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

New training strategies for RBF neural networks for X-ray agricultural product inspection

Processing of real–time X–ray images of randomly oriented and touching pistachio nuts for product inspection
is considered. Processing to isolate individual nuts (segmentation) is emphasized. The processing consisted of a blob coloring
algorithm, filters, and watershed transforms to segment touching nuts, and morphological processing to produce an image
of only the nutmeat. Each operation is detailed and quantitative data for each are presented. These techniques are useful for
many different product inspection problems in agriculture and other areas.

Detection and segmentation of items in x–ray imagery

Synthetic discriminant functions (SDFs) allow distortion invariance to be achieved in optical correlators, thus making such systems more practical. The synthesis of four different types of SDFs is reviewed. Their performance in extensive projection tests on 144 images is presented, together with initial performance tests in the presence of noise. This distortion-invariant and shift-invariant pattern recognition algorithm can also be implemented digitally.

Projection Synthetic Discriminant Function Performance

A general formulation of the properties, optical synthesis, and existence of space variant optical processors using coordinate transformations is provided. General expressions that the coordinate transformations must satisfy are described. Specific examples of the use of the methodology to determine the existence of and methods for the realization of space variant optical processors are included for various specific data deformations and applications.

/pdf/deformation-invariant-optical-processors-using-coordinate-2iemoqfe1a.pdf

Deformation invariant optical processors using coordinate transformations.

An optical processor that realizes a generalized chord transformation is described. The wedge-ring detector samples of an autocorrelation are shown to be the histograms of the chord distributions. This dimensionality reduced set of features is used as the feature vector inputs for a Fisher linear classifier to determine the class of the input object independent of geometrical distortions. Initial discussions on the use of different classifiers, the polarity of the classifier’s output, and selection of the image training set are also advanced.

David P. Casasent

Papers

New training strategies for RBF neural networks for X-ray agricultural product inspection

Detection and segmentation of items in x–ray imagery

Projection Synthetic Discriminant Function Performance

Deformation invariant optical processors using coordinate transformations.

Generalized chord transformation for distortion-invariant optical pattern recognition.