On statistical approaches to target silhouette classification in difficult conditions

In this paper we present a methodical evaluation of the performance of a new and two traditional approaches to automatic target recognition (ATR) based on silhouette representation of objects. Performance is evaluated under the simulated conditions of imperfect localization by a region of interest (ROI) algorithm (resulting in clipping and scale changes) as well as occlusions by other silhouettes, noise and out-of-plane rotations. The two traditional approaches are holistic in nature and are based on moment invariants and principal component analysis (PCA), while the proposed approach is based on local features (object parts) and is comprised of a block-by-block 2D Hadamard transform (HT) coupled with a Gaussian mixture model (GMM) classifier. Experiments show that the proposed approach has good robustness to clipping and, to a lesser extent, robustness to scale changes. The moment invariants based approach achieves poor performance in advantageous conditions and is easily affected by...

• Type: Journal
• Year: 2008
• Where: DSP
• Authors: Conrad Sanderson, Danny Gibbins, Stephen Searle
• Keywords: DSP 2008, Emerging Technology, Performance, Silhouettes, Traditional Approaches