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

This paper presents feasibility studies of a multispectral moving target detection (MTD) system designed to detect and track low-signature moving targets. The system performs multispectral detection based on linear and nonlinear optimal discriminating features (ODF) for low-signature target detection and correlation tracking of slowly moving targets in clutter. Multispectral detection was tested against multispectral data from an acousto-optic tunable filter in the visible and near infrared. Optical Joint Transform Correlator for target tracking was designed and manufactured. The feasibility of correlation target tracking was demonstrated with actual Army MICOM FLIR long wavelength infrared (LWIR: 8 to 12 μm) images of moving vehicles and tanks in clutter.

Optimal discriminating multispectral target detection and optical correlation target tracking

A new application of the triple product processor in the self-synchronization of long spread spectrum codes with high processing gain and a large range delay search is described. The technique presented is useful for fine ranging, message decoding and C3I applications.© (1982) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Spread spectrum product code processing using the triple product processor

Optical linear discriminant functions

Face recognition with both pose and illumination variations is considered. In addition, we consider the ability of the classifier to reject non-member or imposter face inputs; most prior work has not addressed this. Initially, non-registered test and training set imagery is used (this is realistic) and a two-step pose estimation/face recognition processor is employed which includes registration of the test input to the reference poses. A new SVRDM support vector representation and discrimination machine classifier is proposed and initial face recognition-rejection results are presented. Face recognition and verification results are presented.

Distortion-invariant (pose and illumination) face recognition using new SVM

The performance of two new optical correlation filters (G-MACE and MINACE) for large class (many fonts and true class words) OCR (optical character recognition) applications is considered. We consider filters that can recognize many key words in upper case (UC) and mixed case (MC) and various point sizes in the presence of OCR scanner sampling errors. New results are presented and guidelines for large class filters are advanced.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

David P. Casasent

Papers

Optimal discriminating multispectral target detection and optical correlation target tracking

Spread spectrum product code processing using the triple product processor

Optical linear discriminant functions

Distortion-invariant (pose and illumination) face recognition using new SVM

Optical correlation filters for large-class OCR applications