Manual Software Engineering for Image Processing Systems (Image Processing Series)

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Table of contents

Digital image processing deals with manipulation of digital images through a digital computer. It is a subfield of signals and systems but focus particularly on images. DIP focuses on developing a computer system that is able to perform processing on an image. The input of that system is a digital image and the system process that image using efficient algorithms, and gives an image as an output. The most common example is Adobe Photoshop. Ed , Garvin, M. Ed , MacGillivray, T. Ed , Xu, Y. Ed , Zheng, Y.


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Suk, H. Ed , Liu, M. Ed , Yan, P. Ed , Lian, C. Zhu, D. Ed , Fletcher, T. Ed , Durrleman, S. Ed , Sommer, S. Ed , Yan, J. Ed , Huang, H. Ed , Shen, L. Ed , Thompson, P. Ed , Westin, C. Ed , Pennec, X. Ed , Joshi, S. Ed , Nielsen, M. Shen, D. Ed , Liu, T. Ed , Peters, T. Ed , Staib, L. Ed , Essert, C. Ed , Zhou, S. Ed , Yap, P.

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Ed , Khan, A. Schirmer, M. Ed , Venkataraman, A.

Ed , Rekik, I. Ed , Kim, M. Ed , Chung, A. Lin, Z. Studies in the s formed the early foundations for many of the computer vision algorithms that exist today, including extraction of edges from images, labeling of lines, non-polyhedral and polyhedral modeling , representation of objects as interconnections of smaller structures, optical flow , and motion estimation. The next decade saw studies based on more rigorous mathematical analysis and quantitative aspects of computer vision. These include the concept of scale-space , the inference of shape from various cues such as shading , texture and focus, and contour models known as snakes.

Researchers also realized that many of these mathematical concepts could be treated within the same optimization framework as regularization and Markov random fields. Research in projective 3-D reconstructions led to better understanding of camera calibration. With the advent of optimization methods for camera calibration, it was realized that a lot of the ideas were already explored in bundle adjustment theory from the field of photogrammetry. This led to methods for sparse 3-D reconstructions of scenes from multiple images.

Progress was made on the dense stereo correspondence problem and further multi-view stereo techniques.

At the same time, variations of graph cut were used to solve image segmentation. This decade also marked the first time statistical learning techniques were used in practice to recognize faces in images see Eigenface. Toward the end of the s, a significant change came about with the increased interaction between the fields of computer graphics and computer vision. This included image-based rendering, image morphing , view interpolation, panoramic image stitching and early light-field rendering.

Software Tools for Vision

Recent work has seen the resurgence of feature -based methods, used in conjunction with machine learning techniques and complex optimization frameworks. The advancement of Deep Learning techniques has brought further life to the field of computer vision.

The accuracy of deep learning algorithms on several benchmark computer vision data sets for tasks ranging from classification, segmentation and optical flow has surpassed prior methods. Areas of artificial intelligence deal with autonomous planning or deliberation for robotical systems to navigate through an environment. A detailed understanding of these environments is required to navigate through them.

Information about the environment could be provided by a computer vision system, acting as a vision sensor and providing high-level information about the environment and the robot. Artificial intelligence and computer vision share other topics such as pattern recognition and learning techniques. Consequently, computer vision is sometimes seen as a part of the artificial intelligence field or the computer science field in general.

Computer vision is often considered to be part of information engineering.

References

Solid-state physics is another field that is closely related to computer vision. Most computer vision systems rely on image sensors , which detect electromagnetic radiation , which is typically in the form of either visible or infra-red light. The sensors are designed using quantum physics. The process by which light interacts with surfaces is explained using physics. Physics explains the behavior of optics which are a core part of most imaging systems. Sophisticated image sensors even require quantum mechanics to provide a complete understanding of the image formation process.

A third field which plays an important role is neurobiology , specifically the study of the biological vision system.

Digital Signal and Image Processing - ISTE

Over the last century, there has been an extensive study of eyes, neurons, and the brain structures devoted to processing of visual stimuli in both humans and various animals. This has led to a coarse, yet complicated, description of how "real" vision systems operate in order to solve certain vision-related tasks. These results have led to a subfield within computer vision where artificial systems are designed to mimic the processing and behavior of biological systems, at different levels of complexity.

Also, some of the learning-based methods developed within computer vision e.

FPGA Image Processing

Some strands of computer vision research are closely related to the study of biological vision — indeed, just as many strands of AI research are closely tied with research into human consciousness, and the use of stored knowledge to interpret, integrate and utilize visual information. The field of biological vision studies and models the physiological processes behind visual perception in humans and other animals. Computer vision, on the other hand, studies and describes the processes implemented in software and hardware behind artificial vision systems.

Interdisciplinary exchange between biological and computer vision has proven fruitful for both fields. Yet another field related to computer vision is signal processing. Many methods for processing of one-variable signals, typically temporal signals, can be extended in a natural way to processing of two-variable signals or multi-variable signals in computer vision. However, because of the specific nature of images there are many methods developed within computer vision which have no counterpart in processing of one-variable signals.

Together with the multi-dimensionality of the signal, this defines a subfield in signal processing as a part of computer vision. Beside the above-mentioned views on computer vision, many of the related research topics can also be studied from a purely mathematical point of view.

For example, many methods in computer vision are based on statistics , optimization or geometry. Finally, a significant part of the field is devoted to the implementation aspect of computer vision; how existing methods can be realized in various combinations of software and hardware, or how these methods can be modified in order to gain processing speed without losing too much performance.

Computer vision is also used in fashion ecommerce, inventory management, patent search, furniture, and the beauty industry. The fields most closely related to computer vision are image processing , image analysis and machine vision. There is a significant overlap in the range of techniques and applications that these cover. This implies that the basic techniques that are used and developed in these fields are similar, something which can be interpreted as there is only one field with different names.

On the other hand, it appears to be necessary for research groups, scientific journals, conferences and companies to present or market themselves as belonging specifically to one of these fields and, hence, various characterizations which distinguish each of the fields from the others have been presented. Computer graphics produces image data from 3D models, computer vision often produces 3D models from image data.