OpenCV is the largest open-source computer vision library that gives access to thousands of state of the art algorithms with detailed documentation. It has classes/functions for face detection, object identification, extraction of 3D models, tracking of movement, stitching frames, etc. Can be used in C++, Python, Java, and MATLAB. 

OpenCV

Provides algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems. Can be used to train deep and machine learning models and can leverage multicore or GPU systems.

MATLAB Vision Toolbox

The `scipy.ndimage` module provides functions for multidimensional image processing in Python. It includes operations for filtering, mathematical morphology, interpolation, object measurements, and feature extraction on NumPy arrays that represent n-dimensional images.

SciPy ndimage Module

As computer vision methods have become relatively standardized, it is efficient to utilize established software libraries and tools. Prominent examples include OpenCV, the SciPy ndimage Module, TensorFlow, PyTorch Vision, and the MATLAB Vision Toolbox.

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Computer Vision

1- Rapid advances in computer vision is enabling brand new applications the were not possible a few years ago.

2- Since the community is creative, it can inspire applications in other areas.

Why computer vision is interesting?

What is the problem of using deep learning on images?

1- Image classification

2- Image localization

3- Object detection

4- Neural style transfer

Types of Computer Vision Problems

Acquired data is usually messy and can come from different sources, angles, color scales, etc. In order to use machine and deep learning techniques they need to be standardized. Usually the goal is to increase the accuracy while reducing the complexity of the model used. There is no single answer to preprocessing but there are a group of techniques commonly chosen from.

Computer Vision Pre-Processing

In computer vision there is many established datasets used for training and benchmarking different algorithms. Unlike other machine learning areas, computer vision datasets are much more standardized because it takes significantly more effort to create them.

Computer Vision Datasets

- Human computer interaction (HCI)
- Interaction design
- Collaborative and social computing
- Ubiquitous and mobile computing
- Data Visualization
- Accessibility

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