What is Computer Vision?
Computer Vision or CV is a sub-field of Artificial Intelligence and Machine Learning that focuses on teaching computers how to see. It involves the use of general learning algorithms and specialized methods. Everyone, once in life have experienced this without knowing. Also known as an interdisciplinary scientific field, CV is helping computers to gain high-level understanding by analyzing digital videos and images. The end goal of this technology is to understand how the human visual system works and how to automate it.
If we take into account the advancements being made in artificial intelligence, deep learning, and neural networks, computer vision can take incredible steps in the coming years. It will outperform humans in various tasks related to labeling and detecting objects.
The technology measures the data that we generate, then prepare and improve the computer vision algorithms. It comprehends and streamlines the tasks that include visual systems by analyzing, apprehending, and handling the videos and digital pictures.
Image Processing & Computer Vision
Image processing involves the creation of a new image from an existing one through an algorithm. The process simplifies and enhances the image content with its digital signal processing and doesn’t bother with understanding image content.
Computer vision is different from image processing. It may require image processing implication on raw input, such as pre-processing images. Example of image processing are:
- Crop image bounds to center-adjust object in a photograph.
- Adjust the photometric properties of an image like color, brightness, contrast, etc..
- Remove digital noise like digital artifacts from low light levels
Computer Vision Tools for 2020
CV technology is operating in various fields like healthcare services, IoT, security, manufacturing, etc.. Due to this, the experts also noticed a spike in the adoption of computer vision tools for the past few years. The computer vision devices have improved throughout the years and are being offered as a service lately. Also, hardware devices like GPUs, along with machine learning structures and tools are making CV more scalable and reliable in the current day. Cloud service providers like AWS, Google, and Microsoft are also running in this race to become the developer’s choice.
But what computer vision tools you should go for, let’s take a look:
It is an advanced object detection system, preferable for real-time processing. The creators of “You Just Look Once” or YOLO are Ali Farhadi and Joseph Redmon from the University of Washington. The YOLO algorithm applies a neural network to a complete picture, which afterward divides it into grid form and imprints districts with detected items. It is fast and accurate.
It’s a tool for developing image processing applications, and researchers use it for quick prototyping work. One specialty of Matlab is that its code is very concise as compared to C++, which makes it easy to troubleshoot and peruse. It also suggests multiple ways for error handling and how you can make code faster before execution.
It is a multi-platform library and is easy to utilize. It consists of all fundamental algorithms and strategies that you require to perform image and video processing tasks. OpenCV is an open-source library that mainly functions with Python and C++.
It is a comprehensive system for developing computer vision applications. SimpleCV is an open-source framework that provides access to a number of CV tools on any semblance of Pygame, OpenCV, and so on. If you don’t want to go deep into image processing complexities, then it’s the right tool for you. It will help you to perform quick prototyping.
It is an open-source library for real-time computer vision and robotics applications. BoofCV is an Apache 2.0 licensed tool for both business and scholastic use.
Its functionality includes camera calibration, structure-from-motion, recognition, low-level image processing, feature detection & tracking, and fiducial detection. BoofCV IO comprises input and output routines for multiple data structures.
TensorFlow is one of the most used deep learning and machine learning library. It is a free & open-source library with a simplified API for differential programming and data streams. TensorFlow is a symbolic math library that you can use for machine learning applications such as neural networks. TensorFlow 2.0 consists of pre-configured models for picture and speech recognition, recommendations, reinforced learning, object detection, etc.. It will enable you to build your unique and elite solutions.
It is a deep learning library based on Python that consists of different libraries like Theano, CNTK, Microsoft Cognitive Toolkit, PlaidML, and TensorFlow. Keras is meant for quick experimentation with deep neural networks that centers around extensibility, convenience, and measured quality. The library offers basic and steady APIs and follows best practices to decrease the cognitive load.
It is an open-source Python numerical library created by the LISA group (currently acquired by MILA) at the University of Montreal in Canada. You can run Theano on GPU or CPU. It is an advanced compiler for assessing and controlling mathematical expressions, like matrix-valued ones.
It is an OpenGL ES 2.0 based framework that allows you to apply GPU-accelerated channels and impacts to live motion pictures, films, and video. If you want to run custom channels, the GPUImage will require a lot of code to set up and keep up.
CUDA is NVIDIA based computer vision tool for parallel computing, which is very effective, quick, and easy to program. It utilizes the GPU’s power to deliver powerpack performance. CUDA toolbox works together with NVIDIA Performance Primitives Library that consists of sets of signal, video, and image processing functions.
Computer Vision is a rapidly advancing AI subfield that holds a great deal of accountability for other industries. Right now, it occupies USD 2.37 million of the market and aims to develop at a CAGR of 47% till 2023. The amount of data that is generated every day will enable machines to make solutions beyond human capabilities. We can expect a dependable framework that automates content monitoring and moderation. With Google, Microsoft, Facebook, and Apple investing resources in CV technology, it won’t be long when it occupies control over the worldwide market.