Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

Suggested by the structure of the visual nervous system, a new algorithm is proposed for pattern recognition. This algorithm can be realized with a multilayered network consisting of neuron-like cells. The network, “neocognitron”, is self-organized by unsupervised learning, and acquires the ability to recognize stimulus patterns according to the differences in their shapes: Any patterns which we human beings judge to be alike are also judged to be of the same category by the neocognitron. The neocognitron recognizes stimulus patterns correctly without being affected by shifts in position or even by considerable distortions in shape of the stimulus patterns.

Neocognitron--A New Algorithm for Pattern Recognition Tolerant of Deformations and Shifts in Position

Local feature extraction and matching on range images: 2.5D SIFT

Wavelet-Based Off-Line Handwritten Signature Verification

In this paper, a wavelet-based off-line handwritten signature verification system is proposed The proposed system can automatically identify useful and common features which consistently exist within different signatures of the same person and, based on these features, verify whether a signature is a forgery or not The system starts with a closed-contour tracing algorithm The curvature data of the traced closed contours are decomposed into multiresolutional signals using wavelet transforms Then the zero-crossings corresponding to the curvature data are extracted as features for matching Moreover, a statistical measurement is devised to decide systematically which closed contours and their associated frequency data of a writer are most stable and discriminating Based on these data, the optimal threshold value which controls the accuracy of the feature extraction process is calculated The proposed approach can be applied to both on-line and off-line signature verification systems Experimental results show that the average success rates for English signatures and Chinese signatures are 9257% and 9368%, respectively

Wavelet-based off-line handwritten signature verification

A new edge detection technique based on detection of normal changes is proposed. Most of the existing range image-based edge detection algorithms base their detection, criterion on depth or curvature changes. However, the depth change-based approach does not have keen sensitivity in detecting roof (or crease) edges, and the curvature change-based approach suffers from a complicated and tedious principal curvature derivation process. Using normal changes as a detecting criterion, on the other hand, the existence of an edge can be easily detected, even when the change across a boundary is slight. Experimental results using both synthetic and real images demonstrate that the proposed method can efficiently detect both step and roof edges.

Multiscale edge detection on range images via normal changes

In this paper, we propose a cascade fuzzy ART (CFART) neural network which can be used as an extensible database in a model-based object recognition system. The proposed CFART networks can accept both binary and continuous inputs. Besides, it preserves the prominent characteristics of a fuzzy ART network and extends the fuzzy ART's capability toward a hierarchical class representation of input patterns. The learning processes of the proposed network are unsupervised and self-organizing, which include coupled top-down searching and bottom-up learning processes. In addition, a global searching tree is built to speed up the learning and recognition processes.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Cascade fuzzy ART: a new extensible database for model-based object recognition

In this paper, a cascade fuzzy ART (CFART) network is developed and applied to 3D object recognition. The proposed CFART network contains multiple layers which can express a hierarchical representation of an input pattern. The learning processes of the proposed network are unsupervised and self-organizing, which include a top-down search process and a bottom-up learning process. The proposed CFART can accept both binary and analog inputs. With fast learning and categorization capabilities, the proposed network is capable of acting as an extensible database, providing a multi-resolutional representation of 3D objects. In the experiments, we use superquadrics as a demonstration example.

CFART: a multi-resolutional adaptive resonance system

In this report, we propose a cascade fuzzy ART (CFART) neural network which can function as an extensible database in a model-based object recognition system. The proposed CFART network contains multiple layers. It preserves the prominent characteristics of a fuzzy ART network and extends fuzzy ART's capability toward hierarchical representation of input patterns. The learning process of the proposed network is unsupervised and self-organizing, and includes a top-down searching process and a bottom-up learning process. The top-down and bottom-up learning processes interact with each other in a closely coupled manner. Basically, the top-down searching guides the bottom-up learning whereas the bottom-up learning in uences the top-down searching by changing its searching fuzzy boundary. In addition, a global searching tree is built to speed up the learning and recognition processes. The proposed CFART can accept both binary and analog inputs. With fast learning and categorization capabilities, the proposed network is able to function like an extensible database and to provide an e cient multi-resolutional representation capability for 3D objects. Experimental results, using both synthetic and real 3D data, prove that the proposed method is indeed an e cient and powerful representation scheme. 2

CFART: a New Multi-Resolutional Adaptive Resonance System for Object Recognition

A new edge detection technique based on detection of normal changes is proposed. Most of the existing range image-based edge detection algorithms base their detection criterion on depth or curvature changes. However, the depth change-based approach does not have keen sensitivity in detecting roof ( or crease ) edges, and the curvature change-based approach suffers from a complicated and tedious principal curvature derivation process. Using normal changes as a detecting criterion, on the other hand, the existence of an edge can be easily detected, even when the change across a boundary is slight. Experimental results using both synthetic and real images demonstrate that the proposed method can efficiently detect both step and roof edges.

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Hai-Lung Hung

Papers

Multiscale edge detection on range images via normal changes

Cascade fuzzy ART: a new extensible database for model-based object recognition

CFART: a multi-resolutional adaptive resonance system

CFART: a New Multi-Resolutional Adaptive Resonance System for Object Recognition

Multiscale Edge Detection via Normal Changes