Samy Bakheet

Samy Bakheet
Sohag University · Faculty of computers and Information

Engineering doctoral degree (Dr.-Ing.)

About

47
Publications
10,077
Reads
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592
Citations
Introduction
I received the engineering doctoral degree (Dr.-Ing.) in Neuro Information Technology (NIT) from Otto-von-Guericke University Magdeburg, Germany in 2013. I currently serve as an Associate Professor of Computer Science (Major: Intelligent Vision Systems) at Sohag University, Egypt. My research interests are geared towards high-level recognition problems in machine vision, such as human activity/event recognition, human pose estimation, object/scene recognition, etc. I have authored close to 50 refereed technical papers in well journals and international conference/symposium proceedings in the fields of machine vision, pattern recognition, machine learning, medical imaging, biometrics and robotics.
Additional affiliations
February 2019 - present
Sohag University
Position
  • Head of Faculty
May 2016 - December 2017
Otto-von-Guericke-Universität Magdeburg
Position
  • PostDoc Position
March 2016 - September 2016
Otto-von-Guericke-Universität Magdeburg
Position
  • PostDoc Position

Publications

Publications (47)
Article
Full-text available
Amongst all biometric-based personal authentication systems, a fingerprint that gives each person a unique identity is the most commonly used parameter for personal identification. In this paper, we present an automatic fingerprint-based authentication framework by means of fingerprint enhancement, feature extraction, and matching techniques. Initi...
Article
Recently, automatic computer-aided detection (CAD) of COVID-19 using radiological images has received a great deal of attention from many researchers and medical practitioners, and consequently several CAD frameworks and methods have been presented in the literature to assist the radiologist physicians in performing diagnostic COVID-19 tests quickl...
Article
Full-text available
Robust vision-based hand pose estimation is highly sought but still remains a challenging task, due to its inherent difficulty partially caused by self-occlusion among hand fingers. In this paper, an innovative framework for real-time static hand gesture recognition is introduced, based on an optimized shape representation build from multiple shape...
Article
Full-text available
Due to their high distinctiveness, robustness to illumination and simple computation, Histogram of Oriented Gradient (HOG) features have attracted much attention and achieved remarkable success in many computer vision tasks. In this paper, an innovative framework for driver drowsiness detection is proposed, where an adaptive descriptor that possess...
Article
Full-text available
In recent years, researchers have focused on wireless sensor networks (WSNs). Because there are a lot of applications we used. The wireless sensor network consists of many small sensor nodes that contain a small and self-charged battery. Sometimes it is possible to change the power source of the node battery but sometimes it is impossible to do so,...
Article
Full-text available
The American Cancer Society has recently stated that malignant melanoma is the most serious type of skin cancer, and it is almost 100% curable, if it is detected and treated early. In this paper, we present a fully automated neural framework for real-time melanoma detection, where a low-dimensional, computationally inexpensive but highly discrimina...
Article
Full-text available
Despite their recognized merits in terms of discrimination, compactness, and robustness, chord-length shape features have not received a great deal of attention in the literature on license plate recognition. In this paper, we present an innovative k nearest neighbors (kNN) approach for license plate detection and recognition, where a new low-dimen...
Article
Full-text available
Early detection of skin cancer through improved techniques and innovative technologies has the greatest potential for significantly reducing both morbidity and mortality associated with this disease. In this paper, an effective framework of a CAD (Computer-Aided Diagnosis) system for melanoma skin cancer is developed mainly by application of an SVM...
Article
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Since a bank of 2D Gabor filters has a large potential to isolate texture according to particular frequencies and orientations, the usage of Gabor features to simulate the visual features extracted from human hand is a very effective way. In this paper, we propose an optimized Gabor features based framework for real-time hand gesture recognition ex...
Article
Full-text available
Inspired by the overwhelming success of Histogram of Oriented Gradients (HOG) features in many vision tasks, in this paper, we present an innovative compact feature descriptor called fuzzy Histogram of Oriented Lines (f-HOL) for action recognition, which is a distinct variant of the HOG feature descriptor. The intuitive idea of these features is ba...
Article
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Most of the approaches of Content-Based Image Retrieval (CBIR) presume a linear relationship between different image features, and the efficiency of such systems was limited due to the difficulty in representing high-level concepts using low-level features. In this paper, a new architecture for a CBIR system is proposed; the Splines Neural Network-...
