Engineering topics

Cheriyadat, Anil M.

Publications and source records attributed to Cheriyadat, Anil M..

5 records

DOE OSTI2015

Image feature based GPS trace filtering for road network generation and road segmentation

We propose a new method to infer road networks from GPS trace data and accurately segment road regions in high-resolution aerial images. Unlike previous efforts that rely on GPS traces alone, we exploit image features to infer road networks from noisy trace data. The inferred road network is used to guide road segmentation. We show that the number of image segments spanned by the traces and the trace orientation validated with image features are important attributes for identifying GPS traces on road regions. Based on filtered traces , we construct road networks and integrate them with image features to segment road regions. Lastly, our experiments show that the proposed method produces more accurate road networks than the leading method that uses GPS traces alone, and also achieves high accuracy in segmenting road regions even with very noisy GPS data.

58 GEOSCIENCES↗

DOE OSTI2015

Factorization-based texture segmentation

This study introduces a factorization-based approach that efficiently segments textured images. We use local spectral histograms as features, and construct an M × N feature matrix using M-dimensional feature vectors in an N-pixel image. Based on the observation that each feature can be approximated by a linear combination of several representative features, we factor the feature matrix into two matrices-one consisting of the representative features and the other containing the weights of representative features at each pixel used for linear combination. The factorization method is based on singular value decomposition and nonnegative matrix factorization. The method uses local spectral histograms to discriminate region appearances in a computationally efficient way and at the same time accurately localizes region boundaries. Finally, the experiments conducted on public segmentation data sets show the promise of this simple yet powerful approach.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Bag of Lines (BoL) for Improved Aerial Scene Representation

Feature representation is a key step in automated visual content interpretation. In this letter, we present a robust feature representation technique, referred to as bag of lines (BoL), for high-resolution aerial scenes. The proposed technique involves extracting and compactly representing low-level line primitives from the scene. The compact scene representation is generated by counting the different types of lines representing various linear structures in the scene. Through extensive experiments, we show that the proposed scene representation is invariant to scale changes and scene conditions and can discriminate urban scene categories accurately. We compare the BoL representation with the popular scale invariant feature transform (SIFT) and Gabor wavelets for their classification and clustering performance on an aerial scene database consisting of images acquired by sensors with different spatial resolutions. The proposed BoL representation outperforms the SIFT- and Gabor-based representations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

Roadside Tracker Portal-less Portal Monitor

This report documents the full development cycle of the Roadside Tracker (RST) Portal-less Portal monitor (Fig. 1) funded by DHS DNDO. The project started with development of a proof-of-feasibility proto-type, proceeded through design and construction of a proof-of-concept (POC) prototype, a test-and-evaluation phase, participation in a Limited Use Exercise that included the Standoff Radiation Detections Systems developed under an Advanced Technology Demonstration and concluded with participation in a Characterization Study conducted by DNDO.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2013

Detecting multiple moving objects in crowded environments with coherent motion regions

Coherent motion regions extend in time as well as space, enforcing consistency in detected objects over long time periods and making the algorithm robust to noisy or short point tracks. As a result of enforcing the constraint that selected coherent motion regions contain disjoint sets of tracks defined in a three-dimensional space including a time dimension. An algorithm operates directly on raw, unconditioned low-level feature point tracks, and minimizes a global measure of the coherent motion regions. At least one discrete moving object is identified in a time series of video images based on the trajectory similarity factors, which is a measure of a maximum distance between a pair of feature point tracks.

97 MATHEMATICS AND COMPUTING↗