Computer vision is reaching new levels, far beyond basic image processing. This is thanks to the integration of artificial intelligence. AI now allows computers and systems to derive meaningful information from digital images that can be used in advanced industries. Currently, one of the most common applications is in security and surveillance.
A computer vision application is typically divided into small tasks like image classification, object detection, feature extraction, feature matching, image segmentation, edge detection, pose estimation, and others. One of these applications may be responsible for several image processing tasks in sequence to recover the maximum meaning from a specific image or video segment. You can learn more about how computer vision applications work in this article .
Computer vision applications can be programmed in a variety of high-level languages. For example, C++ and Python are the most popular. Although C++ applications run the fastest, Python is easier to use due to its vast repository of libraries and modules.
Generally, AI vision applications use Convolutional Neural Networks (CNN), where each CNN layer performs one or more image processing tasks. The greater the complexity of the application, the more layers are added to the CNN.
Several tools are available for computer vision applications that:
- Provides an integrated environment for programming the application
- Enable the implementation of algorithms for computer vision
- Allow the application to connect to other software components, including cloud services such as Microsoft Azure, Amazon Rekognition, or Google Cloud Vision API.
Popular computer vision tools include:
1. OpenCV
2. MATLAB
3. Simple CV
4. TensorFlow
5. CUDA
6. GPU Image
OpenCV – As its name suggests, the Open-Source Computer Vision (OpenCV) library is an open-source computer vision and machine learning (ML) library, initially released by Intel in 2000. The latest version is OpenCV 4.7. 0, released under an Apache 2. license and free for commercial use.
This tool is written in C++, with new algorithms and libraries in the C++ interface. Interfaces for Python, Java, MATLAB and JavaScript are also available. C++, Python and Java interfaces support Linux, Windows, MacOS, Android and iOS. OpenCV offers programming functions for real-time computer vision. Supported ML algorithms include K-Nearest Neighbor, Random Forest, Decision Tree, Naive Bayes, Support Vector Machine, artificial neural networks, and deep neural networks.
MATLAB – a programming and numerical computing platform for engineers and scientists. MATLAB consists of a computer vision toolbox with many programming functions, algorithms and applications for computer vision, 3D vision and video processing. For example, functions and algorithms are available for object tracking, motion estimation, feature detection, extraction, matching, camera calibration, semantic segmentation, scene classification, instance segmentation, LiDAR and 3D point cloud processing, deep learning and machine learning.
This platform allows the generation and training of object detectors using algorithms such as YOLO, ACF and SSD. Deep learning algorithms include U-Net and Mask R-CNN. The toolbox supports C++ code generation for integration into existing code, embedded vision system deployment, or desktop prototyping.
Simple CV – is an open source platform and a simplified interface for OpenCV. It allows access to various computer vision libraries without the need for concepts such as color spaces, bit depth, file formats, eigenvalues, bitmap storage, buffer management, etc.
Released under the BSD license, the SimpleCV framework is written in Python. It can work with images and video streams from webcams, IP cameras, cell phones, Kinects and FireWire. Your computer vision applications can run on Ubuntu Linux, Windows and MacOS. SimpleCV is also suitable for rapid prototyping of a computer vision application.
TensorFlow – is an open source machine learning framework with multiple tools, libraries, and applications for ML and AI, including computer vision. The framework can train an ML model or a neural network for object detection, object classification, facial recognition, gesture recognition, pose estimation, optical character recognition, and more. The framework has programming interfaces for C, C++, Python, Java, JavaScript, Go, Swift and several other languages. Many programming languages such as MATLAB, Scala, Rust, R, and C# are supported by TensorFlow through third-party APIs.
CUDA – stands for Compute Unified Device Architecture, a parallel computing platform developed by NVIDIA to use graphics processing units (GPU) in general-purpose computing. This platform has many libraries that support image processing, analog signals and video streams. Programming interfaces are available for C, C++, Python, MATLAB and others. Popular CUDA libraries for computer vision include MinGPU, OpenVIDIA, and GPU4Vision. CUDA can also be used for object detection, image classification, segmentation, and neural radiation fields (NeRF).
GPU Image – is an iOS library for GPU-accelerated image and video processing built on OpenGL ES 2.0. The BSD-licensed library is useful for applying GPU-accelerated effects and filters to images, videos, and live streams. GPUImage applies filters using simple function calls rather than requiring programming custom filers from scratch.