Thesis
Full-text available
The overall objective of the research presented in this doctoral thesis is to explore and establish theories and methodologies for accurate representation and recognition of human actions in video data. For the methodological contributions of this thesis, multiple approaches involving diverse conceptualizations are developed to represent and recogn...
Article
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Despite their attractive properties of invariance, robustness and reliability, fuzzy directional features are not hitherto paid the attention they deserve in the activity recognition literature. In this paper, we propose to adopt an innovative approach for activity recognition in real-world scenes, where a new fuzzy motion descriptor is developed t...
Article
Full-text available
We propose an innovative approach for human activity recognition based on affine-invariant shape representation and SVM-based feature classification. In this approach, a compact computationally efficient affine-invariant representation of action shapes is developed by using affine moment invariants. Dynamic affine invariants are derived from the 3D...
Article
Full-text available
Despite their high stability and compactness, chord-length shape features have received relatively little attention in the human action recognition literature. In this paper, we present a new approach for human activity recognition, based on chord-length shape features. The most interesting contribution of this paper is twofold. We first show how a...
Conference Paper
Full-text available
Despite their high stability and compactness, chord-length features have received little attention in activity recognition literature. In this paper, we present an SVM approach for activity recognition, based on chord-length shape features. The main contribution of the paper is two-fold. We first show how a compact computationally-efficient shape d...
Conference Paper
Full-text available
Despite their high stability and compactness, affine moment invariants have received a relatively little attention in action recognition literature. In this paper, we introduce an approach for activity recognition, based on affine moment invariants. In the proposed approach, a compact computationally-efficient shape descriptor is developed by using...
Article
Full-text available
An essential part of any activity recognition system claiming be truly real-time is the ability to perform feature extraction in real-time. We present, in this paper, a quite simple and computationally tractable approach for real-time human activity recognition that is based on simple statistical features. These features are simple and relatively s...
Article
Full-text available
Temporal shape variations intuitively appear to provide a good cue for human activity modeling. In this paper, we lay out a novel framework for human action recognition based on fuzzy log-polar histograms and temporal self-similarities. At first, a set of reliable keypoints are extracted from a video clip (i.e., action snippet). The local descripto...
Article
Full-text available
Temporal shape variations intuitively appear to provide a good cue for human activity modeling. In this paper, we lay out a novel framework for human action recognition based on fuzzy log-polar histograms and temporal self-similarities. At first, a set of reliable keypoints are extracted from a video clip (i.e., action snippet). The local descripto...
Conference Paper
Full-text available
Real-time feature extraction is a key component for any action recognition system that claims to be truly real-time. In this paper we present a conceptually simple and computationally efficient method for real-time human activity recognition based on simple statistical features. Such features are very cheap to compute and form a relatively low dime...
Article
Full-text available
Automatic face detection and localization is a key problem in many computer vision tasks. In this paper, a simple yet effective approach for detecting and locating human faces in color images is proposed. The contribution of this paper is twofold. First, a particular reference to face detection techniques along with a background to neural networks...
Conference Paper
Full-text available
This paper proposes an automatic method that handles hand gesture spotting and recognition simultaneously. To spot meaningful gestures of numbers (0-9) accurately, a stochastic method for designing a non-gesture model with Hidden Markov Models (HMMs) versus Conditional Random Fields (CRFs) is proposed without training data. The non-gesture model pr...
Conference Paper
Full-text available
Temporal invariant shape moments intuitively seem to provide an important visual cue to human activity recognition in video sequences. In this paper, an SVM based method for human activity recognition is introduced. With this method, the feature extraction is carried out based on a small number of computationally-cheap invariant shape moments. When...
Conference Paper
Full-text available
In this work, a schematic model for human activity recognition based on multiple cues is introduced. In the beginning, a sequence of temporal silhouettes of the moving human body parts are extracted from a video clip (i.e., an action snippet). Next, each action snippet is temporally split into several time-slices represented by fuzzy intervals. As...
Conference Paper
Full-text available
