Performance Analysis of 1-D Scale-Space Algorithms for Pulse Detection in Noisy Image Scans

Scale-space representation is a topic of active research in computer vision. The focus of the research so far has been on coarse-to-fine focusing methods, image reconstruction, and computational aspects. However, not much work has been done on the signal detection problem, i.e., detecting the presence or absence of signal models from noisy image scans using the scale-space. In this paper we propose four 1-D signal detection algorithms for separating pulse signals in an image scan from the background in the scale-space domain. These algorithms do not need any thresholding to detect the zero-crossings (zc′s) at any of the scales. The different algorithms are applicable to image scans with different noise and clutter characteristics. A simple algorithm works best for scans having low noise and clutter. When noise and clutter increase sufficiently, a more sophisticated algorithm must be used. The 1-D algorithms for pulse and edge detection can be used to detect 2-D closed objects in cluttered and noisy backgrounds. This is done by scanning the image row-wise (and column-wise) and working on the individual scans. Using this method, the algorithms are demonstrated on several real life images. Another objective of this paper is to conduct comparative analysis of (i) a single-scale system vs a multiscale system and (ii) white noise vs clutter. This is done by conducting an experimental statistical analysis on single-scale and multiscale systems corrupted by white noise or clutter. Performance indices such as probability of detection, probability of false alarms, and delocalization errors are computed. The results indicate that (i) the multiscale approach is better than the single-scale approach and (ii) the degradation in performance is greater with clutter than with white noise.