Advancements in Computer Vision: A Comprehensive Survey of Image Processing and Interdisciplinary Applications

Wen Gendy; Dularia Patel

doi:10.54097/5e1cqw59

Authors

Wen Gendy
Dularia Patel

DOI:

https://doi.org/10.54097/5e1cqw59

Keywords:

Computer vision, image processing, Machine Learning, Object Recognition, Interdisciplinary Innovation.

Abstract

Computer vision and image processing are rapidly evolving fields with broad applications across numerous domains, including healthcare, autonomous driving, surveillance, and entertainment. These fields have transformed from simple data recording techniques into sophisticated systems that incorporate digital image processing, pattern recognition, machine learning, and computer graphics. This evolution has prompted interdisciplinary interest, pushed the technology’s boundaries and expanded its practical uses. This paper offers a comprehensive survey of recent advancements in computer vision, focusing on image processing and its applications across various fields. It delves into the theoretical foundations and technologies that make computer vision a valuable tool for interpreting images and videos, extracting relevant information, recognizing patterns, and understanding events. The ability of computer vision to analyze large datasets across multiple application domains makes it instrumental in tasks such as object identification, facial recognition, scene understanding, and even real-time action prediction. This versatility has established computer vision as a key driver of data-driven insights in both scientific and commercial sectors. The study categorizes computer vision into four main areas: image processing, object recognition, machine learning, and computer graphics. Each of these categories is essential to the functionality of modern computer vision systems. Image processing involves techniques for enhancing image quality and extracting important features. Object recognition and machine learning enable the identification of specific elements within images and allow systems to learn from large datasets, enhancing accuracy over time. Computer graphics, on the other hand, aid in visualizing and interpreting processed data. By offering insights into the latest techniques and evaluating their performance, this survey highlights the current state of computer vision while shedding light on future trends. Computer vision’s expanding utility across various fields underscores its critical role in driving interdisciplinary innovation and addressing complex challenges.

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