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The use of physiological signals is relatively recent development in human emotion recognition. Interest in this field has been motivated by the unbiased nature of such signals, which are generated autonomously from the central nervous system. Generally, these signals can be collected from the cardiovascular system, respiratory system, electrodermal activities, muscular system and brain activities. This paper presents an overview of emotion recognition using physiological signals. The main components of a physiological signals-based emotion recognirion system are explained, including discussion regarding the concepts and problems about the various stages involved in its framework.

The use of physiological signals is relatively recent development in human emotion recognition. Interest in this field has been motivated by the unbiased nature of such signals, which are generated autonomously from the central nervous system. Generally, these signals can be collected from the cardiovascular system, respiratory system, electrodermal activities, muscular system and brain activities. This paper presents an overview of emotion recognition using physiological signals. The main components of a physiological signals-based emotion recognition system are explained, including discussion regarding the concepts and problems about the various stages involved in its framework.

Computer facial animation is not a new endeavour as it had been introduced since 1970s. However, animating human face still presents interesting challenges because of its familiarity as the face is the part used to recognize individuals.Facial modelling and facial animation are important in developing realistic computer facial animation. Both modelling and animation is dependent to drive the animation.This paper reviews several geometric -based modelling (shape interpolation,parameterization and muscle-based animation)and data-driven animation (image-based techniques speech-driven techniques and performance-driven animation) techniques used in computer graphics and vision for facial animation. The main concept s and problems for each technique are highlighted in the paper.

This paper presents a probabilistic technique to localize license plates regions for cars adhering to the standard set by the Malaysian Road Transport Department. Images of the front/rear-view of cars displaying their license plates are firstly preprocessed, followed by features extraction generated from connected components analysis. These features are then used to train a Naïve Bayes classifier for the final task of license plates localization. Experimental results conducted on 144 images have shown that considering two candidates with the highest posterior probabilities better guarantees license plates regions are properly localized, with a recall of 0.98.

The present invention relates to a method of processing a three-dimensional (3D) echocardiography video with a complete cardiac cycle encompassing a ventricular. The method comprises step of obtaining a set of voxels representing a total scanned volume from the three-dimensional (3D) echocardiography video and performing segmentation on the set of voxels representing the total scanned volume.

The present invention relates to a method for determining right ventricle stroke volume comprising the steps of: providing a plurality of three-dimensional images of right cardiac ventricle of a patient over a cardiac cycle; identically slicing each of the plurality of images; selecting an image from the plurality of images, for each time unit of the cardiac cycle; determining a region of interest in each slice of the selected images, wherein the region of interest shows the right ventricle of the patient; determining area of the region of interest; determining volume of the region of interest; determining total volume of the region of interest for each time unit, wherein the maximum total volume is end diastolic volume and the minimum total volume is end systolic volume; and determining the right ventricle stroke volume using an equation: Right Ventricle Stroke Volume = end diastolic volume - end systolic volume (3).

In this paper, we proposed a new method for computer dissection on animal as we use a set of techniques from behavioral deformation model. The reconstruction task was break down into 2 main steps: computation of the collision detection and the modeling of the external shape of neck and the four internal arborescence. The computer simulation tool was developed to manipulate the model and to define surgical resection planes according to internal animals. The first step of dissection simulation was implementing, embedding the model with the behavioral deformation computation. In this paper, we describe how to produce a realistic 3D model of the cow with collision detection for animal and slaughtering training. We address the problems of physical modeling and force feedback and how they relate to 3D representation and behavioral interaction. This research will also to make recommendations on the technologies required to create a virtual environment for the simulation of an animal dissection procedure incorporating haptic feedback. Our initial result is straightforward but is in significant improvements in the efficient physically based simulation of highly detailed parts.

