A formalism for the syntactic description and recognition of two dimensional patterns /

The concept of imaginary parsing is advanced for recognition of partially obscured patterns.A new formalism is proposed for the syntactic description and recognition of two-dimensional patterns. The recognition problem is treated comprehensively from scanning and primitive identification all the way to recognition of patterns syntactically.The vehicle of grammars coupled with ideas of static and dynamic chaining of pattern primitives are used for describing arbitrary two-dimensional patterns. Primitives in the grammar are unquantized vector entities. The grammar derives a "pattern form, " while parametrization of the pattern form yields specific patterns

Neural Network Configurations Analysis for Multilevel Speech Pattern Recognition System with Mixture of Experts

This chapter proposes to analyze two configurations of neural networks to compose the expert set in the development of a multilevel speech signal pattern recognition system of 30 commands in the Brazilian Portuguese language. Then, multilayer perceptron (MLP) and learning vector quantization (LVQ) networks have their performances verified during the training, validation and test stages in the speech signal recognition, whose patterns are given by two-dimensional time matrices, result from mel-cepstral coefficients coding by the discrete cosine transform (DCT). In order to avoid the pattern separability problem, the patterns are modified by a nonlinear transformation to a high-dimensional space through a suitable set of Gaussian radial base functions (GRBF). The performance of MLP and LVQ experts is improved and configurations are trained with few examples of each modified pattern. Several combinations were performed for the neural network topologies and algorithms previously established to determine the network structures with the best hit and generalization results

Three-dimensional object recognition

In the development of an object pattern recognition system, feature construction is always the problem issue. Due to the large amount of information contained in three dimensional (3D) objects, features extracted to efficiently and sufficiently represent 3D objects are difficult to obtain. Thus, current commercially available object recognition systems mostly emphasize the classification of two dimensional objects or patterns. This work presents a paradigm to develop a complete 3D object recognition system that uses simple and efficient features, and supports the integration of CAD/CAM models;In this research, several proposed algorithm for extracting features representing 3D objects are constructed based on the properties of the Radon transform. Two of these algorithms have been successfully implemented for manufacturing applications. The implemented systems use the artificial neural network as the classifier to learn features and to identify 3D objects. A statistical model has also been established based on the output node values of a perceptron neural network to predict the future misclassifications of features which have not been learned by the neural network in the training stage

Hierarchical Associative Memory Based on Oscillatory Neural Network

In this thesis we explore algorithms and develop architectures based on emerging nano-device technologies for cognitive computing tasks such as recognition, classification, and vision. In particular we focus on pattern matching in high dimensional vector spaces to address the nearest neighbor search problem. Recent progress in nanotechnology provides us novel nano-devices with special nonlinear response characteristics that fit cognitive tasks better than general purpose computing. We build an associative memory (AM) by weakly coupling nano-oscillators as an oscillatory neural network and design a hierarchical tree structure to organize groups of AM units. For hierarchical recognition, we first examine an architecture where image patterns are partitioned into different receptive fields and processed by individual AM units in lower levels, and then abstracted using sparse coding techniques for recognition at higher levels. A second tree structure model is developed as a more scalable AM architecture for large data sets. In this model, patterns are classified by hierarchical k-means clustering and organized in hierarchical clusters. Then the recognition process is done by comparison between the input patterns and centroids identified in the clustering process. The tree is explored in a "depth-only" manner until the closest image pattern is output. We also extend this search technique to incorporate a branch-and-bound algorithm. The models and corresponding algorithms are tested on two standard face recognition data-sets. We show that the depth-only hierarchical model is very data-set dependent and performs with 97% or 67% recognition when compared to a single large associative memory, while the branch and bound search increases time by only a factor of two compared to the depth-only search

Wearable device-based gait recognition using angle embedded gait dynamic images and a convolutional neural network

The widespread installation of inertial sensors in smartphones and other wearable devices provides a valuable opportunity to identify people by analyzing their gait patterns, for either cooperative or non-cooperative circumstances. However, it is still a challenging task to reliably extract discriminative features for gait recognition with noisy and complex data sequences collected from casually worn wearable devices like smartphones. To cope with this problem, we propose a novel image-based gait recognition approach using the Convolutional Neural Network (CNN) without the need to manually extract discriminative features. The CNN’s input image, which is encoded straightforwardly from the inertial sensor data sequences, is called Angle Embedded Gait Dynamic Image (AE-GDI). AE-GDI is a new two-dimensional representation of gait dynamics, which is invariant to rotation and translation. The performance of the proposed approach in gait authentication and gait labeling is evaluated using two datasets: (1) the McGill University dataset, which is collected under realistic conditions; and (2) the Osaka University dataset with the largest number of subjects. Experimental results show that the proposed approach achieves competitive recognition accuracy over existing approaches and provides an effective parametric solution for identification among a large number of subjects by gait patterns

Nest Location and Nest Recognition in Two Solitary Bee Species Osmia lignaria Say and Megachile rotundata (F.) (Hymenoptera: Megachilidae)

The visual and olfactory cues used in short-range orientation, specifically nest location and nest recognition, were studied in two solitary bee species Osmia lignaria Say and Megachile rotundata (F.) Osmia lignaria, the blue orchard bee, is an important pollinator of orchard crops, such as apples, cherries, and almonds; and M. rotundata, the alfalfa leafcutting bee, is used in commercial pollination of alfalfa. The general objective was to better understand how these two species locate their nests and how improving nest location could benefit crop pollination. The use of proximal visual landmarks at the nesting site was investigated with M. rotundata, and revealed that females rely more on vertical landmarks than on horizontal landmarks for nest location. Osmia lignaria and M. rotundata were also shown to use 3-dimensional patterns as well as color contrast patterns for nest location. Changing the depth of the 3-dimensional pattern and the color contrast brightness affected nest location ability of both species. Applying these results to commercial situations with M. rotundata showed that providing 3-dimensional patterns to commercial nesting boards, either by separating the boards or by designing 3D boards, allowed M. rotundata females to improve their nest location. The 3D board also decreased the incidence of chalkbrood-related mortality, caused by the fungus Ascosphaera aggregata. Finally, in-nest observations showed O. lignaria females marking their entire nest with abdominal secretions. These secretions provided olfactory cues that O. lignaria females use for individual nest recognition. A chemical analysis of the nest markings revealed the presence of free fatty acids, long chain hydrocarbons, and wax esters. These results have implications for commercial bee management practices, where visual and olfactory cues can be manipulated. Improving the nest location performance of M. rotundata and O. lignaria females would decrease nest location time, thus having important consequences on pollination efficiency and brood production of both species