Engineering Commons^™

Dense Point-Cloud Representation Of A Scene Using Monocular Vision, Yakov Diskin, Vijayan K. Asari

Vijayan K. Asari

We present a three-dimensional (3-D) reconstruction system designed to support various autonomous navigation applications. The system presented focuses on the 3-D reconstruction of a scene using only a single moving camera. Utilizing video frames captured at different points in time allows us to determine the depths of a scene. In this way, the system can be used to construct a point-cloud model of its unknown surroundings. We present the step-by-step methodology and analysis used in developing the 3-D reconstruction technique. We present a reconstruction framework that generates a primitive point cloud, which is computed based on feature matching and depth …

Histogram Of Oriented Phase (Hop): A New Descriptor Based On Phase Congruency, Hussin Ragb, Vijayan K. Asari

Vijayan K. Asari

In this paper we present a low level image descriptor called Histogram of Oriented Phase based on phase congruency concept and the Principal Component Analysis (PCA). Since the phase of the signal conveys more information regarding signal structure than the magnitude, the proposed descriptor can precisely identify and localize image features over the gradient based techniques, especially in the regions affected by illumination changes. The proposed features can be formed by extracting the phase congruency information for each pixel in the image with respect to its neighborhood. Histograms of the phase congruency values of the local regions in the image …

A Modular Approach For Key-Frame Selection In Wide Area Surveillance Video Analysis, Almabrok Essa, Paheding Sidike, Vijayan K. Asari

Vijayan K. Asari

This paper presents an efficient preprocessing algorithm for big data analysis. Our proposed key-frame selection method utilizes the statistical differences among subsequent frames to automatically select only the frames that contain the desired contextual information and discard the rest of the insignificant frames.

We anticipate that such key frame selection technique will have significant impact on wide area surveillance applications such as automatic object detection and recognition in aerial imagery. Three real-world datasets are used for evaluation and testing and the observed results are encouraging.