Mohammed A.-M. Salem

Haralick Features Based Automated Glaucoma Classification Using Back Propagation Neural Network

According to recent researches, glaucoma, an optic nerve disease, is considered as one of the major causes which can lead to blindness. It has affected a huge number of people worldwide. Rise in intraocular pressure of the eye leads to the disease resulting in progressive and permanent visual loss. Texture of normal retinal image and glaucoma image is different. Here texture property of the total image has been extracted from both with and without glaucoma image. In this work, Haralick features have been used to distinguish between normal and glaucoma affected retina. Extracted features have been utilized to train the back propagation neural network. Classification of glaucoma affected eye is successfully achieved with an accuracy of 96%.

Recent Research on Multi Input Multi Output (MIMO) based Mobile ad hoc Network: A Review

Mobile ad hoc Network (MANET) and Multi Input Multi Output (MIMO) communication are emerging techniques in modern communication system. MIMO and MANET have various applications in the wireless communication system. This paper presents a survey on recent advancement of MIMO implemented mobile ad hoc network. A review of more than 40 papers on MIMO communication based mobile ad hoc network and most of the related topics is presented in this work. This paper shows the significant contribution in the field of MIMO communication and mobile ad hoc network. Previous works in this domain can be categorized into four major areas: (a) Mathematical modeling of MIMO channel and Ad hoc networks, (b) Physical Scattering Model of MIMO channel considering fading (c) Spatial multiplexing (OFDM) technique with MIMO channel, (d) Analysis of transmission efficiency of packet radio network. The review paper establishes the advancement in these four areas as well as recent changes in advance communication and networking environments.

Resolution mosaic-based Smart Camera for video surveillance

Video surveillance is one of the most data intensive applications. A typical video surveillance system consists of one or multiple video cameras, a central storage unit, and a central processing unit. At least two bottlenecks exist: First, the transmission capacity is limited, especially for raw data. Second, the central processing unit has to process the incoming data to give results in real time. Therefore, we propose an FPGA-based embedded camera system which performs all steps of image acquisition, region of interest extraction, generation of a multiresolution image, and image transmission. The proposed pipeline-based architecture allows a real time wavelet-based image segmentation and a detection of moving objects for surveillance purposes. The system is integrated in a single FPGA using external RAM as storage for images and for a Linux operating system which controls the data flow. With the pipeline concept and a Linux device driver it is possible to create a system for streaming the results of an image processing through a GbE interface. A real time processing is achieved. The camera transmits the captured images with 30 Mpixel/s, but the system is able to process 100 Mpixel/s.

Fusing directional wavelet local binary pattern and moments for human action recognition

Recently, transformation-based methods have been widely used in many computer vision areas because of their powerful representation ability. One of the most widely used transforms is the wavelet transform that has proved to be very useful in many applications. In this study, a new method for human action representation and description is proposed. This method combines the advantages of local and global descriptions. The method works by fusing the Hu invariant moments as global descriptors with a new local descriptor that is based on three-dimensional stationary wavelet transform and the concept of local binary patterns. The performance of the new method was examined in two different ways. The first one is by fusing the proposed directional global and local features in one feature vector, while the other is using the features of different directional bands separately to train multiple classifiers and then using a voting scheme to vote for the best match. The performance of the proposed method is verified using standard datasets, achieving high accuracy in comparison with state-of-the-art methods. In addition, the proposed method is proved to be robust to the changes in lighting and scale variations, but it exhibits limitations towards dynamic backgrounds.

Spatio-Temporal Motion Detection for Intelligent Surveillance Applications

Intelligent surveillance aims at conceiving reliable and efficient systems that are able to detect and track moving objects in complicated real world scenes. This paper proposes an innovative 3D stationary wavelet-based motion detection technique that fuses spatial and temporal analysis in a single 3D transform. This single transform is composed of applying a 2D transform in the spatial domain followed by 1D transform in the time domain. The results of the proposed technique are compared favorably with those of the recently used stationary wavelet-based technique. In addition of being accurate and has reasonable complexity of O(N2log N), the proposed technique is robust to real world scene variations, including nonuniform and time-varying illumination.

Daubechies Versus Biorthogonal Wavelets for Moving Object Detection in Traffic Monitoring Systems

Moving object detection is a fundamental task for a variety of traffic applications. In this paper the Daubechies and biorthogonal wavelet families are exploited for extracting the relevant movement information in moving image sequences in a 3D wavelet-based segmentation algorithm. The proposed algorithm is applied for traffic monitoring systems. The objective and subjective experimental results obtained by applying both wavelet types are compared and interpreted in terms of the different wavelet properties and the characteristics of the image sequences. The comparisons show the superior performance of the symmetric biorthogonal wavelets in the presence of noisy images and changing lighting conditions when compared to the application of high order Daubechies wavelets. The algorithm is evaluated using simulated images in the Matlab environment.

Action Recognition Using Stationary Wavelet-Based Motion Images

Human action recognition is one of the most important fields in computer vision, because of the large number of applications that employ action recognition. Many techniques have been proposed for representing and classifying actions; yet these tasks are still non-trivial due to a number of challenges and characteristics. In this paper, a new action representation method is proposed. The proposed method utilizes the 3D Stationary Wavelet Analysis to encode the spatio-temporal characteristics of the motion available in the video sequences in a way similar to motion history images. The proposed representation was tested using Weizmann dataset, exhibiting promising results when compared to the existing state – of – the – art methods.

Multi-stage localization given topological map for autonomous robots

Vision-based place recognition is of a particular importance for autonomous systems that aim to navigate intelligently in a human-inhabited environment. Given a topological map of an indoor environment, the autonomous system shall localize itself invariantly with different illumination and imaging conditions. To address these challenges, we propose to use global-local feature extraction and classification in multiple stages. Scale Invariant Feature Transform (SIFT) is used as a local feature detector and descriptor which has been proven to be a robust local invariant feature descriptor. Fourier Transform, Hue Saturation Value (HSV), and Hough Transform are used as global features. The Support Vector Machines (SVM) is used to localize the system by classifying the global features. However the K-nearest neighbors matching technique (K-NN) is used to support SVMs classification in ambiguous decisions by classifying the local features.

Directional Stationary Wavelet-Based Representation for Human Action Classification

This paper proposes a directional wavelet-based representation of natural human actions in realistic videos. This task is very important for human action recognition, which has become one of the most important fields in computer vision. Its importance comes from the large number of applications that employ human action classification and recognition. The proposed method utilizes the 3D Stationary Wavelet Analysis to encode the directional spatio-temporal characteristics of the motion available in video sequences. It was tested using the Weizmann dataset, and produced promising preliminary results (92.47 % classification accuracy) when compared to existing state–of–the–art methods.

Android-based object recognition for the visually impaired

The large number of blind and visually impaired individuals in the society has motivated research groups to search for smart solutions that use vision-based technologies to improve their quality of life. This paper describes an Android-based application for object recognition developed to help the blind understand their environment better. This application is based on extracting local features of the object of interest, which are then matched to the corresponding features of objects saved in a knowledge base previously created. The local features are tested against more than one classification method and the results are analyzed. Deploying the application to a Samsung Galaxy Tab, the system is evaluated using a dataset especially developed for this purpose. The dataset contains more than 600 images of twelve objects under several distortions and viewing condition changes. Results of the analysis show that the system achieves real-time performance with high accuracy under most viewing conditions.