198169122
An adaptive Modular Neural Network with Application to unconstrained Character Recognition
31996759
Analysis and Design of Parallel Thinning Algorithms - A Generic Approach
In this paper, a generic approach for the analysis and design of parallel thinning algorithms is presented. The procedures for designing 4-subcycle/iteration, 2-subcycle/iteration, and 1-subcycle/iteration thinning algorithms are developed. The experimental results show that these thinning algorithms designed by proposed approach preserve image connectivity, produce thinner results, and obtain faster speed than many existing key thinning algorithms. As an example, a 1-subcycle/iteration algorithm and its experimental results were illustrated, which is in general about two times faster than others in the literature.
2857808
A knowledge-based segmentation algorithm for enhanced recognition of handwritten courtesy amounts
Abstract A knowledge-based segmentation algorithm to enhance recognition of courtesy amounts on bank checks is proposed in this paper. This algorithm uses multiple contextual cues to enhance segmentation and recognition. The system described extracts context from the handwritten numerals and uses a syntax parser based on a deterministic finite automaton to provide adequate feedback to enhance recognition. Further feedback is provided by a simple legal amount decoder that determines word count and recognizes several key words (e.g. thousand and hundred). This provides an additional semantic constraint on the dollar section. The segmentation analysis module presented is capable of handling a number of commonly used styles for courtesy amount representation. Both handwritten and machine written courtesy and legal amounts were utilized to test the efficacy of the preprocessor for the check recognition system described in this paper. The substitution error was reduced by 30–40% depending on the input check mix.
41586583
3D Articulated Object Understanding, Learning, and Recognition from 2D Images
This paper is aimed at 3D object understanding from 2D images, including articulated objects in active vision environment, using interactive, and internet virtual reality techniques. Generally speaking, an articulated object can be divided into two portions: main rigid portion and articulated portion. It is more complicated that "rigid" object in that the relative positions, shapes or angles between the main portion and the articulated portion have essentially infinite variations, in addition to the infinite variations of each individual rigid portions due to orientations, rotations and topological transformations. A new method generalized from linear combination is employed to investigate such problems. It uses very few learning samples, and can describe, understand, and recognize 3D articulated objects while the objects status is being changed in an active vision environment.
15652869
Detection of courtesy amount block on bank checks
This paper discusses a technique for locating the courtesy amount block on bank checks. In the analysis and recognition process, connected components in the image are identified first. Then, strings are constructed on the basis of proximity and horizontal alignment of characters. Next, a set of rules and heuristics are applied to these strings to choose the correct one. The chosen string is only accepted if it passes a verification test, which includes an attempt to recognize the currency sign. A deterministic finite automaton system is then used for segmenting the handprinted courtesy amount. Finally, the separated components are passed on to a neural network based recognition system.
38659056
Intelligent Pattern Recognition and Applications
28206858
Tennis Video Analysis Based on Transformed Motion Vectors
Motion Vectors (MV) indicate the motion characteristics between two video frames, and has been widely used in the content-based sports video analysis. Previous works on sports video analysis have proved the effectiveness and efficiency of the MV-based methods. However, in the tennis video, the MV-based methods are seldom applied because the motion represented by MV is greatly deformed relative to the player’s true movement due to the camera’s diagonal shooting. In this paper, an algorithm of MV transformation is proposed to revise the deformed MV using a pinhole camera model. With the transformed MVs, we generate the temporal feature curves and employ Hidden Markov Models to classify two types of player’s basic actions. Evaluation on four hours live tennis videos shows very encouraging results.
5778703
Belief Revision in Probability Theory
In a probability-based reasoning system, Bayes' theorem and its variations are often used to revise the system's beliefs. However, if the explicit conditions and the implicit conditions of probability assignments are properly distinguished, it follows that Bayes' theorem is not a generally applicable revision rule. Upon properly distinguishing belief revision from belief updating, we see that Jeffrey's rule and its variations are not revision rules, either. Without these distinctions, the limitation of the Bayesian approach is often ignored or underestimated. Revision, in its general form, cannot be done in the Bayesian approach, because a probability distribution function alone does not contain the information needed by the operation.
28879338
Reasoning in Practical Situations
An automatic reasoning system usually consists of the following major components: (1) a formal language that represents knowledge, (2) a semantics that defines meaning and truth value in the language, (3) a set of inference rules that derives new knowledge from existing knowledge, (4) a memory that stores knowledge, and (5) a control mechanism that chooses premises and rules in each inference step. The first three components are usually referred to as a logic, or the logical part of the reasoning system, and the last two as an implementation of, or the control part of the system.
17885591
Towards Adaptive Clustering in Self-monitoring Multi-agent Networks
A Decentralised Adaptive Clustering (DAC) algorithm for self-monitoring impact sensing networks is presented within the context of CSIRO-NASA Ageless Aero-space Vehicle project. DAC algorithm is contrasted with a Fixed-order Centralised Adaptive Clustering (FCAC) algorithm, developed to evaluate the comparative performance. A number of simulation experiments is described, with a focus on the scalability and convergence rate of the clustering algorithm. Results show that DAC algorithm scales well with increasing network and data sizes and is robust to dynamics of the sensor-data flux.
16423538
On Decentralised Clustering in self-monitoring networks
A Decentralised Adaptive Clustering (DAC) algorithm for multiagent networks is contrasted with a Fixed-order Centralised Adaptive Clustering algorithm (FCAC). The clustering is done on sensor readings detected within a self-monitoring impact sensing network. Simulation results show that DAC algorithm scales well with increasing network and data sizes and in some cases outperforms FCAC algorithm. While the common-sense intuition suggests that centralised algorithm is always superior, we support the simulation results with a simple counter-example.