In this work we present a technique to extract the heart contours from noisy echocardiograph images. Our technique is based on improving the image before applying contours detection to reduce heavy noise and get better image quality. To perform that, we combine many pre-processing techniques (filtering, morphological operations, and contrast adjustment) to avoid unclear edges and enhance low contrast of echocardiograph images, after implementing these techniques we can get legible detection for heart boundaries and valves movement by traditional edge detection methods.

Problem statement: Medical image analysis is a particularly difficult problem because the inherent characteristics of these images, including low contrast, speckle noise, signal dropouts and complex anatomical structures. An accurate analysis of wall motion in Two-dimensional echocardiography images is "important clinical diagnosis parameter for many cardiovascular diseases". A challenge most researchers faced is how to speed up the clinical decisions and reduce human error of estimating accurately the true wall movements boundaries if can be done automatically will be a useful tool for assessing these diseases qualitatively and quantitatively. Approach: The proposed method involves three stages: First, the pre-processing stage for image contrast enhancement to reduce speckle-noise and to highlight certain features of interest (i.e., myocardial tissue). In the second stage, we applied the segmentation process using thresholding technique by considering a mean value of pixels intensity as a threshold value to distinct image features (e.g., Background and object). After thresholding implementation, the two most common mathematical morphology operators 'erosion' and 'dilation' are applied to improve the efficiency of the wall boundary detection process. Finally, Robert's operator is used as edge detector to identify the wall boundaries. Results: For accuracy measurement, the experimental results of the proposed method are compared to that obtained from medical QLab system qualitatively and quantitatively. Conclusion: The results showed that our proposed method is reliable and its performance accuracy percentages are 50% more acceptable and 42% better than QLab system results.

When herbs plants has disease, they can display a range of symptoms such as colored spots, or streaks that can occur on the leaves, stems, and seeds of the plant. These visual symptoms continuously change their color, shape and size as the disease progresses. Once the image of a target is captured digitally, a myriad of image processing algorithms can be used to extract features from it. The usefulness of each of these features will depend on the particular patterns to be highlighted in the image. A key point in the implementation of optimal classifiers is the selection of features that characterize the image. Basically, in this study, image processing and pattern classification are going to be used to implement a machine vision system that could identify and classify the visual symptoms of herb plants diseases. The image processing is divided into four stages: Image Pre-Processing to remove image noises (Fixed-Valued Impulse Noise, Random-Valued Impulse Noise and Gaussian Noise), Image Segmentation to identify regions in the image that were likely to qualify as diseased region, Image Feature Extraction and Selection to extract and select important image features and Image Classification to classify the image into different herbs diseases classes. This paper is to propose an unsupervised diseases pattern recognition and classification algorithm that is based on a modified Hierarchical Dynamic Artificial Neural Network which provides an adjustable sensitivity-specificity herbs diseases detection and classification from the analysis of noise-free colored herbs images. It is also to proposed diseases treatment algorithm that is capable to provide a suitable treatment and control for each identified herbs diseases.

As computer-assisted virtual reality (CAVR) use has increased during the past few years, educators have expressed concern regarding its effectiveness. The purpose of this quasiexperimental study was to determine if completion of a computerized simulation about halal slaughtering animals for food increased slaughter man and publics’ selfefficacy about caring halal dietary laws found in Quran. This study provides a method to electronically educate, train, prototype, test and evaluate new enhancements to the public. This study detailed the modeling and simulation of a s etal cow. The primary objectives of the study were to assess the use of the VR simulation by public and to assess the potential learning outcomes of them in their use of the VR simulation. The system will consists of an anatomically computer model of a cow, a simulation engine capable of providing soft-tissue [1] modeling, rigid body dynamics, collision detection and response, haptic force calculations, and a number of user interface and display devices to interact with the user. In this paper we describe a new method for computer dissection on animal as we use a set of techniques from medical image analysis. In this paper, we also describe how to produce a realistic 3D model of the cow from bidimensional (2D) medical images for animal and slaughtering training. This research will also to make recommendations on the technologies required to create a virtual environment for the simulation of an animal dissection procedure incorporating haptic feedback.

