Computer vision enables computers and systems to extract useful information from digital photos, videos, and other visual inputs and to conduct actions or offer recommendations in response to that information. Computer vision gives machines the ability to perceive, observe, and understand, much like artificial intelligence gives them the capacity to think.
Human vision has an advantage over computer vision because it has been around longer. With a lifetime of context, human sight has the advantage of learning how to distinguish between things, determine their distance from the viewer, determine whether they are moving, and determine whether an image is correct.
With cameras, data, and algorithms instead of retinas, optic nerves, and the visual cortex, computer vision teaches computers to execute similar tasks in much less time. A system trained to inspect items or monitor a production asset can swiftly outperform humans since it can examine thousands of products or processes per minute while spotting imperceptible flaws or problems.
Energy, utilities, manufacturing, and the automobile industries all use computer vision, and the market is still expanding.
A few typical jobs that computer vision systems can be utilized for are as follows:
Classification of objects. The system analyzes visual data before categorizing an object in a photo or video under a predetermined heading. The algorithm, for instance, can identify a dog among all the items in the image.
Identification of the item. The system analyzes visual data and recognizes a specific object in a picture or video. For instance, the algorithm may pick out a particular dog from the group of dogs in the image.
Tracking of objects. The system analyzes video, identifies the object (or objects) that satisfy the search criteria, and follows that object’s progress.
Top Computer Vision Tools
A software library for machine learning and computer vision is called OpenCV. OpenCV, developed to offer a standard infrastructure for computer vision applications, gives users access to more than 2,500 traditional and cutting-edge algorithms.
These algorithms may be used to identify faces, remove red eyes, identify objects, extract 3D models of objects, track moving objects, and stitch together numerous frames into a high-resolution image, among other things.
A complete platform for computer vision development, deployment, and monitoring, Viso Suite enables enterprises to create practical computer vision applications. The best-in-class software stack for computer vision, which is the foundation of the no-code platform, includes CVAT, OpenCV, OpenVINO, TensorFlow, or PyTorch.
Image annotation, model training, model management, no-code application development, device management, IoT communication, and bespoke dashboards are just a few of the 15 components that make up Viso Suite. Businesses and governmental bodies worldwide use Viso Suite to create and manage their portfolio of computer vision applications (for industrial automation, visual inspection, remote monitoring, and more).
TensorFlow is one of the most well-known end-to-end open-source machine learning platforms, which offers a vast array of tools, resources, and frameworks. TensorFlow is beneficial for developing and implementing machine learning-based computer vision applications.
NVIDIA created the parallel computing platform and application programming interface (API) model called CUDA (short for Compute Unified Device Architecture). It enables programmers to speed up processing-intensive programs by utilizing the capabilities of GPUs (Graphics Processing Units).
The NVIDIA Performance Primitives (NPP) library, which offers GPU-accelerated image, video, and signal processing operations for various domains, including computer vision, is part of the toolkit. In addition, multiple applications like face recognition, image editing, rendering 3D graphics, and others benefit from the CUDA architecture. For Edge AI implementations, real-time image processing with Nvidia CUDA is available, enabling on-device AI inference on edge devices like the Jetson TX2.
Image, video, and signal processing, deep learning, machine learning, and other applications can all benefit from the programming environment MATLAB. It includes a computer vision toolbox with numerous features, applications, and algorithms to assist you in creating remedies for computer vision-related problems.
A Python-based open-source software package called Keras serves as an interface for the TensorFlow framework for machine learning. It is especially appropriate for novices because it enables speedy neural network model construction while offering backend help.
SimpleCV is a set of open-source libraries and software that makes it simple to create machine vision applications. Its framework gives you access to several powerful computer vision libraries, like OpenCV, without requiring a thorough understanding of complex ideas like bit depths, color schemes, buffer management, or file formats. Python-based SimpleCV can run on various platforms, including Mac, Windows, and Linux.
The Java-based computer vision program BoofCV was explicitly created for real-time computer vision applications. It is a comprehensive library with all the fundamental and sophisticated capabilities needed to develop a computer vision application. It is open-source and distributed under the Apache 2.0 license, making it available for both commercial and academic use without charge.
Convolutional Architecture for Fast Feature, or CAFFE A computer vision and deep learning framework called embedding was created at the University of California, Berkeley. This framework supported a variety of deep learning architectures for picture segmentation and classification and was made in the C++ programming language. Due to its incredible speed and image processing capabilities, it is beneficial for research and industry implementation.
A comprehensive computer vision tool, OpenVINO (Open Visual Inference and Neural Network Optimization), helps create software that simulates human vision. It is a free cross-platform toolkit designed by Intel. Models for numerous tasks, including object identification, face recognition, colorization, movement recognition, and others, are included in the OpenVINO toolbox.
The most well-liked open-source computer vision library for deep learning facial recognition at the moment is DeepFace. The library provides a simple method for using Python to carry out face recognition-based computer vision.
One of the fastest computer vision tools in 2022 is You Only Look Once (YOLO). It was created in 2016 by Joseph Redmon and Ali Farhadi to be used for real-time object detection. YOLO, the fastest object detection tool available, applies a neural network to the entire image and then divides it into grids. The odds of each grid are then predicted by the software concurrently. After the hugely successful YOLOv3 and YOLOv4, YOLOR had the best performance up until YOLOv7, published in 2022, overtook it.
FastCV is an open-source image processing, machine learning, and computer vision library. It includes numerous cutting-edge computer vision algorithms along with examples and demos. As a pure Java library with no external dependencies, FastCV’s API ought to be very easy to understand. It is, therefore, perfect for novices or students who want to swiftly include computer vision into their ideas and prototypes.
To easily integrate computer vision functionality into our mobile apps and games, the company also integrated FastCV on Android.
One of the best open-source computer vision tools for processing images in Python is the Scikit-image module. Scikit-image allows you to conduct simple operations like thresholding, edge detection, and color space conversions.
Although it’s not a program you’ll use frequently, it has several practical uses. For instance, with a bit of setup, you could use scikit-image on your camera to snap a picture using infrared light or find watermarks on photos. These are only a few examples of what scikit-image can be used for. If all else fails, image manipulation is an option.
Prathamesh Ingle is a Consulting Content Writer at MarktechPost. He is a Mechanical Engineer and working as a Data Analyst. He is also an AI practitioner and certified Data Scientist with interest in applications of AI. He is enthusiastic about exploring new technologies and advancements with their real life applications