Computer And Machine Vision Theory Algorithms Practicalities

The Fascinating World of Computer and Machine Vision

Computer and Machine Vision is a rapidly evolving field that has revolutionized industries ranging from healthcare to self-driving cars. This branch of Artificial Intelligence focuses on developing algorithms and techniques that enable computers to gather, analyze, and interpret images and videos, mimicking human vision capabilities.

The Theory Behind Computer and Machine Vision

Computer vision theory encompasses an array of techniques that enable computers to process and understand visual information. From low-level image processing tasks such as image filtering and enhancement to high-level computer vision tasks like object detection, recognition, and tracking, the theory behind computer and machine vision is vast and complex.

Computer and Machine Vision: Theory, Algorithms, Practicalities

by E. R. Davies (4th Edition, Kindle Edition)

5 out of 5

Language	:	English
File size	:	15401 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	1755 pages
Paperback	:	26 pages
Item Weight	:	3.04 ounces
Dimensions	:	8.25 x 0.07 x 6 inches

FREE

One prominent theory in computer vision is the concept of pattern recognition. By using machine learning algorithms, computers can identify recurring patterns in an image or video and label them accordingly. This enables applications such as facial recognition technology, barcode scanners, and automated optical inspection systems.

The Algorithms Driving Computer and Machine Vision

Computer and machine vision algorithms form the backbone of this fascinating field. These algorithms are designed to extract meaningful information from images or video sequences, enabling computers to comprehend visual data.

One commonly utilized algorithm in computer vision is the Convolutional Neural Network (CNN). CNNs are a type of deep learning algorithm specifically designed for processing visual data. They consist of multiple layers of interconnected neurons that mimic the human visual cortex. Through a process of training, CNNs can learn features like edges, textures, and shapes, allowing them to perform tasks like object recognition and image classification with astounding accuracy.

Another crucial algorithm in computer vision is the Optical Character Recognition (OCR). OCR algorithms enable computers to detect text within an image or video and convert it into editable and searchable text. This technology has found applications in document processing, automated data entry, and text extraction from images.

The Practicalities of Computer and Machine Vision

While computer and machine vision theory and algorithms provide the foundation for this field, it is the practical implementations that truly showcase its usefulness. From self-driving cars to medical diagnosis, computer and machine vision is transforming several industries with its remarkable capabilities.

Self-driving cars heavily rely on computer vision systems to perceive the surrounding environment, identify objects such as pedestrians, traffic signs, and obstacles, and make informed decisions in real-time. These systems employ image recognition, object tracking, and depth estimation algorithms to accurately navigate through complex traffic situations autonomously.

Medical diagnosis is another area where computer and machine vision proves invaluable. Radiologists and healthcare professionals can leverage computer vision algorithms to analyze medical images like X-rays, CT scans, and MRIs. By automating tasks like tumor detection, anomaly identification, and risk assessment, computer vision streamlines medical processes, enhances accuracy, and saves valuable time.

In

Computer and Machine Vision is a captivating field that combines theory, algorithms, and practical implementations to mimic human vision capabilities. Its impact on various industries is profound, enabling automation, enhancing accuracy, and generating unprecedented insights from visual data.

As computer vision continues to evolve, we can expect to witness even more groundbreaking applications that will reshape the way we interact with technology and the world around us.

Computer and Machine Vision: Theory, Algorithms, Practicalities

by E. R. Davies (4th Edition, Kindle Edition)

5 out of 5

Language	:	English
File size	:	15401 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	1755 pages
Paperback	:	26 pages
Item Weight	:	3.04 ounces
Dimensions	:	8.25 x 0.07 x 6 inches

FREE

Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled Machine Vision) clearly and systematically presents the basic methodology of computer and machine vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fourth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date tutorial text suitable for graduate students, researchers and R&D engineers working in this vibrant subject.

Key features include:

• Practical examples and case studies give the ‘ins and outs’ of developing real-world vision systems, giving engineers the realities of implementing the principles in practice
• New chapters containing case studies on surveillance and driver assistance systems give practical methods on these cutting-edge applications in computer vision
• Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples
• Updated content and new sections cover topics such as human iris location, image stitching, line detection using RANSAC, performance measures, and hyperspectral imaging
• The ‘recent developments’ section now included in each chapter will be useful in bringing students and practitioners up to date with the subject

• Mathematics and essential theory are made approachable by careful explanations and well-illustrated examples
• Updated content and new sections cover topics such as human iris location, image stitching, line detection using RANSAC, performance measures, and hyperspectral imaging
• The ‘recent developments’ section now included in each chapter will be useful in bringing students and practitioners up to date with the subject