Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
Overview of Human and Computer Vision
1. "The eye doesn't see any shapes, it sees
only what is differentiated through light
and dark or through colors."
-Johann Wolfgang Von Goethe
(1749–1832), German poet.
24. Sources and Further Information
Brain and Behavior course website
University of Colorado at Boulder
http://www.colorado.edu/intphys/Class/IPHY3730/07vision.html
* Improving quality inspection of food products by computer vision––a review
Tadhg Brosnan, Da-Wen Sun
** Shape and the stereo correspondence problem
Abhijit S. Ogale and Yiannis Aloimonos
fovea - pit that has provides the greatest focus
rods and cones turn light into electrochemical signals that are sent to the brain
cones are dedicated to bright light and colors
3 kinds of cones
rods are active in processing dim light
hard to see color in dim light
left and right fields get processed together and in parallel
Doesn't show path to the superior colliculus
-- SC serves to generate quick and usually unconscious movements of the eye (saccades);
-- often purely reflexive
-- focuses attention onto regions of interest such as areas where a texture or color is different from its surroundings or where movement has been detected by higher areas of the brain
LGN
-- each LGN has 6 layers of processing, 3 for each eye;
-- exact function is not known, but information is sent to and from V1
Visual Cortex
-- 5 major layers processing: orientation, position, size; _; form and shape; color; motion
-- two paths: "where" and "what" processed in parallel
No one completely knows how vision works in a mathematical / algorithmic sense
Processing involves more than just working with the image coming from the retina
Retinal images don't tell us the difference between a hole in the ground and a shadow.
The brain adds hints to the image for correct interpretation based on probability, past experience, and knowledge.
Brain tweaks the image; may add additional shading or changing perceived colors to synthetically add features like depth cues
What the brain allows you to see isn't always the image that's actually coming in. Not raw data
Huge area of study full of huge sub-areas of study
Things are more objective and discrete with pixels versus fuzzy biological signals
Huge area.
Deals with preprocessing an image to aid higher-level analysis
High/low pass filtering (e.g. sharpening, blurring)
aliasing / antialiasing
Histogram equalization - redistributes the gray-level intensities amongst the pixels, shifting all pixels with a given intensity together (i.e. all pixels that had the same intensity before have the same intensity now, it's just a different value), thus increasing the global contrast
cumulative distribution function - at point X, how many pixels have intensity at or below X
after image is prepared to be analyzed at a high level..
Need to be able to tell difference between the foreground and background, areas / objects of interest in the image, etc.
subproblem of a lot of different high-level problems such as
-- object recognition/detection
-- image classification
discontinuities - adjacent pixel regions where local contrast exceeds some threshold
local contrasts - define edges - define boundaries of shapes - define objects
Canny edge detector
problems:
-- can create edges that don't actually exist
-- can ignore edges that do exist
-- no inherent way to tell if an edge is part of an object or is an object boundary; e.g. textures
looking for homogeneity wrt certain features (e.g. color, texture, etc);
think of the paint bucket tool in photoshop; spread out in all directions looking for contiguous pixels that are similar
can be used in conjunction with edge detection
High level image processing
feature detection
Images from a system that classifies pizzas as good or bad based on the pattern and distribution of toppings
Disparity - thumb exercise
Brain offers hints and cues for distance
-- parallax - moving head, closer objects move across field of view faster; moon follows you wherever you go
-- shadows
-- knowledge of what things look like
Correspondence problem
-- some pixels don't correspond at all due to occlusions; can see more AROUND the left side with left eye, right side with right eye
-- some areas are going to appear as different widths in the different images (e.g. slanted)
Smoothed image to eliminate noise
Segmented based mostly on color and contrast.
-- colors weren't the same due to different cameras, different lighting from different angles, noise, etc.
Triangulation using distance between each camera, focal points, and relative positions of the corresponding segments
