Partial Shape Matching Using Genetic Algorithms

Shape recognition is a challenging task when images contain overlapping, noisy, occluded, partial shapes. This paper addresses the task of matching input shapes with model shapes described in terms of features such as line segments and angles. The quality of matching is gauged using a measure derived from attributed shape grammars. We apply genetic algorithms to the partial shape-matching task. Preliminary results, using model shapes with 6 to 70 features each, are extremely encouraging

Two-dimensional object recognition through two-stage string matching

[[abstract]]A two-stage string matching method for the recognition of two-dimensional (2-D) objects is proposed in this work. The first stage is a global cyclic string matching. The second stage is a local matching with local dissimilarity measure computing. The dissimilarity measure function of the input shape and the reference shape is obtained by combining the global matching cost and the local dissimilarity measure. The proposed method has the advantage that there is no need to set any parameter in the recognition process. Experimental results indicate that the two-stage string matching approach significantly improves the recognition rates while comparing to the one-stage string matching method.[[fileno]]2020405010059[[department]]工工

Process grammar and process history for 2D objects

This project is the written report for the course in Picture Processing at the Department of Computer Science, Aarhus University. The starting point is a paper by Michael Leyton in Artificial Intelligence 34, 1988: "A process grammar for shape". The paper describes how it is possible to derive the process history for an object from its state at two stages in its development. The aim of this project is to describe and test an algorithm for doing so

Automatic visual recognition using parallel machines

Invariant features and quick matching algorithms are two major concerns in the area of automatic visual recognition. The former reduces the size of an established model database, and the latter shortens the computation time. This dissertation, will discussed both line invariants under perspective projection and parallel implementation of a dynamic programming technique for shape recognition. The feasibility of using parallel machines can be demonstrated through the dramatically reduced time complexity. In this dissertation, our algorithms are implemented on the AP1000 MIMD parallel machines. For processing an object with a features, the time complexity of the proposed parallel algorithm is O(n), while that of a uniprocessor is O(n2). The two applications, one for shape matching and the other for chain-code extraction, are used in order to demonstrate the usefulness of our methods. Invariants from four general lines under perspective projection are also discussed in here. In contrast to the approach which uses the epipolar geometry, we investigate the invariants under isotropy subgroups. Theoretically speaking, two independent invariants can be found for four general lines in 3D space. In practice, we show how to obtain these two invariants from the projective images of four general lines without the need of camera calibration. A projective invariant recognition system based on a hypothesis-generation-testing scheme is run on the hypercube parallel architecture. Object recognition is achieved by matching the scene projective invariants to the model projective invariants, called transfer. Then a hypothesis-generation-testing scheme is implemented on the hypercube parallel architecture

A two-stage framework for polygon retrieval.

by Tung Lun Hsing.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 80-84).Abstract --- p.iAcknowledgement --- p.iiChapter 1 --- Introduction --- p.1Chapter 2 --- Literature Survey --- p.8Chapter 2.1 --- The Freeman Chain Code Approach --- p.8Chapter 2.2 --- The Moment Approach --- p.10Chapter 2.3 --- The Rectangular Cover Approach --- p.12Chapter 2.4 --- The Potential-Based Approach --- p.15Chapter 2.5 --- The Normalized Coordinate System Approach --- p.17Chapter 2.6 --- The Hausdorff Distance Method --- p.20Chapter 2.7 --- The PCA Approach --- p.22Chapter 3 --- Binary Shape Descriptor --- p.26Chapter 3.1 --- Basic idea --- p.26Chapter 3.2 --- Standardized Binary String Descriptor --- p.27Chapter 3.3 --- Number of equivalent classes for n-gons --- p.28Chapter 4 --- The Two-Stage Framework --- p.30Chapter 5 --- Multi-Resolution Area Matching --- p.33Chapter 5.1 --- The idea --- p.33Chapter 5.2 --- Computing MRAI --- p.34Chapter 5.3 --- Measuring similarity using MRAI --- p.36Chapter 5.4 --- Query processing using MRAM --- p.38Chapter 5.5 --- Characteristics and Discussion --- p.40Chapter 6 --- Circular Error Bound and Minimum Circular Error Bound --- p.41Chapter 6.1 --- Polygon Matching using Circular Error Bound --- p.41Chapter 6.1.1 --- Translation --- p.43Chapter 6.1.2 --- Translation and uniform scaling in x-axis and y-axis directions --- p.45Chapter 6.1.3 --- Translation and independent scaling in x-axis and y-axis directions --- p.47Chapter 6.2 --- Minimum Circular Error Bound --- p.48Chapter 6.3 --- Characteristics --- p.49Chapter 7 --- Experimental Results --- p.50Chapter 7.1 --- Setup --- p.50Chapter 7.1.1 --- Polygon generation --- p.51Chapter 7.1.2 --- Database construction --- p.52Chapter 7.1.3 --- Query processing --- p.54Chapter 7.2 --- Running time comparison --- p.55Chapter 7.2.1 --- Experiment I --- p.55Chapter 7.2.2 --- Experiment II --- p.58Chapter 7.2.3 --- Experiment III --- p.60Chapter 7.3 --- Visual ranking comparison --- p.61Chapter 7.3.1 --- Experiment I --- p.61Chapter 7.3.2 --- Experiment II --- p.62Chapter 7.3.3 --- Experiment III --- p.63Chapter 7.3.4 --- Conclusion on visual ranking experiments --- p.66Chapter 8 --- Discussion --- p.68Chapter 8.1 --- N-ary Shape Descriptor --- p.68Chapter 8.2 --- Distribution of polygon equivalent classes --- p.69Chapter 8.3 --- Comparing polygons with different number of vertices --- p.72Chapter 8.4 --- Relaxation of assumptions --- p.73Chapter 8.4.1 --- Non-degenerate --- p.74Chapter 8.4.2 --- Simple --- p.74Chapter 8.4.3 --- Closed --- p.76Chapter 9 --- Conclusion --- p.78Bibliography --- p.8

