Computer vision is a field of artificial intelligence that enables computers to interpret and understand the visual world. It involves the development of algorithms and techniques that allow machines to extract information from images or videos and make decisions based on that information.
Human Vision vs. Computer Vision
Researchers draw inspiration from human vision to develop computer vision. The structure and functioning of the human visual system, such as the arrangement of neurons in the visual cortex and mechanisms for object recognition, inspire the design of neural networks and algorithms for image processing and pattern recognition.
Human vision involves the eyes capturing light and sending signals to the brain for interpretation. It is a complex process that involves perception, recognition, and interpretation of visual information. Computer vision, on the other hand, is the process of enabling computers to interpret and understand the visual world through digital images or videos.
How Does It Work?
Computer vision works by using image processing techniques to analyze and interpret visual data. This involves tasks such as image classification, object localizing, object detection, and image segmentation. These tasks are typically achieved through machine learning algorithms, such as convolutional neural networks, trained on large datasets of labeled images.
There are three stages of operation for a computer vision system.
Acquiring images: Initially, a computer vision system uses a camera or sensor to gather photos, movies, or other visual information (such as scans). The recorded pictures, videos, or streams are moved to a computer system so that they may be processed further.
Process images: To accurately depict the relevant data, the raw photos must be prepared. Images are pre-processed to do this, including noise reduction, contrast adjustment, rescaling, and cropping. A computer vision system does most of these tasks automatically. Hardware is already used to carry out some of these procedures.
Understanding images: The most important part of a computer vision system is this. It involves carrying out the real computer vision work with the aid of a deep learning model or a traditional method of image processing.
At a certain level Computer vision is all about pattern recognition. to understand visual data by feeding it images — many, many, thousands, even millions of them — that have been labelled. The computer can then search for patterns in all the elements that are related to those labels by applying different software techniques, or algorithms, to those images.
A straightforward depiction involves the grayscale image buffer used to store an individual’s image. The brightness of each pixel is denoted by a single 8-bit number, ranging from 0 (representing black) to 255 (representing white). In computer systems, color is typically interpreted as a trio of values — red, green, and blue (RGB) — within the same 0–255 scale. Consequently, each pixel is characterized not only by its position but also by three values to store.
This necessitates a significant amount of memory for a single image, considering the multitude of pixels that an algorithm must iterate over. However, achieving meaningful accuracy in training a model, particularly in the context of Deep Learning, requires many images. Typically, tens of thousands of images are preferred for optimal results, with a larger dataset contributing to improved model performance.
Types of Computer vision
Image Classification
Image classification, also known as image recognition, is a fundamental task in computer vision that involves associating one or more labels with a given image. In single-label classification, the goal is to assign a single label to an image from a predefined set of categories. In multi-label classification, an image may be associated with multiple labels simultaneously.
Object Localization
Object localization is the process of identifying the location of an object in an image or video, usually by using a bounding-box. It is a common task in computer vision. In Object Localization, only a single object can appear in the image.
Object Detection
Object detection serves as an extension of image classification Classify and detect all objects in the image. Assign a class to each object and draw a bounding box around it. The difference between object localization and object detection is subtle. Simply, object localization aims to locate the main object in an image while object detection tries to find out all the objects and their boundaries.
Image Segmentations
Image segmentation is segmenting an image into fragments and assigning a label to each of those. This occurs on a pixel level to define the precise outline of an object within its frame and class. Those outlines, otherwise known as the output, are highlighted with either one or more colors, depending on the type of segmentation.
Instance segmentation
Classify every pixel in the image to a class so that each pixel is assigned to a different instance of an object.
Semantic segmentation
Classify every pixel in the image to a class according to its context, so that each pixel is assigned to an object.
Applications of Computer Vision
Computer vision enables a wide range of technological innovations across all industries.
Manufacturing Industry: In the manufacturing industry, computer vision is employed to enhance quality control on the production line, identifying and preventing the shipment of defective products to customers. This technology ensures precision in assembly processes, reducing errors and improving overall product quality. Manufacturers also utilize computer vision to optimize maintenance schedules for machinery, minimizing downtime and increasing operational efficiency. Additionally, it aids in inventory management by automating the tracking and monitoring of raw materials and finished goods, streamlining the supply chain.
Agriculture Industry
Computer vision in agriculture provides crucial benefits beyond crop health monitoring. It helps in defect detection during the production process, ensuring the quality of agricultural products. By analyzing images, it can identify defects or irregularities in fruits, vegetables, or other crops, allowing for the removal of substandard items before reaching consumers. This capability improves the overall quality of agricultural produce and reduces waste and enhances the supply chain’s efficiency.
