Rosdiyana Samad

Real-time rotation invariant hand tracking using 3D data

Hand tracking is a common task in a gesture recognition system. Many techniques have been introduced to make successful hand tracking. In hand tracking system, most of previous works tracked the hand position using attached marker on hands. Several researchers have used a color image for skin color detection. However, using marker based need to attach marker on hands or wear gloves to make hand can be detected. When using color information, there is a need to extract many different skin colors. Furthermore, the lighting and background on the situation also need to be concerned to avoid a cluttered background that can affect the detection and tracking. This paper presents the real-time hand tracking using three dimensional (3D) data. This 3D data is coming from the Kinect sensor, which is working in real-time. 3D data from Kinect sensor is depth image data which can be used to detect and track the motion of the hand. This paper proposes hand tracking method using a hand tracker algorithm released by NiTE, hands segmentation method, hand contour detection and center of palm detection. The hands segmentation method consists of the ROI of the hands area and background subtraction. The propose hand tracking algorithm is rotation invariant, since it can detect and track various rotations of hand. Additionally, it also can remove unwanted object (noise) that also moving parallelly with the hands position.

Initial experimental results of real-time variant pose face detection and tracking system

A face detection system is a computer application for automatically detecting a human face from digital image or video frame. This paper presents a face detection system that used web camera to detect and track a face in real-time. To detect a face in the image, a simple method of skin color detection is used. By using color detection method in this project, the face can be segmented easily from the complex background. However, to detect a face in real-time is quite challenging especially when a face is moving and the real-time environment has uneven illumination. This paper presents the preliminary result of face detection and tracking system, which is the system, detects a face that has different poses in a real-time situation, where the light condition is uneven. Here, to complete the detection process, contour detection method is added so that the detection is more accurate. This system can be applied in many applications such as banking system to reduce the number of forgery, security system, and human-computer interaction (HCI).

Computer vision-based hand deviation exercise for rehabilitation

Computerized monitoring of the home based rehabilitation exercise has many benefits and it has attracted considerable interest among the computer vision community. Nowadays, many rehabilitation systems are proposed, most of the targeted disability is for stroke patient. Some of patient or user just wants to take certain part for rehabilitation. Therefore, this paper is focusing on hand rehabilitation system. The importance of the rehabilitation system is to implement the specific exercise for the specific requirements of the patients that needs rehabilitation therapy. This paper presents the specific hand rehabilitation system using computer vision method. The specific hand rehabilitation implemented in this system is a hand deviation exercise. This exercise is benefited to improve the mobility of the hand and reduce the pain. The hand tracking and finger detection method are used in this hand rehabilitation system. The result of the exercise can be used as a training data for the analysis of the injured hand recovery and healing process.

Finger application using K-Curvature method and Kinect sensor in real-time

Gesture is one of the important aspects of human interaction and also in the context of human computer interaction. Gesture recognition is the mathematical interpretation of a human motion by a computing device. It is often used hand gestures for input commands in personal computers. By recognizing the hand gesture as input, it allows the user to access the computer interactively and makes interaction more natural. This paper presents a finger detection application by using Kinect. Kinect is a depth sensor that is an effective device to capture the gesture in real-time. To detect and recognize the fingertips, it needs to extract the detail of the captured hand image using image processing methods. In this paper, the proposed method is to detect and recognize the fingertips by using the K-Curvature algorithm. Finally, the finger counting application is applied and the proposed method is discussed at the end of this paper. The results obtained from the experiment show that the acceptable average accuracy for the fingertips detection is 73.7% and the average processing time is 15.73 ms. By considering this result, the application of the proposed method can be extended to the hand rehabilitation system.

BreAst Cancer Segmentation Based On GVF snake

Breast cancer is a mysterious disease that forms through the formation of cancer in the cells of the breast. It is one of the most dangerous cancer diagnosed in women and may lead to the increasing number of deaths all over the world. In this paper, Gradient Vector Flow (GVF) Snake technique is proposed to detect cancerous cells in breast tissues. Ten images of mammograms were tested by using the GVF Snake technique. The mammography images were pre-processed at first using Gaussian Low Pass Filter to remove the unwanted noise and followed by contrast enhancement. After that, the GVF Snake technique is then used to segment the particular cancerous region in the breast tissue as it is allows for flexible initialization of deformable surface and encourages convergence to boundary concavities. The GVF Snake technique showed positive results in segmenting the cancerous region in breast tissue.

