Rahmita Wirza O. K. Rahmat

Skin Segmentation Using YUV and RGB Color Spaces

Skin detection is used in many applications, such as face recognition, hand tracking, and human-computer interaction. There are many skin color detection algorithms that are used to extract human skin color regions that are based on the thresholding technique since it is simple and fast for computation. The efficiency of each color space depends on its robustness to the change in lighting and the ability to distinguish skin color pixels in images that have a complex background. For more accurate skin detection, we are proposing a new threshold based on RGB and YUV color spaces. The proposed approach starts by converting the RGB color space to the YUV color model. Then it separates the Y channel, which represents the intensity of the color model from the U and V channels to eliminate the effects of luminance. After that the threshold values are selected based on the testing of the boundary of skin colors with the help of the color histogram. Finally, the threshold was applied to the input image to extract skin parts. The detected skin regions were quantitatively compared to the actual skin parts in the input images to measure the accuracy and to compare the results of our threshold to the results of other’s thresholds to prove the efficiency of our approach. The results of the experiment show that the proposed threshold is more robust in terms of dealing with the complex background and light conditions than others.

Facial feature extraction for face recognition: a review

Over the last decade facial feature extraction has been actively researched for face recognition. This paper provides an up-to-date review of major human facia recognition research. Earlier sections we presented an overview of face recognition and its applications. In later sections, a literature review of the most recent face recognition technique is presented. The most prominent feature extraction and the techniques are also given. Finally, we summarized all research results discussed.

Region-based facial expression recognition in still images.

In Facial Expression Recognition Systems (FERS), only particular regions of the face are utilized for discrimination. The areas of the eyes, eyebrows, nose, and mouth are the most important features in any FERS. Applying facial features descriptors such as the local binary pattern (LBP) on such areas results in an effective and efficient FERS. In this paper, we propose an automatic facial expression recognition system. Unlike other systems, it detects and extracts the informative and discriminant regions of the face (i.e., eyes, nose, and mouth areas) using Haar-feature based cascade classifiers and these region-based features are stored into separate image files as a preprocessing step. Then, LBP is applied to these image files for facial texture representation and a feature-vector per subject is obtained by concatenating the resulting LBP histograms of the decomposed region-based features. The one-vs.-rest SVM, which is a popular multi-classification method, is employed with the Radial Basis Function (RBF) for facial expression classification. Experimental results show that this approach yields good performance for both frontal and near-frontal facial images in terms of accuracy and time complexity. Cohn-Kanade and JAFFE, which are benchmark facial expression datasets, are used to evaluate this approach.

AUTOMATIC DETECTION OF THE END-DIASTOLIC AND END-SYSTOLIC FROM 4D ECHOCARDIOGRAPHIC IMAGES

Accurate detection of the End-Diastolic (ED) and End-Systolic (ES) frames of a cardiac cycle are significant factors that may affect the accuracy of abnormality assessment of a ventricle. This process is a routine step of the ventricle assessment procedure as most of the time in clinical reports many parameters are measured in these two frames to help in diagnosing and dissection making. According to the previous works the process of detecting the ED and ES remains a challenge in that the ED and ES frames for the cavity are usually determined manually by review of individual image phases of the cavity and/or tracking the tricuspid valve. The proposed algorithm aims to automatically determine the ED and ES frames from the four Dimensional Echocardiographic images (4DE) of the Right Ventricle (RV) from one cardiac cycle. By computing the area of three slices along one cardiac cycle and selecting the maximum area as the ED frame and the minimum area as the ES frame. This method gives an accurate determination for the ED and ES frames, hence avoid the need for time consuming, expert contributions during the process of computing the cavity stroke volume.