Echocardiography is a widely used imaging technique to examine various cardiac functions, especially to detect the left ventricular wall motion abnormality. Unfortunately the quality of echocardiograph images and complexities of underlying motion captured, makes it difficult for an in-experienced physicians/ radiologist to describe the motion abnormalities in a crisp way, leading to possible errors in diagnosis. In this study, we present a method to analyze left ventricular wall motion, by using optical flow to estimate velocities of the left ventricular wall segments and find relation between these segments motion. The proposed method will be able to present real clinical help to verify the left ventricular wall motion diagnosis.

When herbs plants has disease, they can display a range of symptoms such as colored spots, or streaks that can occur on the leaves, stems, and seeds of the plant. These visual symptoms continuously change their color, shape and size as the disease progresses. Once the image of a target is captured digitally, a myriad of image processing algorithms can be used to extract features from it. The usefulness of each of these features will depend on the particular patterns to be highlighted in the image. A key point in the implementation of optimal classifiers is the selection of features that characterize the image. Basically, in this study, image processing and pattern classification are going to be used to implement a machine vision system that could identify and classify the visual symptoms of herb plants diseases. The image processing is divided into four stages: Image Pre-Processing to remove image noises (Fixed-Valued Impulse Noise, Random-Valued Impulse Noise and Gaussian Noise), Image Segmentation to identify regions in the image that were likely to qualify as diseased region, Image Feature Extraction and Selection to extract and select important image features and Image Classification to classify the image into different herbs diseases classes. This paper is to propose an unsupervised diseases pattern recognition and classification algorithm that is based on a modified Hierarchical Dynamic Artificial Neural Network which provides an adjustable sensitivity-specificity herbs diseases detection and classification from the analysis of noise-free colored herbs images. It is also to proposed diseases treatment algorithm that is capable to provide a suitable treatment and control for each identified herbs diseases.

Purpose – The aim of this paper is to propose a strategy for extracting information from web tables. Design/methodology/approach – The paper presents a strategy for extracting information from web tables of semi-structured web pages (WPs) by handling the issue of synonym which emerges as these WPs have been designed and created without referring to any standards or guidelines. Findings – The paper finds that this strategy extracts information with high precision, and extracts the attributes besides the sub-attributes that describe the extracted attributes and values of the sub-attributes. Practical implications – Experiment conducted on the Nokia products domain demonstrated that the proposed strategy extracts information from web tables with high precision which is 98.98 percent. Originality/value – This paper contributes to the research on extracting information.

Extracting information from the web data sources becomes very important because the massive and increasing amount of diverse semi-structured information sources in the Internet that are available to users, and the variety of web pages making the process of information extraction from web a challenging problem. This paper proposes a framework for extracting, classifying, analyzing, and presenting semi-structured web data sources. The framework is able to extract relevant information from different web data sources, and classify the extracted information based on the standard classification scheme of Nokia products, which has been chosen as the case study.

Contours extraction from two dimensional echocardiographic images has been a challenge in digital image processing. This is essentially due to the heavy noise, poor quality of these images and some artifacts like papillary muscles, intra-cavity structures as chordate, and valves that can interfere with the endocardial border tracking. In this paper, we will present a technique to extract the contours of heart boundaries from a sequence of echocardiographic images, where it started with pre-processing to reduce noise and produce better image quality. By pre-processing the images, the unclear edges are avoided, and we can get an accurate detection of both heart boundary and movement of heart valves.

This work discusses the application of an Artificial Intelligence technique called data extraction and a process-based ontology in constructing experimental qualitative models for video retrieval and detection. We present a framework architecture that uses multimodality features as the knowledge representation scheme to model the behaviors of a number of human actions in the video scenes. The main focus of this paper placed on the design of two main components (model classifier and inference engine) for a tool abbreviated as VSAD (Video Action Scene Detector) for retrieving and detecting human actions from video scenes. The discussion starts by presenting the workflow of the retrieving and detection process and the automated model classifier construction logic. We then move on to demonstrate how the constructed classifiers can be used with multimodality features for detecting human actions. Finally, behavioral explanation manifestation is discussed. The simulator is implemented in bilingual; Math Lab and C++ are at the backend supplying data and theories while Java handles all front-end GUI and action pattern updating.

