Moments and Moment Invariants in Pattern Recognition

Moments and Moment Invariants in Pattern Recognition
Author: Jan Flusser
Publisher: John Wiley & Sons
Total Pages: 312
Release: 2009-11-04
Genre: Technology & Engineering
ISBN: 9780470684764

Moments as projections of an image’s intensity onto a proper polynomial basis can be applied to many different aspects of image processing. These include invariant pattern recognition, image normalization, image registration, focus/ defocus measurement, and watermarking. This book presents a survey of both recent and traditional image analysis and pattern recognition methods, based on image moments, and offers new concepts of invariants to linear filtering and implicit invariants. In addition to the theory, attention is paid to efficient algorithms for moment computation in a discrete domain, and to computational aspects of orthogonal moments. The authors also illustrate the theory through practical examples, demonstrating moment invariants in real applications across computer vision, remote sensing and medical imaging. Key features: Presents a systematic review of the basic definitions and properties of moments covering geometric moments and complex moments. Considers invariants to traditional transforms – translation, rotation, scaling, and affine transform - from a new point of view, which offers new possibilities of designing optimal sets of invariants. Reviews and extends a recent field of invariants with respect to convolution/blurring. Introduces implicit moment invariants as a tool for recognizing elastically deformed objects. Compares various classes of orthogonal moments (Legendre, Zernike, Fourier-Mellin, Chebyshev, among others) and demonstrates their application to image reconstruction from moments. Offers comprehensive advice on the construction of various invariants illustrated with practical examples. Includes an accompanying website providing efficient numerical algorithms for moment computation and for constructing invariants of various kinds, with about 250 slides suitable for a graduate university course. Moments and Moment Invariants in Pattern Recognition is ideal for researchers and engineers involved in pattern recognition in medical imaging, remote sensing, robotics and computer vision. Post graduate students in image processing and pattern recognition will also find the book of interest.

Proceedings of International Conference on Computer Vision and Image Processing

Proceedings of International Conference on Computer Vision and Image Processing
Author: Balasubramanian Raman
Publisher: Springer
Total Pages: 556
Release: 2016-12-22
Genre: Technology & Engineering
ISBN: 9811021074

This edited volume contains technical contributions in the field of computer vision and image processing presented at the First International Conference on Computer Vision and Image Processing (CVIP 2016). The contributions are thematically divided based on their relation to operations at the lower, middle and higher levels of vision systems, and their applications. The technical contributions in the areas of sensors, acquisition, visualization and enhancement are classified as related to low-level operations. They discuss various modern topics – reconfigurable image system architecture, Scheimpflug camera calibration, real-time autofocusing, climate visualization, tone mapping, super-resolution and image resizing. The technical contributions in the areas of segmentation and retrieval are classified as related to mid-level operations. They discuss some state-of-the-art techniques – non-rigid image registration, iterative image partitioning, egocentric object detection and video shot boundary detection. The technical contributions in the areas of classification and retrieval are categorized as related to high-level operations. They discuss some state-of-the-art approaches – extreme learning machines, and target, gesture and action recognition. A non-regularized state preserving extreme learning machine is presented for natural scene classification. An algorithm for human action recognition through dynamic frame warping based on depth cues is given. Target recognition in night vision through convolutional neural network is also presented. Use of convolutional neural network in detecting static hand gesture is also discussed. Finally, the technical contributions in the areas of surveillance, coding and data security, and biometrics and document processing are considered as applications of computer vision and image processing. They discuss some contemporary applications. A few of them are a system for tackling blind curves, a quick reaction target acquisition and tracking system, an algorithm to detect for copy-move forgery based on circle block, a novel visual secret sharing scheme using affine cipher and image interleaving, a finger knuckle print recognition system based on wavelet and Gabor filtering, and a palmprint recognition based on minutiae quadruplets.

Moments and Moment Invariants in Pattern Recognition

Moments and Moment Invariants in Pattern Recognition
Author: Jan Flusser
Publisher: Wiley
Total Pages: 312
Release: 2009-12-14
Genre: Technology & Engineering
ISBN: 9780470699874

Moments as projections of an image’s intensity onto a proper polynomial basis can be applied to many different aspects of image processing. These include invariant pattern recognition, image normalization, image registration, focus/ defocus measurement, and watermarking. This book presents a survey of both recent and traditional image analysis and pattern recognition methods, based on image moments, and offers new concepts of invariants to linear filtering and implicit invariants. In addition to the theory, attention is paid to efficient algorithms for moment computation in a discrete domain, and to computational aspects of orthogonal moments. The authors also illustrate the theory through practical examples, demonstrating moment invariants in real applications across computer vision, remote sensing and medical imaging. Key features: Presents a systematic review of the basic definitions and properties of moments covering geometric moments and complex moments. Considers invariants to traditional transforms – translation, rotation, scaling, and affine transform - from a new point of view, which offers new possibilities of designing optimal sets of invariants. Reviews and extends a recent field of invariants with respect to convolution/blurring. Introduces implicit moment invariants as a tool for recognizing elastically deformed objects. Compares various classes of orthogonal moments (Legendre, Zernike, Fourier-Mellin, Chebyshev, among others) and demonstrates their application to image reconstruction from moments. Offers comprehensive advice on the construction of various invariants illustrated with practical examples. Includes an accompanying website providing efficient numerical algorithms for moment computation and for constructing invariants of various kinds, with about 250 slides suitable for a graduate university course. Moments and Moment Invariants in Pattern Recognition is ideal for researchers and engineers involved in pattern recognition in medical imaging, remote sensing, robotics and computer vision. Post graduate students in image processing and pattern recognition will also find the book of interest.

