MOMENTOS DE LOS LÍMITES GEOMÉTRICOS Y SU APLICACIÓN AL CONTROL DE CALIDAD AUTOMATIZADO EN LA INDUSTRIA

In this research the performance of the Chen's Improved (Boundary) Moments is carefully compared to that of the traditional (Massive) Moments. To achieve this investigation, the pattern recognition power of the former is thoroughly assessed against that of the latter. The boundary moments are evaluated by two methods, in the first by edge-tracing, in the second method the edge pixels are considered as though they are met when sweeping the image space. It is concluded that the computation of the Boundary Moments by sweeping the image space associates minimum computational complexity to a high enough object classification efficiency, thus they may be used in lieu of the traditional moments.En esta investigación se lleva a cabo una detallada comparación de la performance de los Momentos Mejorados (de Borde), de C.C. Chen, con los Momentos Masivos tradicionales, para ejecutar este examen, el poder de reconocimiento de objetos de los primeros es cuidadosamente comparado con aquella de los últimos. Los Momentos de Borde son evaluados usando dos métodos, en el primero, mediante Trazado de Bordes, y en el segundo, mediante Barrido de Imagen. Se concluye que el cálculo de los Momentos de Borde mediante Barrido de Imagen, asocia una Complejidad Computacional mínima a una suficientemente alta eficiencia en la clasificación de objetos, pudiendo entonces ser usados en lugar de los Momentos Tradicionales

Classification of Marine Vessels in a Littoral Environment Using a Novel Training Database

Research into object classification has led to the creation of hundreds of databases for use as training sets in object classification algorithms. Datasets made up of thousands of cars, people, boats, faces and everyday objects exist for general classification techniques. However, no commercially available database exists for use with detailed classification and categorization of marine vessels commonly found in littoral environments. This research seeks to fill this void and is the combination of a multi-stage research endeavor designed to provide the missing marine vessel ontology. The first of the two stages performed to date introduces a novel training database called the Lister Littoral Database 900 (LLD-900) made up of over 900 high-quality images. These images consist of high-resolution color photos of marine vessels in working, active conditions taken directly from the field and edited for best possible use. Segmentation masks of each boat have been developed to separate the image into foreground and background sections. Segmentation masks that include boat wakes as part of the foreground section are the final image type included. These are included to allow for wake affordance detection algorithms rely on the small changes found in wakes made by different moving vessels. Each of these three types of images are split into their respective general classification folders, which consist of a differing number of boat categories dependent on the research stage. In the first stage of research, the initial database is tested using a simple, readily available classification algorithm known as the Nearest Neighbor Classifier. The accuracy of the database as a training set is tested and recorded and potential improvements are documented. The second stage incorporates these identified improvements and reconfigures the database before retesting the modifications using the same Nearest Neighbor Classifier along with two new methods known as the K-Nearest Neighbor Classifier and the Min-Mean Distance Classifier. These additional algorithms are also readily available and offer basic classification testing using different classification techniques. Improvements in accuracy are calculated and recorded. Finally, further improvements for a possible third iteration are discussed. The goal of this research is to establish the basis for a training database to be used with classification algorithms to increase the security of ports, harbors, shipping channels and bays. The purpose of the database is to train existing and newly created algorithms to properly identify and classify all boats found in littoral areas so that anomalous behavior detection techniques can be applied to determine when a threat is present. This research represents the completion of the initial steps in accomplishing this goal delivering a novel framework for use with littoral area marine vessel classification. The completed work is divided and presented in two separate papers written specifically for submission to and publication at appropriate conferences. When fully integrated with computer vision techniques, the database methodology and ideas presented in this thesis research will help to provide a vital new level of security in the littoral areas around the world

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