Open your phone and check the first image in your gallery? You can easily identify the people in the image and even describe it to your friend. That’s because it is very easy for humans to see things and describe what they are seeing but is that the same for computers? Not at all! It isn’t that easy for computers to “see” as it is for humans. And that is why the field of Computer Vision is so important as it tries to find better and faster ways for computers to “see”.
This world is currently overloaded with images and videos. People can take pics in a second and post them on Instagram or make videos instantly and upload them on YouTube. With so much image and video content, it is very difficult to index and maintain this content as computer algorithms cannot “see” images and videos like humans. At best, algorithms can only organize them using the meta descriptions provided with them. And that is why Computer vision is so important. It is dedicated to helping computers “see” the images and videos so that they can be understood and organized in better ways.
Computer vision is a very complex field that involves computers obtaining information from images or videos. This is a multidisciplinary field that combines artificial intelligence and machine learning to process and analyze images and videos to obtain useful information from them. Some of the tasks for learning algorithms in Computer vision include facial recognition, object identification, video tracking, image restoration, scene reconstruction, etc.
Currently, there are various online tools that provide algorithms for Computer Vision and a platform to execute these algorithms or create new ones. These tools also provide an environment for connecting with various other software and technologies in conjugation with computer vision. So let’s check out some of the Computer Vision tools now!
OpenCV (Open Source Computer Vision Library) is an open-source computer vision library that contains many different functions for computer vision and machine learning. It was created by Intel and originally released in 2000. OpenCV has many different algorithms related to computer vision that can perform a variety of tasks including facial detection and recognition, object identification, monitoring moving objects, tracking camera movements, tracking eye movements, extracting 3D models of objects, creating an augmented reality overlay with a scenery, recognizing similar images in an image database, etc. OpenCV has interfaces for C++, Java Python, MATLAB, etc. and it supports various operating systems such as Windows, Android, Mac OS, Linux, etc.
TensorFlow is a free open-source platform that has a wide variety of tools, libraries, and resources for Artificial Intelligence and Machine Learning which includes Computer Vision. It was created by the Google Brain team and initially released on November 9, 2015. You can use TensorFlow build and train Machine Learning models related to computer vision that include facial recognition, object identification, etc. Google also released the Pixel Visual Core (PVC) in 2017 which is an image, vision, and Artificial Intelligence processor for mobile devices. This Pixel Visual Core also supports TensorFlow for machine learning. TensorFlow supports languages such as Python, C, C++, Java, JavaScript, Go, Swift, etc. but without an API backward compatibility guarantee. There are also third-party packages for languages like MATLAB, C#, Julia, Scala, R, Rust, etc.
Matlab is a numerical computing environment that was developed by MathWorks in 1984. It contains the Computer Vision Toolbox which provides various algorithms and functions for computer vision. These include object detection, object tracking, feature detection, feature matching, camera calibration in 3-D, 3D reconstruction, etc. You can also create and train custom object detectors in Matlab using machine learning algorithms such as YOLO v2, ACF, Faster R-CNN, etc. These algorithms can also be run on multicore processors and GPUs to make them much faster. The Matlab toolbox algorithms support code generation in C and C++.
CUDA or the Compute Unified Device Architecture)is a parallel computing platform that was created by Nvidia and released in 2007. It is used by software engineers for general purpose processing using the CUDA-enabled graphics processing unit or GPU. CUDA also has the Nvidia Performance Primitives library that contains various functions for image, signal, and video processing. Some other libraries and collections include GPU4Vision, OpenVIDIA for popular computer vision algorithms on CUDA, MinGPU which is a minimum GPU library for Computer Vision, etc. Developers can program in various languages like C, C++, Fortran, MATLAB, Python, etc. while using CUDA.
SimpleCV is an open-source computer vision framework that can be used for building various computer vision applications. SimpleCV is simple (as the name suggests!) and you can use various advanced computer vision libraries with it such as OpenCV without learning all the CV concepts in-depth such as file formats, buffer management, color spaces, eigenvalues, bit depths, matrix storage, bitmap storage, etc. SimpleCV allies you to experiment in computer vision using the images or video streams from webcams, FireWire, mobile phones, Kinects, etc. It is the best framework if you need to perform some quick prototyping. You can use SimpleCV with Mac, Windows, and Ubuntu Linux operating systems.
YOLO or You only look once! is a latest and cutting edge real-time object detection system. It was created by Joseph Redmon and Ali Farhadi from the University of Washington and it is extremely fast and accurate as compared to the other object detectors. The YOLO algorithm is so fast as compared to other object detection algorithms because it applies a neural network to the full image in order to classify the objects. The neural network then partitions the image into regions and predicts probabilities for each region. On the other hand, the rest of the commonly used object detection algorithms apply the neural network to an image at many different locations and scales. So YOLO is fast as It looks at the whole image so its predictions are informed by a holistic context of the image.
BoofCV is an open-source library that is written specifically for real-time computer vision. It was released under an Apache 2.0 license for both academic and commercial use. There are options for various branches of CV in BoofCV including low-level image processing, feature detection and tracking, camera calibration, etc. Some of the packages in BoofCV include Image processing functions with image processing functions that operate on pixels, Geometric vision for extracting image features using 2D and 3D geometry, Calibration that has functions to determine the camera’s intrinsic and extrinsic parameters, Recognition for recognizing complicated visual objects, etc.
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