Video content analysis is an active research domain due to the availability and the increment of audiovisual data in the digital format. There is a need to automatically extracting video content for efficient access, understanding,browsing and retrieval of videos. To obtain the information that is of interest and to provide better entertainment, tools are needed to help users extract relevant content and to effectively navigate through the large amount of available video information. Existing methods do not seem to attempt to model and estimate the semantic content of the video. Detecting and interpreting human presence,actions and activities is one of the most valuable functions in this proposed framework. The general objectives of this research are to analyze and process the audio-video streams to a robust audiovisual action recognition system by integrating, structuring and accessing multimodal information via multidimensional retrieval and extraction model. The proposed technique characterizes the action scenes by integrating cues obtained from both the audio and video tracks. Information is combined based on visual features (motion,edge, and visual characteristics of objects), audio features and video for recognizing action. This model uses HMM and GMM to provide a framework for fusing these features and to represent the multidimensional structure of the framework. The action-related visual cues are obtained by computing the spatio temporal dynamic activity from the video shots and by abstracting specific visual events. Simultaneously, the audio features are analyzed by locating and compute several sound effects of action events that embedded in the video. Finally, these audio and visual cues are combined to identify the action scenes. Compared with using single source of either visual or audio track alone, such combined audio visual information provides more reliable performance and allows us to understand the story content of movies in more detail. To compare

This work discusses the application of an Artificial Intelligence technique called data extraction and a process-based ontology in constructing experimental qualitative models for video retrieval and detection. We present a framework architecture that uses multimodality features as the knowledge representation scheme to model the behaviors of a number of human actions in the video scenes. The main focus of this paper placed on the design of two main components (model classifier and inference engine) for a tool abbreviated as VASD (Video Action Scene Detector) for retrieving and detecting human actions from video scenes. The discussion starts by presenting the workflow of the retrieving and detection process and the automated model classifier construction logic. We then move on to demonstrate how the constructed classifiers can be used with multimodality features for detecting human actions. Finally, behavioral explanation manifestation is discussed. The simulator is implemented in bilingual; Matlab and C++ are at the backend supplying data and theories while Java handles all front-end GUI and action pattern updating.

Understanding human action can be posed as a pattern recognition problem. Actions are complex entities, possessing several representations - linguistic, visual, cognitive, and motor. The ability to recognize the actions, the ability to understand, and to react to, human actions is crucial for cognitive systems of the future. Recognition of human action is divided into two main problems. First is the problem of getting whole body motion data. For this, various techniques such as stereo vision or motion capture can be considered. Second is the problem of interpretation of the human motion, which includes modelling of action, feature extraction, classification, and detection of action. The focus of this research is attended to the second problem. This paper is to create a framework that is able to segment and classify individual actions from a stream of human motion using video data. Based on this framework, a model is trained to automatically segment and classify an activity sequence into sub-actions during inferencing. Our goal is to make our system adaptable to different events in different domains. Hidden Markov models (HMMs) originally emerged in the domain of speech recognition. In recent years, they have attracted growing interest in the area of computer vision as well. This report presents a method for determining action scene using a multi-dimensional hidden Markov model (HMM). Instead of using geometric features, actions are converted into sequential symbols. HMMs are employed to represent the action and their parameters are learned from the training data. Based on the most likely performance criterion, the action can be recognized through evaluating the trained HMMs. We will be developing a prototype system to demonstrate the feasibility of the proposed method. The proposed method is applicable to any action represented by a multi-dimensional signal, and will be a valuable tool in human computer interfaces.