Texture Classification Based on Complex Network Model with Spatial Information

This paper proposes a method for image texture classification based on a complex network model. Finding relevant and valuable information in an image texture is an essential issue for image classification and remains a challenge. Recently, a complex network model has been used for texture analysis and classification. However, with current analysis methods, important empirical properties of image texture such as spatial information are discarded from consideration. Accordingly, we propose local spatial pattern mapping (LSPM) method for manipulating the spatial information in an image texture with multi-radial distance analysis to capture the texture pattern. In experiments, the feature properties under the traditional complex network model and those with the proposed method are analyzed by using the Brodatz, UIUC, and Outex databases. As results, the proposed method is shown to be effective for texture classification, providing an improved classification rate as compared to the traditional complex network model

Adaptive Skin Color Prediction Using Multi Skin Color Models

In this paper, a new skin color detection method, in which a skin color model for a given image is adequately selected from a set of models to realize adaptive detection, is proposed. In the proposed method, multiple skin color models are tuned by a learning based on a concept of self-organizing adaptive controller. The skin color models for various lighting conditions can be obtained from small number of images. The effectiveness of the proposed method is verified by simulation results

A Modified Real-Coded Genetic Algorithm Considering with Fitness-based Variability

A genetic algorithm (GA) is a search algorithm based on the mechanism of natural genetics. In various GAs, a real-coded GA (RCGA) employing individuals represented by real valued-genes has been proposed to solve the optimization problem in the continuous searching space. However, the conventional RCGA yields ineffective searches due to insufficient genetic diversity in the selection process. In this paper, we propose a modified RCGA with variability operator maintaining the genetic diversity of the population. In the proposed method, a variability term is newly added to the individuals selected by the ordinary selection. The degree of the variability is decided considering the fitness value of the individual. The searching performance of the proposed method is better than the conventional methods. The effectiveness and the validity of the proposed method are verified by applying it to optimization problems of continuous benchmark functions and signal sources localization

Human Tracking Using Particle Filter with Reliable Appearance Model

In this paper, we present a human tracking algorithm that can work robustly in complex environments such that serious occlusion, various appearances and abrupt motion changes occur in the scenario. Our tracking framework is well known particle filter based on Condensation algorithm. In the observation model of the particle filter, we establish RAM(Reliable Appearance Model) which exhibits high discriminative performance in particular for human tracking. The RAM is to describe a target as features from local descriptors. In order to extract practical features from a larger number of local descriptors for robust tracking, the features were employed by boosting algorithm. The components of the features are utilized color and shape based-models. Experimental results demonstrate that our approach tracks the target accurately and reliably when position and scale are changing as well as occurrence of occlusion.SICE Annual Conference 2013 - International conference on Instrumentation, Control, Information Technology and System Integration, September 14-17, 2013, Nagoya University, Nagoya, Japa

Human Tracking Using Particle Filter with Reliable Appearance Model

In this paper, we present a human tracking algorithm that can work robustly in complex environments such that serious occlusion, various appearances and abrupt motion changes occur in the scenario. Our tracking framework is well known particle filter based on Condensation algorithm. In the observation model of the particle filter, we establish RAM(Reliable Appearance Model) which exhibits high discriminative performance in particular for human tracking. The RAM is to describe a target as features from local descriptors. In order to extract practical features from a larger number of local descriptors for robust tracking, the features were employed by boosting algorithm. The components of the features are utilized color and shape based-models. Experimental results demonstrate that our approach tracks the target accurately and reliably when position and scale are changing as well as occurrence of occlusion.SICE Annual Conference 2013 - International conference on Instrumentation, Control, Information Technology and System Integration, September 14-17, 2013, Nagoya University, Nagoya, Japa

Discrimination of Oral Mucosal Disease Inspired by Diagnostic Process of Specialist

A discrimination of oral mucosal diseases is very important in clinical site. Therefore, a development of a screening support system for oral mucosal diseases which supports the diagnosis of clinical dentist is required. In this paper, a discrimination method based on fuzzy inference using four attributes (existence of vitiligos, bulges, granular patterns, and reddening) for oral mucosal diseases is proposed. As the results of the experiment, the discrimination rates of squamous cell carcinoma, leukoplakia and lichen planus were 87%, 70% and 87%, respectively. The results suggest that the proposed method is effective in discriminating oral mucosal diseases

Discrimination of Oral Mucosal Disease Inspired by Diagnostic Process of Specialist

Abstract-A discrimination of oral mucosal diseases is very important in clinical site. Therefore, a development of a screening support system for oral mucosal diseases which supports the diagnosis of clinical dentist is required. In this paper, a discrimination method based on fuzzy inference using four attributes (existence of vitiligos, bulges, granular patterns, and reddening) for oral mucosal diseases is proposed. As the results of the experiment, the discrimination rates of squamous cell carcinoma, leukoplakia and lichen planus were 87%, 70% and 87%, respectively. The results suggest that the proposed method is effective in discriminating oral mucosal diseases. Index Terms-oral mucosal disease, diagnosis support system, fuzzy inference, intraoral imag

Handwritten Character Recognition Based on Relative Position of Local Features Extracted by Self-Organizing Maps

This paper describes a new pattern recognition method which is based on relative position of local features. We use a self-organizing map to detect a position of features, thus the relative position of them are automatically defined based on arrangement of the competing units. The local features are detected using filter constructed by adaptive subspace self-organizing maps