A topological approach for segmenting human body shape

Segmentation of a 3D human body, is a very challenging problem in applications exploiting human scan data. To tackle this problem, the paper proposes a topological approach based on the discrete Reeb graph (DRG) which is an extension of the classical Reeb graph to handle unorganized clouds of 3D points. The essence of the approach concerns detecting critical nodes in the DRG, thereby permitting the extraction of branches that represent parts of the body. Because the human body shape representation is built upon global topological features that are preserved so long as the whole structure of the human body does not change, our approach is quite robust against noise, holes, irregular sampling, frame change and posture variation. Experimental results performed on real scan data demonstrate the validity of our method

A statistical multiresolution approach for face recognition using structural hidden Markov models

This paper introduces a novel methodology that combines the multiresolution feature of the discrete wavelet transform (DWT) with the local interactions of the facial structures expressed through the structural hidden Markov model (SHMM). A range of wavelet filters such as Haar, biorthogonal 9/7, and Coiflet, as well as Gabor, have been implemented in order to search for the best performance. SHMMs perform a thorough probabilistic analysis of any sequential pattern by revealing both its inner and outer structures simultaneously. Unlike traditional HMMs, the SHMMs do not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the SHMMs. Therefore, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. SHMMs have not previously been applied to the problem of face identification. The results reported in this application have shown that SHMM outperforms the traditional hidden Markov model with a 73% increase in accuracy

Face Recognition using DWT with HMM

This paper presents an efficient face recognition system based on Hidden Markov Model (HMM) and the simplest type “Haar” of the Discrete Wavelet Transform (DWT). The one dimensional ergodic HMM with Gaussian outputs, which represent the simplest and robust type of HMM, is used in the proposed work. A novel method is introduced for selecting the training images implemented by choosing the images that have the odd identifying numbers from the database. Some of these images are replaced according to the trial-and-error results. The proposed work achieves the maximum recognition rate (100%), where the experiments are carried out on the ORL face database

A Survey of Geometric Analysis in Cultural Heritage

We present a review of recent techniques for performing geometric analysis in cultural heritage (CH) applications. The survey is aimed at researchers in the areas of computer graphics, computer vision and CH computing, as well as to scholars and practitioners in the CH field. The problems considered include shape perception enhancement, restoration and preservation support, monitoring over time, object interpretation and collection analysis. All of these problems typically rely on an understanding of the structure of the shapes in question at both a local and global level. In this survey, we discuss the different problem forms and review the main solution methods, aided by classification criteria based on the geometric scale at which the analysis is performed and the cardinality of the relationships among object parts exploited during the analysis. We finalize the report by discussing open problems and future perspectives

Multi-Agent Framework in Visual Sensor Networks

21 pages, 21 figures.-- Journal special issue on Visual Sensor Networks.The recent interest in the surveillance of public, military, and commercial scenarios is increasing the need to develop and deploy intelligent and/or automated distributed visual surveillance systems. Many applications based on distributed resources use the so-called software agent technology. In this paper, a multi-agent framework is applied to coordinate videocamera-based surveillance. The ability to coordinate agents improves the global image and task distribution efficiency. In our proposal, a software agent is embedded in each camera and controls the capture parameters. Then coordination is based on the exchange of high-level messages among agents. Agents use an internal symbolic model to interpret the current situation from the messages from all other agents to improve global coordination.This work was funded by projects CICYT TSI2005-07344, CICYT TEC2005-07186, and CAM MADRINET S-0505/TIC/0255.Publicad

Combination of Accumulated Motion and Color Segmentation for Human Activity Analysis

The automated analysis of activity in digital multimedia, and especially video, is gaining more and more importance due to the evolution of higher-level video processing systems and the development of relevant applications such as surveillance and sports. This paper presents a novel algorithm for the recognition and classification of human activities, which employs motion and color characteristics in a complementary manner, so as to extract the most information from both sources, and overcome their individual limitations. The proposed method accumulates the flow estimates in a video, and extracts “regions of activity†by processing their higher-order statistics. The shape of these activity areas can be used for the classification of the human activities and events taking place in a video and the subsequent extraction of higher-level semantics. Color segmentation of the active and static areas of each video frame is performed to complement this information. The color layers in the activity and background areas are compared using the earth mover's distance, in order to achieve accurate object segmentation. Thus, unlike much existing work on human activity analysis, the proposed approach is based on general color and motion processing methods, and not on specific models of the human body and its kinematics. The combined use of color and motion information increases the method robustness to illumination variations and measurement noise. Consequently, the proposed approach can lead to higher-level information about human activities, but its applicability is not limited to specific human actions. We present experiments with various real video sequences, from sports and surveillance domains, to demonstrate the effectiveness of our approach

Ensemble of binary classifiers: combination techniques and design issues

In this thesis the problem of the combination of binary classifiers ensamble is faced. For each pattern a binary classifier (or binary expert) assigns a similarity score, and according to a decision threshold a class is assigned to the pattern (i.e., if the score is higher than the threshold the pattern is assigned to the “positive” class, otherwise to the “negative” one). An example of this kind of classifier is an authentication biometric expert, where the expert must distinguish between the “genuine” users, and the “impostor” users. The combination of different experts is currently investigated by researchers to increase the reliability of the decision. Thus in this thesis the following two aspects are investigated: a score “selection” methodology, and diversity measures of ensemble effectiveness. In particular, a theory on ideal score selection has been developed, and a number of selection techniques based on it have been deployed. Moreover some of them are based on the use of classifier as a selection support, thus different use of these classifier is analyzed. The influence of the characteristics of the individual experts to the final performance of the combined experts have been investigated. To this end some measures based on the characteristics of the individual experts were developed to evaluate the ensemble effectiveness. The aim of these measures is to choose which of the individual experts from a bag of experts have to be used in the combination. Finally the methodologies developed where extensively tested on biometric datasets

Ensemble of binary classifiers: combination techniques and design issues

In this thesis the problem of the combination of binary classifiers ensamble is faced. For each pattern a binary classifier (or binary expert) assigns a similarity score, and according to a decision threshold a class is assigned to the pattern (i.e., if the score is higher than the threshold the pattern is assigned to the “positive” class, otherwise to the “negative” one). An example of this kind of classifier is an authentication biometric expert, where the expert must distinguish between the “genuine” users, and the “impostor” users. The combination of different experts is currently investigated by researchers to increase the reliability of the decision. Thus in this thesis the following two aspects are investigated: a score “selection” methodology, and diversity measures of ensemble effectiveness. In particular, a theory on ideal score selection has been developed, and a number of selection techniques based on it have been deployed. Moreover some of them are based on the use of classifier as a selection support, thus different use of these classifier is analyzed. The influence of the characteristics of the individual experts to the final performance of the combined experts have been investigated. To this end some measures based on the characteristics of the individual experts were developed to evaluate the ensemble effectiveness. The aim of these measures is to choose which of the individual experts from a bag of experts have to be used in the combination. Finally the methodologies developed where extensively tested on biometric datasets