Adjunct Faculty
The University of Texas at Arlington, Department of Computer Science and Engineering
Brief Biography: Ali Sharifara received his Ph.D. and M.Sc. in Computer Science from the University of Technology, Malaysia in 2016 and 2012, respectively. He worked as a postdoctoral researcher in Heracleia Human-Centered Computing Lab at the University of Texas at Arlington, the U.S. from 2016 to 2017, and he is currently carrying out his research using computer vision methods for Robotic Systems. Contact Information: Office Hours: Wednesday 2:00 p.m. - 3:45 p.m. or by appointment Office Location : ERB 321, CSE@UTA Email : ali.sharifara@uta.edu
The University of Texas at Arlington, Department of Computer Science and Engineering
The University of Texas at Arlington, Department of Computer Science and Engineering
University of Technology, Malaysia, Faculty of Computing
University of Technology, Malaysia, Faculty of Computing
Ph.D. in Computer Science
University of Technology, Malaysia
Master of Computer Science
University of Technology, Malaysia
Bachelor of Software Engineering
University of Applied science and Technology, Iran
Ali Sharifara's current study involving smart vocational assessment and intervention service systems. He has been working with other research members to develop a system that collects and analyzes multi-sensing human-robot interaction data to recommend personalized interventions to provide improvements. He currently conducts experiments with different robots, namely Socially Assistive Robotics and Physically Assistive Robots to assess cognitive, physical, and collaboration worker skills.
He is incorporating state of the art methods in machine learning, data mining, collaborative filtering, and human-robot interaction, and seek to take advantage of the latest developments in sensing technologies, robotics, and intelligent communications. The purpose of this research is to design better human-robot collaboration methods as well as advance the intelligence of robots with human-like learning and collaboration capabilities.
Face detection is an interesting area in research application of computer vision and pattern recognition, especially during the past several years. It is also plays a vital role in surveillance systems which is the first steps in face recognition systems. The high degree of variation in the appearance of human faces causes the face detection as a complex problem in computer vision. The face detection systems aimed to decrease false positive rate and increase the accuracy of detecting face especially in complex background images. The main aim of this paper is to present an up-to-date review of face detection methods including feature-based, appearance-based, knowledge-based and template matching. Also, the study presents the effect of applying Haar-like features along with neural networks. We also conclude this paper with some discussions on how the work can be taken further.
The main aim of this paper is to introduce a novel approach to preserve digital images' copyrights. Hence as ISB scheme was selected in relation to the approach in an attempt conquer the issues of robustness and imperceptibility in watermarked imagery. According to the literature review, embedding the aimed secret bits (Watermark) is a problematic issue inside a host image (normal 8-bit, grey-scale) in a sense to make it undetectable by the HVS (Human Visual System) in addition to the matter that it is predicted to receive any attacks. The suggested method here represents an improved scheme for the embedding of ISB which maintains the robustness and cultivates the rate of security by employing repeated bits in various bit planes over an irregular order and it develops the LSB technique specifically in circumstances where robustness and imperceptibility are main points of assessment.
One of the main challenging issues in computer vision is automatic detection and recognition of object classes. In particular, the detection of the class of human faces is a challenging issue, which makes special attention due to the large number of its practical applications, which use face detection as the main and primary step such as face recognition, video surveillance systems, etc. The main aim of face detection is locating human face in images or videos regardless of variations, which are associated to the face detection problem including pose, illumination, and occlusion. The present research distinguishes by two main contributions, which aims to cope with the problem of face detection to locate faces in different poses precisely. The first contribution is the segmentation of face images, based on skin color, which allows discarding the background regions of image quickly. The process aims to decrease the search space and reduce the computation time for feature extraction process. The Second contribution is applying a validation phase in order to reject false alarms. In this phase, the algorithm uses the enhanced local binary pattern and Support Vector Machine (SVM) to extract features of face and classification the features, respectively. In the proposed framework, the intra-class variability of faces is accomplished in a learning module. The learning module used enhanced Haar-like features in order to extract features from human face.
