From the Publisher:
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.

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Neural networks for pattern recognition

The core component of most modern trackers is a discriminative classifier, tasked with distinguishing between the target and the surrounding environment. To cope with natural image changes, this classifier is typically trained with translated and scaled sample patches. Such sets of samples are riddled with redundancies—any overlapping pixels are constrained to be the same. Based on this simple observation, we propose an analytic model for datasets of thousands of translated patches. By showing that the resulting data matrix is circulant, we can diagonalize it with the discrete Fourier transform, reducing both storage and computation by several orders of magnitude. Interestingly, for linear regression our formulation is equivalent to a correlation filter, used by some of the fastest competitive trackers. For kernel regression, however, we derive a new kernelized correlation filter (KCF), that unlike other kernel algorithms has the exact same complexity as its linear counterpart. Building on it, we also propose a fast multi-channel extension of linear correlation filters, via a linear kernel, which we call dual correlation filter (DCF). Both KCF and DCF outperform top-ranking trackers such as Struck or TLD on a 50 videos benchmark, despite running at hundreds of frames-per-second, and being implemented in a few lines of code (Algorithm 1). To encourage further developments, our tracking framework was made open-source.

High-Speed Tracking with Kernelized Correlation Filters

We present a neural network-based upright frontal face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We present a straightforward procedure for aligning positive face examples for training. To collect negative examples, we use a bootstrap algorithm, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting nonface training examples, which must be chosen to span the entire space of nonface images. Simple heuristics, such as using the fact that faces rarely overlap in images, can further improve the accuracy. Comparisons with several other state-of-the-art face detection systems are presented, showing that our system has comparable performance in terms of detection and false-positive rates.

Neural network-based face detection

A major new professional reference work on fingerprint security systems and technology from leading international researchers in the field Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators

/pdf/handbook-of-fingerprint-recognition-16mh887j24.pdf

Handbook of Fingerprint Recognition

http://romisatriawahono.net/lecture/rm/survey/machine%20learning/Chandrashekar%20-%20Feature%20Selection%20Methods%20-%202014.pdf

A survey on feature selection methods

Many linear algebra operations, matrix inversions, etc. are required in pattern recogni­ tion as well as in signal processing. In this paper, we concentrate on feature extraction pattern recognition techniques (specifically a chord distribution and a moment feature space). For these two case studies, we note the various linear algebra operations required in distortion- invariant pattern recognition. Systolic processors can easily perform all required linear algebra functions. 1. INTRODUCTION Linear algebra operations are required in many signal processing applications and these have been extensively discussed elsewhere in this volume. In this paper, I note that similar operations are also required in many pattern recognition and object identification applica­ tions. In this paper, specific attention is given to feature extraction or feature space based pattern recognition problems and to viable applications such as achieving object rec­ ognition in the face of geometrical distortions in the input image. The two feature extrac­ tion case studies considered are the use of a chord distribution and a moment feature space. Each of these results in considerably different linear algebra operations required on the object features to achieve the desired object identification. Sections 2 and 3 address the chord distribution feature space and Section 4 addresses the moment feature space case study.

Linear Algebra Techniques For Pattern Recognition: Feature Extraction Case Studies

Partitioning of an object into N parts and the use of M filters with different output patterns are used to produce an NM digit symbolic encoding of the input object. The rule based system and techniques to update partitions of the object are emphasized in this paper. Three-dimensional aspect-invariant, shift-invariant and distortion-invariant pattern recognition data are considered and provided to demonstrate the usefulness of this technique for adaptive image processing.

Adaptive Learning Optical Symbolic Processor

There are presented the results of the investigations of the fingerprints' images correlation recognition in conditions of different distortions - scale, angular orientation change, image's surface reducing, noises' influence. There are examined possibilities of the persons' identification and their verification. There are proposed and investigated the method of the fingerprints' semi-spectrums recognition and the method of the fingerprints' space-dependent recognition. There are presented the structures of the special purpose mono-channel and multi-channel optical-electronic systems and are described computing processes in the systems at the realization of the different fingerprints recognition algorithms: ,,FSR-1, FSR-2, FSDR-1, FSDR-2, FICR. Also, there are presented the results of systems investigations: fingerprints time recognition, systems productivity at the fingerprints comparison step, systems prices.

Perspective optical-electronic technologies for persons identification and verification on the bases of the fingerprints

Specific optical architectures for performing logic and numeric functions using symbolic substitutionare detailed. Optical laboratory results of the operations are presented and advanced considerationsconcerning these operations are discussed. 1 Introduction The technique of symbolic substitution was introduced by Huang i to alleviate the speed bottlenecksin electronic hardware. In its most basic form, symbolic substitution involves locating the occurrence of a specific symbol in an input environment, and replacing it with another symbol in the output.The specific replacements to be done are defined by substitution rules in the form in out, wherein is the input symbol and out is the output symbol. Symbolic substitution can perform any logic ornumerical operation. 1 -3 The recognition and replacement steps of symbolic substitution can both bedone in parallel using optics. We and others 2,4 -6 have detailed a correlator architecture to implementsymbolic substitution. We find this architecture attractive, since one optical architecture can be used toachieve logic, numerical, morphological and inference functions. ' The operations to be performed arecontrolled adaptively by filter functions. Morphology is the algebra of sets and is used extensively toimplement image -processing operations.

Optical Laboratory Symbolic Substitution Logic And Numeric Processor

A new algorithm based on scale-space median and gabor filtering is used to find boundaries in electron microscopy images under noise and low contrast. Boundary information from different scales are fused to find triple junctions and dihedral angles that are of use in material science. The proposed algorithm has been tested on various electron microscopy images and has been shown to be robust to changing imaging conditions, noise and contrast.

David P. Casasent

Papers

Linear Algebra Techniques For Pattern Recognition: Feature Extraction Case Studies

Adaptive Learning Optical Symbolic Processor

Perspective optical-electronic technologies for persons identification and verification on the bases of the fingerprints

Optical Laboratory Symbolic Substitution Logic And Numeric Processor

Scale-space median and gabor filtering for boundary detection in electron microscopy images