AN OVERVIEW OF IMAGE SEGMENTATION ALGORITHMS

Image segmentation is a puzzled problem even after four decades of research. Research on image segmentation is currently conducted in three levels. Development of image segmentation methods, evaluation of segmentation algorithms and performance and study of these evaluation methods. Hundreds of techniques have been proposed for segmentation of natural images, noisy images, medical images etc. Currently most of the researchers are evaluating the segmentation algorithms using ground truth evaluation of (Berkeley segmentation database) BSD images. In this paper an overview of various segmentation algorithms is discussed. The discussion is mainly based on the soft computing approaches used for segmentation of images without noise and noisy images and the parameters used for evaluating these algorithms. Some of these techniques used are Markov Random Field (MRF) model, Neural Network, Clustering, Particle Swarm optimization, Fuzzy Logic approach and different combinations of these soft techniques

Utilizing Analytical Hierarchy Process for Pauper House Programme in Malaysia

In Malaysia, the selection and evaluation of candidates for Pauper House Programme (PHP) are done manually. In this paper, a technique based on Analytical Hierarchy Technique (AHP) is designed and developed in order to make an evaluation and selection of PHP application. The aim is to ensure the selection process is more precise, accurate and can avoid any biasness issue. This technique is studied and designed based on the Pauper assessment technique from one of district offices in Malaysia. A hierarchical indexes are designed based on the criteria that been used in the official form of PHP application. A number of 23 samples of data which had been endorsed by Exco of State in Malaysia are used to test this technique. Furthermore the comparison of those two methods are given in this paper. All the calculations of this technique are done in a software namely Expert Choice version 11.5. By comparing the manual and AHP shows that there are three (3) samples that are not qualified. The developed technique also satisfies in term of ease of accuracy and preciseness but need a further study due to some limitation as explained in the recommendation of this paper

Radon transform-based invariant image recognition

http://www.worldcat.org/oclc/2720424

Neural units with higher-order synaptic operations with applications to edge detection and control systems

The biological sense organ contains infinite potential. The artificial neural structures have emulated the potential of the central nervous system; however, most of the researchers have been using the linear combination of synaptic operation. In this thesis, this neural structure is referred to as the neural unit with linear synaptic operation (LSO). The objective of the research reported in this thesis is to develop novel neural units with higher-order synaptic operations (HOSO), and to explore their potential applications. The neural units with quadratic synaptic operation (QSO) and cubic synaptic operation (CSO) are developed and reported in this thesis. A comparative analysis is done on the neural units with LSO, QSO, and CSO. It is to be noted that the neural units with lower order synaptic operations are the subsets of the neural units with higher-order synaptic operations. It is found that for much more complex problems the neural units with higher-order synaptic operations are much more efficient than the neural units with lower order synaptic operations. Motivated by the intensity of the biological neural systems, the dynamic nature of the neural structure is proposed and implemented using the neural unit with CSO. The dynamic structure makes the system response relatively insensitive to external disturbances and internal variations in system parameters. With the success of these dynamic structures researchers are inclined to replace the recurrent (feedback) neural networks (NNs) in their present systems with the neural units with CSO. Applications of these novel dynamic neural structures are gaining potential in the areas of image processing for the machine vision and motion controls. One of the machine vision emulations from the biological attribution is edge detection. Edge detection of images is a significant component in the field of computer vision, remote sensing and image analysis. The neural units with HOSO do replicate some of the biological attributes for edge detection. Further more, the developments in robotics are gaining momentum in neural control applications with the introduction of mobile robots, which in turn use the neural units with HOSO; a CCD camera for the vision is implemented, and several photo-sensors are attached on the machine. In summary, it was demonstrated that the neural units with HOSO present the advanced control capability for the mobile robot with neuro-vision and neuro-control systems

