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Computer Vision : Series

by akshara

ğ‚ğŽğŒğğ”ğ“ğ„ğ‘ ğ•ğˆğ’ğˆğŽğ is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models machines can accurately identify and classify objects and then react to what they "see". It is the field of computer science that focuses on replicating parts of the complexity of the human vision system and enabling computers to identify and process objects in images and videos.
𝑯𝑰𝑺𝑻𝑶𝑹𝒀 𝑶𝑭 𝑪𝑶𝑴𝑷𝑼𝑻𝑬𝑹 𝑽𝑰𝑺𝑰𝑶𝑵: In the 1950s, using some of the first neural networks to detect the edges of an object and to sort simple objects into categories like circles. In the 1970s, the first commercial use of computer vision interpreted typed or handwritten text using optical character recognition. In the 1990s, making large sets of images available online for analysis, facial recognition programs flourished. 𝑪𝑶𝑴𝑷𝑼𝑻𝑬𝑹 𝑽𝑰𝑺𝑰𝑶𝑵 𝑾𝑶𝑹𝑲𝑰𝑵𝑮: Acquiring an image Processing the image Understanding the image 𝑪𝑶𝑴𝑷𝑼𝑻𝑬𝑹 𝑽𝑰𝑺𝑰𝑶𝑵 𝑨𝑺 𝑨 𝑱𝑰𝑮𝑺𝑨𝑾 𝑷𝑼𝒁𝒁𝑳𝑬: Computers assemble visual images like a jigsaw puzzle. All these pieces of the jigsaw are to be assembled into an image and that's how neural networks work for computer vision. They distinguish many different pieces of the image, they identify the edges and then model the subcomponents. Using filtering and a series of actions through deep network layers, they can piece all the parts of the image together. the computer is often fed hundreds or thousands of related images for training and recognizing specific objects. 𝑨𝑷𝑷𝑳𝑰𝑪𝑨𝑻𝑰𝑶𝑵 𝑶𝑭 𝑪𝑶𝑴𝑷𝑼𝑻𝑬𝑹 𝑽𝑰𝑺𝑰𝑶𝑵: Computer Vision for Defect Detection Computer Vision for Intruder Detection Computer Vision for Screen Reader Computer Vision for Code and Character Reader (OCR) Computer Vision in robotics for Bin Picking

𝑪𝑶𝑴𝑷𝑼𝑻𝑬𝑹 𝑽𝑰𝑺𝑰𝑶𝑵 𝑺𝑬𝑹𝑰𝑬𝑺

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Image classification is used to categorize whether an object is present in the likelihood of an image. Feature extraction is a part of the dimensionality reduction process, in which, an initial set of the raw data is divided and reduced into more manageable groups. Image classification is the process of categorizing and labeling groups of pixels or vectors within an image based on specific rules.

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Object Detection is used to recognize the object with a bounding box in an image. Object detection is an important computer vision task used to detect instances of visual objects of certain classes (for example, humans, animals, cars, or buildings) in digital images such as photos or video frames. The goal of object detection is to develop computational models that provide the most fundamental information needed by computer vision applications.

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Instance Segmentation is identifying each object instance for every known object within an image. Instance segmentation assigns a label to each pixel of the image. Instance segmentation is the task of detecting and delineating each distinct object of interest appearing in an image.