Constraint optimization techniques for graph matching applicable to 3-D object recognition.

by Chi-Min Pang.Thesis (M.Phil.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 110-[115]).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Range Images --- p.1Chapter 1.2 --- Rigid Body Model --- p.3Chapter 1.3 --- Motivation --- p.4Chapter 1.4 --- Thesis Outline --- p.6Chapter 2 --- Object Recognition by Relaxation Processes --- p.7Chapter 2.1 --- An Overview of Probabilistic Relaxation Labelling --- p.8Chapter 2.2 --- Formulation of Model-matching Problem Solvable by Probabilistic Relaxation --- p.10Chapter 2.2.1 --- Compatibility Coefficient --- p.11Chapter 2.2.2 --- Match Score --- p.13Chapter 2.2.3 --- Iterative Algorithm --- p.14Chapter 2.2.4 --- A Probabilistic Concurrent Matching Scheme --- p.15Chapter 2.3 --- Formulation of Model-merging Problem Solvable by Fuzzy Relaxation --- p.17Chapter 2.3.1 --- Updating Mechanism --- p.17Chapter 2.3.2 --- Iterative Algorithm --- p.19Chapter 2.3.3 --- Merging Sub-Rigid Body Models --- p.20Chapter 2.4 --- Simulation Results --- p.21Chapter 2.4.1 --- Experiments in Model-matching Using Probabilistic Relaxation --- p.22Chapter 2.4.2 --- Experiments in Model-matching Using Probabilistic Concur- rent Matching Scheme --- p.26Chapter 2.4.3 --- Experiments in Model-merging Using Fuzzy Relaxation --- p.33Chapter 2.5 --- Summary --- p.36Chapter 3 --- Object Recognition by Hopfield Network --- p.37Chapter 3.1 --- An Overview of Hopfield Network --- p.38Chapter 3.2 --- Model-matching Problem Solved by Hopfield Network --- p.41Chapter 3.2.1 --- Representation of the Solution --- p.41Chapter 3.2.2 --- Energy Function --- p.42Chapter 3.2.3 --- Equations of Motion --- p.46Chapter 3.2.4 --- Interpretation of Solution --- p.49Chapter 3.2.5 --- Convergence of the Hopfield Network --- p.50Chapter 3.2.6 --- Iterative Algorithm --- p.51Chapter 3.3 --- Estimation of Distance Threshold Value --- p.53Chapter 3.4 --- Cooperative Concurrent Matching Scheme --- p.55Chapter 3.4.1 --- Scheme for Recognizing a Single Object --- p.56Chapter 3.4.2 --- Scheme for Recognizing Multiple Objects --- p.60Chapter 3.5 --- Simulation Results --- p.60Chapter 3.5.1 --- Experiments in the Model-matching Problem Using a Hopfield Network --- p.61Chapter 3.5.2 --- Experiments in Model-matching Problem Using Cooperative Concurrent Matching --- p.69Chapter 3.5.3 --- Experiments in Model-merging Problem Using Hopfield Network --- p.77Chapter 3.6 --- Summary --- p.80Chapter 4 --- Genetic Generation of Weighting Parameters for Hopfield Network --- p.83Chapter 4.1 --- An Overview of Genetic Algorithms --- p.84Chapter 4.2 --- Determination of Weighting Parameters for Hopfield Network --- p.86Chapter 4.2.1 --- Chromosomal Representation --- p.87Chapter 4.2.2 --- Initial Population --- p.88Chapter 4.2.3 --- Evaluation Function --- p.88Chapter 4.2.4 --- Genetic Operators --- p.89Chapter 4.2.5 --- Control Parameters --- p.91Chapter 4.2.6 --- Iterative Algorithm --- p.94Chapter 4.3 --- Simulation Results --- p.95Chapter 4.3.1 --- Experiments in Model-matching Problem using Hopfield Net- work with Genetic Generated Parameters --- p.95Chapter 4.3.2 --- Experiments in Model-merging Problem Using Hopfield Network --- p.101Chapter 4.4 --- Summary --- p.104Chapter 5 --- Conclusions --- p.106Chapter 5.1 --- Conclusions --- p.106Chapter 5.2 --- Suggestions for Future Research --- p.109Bibliography --- p.110Chapter A --- Proof of Convergence of Fuzzy Relaxation Process --- p.11

NASA JSC neural network survey results

A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc

Learning as a Nonlinear Line of Attraction for Pattern Association, Classification and Recognition

Development of a mathematical model for learning a nonlinear line of attraction is presented in this dissertation, in contrast to the conventional recurrent neural network model in which the memory is stored in an attractive fixed point at discrete location in state space. A nonlinear line of attraction is the encapsulation of attractive fixed points scattered in state space as an attractive nonlinear line, describing patterns with similar characteristics as a family of patterns. It is usually of prime imperative to guarantee the convergence of the dynamics of the recurrent network for associative learning and recall. We propose to alter this picture. That is, if the brain remembers by converging to the state representing familiar patterns, it should also diverge from such states when presented by an unknown encoded representation of a visual image. The conception of the dynamics of the nonlinear line attractor network to operate between stable and unstable states is the second contribution in this dissertation research. These criteria can be used to circumvent the plasticity-stability dilemma by using the unstable state as an indicator to create a new line for an unfamiliar pattern. This novel learning strategy utilizes stability (convergence) and instability (divergence) criteria of the designed dynamics to induce self-organizing behavior. The self-organizing behavior of the nonlinear line attractor model can manifest complex dynamics in an unsupervised manner. The third contribution of this dissertation is the introduction of the concept of manifold of color perception. The fourth contribution of this dissertation is the development of a nonlinear dimensionality reduction technique by embedding a set of related observations into a low-dimensional space utilizing the result attained by the learned memory matrices of the nonlinear line attractor network. Development of a system for affective states computation is also presented in this dissertation. This system is capable of extracting the user\u27s mental state in real time using a low cost computer. It is successfully interfaced with an advanced learning environment for human-computer interaction

Data comparison schemes for Pattern Recognition in Digital Images using Fractals

Pattern recognition in digital images is a common problem with application in remote sensing, electron microscopy, medical imaging, seismic imaging and astrophysics for example. Although this subject has been researched for over twenty years there is still no general solution which can be compared with the human cognitive system in which a pattern can be recognised subject to arbitrary orientation and scale. The application of Artificial Neural Networks can in principle provide a very general solution providing suitable training schemes are implemented. However, this approach raises some major issues in practice. First, the CPU time required to train an ANN for a grey level or colour image can be very large especially if the object has a complex structure with no clear geometrical features such as those that arise in remote sensing applications. Secondly, both the core and file space memory required to represent large images and their associated data tasks leads to a number of problems in which the use of virtual memory is paramount. The primary goal of this research has been to assess methods of image data compression for pattern recognition using a range of different compression methods. In particular, this research has resulted in the design and implementation of a new algorithm for general pattern recognition based on the use of fractal image compression. This approach has for the first time allowed the pattern recognition problem to be solved in a way that is invariant of rotation and scale. It allows both ANNs and correlation to be used subject to appropriate pre-and post-processing techniques for digital image processing on aspect for which a dedicated programmer's work bench has been developed using X-Designer