Understanding Target Trajectory Behavior: A Dynamic Scene Modeling Approach

[Resumen] El análisis de comportamiento humano es uno de los campos más activos en la rama de visión por computador. Con el incremento de cámaras, especialmente en entornos controlados tales como aeropuertos, estaciones de tren o museos, se hace cada vez más necesario el uso de sistemas automáticos que puedan catalogar la información proporcionada. En el caso de entornos concurridos, es muy difícil el poder distinguir el comportamiento de personas en base a sus gestos, debido a la falta de visión de su cuerpo al completo. Por ende, el análisis de comportamiento se realiza en base a sus trayectorias, añadiendo técnicas de razonamiento de alto nivel para ulilizar dicha información en múltiples aplicaciones, tales como la video vigilancia o el análisis de tráfico. El propósito de esta investigación es el desarrollo de un sistema totalmente automático para el análisis de comportamiento de las personas. Por una parte, se presentan dos sistemas para el seguimiento de múltiples objetivos, así como un sistema novedoso para la re-identificación de personas, con la intención de detectar todo objeto de interés en la escena, devolviendo sus trayectorias como salida. Por otra parte, se presenta un sistema novedoso para el análisis de comportamiento basado en información del entorno de la escena. Está basado en la idea que que toda persona,cuando intenta llegar a un cierto lugar, tiende a seguir el mismo camino que suele utilizar la mayoría de la gente. Se presentan una serie de métricas para la detección de movimientos anómalos, haciendo que este método sea ideal para su utilización en sistemas de tiempo real.[Abstract] Human behavior analysis is one of the most active computer vision research fields. As the number of cameras are increased, especially in restricted environments, like airports, train stations or museums, the need of automatic systems that can catalog the information provided by the cameras becomes crucial. In the case of crowded scenes, it is very difficult to distinguish people behavior because of the lack of visual contact of the whole body. Thus, behavior analysis remains in the evaluation of trajectories, adding high-level knowledge approaches in order to use that information in several applications like video surveillance or traffic analysis. The proposal of this research is the design of a fully-automatic human behavior system from a distance. On the one hand, two different multiple-target tracking methods and a target re-identification procedure are presented to detect every target in the scene, returning their trajectories as output. On the other hand, a novel behavior analysis system, which includes information about the environment, is provided. It is based in the idea that every person tries to reach a goal in the scene following the same path the majority of people should use. An extremely fast abnormal behavior metric is presented, providing our method with the capabilities needed to be used in real-time scenarios[Resumo] A análise do comportamento humano é un dos campos máis activos na rama da visión por computadora. Co incremento de cámaras, especialmente en entornos controlados tales coma aeroportos, estacións de tren ou museos, faise cada vez máis necesario o uso de sistemas automáticos que poidan catalogar a información proporcionada. No caso de entornos concurridos, é moi complicado de poder distinguir o comportamento de persoas dacordo cos seus xestos, debido á falta dunha visión completa do corpo do suxeito. Por tanto, a análise de comportamento tende a realizarse en base á traxectoria, engadindo técnicas de razoamento de alto nivel para utilizar dita información en diversas aplicacións, tales coma a video vixiancia ou a análise de tráfico. O propósito desta investigación é o desenrolo dun sistema totalmente automático para a análise do comportamento das persoas. Por unha parte, preséntanse dous sistemas para o seguimento de múltiples obxectivos, así coma un sistema novidoso para a re-identificación de persoas, coa intención de detectar todo obxecto de interés na escena, devolvendo as traxectorias asociadas como saída. Por outra parte, preséntase un sistema novidoso para a análise de comportamente baseada na informaci ón do entorno da escena. Está baseado na idea de que toda persoa, cando intenta acadar un certo luegar, tende a seguir o mesmo cami~no que xeralmente usa a maioría da xente. Preséntanse unha serie de métricas para a detección de movementos anómalos, facendo posible que este método poida ser utilizado en sistemas de tempo real

Intelligent optical methods in image analysis for human detection

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.This thesis introduces the concept of a person recognition system for use on an integrated autonomous surveillance camera. Developed to enable generic surveillance tasks without the need for complex setup procedures nor operator assistance, this is achieved through the novel use of a simple dynamic noise reduction and object detection algorithm requiring no previous knowledge of the installation environment and without any need to train the system to its installation. The combination of this initial processing stage with a novel hybrid neural network structure composed of a SOM mapper and an MLP classifier using a combination of common and individual input data lines has enabled the development of a reliable detection process, capable of dealing with both noisy environments and partial occlusion of valid targets. With a final correct classification rate of 94% on a single image analysis, this provides a huge step forwards as compared to the reported 97% failure rate of standard camera surveillance systems

Intelligent optical methods in image analysis for human detection

This thesis introduces the concept of a person recognition system for use on an integrated autonomous surveillance camera. Developed to enable generic surveillance tasks without the need for complex setup procedures nor operator assistance, this is achieved through the novel use of a simple dynamic noise reduction and object detection algorithm requiring no previous knowledge of the installation environment and without any need to train the system to its installation. The combination of this initial processing stage with a novel hybrid neural network structure composed of a SOM mapper and an MLP classifier using a combination of common and individual input data lines has enabled the development of a reliable detection process, capable of dealing with both noisy environments and partial occlusion of valid targets. With a final correct classification rate of 94% on a single image analysis, this provides a huge step forwards as compared to the reported 97% failure rate of standard camera surveillance systems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

On-line lot-sizing with perceptrons

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