Ashraf Saad Hussein

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.

Multi-label automatic GrabCut for image segmentation

This paper presents a multi-label automatic GrabCut technique for the problem of image segmentation. GrabCut is considered as one of the binary-label segmentation techniques because it is based on the famous s/t graph cut minimization technique for image segmentation. This paper extends the automatic binary-label GrabCut to a multi-label technique that can segment a given image into its natural segments without user intervention. Since multi-label segmentation is an NP-hard problem, the proposed algorithm converts the segmentation problem into multiple iterative piecewise binary label GrabCut segmentations. This implies separating one segment from the image, under consideration, per iteration. In this way, the proposed algorithm maintains the powerful advantage of the GrabCut to get the optimal solution for the segmentation problem. Evaluation of the segmentation results was carried out using different accuracy metrics from the literature. The evaluations were conducted with human ground truth segmentations from Berkeley benchmark dataset of natural images. Although human segmentations are semantically more meaningful, experiments showed that the proposed multi-label GrabCut provided matching segmentation results to that of individual humans with acceptable accuracy.

A Comparative Study of Different Color Space Models Using FCM-Based Automatic GrabCut for Image Segmentation

GrabCut is one of the powerful color image segmentation techniques. One main disadvantage of GrabCut is the need for initial user interaction to initialize the segmentation process which classifies it as a semi-automatic technique. The paper presents the use of Fuzzy C-means clustering as a replacement of the user interaction for the GrabCut automation. Several researchers concluded that no single color space model can produce the best results of every image segmentation problem. This paper presents a comparative study of different color space models using automatic GrabCut for the problem of color image segmentation. The comparative study includes the test of five color space models; RGB, HSV, XYZ, YUV and CMY. A dataset of different 30 images are used for evaluation. Experimental results show that the YUV color space is the one generating the best segmentation accuracy for the used dataset of images.

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.

Automatic GrabCut for Bi-label Image Segmentation Using SOFM

This paper proposes a new technique for the problem of color image segmentation using GrabCut. GrabCut is considered as one of the semi-automatic segmentation techniques, since it requires user interaction for the initialization of the segmentation process, via dragging a rectangle around an object to extract it. This restricts GrabCut for bi-label segmentation, where the image cannot be segmented into more than two; foreground and background segments. In order to set up for multi-label segmentation, this paper presents the use of SOFM as a powerful unsupervised clustering technique for the GrabCut initialization process. This converts the GrabCut from a semi-automatic into a complete automatic segmentation technique. The use of different SOFM architectures for the process of image segmentation was tested for real experiments. Evaluation and comparison with the original GrabCut show the efficiency of the proposed automatic technique in terms of segmentation quality and accuracy.

An Efficient System for Stock Market Prediction

This paper presents an efficient system for accurate, confident, general and responsive stock market prediction, employing Artificial Neural Networks (ANN). For technical indicators, Multi-Layer Perceptron (MLP) ANN is used and trained with Kullback Leibler Divergence (KLD) learning algorithm because it converges fast in addition to offering generalization in the learning mechanism. On the other hand, Radial Basis Function Neural Network (RBFNN) trained with Localized Generalization Error (L-GEM) is used for candlesticks patterns. The accuracy, generalization and statistical-significance of the developed system were confirmed through various local and international data sets. Next, sensitivity analysis was conducted for the different parameters that influence the system efficiency metrics. In order to have responsive prediction, the proposed system was evolved, employing concurrent programming to get benefit from the off-the-shelf multi-core architectures. Then, the performance of the developed system was evaluated to confirm acceptance scalability and utilization.

Human action recognition via multi-scale 3D stationary wavelet analysis

Multi-scale methods, especially wavelets, are being used in various computer vision applications, including surveillance, robotics, and human-centered computing. Human action recognition is one of the core areas that dominate the aforementioned applications. In this paper, the 3D multi-scale stationary wavelet analysis is used to build a view-based multi-scale spatio-temporal representation of the human actions. The proposed representation benefits from the ability of the 3D stationary wavelet transform to fuse the spatio-temporal information highlighted at different scales and orientations. Experimental results using Weizmann and KTH datasets revealed a good performance in various scenarios with different conditions.

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.

Clustering-based Image Segmentation using Automatic GrabCut

GrabCut is one of the most powerful semi-automatic segmentation techniques. One main drawback of GrabCut is the need for user interaction in order to initialize the segmentation process. User interaction involves dragging a rectangle around the object of interest in the image to extract it. This restricts GrabCut for binary-label segmentation, where the image cannot be segmented into more than two; foreground and background segments. Unsupervised clustering is a powerful automatic tool for dividing images into a specified number of regions based on defined image features. The authors had introduced the SOFM clustering technique for GrabCut automation as a replacement to the user interaction. In this paper, K-means and Fuzzy C-means are introduced as new clustering techniques to automate GrabCut and improve the segmentation accuracy. Experimental results and comparisons with the previous results are carried out to test the efficiency of the proposed techniques in terms of segmentation quality and accuracy.

3D Mesh Segmentation Based on Unsupervised Clustering

3D mesh segmentation is considered an important process in the field of computer graphics. It is a fundamental process in different applications such as shape reconstruction in reverse engineering, 3D models retrieval, and CAD/CAM applications, etc. It consists of subdividing a polygonal surface into patches of uniform properties either from a geometrical point of view or from a perceptual/semantic point of view. In this paper, unsupervised clustering techniques for the 3D mesh segmentation problem are introduced. The K-means and the Fuzzy C-means (FCM) clustering techniques are selected for the development of the proposed clustering-based 3D mesh segmentation techniques. Since the mesh faces are considered the main element, the clustering technique is applied to the dual mesh. The 3D Euclidean distance is used as the distance measure to compute matching between mesh elements. Based on empirical results on a benchmark dataset of 3D mesh models, the FCM-based mesh segmentation technique outperforms the K-means-based one in terms of accuracy and consistency with human segmentations.