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Full-Text Articles in Physics
Volume Component Analysis For Classification Of Lidar Data, Nina M. Varney, Vijayan K. Asari
Volume Component Analysis For Classification Of Lidar Data, Nina M. Varney, Vijayan K. Asari
Vijayan K. Asari
One of the most difficult challenges of working with LiDAR data is the large amount of data points that are produced. Analysing these large data sets is an extremely time consuming process. For this reason, automatic perception of LiDAR scenes is a growing area of research. Currently, most LiDAR feature extraction relies on geometrical features specific to the point cloud of interest. These geometrical features are scene-specific, and often rely on the scale and orientation of the object for classification. This paper proposes a robust method for reduced dimensionality feature extraction of 3D objects using a volume component analysis (VCA) …
Multiple Object Detection In Hyperspectral Imagery Using Spectral Fringe-Adjusted Joint Transform Correlator, Paheding Sidike, Vijayan K. Asari, Mohammad S. Alam
Multiple Object Detection In Hyperspectral Imagery Using Spectral Fringe-Adjusted Joint Transform Correlator, Paheding Sidike, Vijayan K. Asari, Mohammad S. Alam
Vijayan K. Asari
Hyperspectral imaging (HSI) sensors provide plenty of spectral information to uniquely identify materials by their reflectance spectra, and this information has been effectively used for object detection and identification applications. Joint transform correlation (JTC) based object detection techniques in HSI have been proposed in the literatures, such as spectral fringe-adjusted joint transform correlation (SFJTC) and with its several improvements. However, to our knowledge, the SFJTC based techniques were designed to detect only similar patterns in hyperspectral data cube and not for dissimilar patterns. Thus, in this paper, a new deterministic object detection approach using SFJTC is proposed to perform multiple …
Directional Ringlet Intensity Feature Transform For Tracking, Evan Krieger, Paheding Sidike, Theus H. Aspiras, Vijayan K. Asari
Directional Ringlet Intensity Feature Transform For Tracking, Evan Krieger, Paheding Sidike, Theus H. Aspiras, Vijayan K. Asari
Vijayan K. Asari
The challenges existing for current intensity-based histogram feature tracking methods in wide area motion imagery include object structural information distortions and background variations, such as different pavement or ground types. All of these challenges need to be met in order to have a robust object tracker, while attaining to be computed at an appropriate speed for real-time processing. To achieve this we propose a novel method, Directional Ringlet Intensity Feature Transform (DRIFT), that employs Kirsch kernel filtering and Gaussian ringlet feature mapping. We evaluated the DRIFT on two challenging datasets, namely Columbus Large Image Format (CLIF) and Large Area Image …
Histogram Of Oriented Phase (Hop): A New Descriptor Based On Phase Congruency, Hussin Ragb, Vijayan K. Asari
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 Robust Fringe-Adjusted Joint Transform Correlator For Efficient Object Detection, Paheding Sidike, Vijayan K. Asari, Mohammad S. Alam
A Robust Fringe-Adjusted Joint Transform Correlator For Efficient Object Detection, Paheding Sidike, Vijayan K. Asari, Mohammad S. Alam
Vijayan K. Asari
The fringe-adjusted joint transform correlation (FJTC) technique has been widely used for real-time optical pattern recognition applications. However, the classical FJTC technique suffers from target distortions due to noise, scale, rotation and illumination variations of the targets in input scenes. Several improvements of the FJTC have been proposed in the literature to accommodate these problems. Some popular techniques such as synthetic discriminant function (SDF) based FJTC was designed to alleviate the problems of scale and rotation variations of the target, whereas wavelet based FJTC has been found to yield better performance for noisy targets in the input scenes. While these …