Object detection in noisy images

Scale-space representation is a topic of active research in computer vision. Several researchers have studied the behavior of signals in the scale-space domain and how a signal can be reconstructed from its scale-space. However, not much work has been done on the signal detection problem, i.e. detecting the presence or absence of signal models from a given scale-space representation. In this paper we propose a model-based object detection algorithm for separating the objects from the background in the scale-space domain. There are a number of unresolved issues, some of which are discussed here. The algorithm is used to detect an infrared image of a tank in a noisy background. The performance of a multiscale approach is compared with that of a single scale approach by using a synthetic image and adding controlled amounts of noise. A synthetic image of randomly placed blobs of different sizes is used as the clean image. Two classes of noisy images arc considered. The first class is obtained by adding clutter (i.e. colored noise) and the second class by adding an equivalent amount of white noise. The multiscale and single scale algorithms are applied to delect the blobs, and performance indices such as number of detections, number of false alarms, delocalization errors etc. 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.