SKF-Based Image Template Matching for Distance Measurement by Using Stereo Vision

In this paper, a novel image template matching approach to tackle distance measurement problem has been proposed. There are many conventional algorithms to increase the accuracy of distance measurement as reported in the literature such as Semi-global algorithm to produce the disparity map. Meanwhile, in this paper, the reverse engineering technique had been implemented to get the correct depth value by applying the image template matching method as reference for the distance measurement. The traditional algorithm to solve image matching problem take a lot of memory and computational time. Therefore, image matching problem can be considered to optimization problem and can be solved precisely. The search of the image template has been performed exhaustively by using Simulated Kalman Filter (SKF) algorithm. The experiment is conducted with a set of images taken by using stereo vision system. Experimental results show the accuracy of the distance measurement by using stereo camera, after applying (1) the estimate error model, (2) SKF and (3) PSO algorithm are 89.95%, 96.09%, 95.29% and 58.51% respectively. The limitation of estimate error model that it can only be applied into the same setup of the experiment, environment, parameters of the camera and acquired images. Instead, the proposed algorithm which is SKF can be applied to original image and image under the vision problems like illumination and partially occluded. The SKF algorithm shows more robust, more efficient and more accurate to solve the distance measurement problem.

Multi-objective evolutionary programming for static var compensator (SVC) in power system considering contingencies (N-m)

Efficiency, reliability, high power quality and continuous operation are important aspects in electric vehicle attraction system. Therefore, quick fault detection, isolation and enhanced fault-tolerant control for open-switches faults in inverter driving systems become more and more required in this filed. However, fault detection and localization algorithms have been known to have many performance limitations due to speed variations such as wrong decision making of fault occurrence. Those weaknesses are investigated and solved in this paper using currents magnitudes fault indices, current direct component fault indices and a decision system. A simulation model and experimental setup are utilized to validate the proposed concept. Many simulation and experimental results are carried out to show the effectiveness of the proposed fault detection approach.The inverter with critical loads should be able to provide critical loads with a stable and seamless voltage during control mode change as well as clearing time. The indirect current control has been proposed for providing stable voltage with critical load during clearing time and seamless control mode transfer of inverters. However, the islanding detection is difficult since with the indirect current control the magnitude and frequency of voltage do not change when the islanding occurs. The conventional anti-islanding method based on the magnitude and frequency of voltage variation cannot apply to the indirect current control. This paper proposes an islanding detection method for the indirect current control. The proposed islanding detection method can detect the islanding using reactive power perturbation and observation when the frequency and magnitude of voltage don’t vary during clearing time. In order to verify the proposed anti-islanding method, the experimental results of a 600W three-phase inverter are provided.

Correlation of Objective Assessment of Facial Paralysis with House-Brackmann Score

This article illustrated a brief review of some objective methods in assessing facial nerve function for facial nerve paralysis which were correlated with House-Brackmann Grading System (HBGS). A rigorous search of online databases such as Springer, Elsevier and IEEE was conducted from June, 2015 to November, 2016 to discover and analyze the previous works in facial nerve assessment methods for facial paralysis. Several domains such as facial grading system and methods used to evaluate the facial nerve function were extracted for further analysis. Different keywords were used to acquire the studies based on the desire criteria. A total of 8 articles were identified and were analyzed for inclusion in this search. In conclusion, this review has presented an initial overview for further improvements in objective facial nerve assessment which has to be correlated with subjective assessment to make it more reliable and useful in clinical practice.

Fault detection and isolation for complex system

Fault Detection and Isolation (FDI) is a method to monitor, identify, and pinpoint the type and location of system fault in a complex multiple input multiple output (MIMO) non-linear system. A two wheel robot is used as a complex system in this study. The aim of the research is to construct and design a Fault Detection and Isolation algorithm. The proposed method for the fault identification is using hybrid technique that combines Kalman filter and Artificial Neural Network (ANN). The Kalman filter is able to recognize the data from the sensors of the system and indicate the fault of the system in the sensor reading. Error prediction is based on the fault magnitude and the time occurrence of fault. Additionally, Artificial Neural Network (ANN) is another algorithm used to determine the type of fault and isolate the fault in the system.

Levenberg-Marquardt flood prediction for Sungai Isap residence

The flood can cause wide destroy to property and life because of the supreme corrosive force and can be highly damaging. In order to decrease the damages cause by the flood, an Artificial Neural Network (ANN) model has been established to predict flood in Sungai Isap, Kuantan, Pahang, Malaysia. This model is able to imitate same as the brain thinking process and avoid any influence to the predict judgment. This study proposed Levenberg-Marquardt (LM) back-propagation with two different ratios that is (80%: 10%: 10%) and (70%: 15%: 15%) for training sample, testing sample, and validation sample. The data collected in terms of temperature, precipitation, dew point, humidity, sea level pressure, visibility, wind and river level data were collected from January 2013 until May 2015. The results are shown on the basic of mean square error (MSE) and regression (R). The prediction by Levenberg-Marquardt with 80% training sample was shown better result compared with 70% training sample.