Alaa E. Abdel-Hakim

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SIFT has been proven to be the most robust local invariant feature descriptor. SIFT is designed mainly for gray images. However, color provides valuable information in object description and matching tasks. Many objects can be misclassified if their color contents are ignored. This paper addresses this problem and proposes a novel colored local invariant(More)
In this paper we present a novel and robust approach for detection, categorization and recognition of road signs. It is known that the standard road signs contain few and easily distinguishable colors, such as red for prohibition, yellow for warnings, green, blue and white. We use a Bayesian approach for detecting road signs in the captured images based on(More)
We propose a novel approach for rain/snow removal from videos using low-rank recovery. Rain/snowdistorted video frames are treated as a distorted 3D signal. The main goal is to separate the distortion, which is the rain or snow additive signal, from the original rain-free signal. Inter-frame information is exploited to put the problem in a convex(More)
In this paper, we propose a general and robust robotic path planning framework for both planar and terrain environments using level set methods. The framework is general in the sense that it can be used for both 2D and 3D environments. It generates a collision-free optimum paths for the entire or a portion of the configuration space. The optimum planned(More)
In this paper, we propose a general, robust, and fast path planning framework for robotic navigation using level set methods. A medial point of the map is selected automatically to be a point source that transmits two wave fronts of different speeds. The first front propagates with a moderate speed to capture the map topology, while the second one(More)
While the performance of Robust Principal Component Analysis (RPCA), in terms of the recovered low-rank matrices, is quite satisfactory to many applications, the time efficiency is not, especially for scalable data. We propose to solve this problem using a novel fast incremental RPCA (FRPCA) approach. The low rank matrices of the incrementally-observed data(More)
In this paper, we present a novel and robust approach for camera planning in smart vision systems. The proposed approach uses virtual forces to adjust the camera parameters (pan, tilt, translation ... etc.) toward the most proper values with respect to the application. It employs the physical spring model to direct the motion of the camera toward its(More)
In this paper, we present a sensor planning approach for a mobile trinocular active vision system. At the stationary state (i.e., no motion) the sensor planning system calculates the generalized cameras' parameters (i.e., translational distance from the center, zoom, focus and vergence) using deterministic geometric specifications of both the sensors and(More)