Border Detection of Ventricle Wall Motion in Echocardiographic Images: A Survey

Echocardiography imaging is one of the most widely used diagnostic tests for cardiovascular diseases which allow direct visualization of cardiac structure and ventricles wall motion. It can provide useful information, including the size and shape of the heart. An accurate method for border detection of ventricle wall motion is still important clinical diagnosis tool. Therefore, most of common clinical parameters measurement has become a difficult challenge for many interested researchers especially in the field of Computer Aided Diagnostic (CAD). This paper reviews a number of investigative methods for border detection focusing on segmentation techniques developed in Two-dimensional echocardiographic images.

Quantitative Detection of Left Ventricular Wall Motion Abnormality by Two-Dimensional Echocardiography

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.

3 Dimensional Reconstruction of Tricuspid Valve Using Transesophagel Echocardiography Images

This research aims to develop three dimensional geometrical tricuspid valve model using transesophagel echocardiography raw images (3DTEE). Main motivation that derives this research is the needs of volumetric image segmentation for surgical planning, post-surgical assessment, abnormality detection, and many other medical application. The challenge stands tall especially in regions with abnormal color and shape which needs to be identified by researchers for future studies. Volumetric images contain complicated structures that require precise and most accurate segmentation for diagnosis. Using Level set technique for segmentation, this research promising an accurate and better result that can be used for 3D tricuspid valve reconstruction. The solution of this research will be an alternative segmentation method to assist in surgical planning and indirectly eliminating the subjectively of manual segmentation.

Removing Shadow for Hand Segmentation Based on Background Subtraction

Hand segmentation is an important stage for accurate hand detection and background subtraction is one of the best solutions to detect the hand motion accurately, however the shadow is the critical problem in this technique which is not easy to separate the hand region from the shadow area. Removing shadow using an automatic threshold will be a good solution to detect the hand region where the variety of skin color and lighting condition affect the hand segmentation. The proposed approach involves three stages: First, we convert RGB color model to YUV space to get the benefit of separation the luminance channel (Y) from the chrominance channels (U, V) to reduce the effect of shadow, reflections and, etc. In the second stage, we applied background subtraction technique to the V channel to remove the unwanted background noise and to get the hand and shadow pixels. Finally, we used shareholding technique by considering a mean value of the pixels of foreground image (the hand and shadow pixels) as automatic threshold value and other tow static thresholds to distinguish the hand region from shadow pixels. After background subtraction, we used the famous morphology techniques (Erosion and Dilation) to enhance the accuracy of hand detection. We measure the accuracy for the results by compare the detect hand pixels to the actual hand pixels quantitatively. From the results, we noticed that our proposed approach is accurate and suitable for real time application systems.

A review on gesture recognition technology in children's interactive storybook

Over the past few years, gesture recognition has made its debut in education and virtual reality environment. This paper reviews the current literature in gesture recognition technology for interactive storybooks and the existing methods and challenges for this technology. A conceptual framework is proposed to resolve two main challenges that have been reviewed from previous work: to provide a novel interaction to young children and to ensure accuracy of gesture when using gesture based input sensor. The proposed method is the future work which provides the direction towards developing virtual reality storybooks for children.

Age Range Estimation Based on Facial Wrinkle Analysis Using Hessian Based Filter

Aging is a normal process that has an effect on different parts of the human body under the influence of various biological and environmental aspects. The most prominent changes that occur on the face are the form of the skin wrinkles, which are the main objective of this research. Specific wrinkle detection is an important task in face textural analysis. Previously, some researchers have been proposed the age range estimation based on wrinkle analysis in literature, but poor localization limits the performance of the whole age estimation process. This is because, when less number of wrinkles are detected or extracted, it will consequently affect the process to estimate the correct age. Therefore, we address this issue to enhance age range estimation method using a new approach to extract correct facial wrinkles for further analysis. We propose a method to extract facial wrinkle in face image using Hessian based filter (HBF) for age estimation. In other word, this research focus on age range estimation method based on facial wrinkle analysis extracted from facial image obtained from FG-NET database using hessian based filter. The proposed filter is theoretically straightforward, however, it significantly increases the wrinkle analysis result compared to previous methods. The result shows that HBF successfully obtained higher accuracy with over 90 % estimation rate.