A Human Visual System-Driven Image Segmentation Algorithm

This paper presents a novel image segmentation algorithm driven by human visual system (HVS) properties. Quality metrics for evaluating the segmentation result, from both region-based and boundary-based perspectives, are integrated into an objective function. The objective function encodes the HVS properties into a Markov random fields (MRF) framework, where the just-noticeable difference (JND) model is employed when calculating the difference between the image contents. Experiments are carried out to compare the performances of three variations of the presented algorithm and several representative segmentation algorithms available in the literature. Results are very encouraging and show that the presented algorithms outperform the state-of-the-art image segmentation algorithms

Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation

International audienceIn this work, we present a novel multiscale texture model, and a related algorithm for the unsupervised segmentation of color images. Elementary textures are characterized by their spatial interactions with neighboring regions along selected directions. Such interactions are modeled in turn by means of a set of Markov chains, one for each direction, whose parameters are collected in a feature vector that synthetically describes the texture. Based on the feature vectors, the texture are then recursively merged, giving rise to larger and more complex textures, which appear at different scales of observation: accordingly, the model is named Hierarchical Multiple Markov Chain (H-MMC). The Texture Fragmentation and Reconstruction (TFR) algorithm, addresses the unsupervised segmen- tation problem based on the H-MMC model. The “fragmentation” step allows one to find the elementary textures of the model, while the “reconstruction” step defines the hierarchical image segmentation based on a probabilistic measure (texture score) which takes into account both region scale and inter-region interactions. The performance of the proposed method was assessed through the Prague segmentation benchmark, based on mosaics of real natural textures, and also tested on real-world natural and remote sensing images

Modelo de perda de pacote para projeto e simulação de sistemas de controle em rede sem fio

Orientador : Prof. Dr. Eduardo Parente RibeiroDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa: Curitiba, 12/05/2016Inclui referências : f. 61-63Área de concentração: Sistemas eletrônicosResumo: A compreensão da dinâmica intr?nseca em um sistema de controle de rede sem fios (WNCS - Wireless Networked Control System) é relevante para o desenvolvimento e análise de estratégias de controle que proporcionem o funcionamento do sistema em condições adversas, como em casos onde ocorre uma alta taxa de perda de pacotes durante a comunicação. A perda de pacotes é uma das principais deficiências presentes na transmissão de dados sem fios, as quais afetam diretamente a qualidade do sistema de controle. Desse modo um modelo de perda de pacotes preciso é muito importante para o projeto e simulação de WNCS. Neste sentido o presente trabalho analisou o comportamento de um processo, em diferentes níveis de perda de pacotes utilizando o protocolo IEEE (Institute of Electrical and Electronic Engineers) 802.15.4. Foram comparados dois modelos de perda de pacotes, a fim de verificar qual o modelo pode representar melhor esse comportamento em um WNCS. O comportamento real da transmissão foi obtido mediante comunicação entre dois n'os xbees modelo s1. Sobre as perdas reais foram ajustados dois modelos de perdas de pacotes, sendo estes os modelos de Bernoulli, modelo utilizado em softwares de simulação de WNCS/NCS, tal qual TRUETIME e o modelo de Gilbert-Elliot. As análises mostraram em que condições os modelos de perdas diferem na representação do comportamento real do WNCS. Ambos os modelos representaram bem baixas taxas de perdas, mas o modelo de Gilbert-Elliot mostrou ser uma melhor representação para taxas de perdas mais elevadas. Palavras-chave: Sistema de controle em rede, perda de pacote, modelo de Gilbert- Elliot, modelo de Bernoulli.Abstract: The understanding of intrinsic dynamics of a wireless networked control system (WNCS) is relevant to the development and analysis of control strategies to enable the operation of the system under adverse conditions. Packet loss is one of the main deficiencies present in wireless data transmission. An accurate packet loss model is very important to WNCS design and simulation. We analyzed the behavior of a plant under different levels of packet loss using the IEEE 802.15.4 protocol. We compared two models of packet loss in order to check which model can better represent this behavior in a WNCS. The results demonstrate in which conditions the Gilbert-Elliot and Bernoulli models differ in the representation of packet loss for a WNCS. Gilbert model showed to be a better representation specially for higher loss ratios. Key words: WNCS, packet loss, Gilbert-Elliot model, Bernoulli model

A Segmentation Model Using Compound Markov Random Fields Based on a Boundary Model

Abstract—Markov random field (MRF) theory has been widely applied to the challenging problem of image segmentation. In this paper, we propose a new nontexture segmentation model using compound MRFs, in which the original label MRF is coupled with a new boundary MRF to help improve the segmentation performance. The boundary model is relatively general and does not need prior training on boundary patterns. Unlike some existing related work, the proposed method offers a more compact interaction between label and boundary MRFs. Furthermore, our boundary model systematically takes into account all the possible scenarios of a single edge existing in a 3 3 neighborhood and, thus, incorporates sophisticated prior information about the relation between label and boundary. It is experimentally shown that the proposed model can segment objects with complex boundaries and at the same time is able to work under noise corruption. The new method has been applied to medical image segmentation. Experiments on synthetic images and real clinical datasets show that the proposed model is able to produce more accurate segmentation results and satisfactorily keep the delicate boundary. It is also less sensitive to noise in both high and low signal-to-noise ratio regions than some of the existing models in common use. Index Terms—Boundary model, Markov random fields (MRFs), medical image segmentation

Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits

This dissertation investigates the problem of image processing based on stochastic resonance (SR) noise and human visual system (HVS) properties, where several novel frameworks and algorithms for object detection in images, image enhancement and image segmentation as well as the method to estimate the performance limit of image segmentation algorithms are developed. Object detection in images is a fundamental problem whose goal is to make a decision if the object of interest is present or absent in a given image. We develop a framework and algorithm to enhance the detection performance of suboptimal detectors using SR noise, where we add a suitable dose of noise into the original image data and obtain the performance improvement. Micro-calcification detection is employed in this dissertation as an illustrative example. The comparative experiments with a large number of images verify the efficiency of the presented approach. Image enhancement plays an important role and is widely used in various vision tasks. We develop two image enhancement approaches. One is based on SR noise, HVS-driven image quality evaluation metrics and the constrained multi-objective optimization (MOO) technique, which aims at refining the existing suboptimal image enhancement methods. Another is based on the selective enhancement framework, under which we develop several image enhancement algorithms. The two approaches are applied to many low quality images, and they outperform many existing enhancement algorithms. Image segmentation is critical to image analysis. We present two segmentation algorithms driven by HVS properties, where we incorporate the human visual perception factors into the segmentation procedure and encode the prior expectation on the segmentation results into the objective functions through Markov random fields (MRF). Our experimental results show that the presented algorithms achieve higher segmentation accuracy than many representative segmentation and clustering algorithms available in the literature. Performance limit, or performance bound, is very useful to evaluate different image segmentation algorithms and to analyze the segmentability of the given image content. We formulate image segmentation as a parameter estimation problem and derive a lower bound on the segmentation error, i.e., the mean square error (MSE) of the pixel labels considered in our work, using a modified Cramér-Rao bound (CRB). The derivation is based on the biased estimator assumption, whose reasonability is verified in this dissertation. Experimental results demonstrate the validity of the derived bound