Active contour‐based clutter defiance scheme for correlation filters

Abstract

The negative impact of clutters on correlation output still remains the problem not handled in correlation pattern recognition (CPR) paradigm. Some of the known impacts caused by the presence of clutters include pronounced side-lobe generation and reduction in height of correlation peak. The occurrence of the side-lobes results in the distortion of actual correlation output while making the class decision a difficult task. Whereas, reduction in correlation height may cause class decisions to go wrong. The authors propose a novel clutter defiance strategy based on object segmentation through active contours approach thus evading a negative effect on the correlation output. Instead of allowing clutters to participate in CPR-based classification decision they recommend blocking irrelevant details to avoid distortion in the correlation process. An appropriate tweaking in the optimal logarithmic maximum average correlation height filter design yields a performance gain up to 80% in the correlation peak to side-lobe ratio, thus making it well pronounced to classify the true class objects correctly. The improvement remains consistent throughout the range of in-plane angular distortions against synthetically cluttered challenging images.

Weight Quantization Retraining for Sparse and Compressed Spatial Domain Correlation Filters

“…Gardezi et al [1] use Affine Scale Shift Invariant Feature Transform (ASIFT) along with a spatial correlation filter that enables the fully-invariant filter. Similarly, Awan et al [4] devise an auto-contour-based technique to reduce the side lobes. This method assures higher accuracy through prior object segregation before correlation with the reference template; however, the mentioned techniques do not target the filters' compression, efficiency, or memory requirements.…”