08/understanding-real-world-imaging-challenges-for-adas- and-autonomous-vision-systems-ieee-p2020-a-presentation-from-algolux/ Notable examples included on our reference page: IEEE P2020 Automotive Image Quality (Computer Vision) White Paper Electronic Imaging (January 2023) and Imaging.org University of Westminster & Nvidia’s Collaboration on Image Quality Metrics 44 https://commonlands.com/summit2022 © 2022 Commonlands LLC
  • 46. Contrast loss impacts both human visual perception and CNN-based methods. 46 Geirhos, et. al. “Comparing deep neural networks against humans: object recognition when the signal gets weaker” © 2022 Commonlands LLC
  • 47. Pezzementi, et. al. “Putting Image Manipulations in Context: Robustness Testing for Safe Perception” • ADR= Average Detection Rate Quantitative Degradation of Agricultural Outdoor Detection Based on Image Quality 47 © 2022 Commonlands LLC
  • 48. Karahan, et. al. “How Image Degradations Affect Deep CNN- based Face Recognition?” Quantitative Degradation of Facial Recognition Networks Based on Image Quality 48 © 2022 Commonlands LLC
  • 49. Roy, et. Al. “Effects of Degradations on Deep Neural Network Architectures” Quantitative Degradation of A Variety of Images and Datasets 49 © 2022 Commonlands LLC
  • 50. Example of CV impact • Must select line detection and/or dewarping methods carefully as camera to camera variations can throw off Hough transfroms and RANSAC • Fewer pixels for detection tasks at edges of negative FΘ lenses KPI • % Distortion (Optical, TV, SMIA TV) Reference: ISO17850 6) Distortion is the change in angular resolution (magnification) across field 50 Negative F-Θ Rectilinear Two 90° HFoV lenses on IMX477. Left 10% of image is shown. Original Images © 2022 Commonlands LLC