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

A hybrid time- and space-integrating optical signal processor for spread spectrum
					application is described. The system is self-synchronizing and capable of
					decoding frequency-hopped spread spectrum signals with a large
					time–bandwidth product. Extensions to hybrid
					frequency-hopped/direct-sequence systems and to ambiguity function processors
					are provided. Both theory and experimental verification are included.

Spread spectrum time- and space-integrating optical processor

An optical image plane correlator is described which computes the ambiguity function for passive, wideband, complex, and basebanded signals. Quadrature input modulation, single-sideband filtering, and a modified image plane correlator requiring no moving parts are used to achieve the required complex correlations. The scenario, theory, and design are provided together with experimental verification on LFM and Barker-coded waveforms as well as synthetic data.

Optical image plane correlator for ambiguity surface computation

This paper presents the status of our SAR automatic target recognition (ATR) work on the Moving and Stationary Target Acquisition and Recognition (MSTAR) public database using the minimum noise and correlation energy (MINACE) distortion-invariant filter (DIF). We use a subset of the MSTAR public database for the benchmark three-class problem and we address confuser and clutter rejection. To handle the full 360° range of aspect view in MSTAR data, we use a set of Minace filters for each object; each filter should recognize the object (and its variants) in some angular range. We use fewer DIFs per object than prior work did. The Minace parameter c trades-off distortion-tolerance (recognition) versus discrimination (confuser/clutter rejection) performance. Our filter synthesis algorithm automatically selects the Minace filter parameter c and selects the training set images to be included in the filter, so that the filter can achieve both good recognition and good confuser and clutter rejection performance; this is achieved using a training and validation set. In our new filter synthesis method, no confuser, clutter, or test set data are used. The peak-to-correlation energy (PCE) ratio is used as the correlation plane metric in both filter synthesis and in tests, since it works better than correlation peak height. In tests, we do not assume that the test input's pose is known (as most prior work does), since pose estimation of SAR objects has a large margin of error; we describe our procedure for proper use of pose estimates in MSTAR recognition. The use of circular versus linear correlations is addressed. We also address the use of multi-look SAR data to improve performance.© (2006) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

MSTAR object classification and confuser and clutter rejection using Minace filters

We propose a binary hierarchical classification structure to address the multi-class classification problem with a new hierarchical design method, k-means SVRM (support vector representation machine) clustering. This greatly improves upon our prior IJCNN hierarchical design. At each node in the hierarchy, we apply the SVRDM (support vector representation and discrimination machine) classifier, which offers generalization and good rejection ability. We also provide new theoretical bases and methods for our choice of the kernel function and new SVRDM parameter selection rules. Classification and rejection test results are presented on new databases of both simulated and real infra-red (IR) data.

Hierarchical K-means Clustering Using New Support Vector Machines for Multi-class Classification

A technique for encoding binary outputs from optical filters or matrix memories used in an associative processor for object recognition is discussed. Binary coded output vectors (rather than unit vectors) are used and considerably improve storage capacity. The output codes or matrix memories are chosen from coding theory to enable error correction and detection. The error classification rate for the coded scheme is compared to the noncoded version for different amounts of noise in the input and output planes. Discussion of extensions to more classes, more errors, and multilevel coding are included.

David P. Casasent

Papers

Spread spectrum time- and space-integrating optical processor

Optical image plane correlator for ambiguity surface computation

MSTAR object classification and confuser and clutter rejection using Minace filters

Hierarchical K-means Clustering Using New Support Vector Machines for Multi-class Classification

Error-correction coding in an associative processor.