Emerging Trends in Image Processing, Computer Vision and Pattern Recognition (Emerging Trends in Computer Science and Applied Computing)
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Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition discusses the latest in trends in imaging science which at its core consists of three intertwined computer science fields, namely: Image Processing, Computer Vision, and Pattern Recognition. There is significant renewed interest in each of these three fields fueled by Big Data and Data Analytic initiatives including but not limited to; applications as diverse as computational biology, biometrics, biomedical imaging, robotics, security, and knowledge engineering. These three core topics discussed here provide a solid introduction to image processing along with low-level processing techniques, computer vision fundamentals along with examples of applied applications and pattern recognition algorithms and methodologies that will be of value to the image processing and computer vision research communities.
Drawing upon the knowledge of recognized experts with years of practical experience and discussing new and novel applications Editors’ Leonidas Deligiannidis and Hamid Arabnia cover;
- Many perspectives of image processing spanning from fundamental mathematical theory and sampling, to image representation and reconstruction, filtering in spatial and frequency domain, geometrical transformations, and image restoration and segmentation
- Key application techniques in computer vision some of which are camera networks and vision, image feature extraction, face and gesture recognition and biometric authentication
- Pattern recognition algorithms including but not limited to; Supervised and unsupervised classification algorithms, Ensemble learning algorithms, and parsing algorithms.
- How to use image processing and visualization to analyze big data.
- Discusses novel applications that can benefit from image processing, computer vision and pattern recognition such as computational biology, biometrics, biomedical imaging, robotics, security, and knowledge engineering.
- Covers key application techniques in computer vision from fundamentals to mid to high level processing some of which are camera networks and vision, image feature extraction, face and gesture recognition and biometric authentication.
- Presents a number of pattern recognition algorithms and methodologies including but not limited to; supervised and unsupervised classification algorithms, Ensemble learning algorithms, and parsing algorithms.
- Explains how to use image processing and visualization to analyze big data.
Steve Elliot (Elsevier Executive Editor) and Kaitlin Herbert (Elsevier Editorial Project Manager). I hope that you enjoy reading this book. xxxiii This page intentionally left blank Introduction It gives me immense pleasure to present this edited book to the imaging science research community. As the title of this book (Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition) suggests, this book addresses problems in the three intertwined areas: image processing,
standard deviations of the mean are thrown out, and the means and standard deviations are recalculated. Again, the whole piece is not thrown out, just the piece’s individual metric(s). FPC updates each Classifier object with the new mean(s) and standard deviation(s) and then produces TXT files with the same information. Figure 11 provides an example to illustrate the process. 6 CLASSIFYING TEST PIECES Three techniques were used to classify test pieces from metric data: Unweighted Points,
highdimensional space. Here, it focuses on one Classifier at a time, taking the square root of the sums of each metric difference (between test piece and classifier) squared. This is illustrated in Figure 12, where p is the classifier, q is the test piece, and there are n metrics. Euclidean distance is calculated for each classifier, and the classifier with the smallest distance from the test piece is chosen as the classification. 6.2 USER INTERFACE A row of four buttons allows the user to load
vertical directions. 2 Methodology Initial step would be creating a mask to define the region of interest . Once the region of interest is defined, the radius can then be estimated. To estimate the radius, it is important to locate the first and last nonzero pixel (white pixel) across the columns of the image. The middle value between these two estimated points can then be used as a preliminary location for the center. This is then followed by finding the first and last nonzero pixel
coefficient matrix as ^ being 2g Â 2g be the size of H and 4g’ H and the interleaved resultant vector as H, the size of H, where g, is the Hilbert curve level. Algorithm 1 generates a Hilbert mapping matrix y with level g, expressing each curve as four consecutive indexes. The level g of y is acquired concatenating four different y transformations in the previous level, g À 1. Algorithm 1 generates the Hilbert mapping matrix y, transpose of b. where e b refers a 180 rotation of b and bT is the