Automatic face detection is one of the interesting and challenging tasks in the field of computer vision. Face detection is the first and main step in many applications, especially in surveillance systems. In the present paper, a hybrid method is proposed to detect human face under different lighting conditions and complex backgrounds of color images. The proposed method have used skin color segmentation methods as well as edge detection to detect face in color images. In addition, a template matching process is applied based on a linear transformation in order to detect face for the selected regions. Thus, the process can be helpful in reducing false selected regions, which have same color as face.
HTKS is a game-like cognitive assessment method, designed for children between four and eight years of age. During the HTKS assessment, a child responds to a sequence of requests, such as "touch your head" or "touch your toes". The cognitive challenge stems from the fact that the children are instructed to interpret these requests not literally, but by touching a different body part than the one stated. In prior work, we have developed the CogniLearn system, that captures data from subjects performing the HTKS game, and analyzes the motion of the subjects. In this paper we propose some specific improvements that make the motion analysis module more accurate. As a result of these improvements, the accuracy in recognizing cases where subjects touch their toes has gone from 76.46% in our previous work to 97.19% in this paper.
Teleradiology enables medical images to be transferred over the computer networks for many purposes including clinical interpretation, diagnosis, archive, etc. In telemedicine, medical images can be manipulated while transferring. In addition, medical information security requirements are specified by the legislative rules, and concerned entities must adhere to them. In this research, we propose a new scheme based on 2-dimensional Discrete Wavelet Transform (2D DWT) to improve the robustness and authentication of medical images. In addition, the current research improves security and capacity of watermarking using encryption and compression in medical images. The evaluation is performed on the personal dataset, which contains 194 CTI and 68 MRI cases.
Face detection is one of the challenging tasks in computer vision. Human face detection plays an essential role in the first stage of face processing applications such as face recognition, face tracking, image database management, etc. In these applications, face objects often come from an inconsequential part of images that contain variations, namely different illumination, poses, and occlusion. These variations can decrease face detection rate noticeably. Most existing face detection approaches are not accurate, as they have not been able to resolve unstructured images due to large appearance variations and can only detect human faces under one particular variation. Existing frameworks of face detection need enhancements to detect human faces under the stated variations to improve detection rate and reduce detection time. In this study, an enhanced face detection framework is proposed to improve detection rate based on skin color and provide a validation process. A preliminary segmentation of the input images based on skin color can significantly reduce search space and accelerate the process of human face detection. The primary detection is based on Haar-like features and the Adaboost algorithm. A validation process is introduced to reject non-face objects, which might occur during the face detection process. The validation process is based on two-stage Extended Local Binary Patterns. The experimental results on the CMU-MIT and Caltech 10000 datasets over a wide range of facial variations in different colors, positions, scales, and lighting conditions indicated a successful face detection rate.
The rapid growth of computer technologies have been increased over the last half century in terms of amount and complexity of data. Broadcasting of digital contents on the networks (especially Internet) has become more important and access to the data also has become much easier than before. Digital atermarking techniques are used to protect the copyrights of multimedia data by embedding secret nformation inside them. For example, embedding watermark in images, audios, and videos. Digital Image watermarking also has been using to detect original images against forged images by embedding an vidence of the owner of the digital image. Imperceptibility, on the other hand, is one of the problems in digital image watermarking which a repeated method in different bit planes of cover image has been presented to improve the imperceptibility of watermarking in both embedding and extracting processes. Moreover, embedding process aims to embed watermark in different bit planes by using a nonsequential method to improve security of image rather than simple sequential embedding.
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CSE 5321/4321
CSE 5369/6369- Guest Lecturer
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Office Hours: Wednesday from 2:00 p.m to 3:45 p.m or by appointment
Office Location: ERB 321, CSE@UTA