Fast algorithms for sequence data searching.

by Sze-Kin Lam.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 71-76).Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 2 --- Related Work --- p.6Chapter 2.1 --- Sequence query processing --- p.8Chapter 2.2 --- Text sequence searching --- p.8Chapter 2.3 --- Numerical sequence searching --- p.11Chapter 2.4 --- Indexing schemes --- p.17Chapter 3 --- Sequence Data Searching using the Projection Algorithm --- p.21Chapter 3.1 --- Sequence Similarity --- p.21Chapter 3.2 --- Searching Method --- p.24Chapter 3.2.1 --- Sequential Algorithm --- p.24Chapter 3.2.2 --- Projection Algorithm --- p.25Chapter 3.3 --- Handling Scaling Problem by the Projection Algorithm --- p.33Chapter 4 --- Sequence Data Searching using Hashing Algorithm --- p.37Chapter 4.1 --- Sequence Similarity --- p.37Chapter 4.2 --- Hashing algorithm --- p.39Chapter 4.2.1 --- Motivation of the Algorithm --- p.40Chapter 4.2.2 --- Hashing Algorithm using dynamic hash function --- p.44Chapter 4.2.3 --- Handling Scaling Problem by the Hashing Algorithm --- p.47Chapter 5 --- Comparisons between algorithms --- p.50Chapter 5.1 --- Performance comparison with the sequence searching algorithms --- p.54Chapter 5.2 --- Comparison between indexing structures --- p.54Chapter 5.3 --- Comparison between sequence searching algorithms in coping some deficits --- p.55Chapter 6 --- Performance Evaluation --- p.58Chapter 6.1 --- Performance Evaluation using Projection Algorithm --- p.58Chapter 6.2 --- Performance Evaluation using Hashing Algorithm --- p.61Chapter 7 --- Conclusion --- p.66Chapter 7.1 --- Motivation of the thesis --- p.66Chapter 7.1.1 --- Insufficiency of Euclidean distance --- p.67Chapter 7.1.2 --- Insufficiency of orthonormal transforms --- p.67Chapter 7.1.3 --- Insufficiency of multi-dimensional indexing structure --- p.68Chapter 7.2 --- Major contribution --- p.68Chapter 7.2.1 --- Projection algorithm --- p.68Chapter 7.2.2 --- Hashing algorithm --- p.69Chapter 7.3 --- Future work --- p.70Bibliography --- p.7

Fourier Transform to Detect Pine Seedlings in a Digital Image

Each year, u.s. forest nurseries produce approximately 200 million pine seedlings. Forest companies depend on an adequate number of seedlings in order to replant timber land. To monitor the progress of seedlings, nurseries periodically conduct an inventory. The procedure is performed manually and is based on a statistical estimate. The process is slow, tedious, and imprecise. Automating the inventory procedure is subject of this dissertation. A digital image processing technique to visually count pine seedlings is investigated. The technique is based on a proposed imaging system which resides on a platform behind a tractor. As the system passes over the seedling bed, image sensors capture an overhead view of individual seedlings. A computer analyzes the sensor values in order to detect and count individual seedlings. This dissertation is concerned with developing a computer algorithm. Several test images were obtained. Pertinent seedling features in the images are gray level contrast, lines formed by the needles, and circular distribution of the needles. Four different techniques were investigated in an attempt to use these features to detect pine seedlings. These techniques are gray level peaks geometric intersection of needle lines, gray level contour encoding 1 and a technique based on the Fourier transform.Agricultural Engineerin

Visual recognition of objects : behavioral, computational, and neurobiological aspects

I surveyed work on visual object recognition and perception. In animals, vision has been studied mainly on the behavioral and neurobiological levels. Behavioral data typically show what the visual system, by itself or together with the rest of the organism, is capable of. They show, for example, that humans can recognie objects regardless of size and position, but that rotated objects pose problems. Important insights into the organization of behavior have also been provided by people who suffered localized brain damage. We have learned that the brain is divided into areas subserving different and relatively well-defined behaviors. The visual system itself is also organized in different subsystems; the visual cortex alone contains nearly twenty maps of the visual field. And individual neurons respond selectively to visual stimuli, e.g., the orientation of line segments, color, direction of motion, and, most intriguingly, faces. The question is how the actions of all these neurons produce the behavior we observe. How do neurons represent the shape of objects such that they can be recognized? Before we can answer the question, we have to understand the computational aspect of shape representation, the nature of the problem as it were. Many methods for representing shape have been explored, mainly by computer scientists, but so far no satisfactory answers have been found