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1. TOP 13 IMAGE
PROCESSING
TOOLS TO EXPECT
IN 2 0 2 3
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, Tutors India
Group www.tutorsindia.com
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2. As the name suggests, processing an image entails a
number of steps before we reach our goal.
The end result may be an image or a feature that is
similar to that image.
Additional research and decision-making can be done
using this information. Below are the best image
processing tools used in machine learning.
INTRODUCTI
ON

3. Figure1: Best image Processing Tools Used in Machine Learning

4. OPENCV
A popular library that is simple to use is multi-platform. It
flawlessly integrates with C++ and Python and provides
all the essential methods and algorithms needed to
complete a few image and video processing tasks (Salvi
et al., 2021) .
MATLAB
Since it allows for rapid prototyping, Matlab is a great tool
for creating image processing systems and is
extensively used in research. Another interesting point is
how much shorter Matlab code is than C++ code, making
it simpler to read and troubleshoot. It addresses errors
prior to execution by offering a number of options to
expedite the process.

5. CUD
ANVIDIA, which is fast, highly efficient, and easy to program, serves as the foundation for
parallel computing. It gives outstanding performance by utilizing GPU power. Incorporated
into its toolset is the NVIDIA Performance Primitives library, which offers a variety of image,
signal, and video processing techniques (Abdulrahman et al., 2021) .
TENSORFLOW
The most used deep learning and machine learning library currently in use is this one. It
quickly gained popularity and surpassed competing libraries due to the ease of use of the
API. The differential programming and data stream library TensorFlow is open-source and
free. It is a symbolic math library that is utilized by neural networks and other machine
learning applications.

6. TensorFlow 2.0 encourages the use of pre-built models for a variety of applications,
including object detection, reinforcement learning, voice and picture recognition. These
reference models give you the ability to employ certain best business practices and act as a
platform for creating your superior solutions (Reinke et al., 2021) .
SIMPLECV
A system for creating computer vision applications is called SimpleCV. It provides access to
several computer vision technologies on any OpenCV, pygame, and other platforms. You
need this program if all you need to do is finish the task and you don't want to learn all the
specifics of image processing. SimpleCV is the best option if rapid prototyping is required.

7. Machine learning open-source platform PyTorch. The process is
designed to accelerate the transition from a research prototype to
commercial development.
Easy production transition
Performance enhancement with distributed adaptive learning
Thriving ecosystem for tools and libraries
Excellent support for the primary cloud platforms
Optimization modules and independent differentiation
PYTOR
CH

8. Tensorflow, Theano, and CNTK are just a few of the libraries that Keras, a deep learning
Python library, includes. In comparison to competitors like Scikit-learn and PyTorch, Keras
has an advantage because it is developed on top of Tensorflow.
Any of the following can be utilized with Keras: TensorFlow, Microsoft Cognitive Toolkit,
Theano, or PlaidML. It is made for quick deep neural network experimentation and places a
premium on convenience, quantitative quality, and extensibility. Keras adheres to best
practices for decreasing cognitive load by offering stable and basic APIs and limiting the
amount of user involvement required for typical use cases (Zhang, 2021).
KER
AS

9. THEANO
Theano is a swift Python numerical library that may be
utilized with a CPU or GPU. At the Canadian University of
Montreal, it was created by the LISA group, which is now
known as MILA. Theano is a developing compiler for
controlling and assessing mathematical expressions,
particularly those using matrix values.
EmguCV is a platform-agnostic image processing
program. a Net extension for OpenCV. IronPython, C#,
VB, and VC++ are among the.NET compatible languages
that it supports. It runs on Windows, Linux, Mac OS, iOS,
and Android platforms. It is also compatible with Visual
Studio, Xamarin Studio, and Unity.
EMGU
CV

10. GPUIMA
GE
It is an OpenGL ES 2.0-based framework that enables the addition of GPU-accelerated
effects and channels to the live-act video side, still images, and motion pictures. To set up
and keep running custom channels on a GPU, a significant amount of code must be
developed.
YOLO
"You Just Look Once" (YOLO), an approach to object detection, was created with real-time
processing in mind. Joseph Redmon and Ali Farhadi, researchers at the University of
Washington, created the advanced real-time object detection technology known as YOLO.
Their method uses a neural network to divide the entire image into districts with the found
items, leaving imprints of those districts on the grid (Ding et al., 2021) .

11. VXL is a collection of open-source C++ libraries. This image editing application has the ability to
open, save, and edit photos in a variety of frequently used file types, including huge images.
Geometry for points, curves, and other fundamental objects in 1, 2, or 3 dimensions.
Camera physics
regaining stability following movement
Implementing a graphical user interface
3D topology pictures
VX
L

12. BoofCV is an open-source Java framework with an Apache 2.0 license that may be used for
both professional and academic real-time robotics and computer vision applications.
Structure-from-motion, feature tracking, camera alignment, and efficient low-level image
processing techniques are some of its features (C et al., 2022) .
BOOF
CV

13. Deep learning's use of Broadway lingo and advances in
image processing are changing the world. This is just the
beginning of the learning process because scientists are
constantly creating better techniques to optimize the
entire field of image processing. All the tasks you must
accomplish can be completed using a variety of image
processing techniques.
Looking to write a research proposal on image processing
techniques? At tutorsindia, Our expertise will assist you
from identifying problem to the research solutions
CONCLUSI
ON

14. Abdulrahman, A.A., Rasheed, M. & Shihab, S. (2021). The Analytic of Image Processing Smoothing Spaces Using
Wavelet. Journal of Physics: Conference Series. [Online]. 1879 (2). pp. 022118. Available from:
https://iopscience.iop.org/article/10.1088/1742-6596/1879/2/022118.
C, D., N, N.U., Maddikunta, P.K.R., Gadekallu, T.R., Iwendi, C., Wei, C. & Xin, Q. (2022). Identification of
malnutrition and prediction of BMI from facial images using real‐time image processing and machine learning. IET
Image Processing. [Online]. 16 (3). pp. 647–658. Available from:
https://onlinelibrary.wiley.com/doi/10.1049/ipr2.12222.
Ding, K., Ma, K., Wang, S. & Simoncelli, E.P. (2021). Comparison of full-reference image quality models for
optimization of image processing systems. International Journal of Computer Vision. [Online]. 129 (4). pp. 1258–
1281. Available from: https://link.springer.com/article/10.1007/s11263-020-01419-7.
Reinke, A., Tizabi, M.D., Sudre, C.H., Eisenmann, M., Radsch, T., Baumgartner, M., Acion, L., Antonelli, M., Arbel,
T. & Bakas, S. (2021). Common limitations of image processing metrics: A picture story. arXiv preprint
arXiv:2104.05642. [Online]. Available from: https://arxiv.org/abs/2104.05642.
Salvi, M., Acharya, U.R., Molinari, F. & Meiburger, K.M. (2021). The impact of pre- and post-image processing
techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis. Computers
in Biology and Medicine. [Online]. 128. pp. 104129. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S0010482520304601.
Zhang, Y.-J. (2021). Image Engineering. In: Handbook of Image Engineering. [Online]. Singapore: Springer
Singapore, pp. 55–83. Available from: http://link.springer.com/10.1007/978-981-15-5873-3_2.
REFEREN
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