Automatic skin detection is a key enabler of various imaging applications, such as face detection, human tracking, and adult content filtering. In 1996, the first paper on identifying nude pictures was published. Since then, different researchers argue different color models to be the best choice for skin detection. But, to the best our knowledge,...
Conference Paper
Full-text available
Recently, the problem of automatic traffic accident recognition has appealed to the machine vision community due to its implications on the development of autonomous Intelligent Transportation Systems (ITS). In this paper, a new framework for real-time automated traffic accidents recognition using Histogram of Flow Gradient (HFG) is proposed. This...
Chapter
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In this paper, a fast and efficient approach for region-based image classification and retrieval using multi-level neural network model is proposed. The advantages of this particular model in image classification and retrieval domain will be highlighted. The proposed approach accomplishes its goal in two main steps. First, by aid of a mean-shift ba...
Chapter
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In 1953, a functional extension by A. Rènyi to generalize traditional Shannon’s entropy known as α-entropies was proposed. The functionalities of α-entropies share the major properties of Shannon’s entropy. Moreover, these entropies can be easily estimated using a kernel estimate. This makes their use by many researchers in computer vision communit...
Article
Full-text available
Retrieving images from large and varied repositories using visual contents has been one of major research items, but a challenging task in the image management community. In this paper we present an efficient approach for region-based image classification and retrieval using a fast multi-level neural network model. The advantages of this neural mod...
Article
Full-text available
Over the past decade, automatic traffic accident recognition has become a prominent objective in the area of machine vision and pattern recognition because of its immense application potential in developing autonomous Intelligent Transportation Systems (ITS). In this paper, we present a new framework toward a real-time automated recognition of traf...
Conference Paper
Full-text available
Retrieving human actions from video databases is a paramount but challenging task in computer vision. In this work, we develop such a framework for robustly recognizing human actions in video sequences. The contribution of the paper is twofold. First a reliable neural model, the Multi-level Sigmoidal Neural Network (MSNN) as a classifier for the ta...
Article
Full-text available
Over the past decade, automatic traffic accident recognition has become a prominent objective in the area of machine vision and pattern recognition because of its immense application potential in developing autonomous Intelligent Transportation Systems (ITS). In this paper, we present a new framework toward a real-time automated recognition of traf...
Conference Paper
Full-text available
Image classification problem is one of the most challenges of computer vision. In this paper, a robust image classification approach using multilevel neural networks is proposed. In this approach, each image is fixedly divided into five regions each equal to half of the original image. Then these regions are classified by the multilevel neural clas...
Conference Paper
Full-text available
Research in content-based image retrieval (CBIR) shows that high-level semantic concepts in image cannot be constantly depicted using low-level image features. So the process of designing a CBIR system should take into account diminishing the existing gap between low-level visual image features and the high-level semantic concepts. In this paper, w...
Article
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In this paper, we propose a supervised method for color image classification based on a multilevel sigmoidal neural network (MSNN) model. In this method, images are classified into five categories, i.e., "Car", "Building", "Mountain", "Farm" and "Coast". This classification is performed without any segmentation processes. To verify the learning cap...
Article
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In this paper, we propose a novel approach for image segmentation via fuzzification of Rènyi Entropy of Generalized Distributions (REGD). The fuzzy REGD is used to precisely measure the structural information of image and to locate the optimal threshold desired by segmentation. The proposed approach draws upon the postulation that the optimal thres...
Conference Paper
Full-text available
Detecting human faces in color images is a key problem in human-computer interaction. In this paper, an approach for precisely detecting and locating human faces in color images is proposed. An adaptive multi-hidden layers neural network is used to perform face localization in uncontrolled environments. A particular reference to face detection tech...
Article
Full-text available
A robust model for skin detection is the primary need of many fields of computer vision, including face detection, gesture recognition, and pornography detection. In 1996, the first paper on pornographic image detection was published. Since then, different researchers argue different color spaces to be the best choice for skin detection in pornogra...

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