2D and 3D Image Analysis by Moments

2D and 3D Image Analysis by Moments
Author: Jan Flusser
Publisher: John Wiley & Sons
Total Pages: 555
Release: 2016-12-19
Genre: Technology & Engineering
ISBN: 1119039355

Presents recent significant and rapid development in the field of 2D and 3D image analysis 2D and 3D Image Analysis by Moments, is a unique compendium of moment-based image analysis which includes traditional methods and also reflects the latest development of the field. The book presents a survey of 2D and 3D moment invariants with respect to similarity and affine spatial transformations and to image blurring and smoothing by various filters. The book comprehensively describes the mathematical background and theorems about the invariants but a large part is also devoted to practical usage of moments. Applications from various fields of computer vision, remote sensing, medical imaging, image retrieval, watermarking, and forensic analysis are demonstrated. Attention is also paid to efficient algorithms of moment computation. Key features: Presents a systematic overview of moment-based features used in 2D and 3D image analysis. Demonstrates invariant properties of moments with respect to various spatial and intensity transformations. Reviews and compares several orthogonal polynomials and respective moments. Describes efficient numerical algorithms for moment computation. It is a "classroom ready" textbook with a self-contained introduction to classifier design. The accompanying website contains around 300 lecture slides, Matlab codes, complete lists of the invariants, test images, and other supplementary material. 2D and 3D Image Analysis by Moments, is ideal for mathematicians, computer scientists, engineers, software developers, and Ph.D students involved in image analysis and recognition. Due to the addition of two introductory chapters on classifier design, the book may also serve as a self-contained textbook for graduate university courses on object recognition.

Computer Vision and Image Processing

Computer Vision and Image Processing
Author: Neeta Nain
Publisher: Springer Nature
Total Pages: 440
Release: 2020-03-28
Genre: Computers
ISBN: 9811540152

This two-volume set (CCIS 1147, CCIS 1148) constitutes the refereed proceedings of the 4th International Conference on Computer Vision and Image Processing. held in Jaipur, India, in September 2019. The 73 full papers and 10 short papers were carefully reviewed and selected from 202 submissions. The papers are organized according to the following topics:​ Part I: Biometrics; Computer Forensic; Computer Vision; Dimension Reduction; Healthcare Information Systems; Image Processing; Image segmentation; Information Retrieval; Instance based learning; Machine Learning.Part II: ​Neural Network; Object Detection; Object Recognition; Online Handwriting Recognition; Optical Character Recognition; Security and Privacy; Unsupervised Clustering.

Image Reconstruction from Zernike Moments Under Symmetry Constraint

Image Reconstruction from Zernike Moments Under Symmetry Constraint
Author: Gurmukh Singh Panesar
Publisher:
Total Pages: 0
Release: 2020
Genre:
ISBN:

Symmetry is an important aspect that can be observed in various man made and natural objects. Symmetry is more likely to be a continuous feature rather than a binary feature. Quantitative analysis of the asymmetry, present in an object, is directly related to the measure of the difference between the given object and its symmetric counterpart. In our study, we propose a method, that reconstructs an image using Zernike moments, which represents the symmetric version of the given image. We use Zernike moments due to their invariance properties to various image transformations and ability to unique represent an image as the corresponding Zernike functions form an orthogonal basis. Ordinary least squares regression fails to calculate the Zernike moments. It is due to the presence of collinearity among the covariates obtained using Zernike functions. Therefore, we present the modified Ridge regression strategy to estimate Zernike moments, under a symmetry constraint, which enforces bilateral symmetry in planar images. An angle of the symmetry axis is estimated by minimizing the cost function, which determines the possible asymmetry present in the image. The given model is tested in three experiments involving image reconstruction and symmetry estimation. In the first experiment, images were reconstructed using Zernike moments calculated using Ridge regression. The second experiment consists of two parts. It starts with estimation of the symmetry axis evaluated for images showing bilateral symmetry at various angles. These experiments are then followed by image reconstruction with enforced symmetry. From these results the conclusion can be drawn that, Zernike moments estimated using Ridge regression can be efficiently used for the image reconstruction under the symmetry constraint. The symmetry constraint can be effectively utilized to estimate symmetry axis, and, in conjunction with Ridge regression, it can reconstruct an image which is the closest symmetric version of the given asymmetric image.

Accuracy of Rotation Invariant Moments for Image Analysis

Accuracy of Rotation Invariant Moments for Image Analysis
Author: Rahul Upneja
Publisher: LAP Lambert Academic Publishing
Total Pages: 88
Release: 2014-03
Genre:
ISBN: 9783659273988

Image processing has become a critical component in Engineering and technology and has numerous applications ranging from nanotechnologies to the digital communication technologies. This book is about the problem of accurate computation of orthogonal rotation invariant moments. The moments and orthogonal polynomials are classical concepts in mathematical analysis due to its attractive properties. This book gives excellent overview of the various image moments. Several new techniques to increase the accuracy and efficiency of moment descriptors are proposed. We utilize these results for solving the problem of reconstruction of images from orthogonal moments. The proposed method provides much improved accuracy of rotation invariant moments which provide very accurate reconstructed images, numerical stability and invariance.

Pattern Recognition

Pattern Recognition
Author: Tieniu Tan
Publisher: Springer
Total Pages: 800
Release: 2016-10-21
Genre: Computers
ISBN: 9811030022

The two-volume set CCIS 662 and CCIS 663 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition, CCPR 2016, held in Chengdu, China, in November 2016.The 121 revised papers presented in two volumes were carefully reviewed and selected from 199 submissions. The papers are organized in topical sections on robotics; computer vision; basic theory of pattern recognition; image and video processing; speech and language; emotion recognition.