Cooperative use of parallel processing with time or frequency-domain filtering for shape recognition

For many computer vision applications, detection of blobs and/or tubular structures in images are of great importance. In this paper, we have developed a parallel signal processing framework for speeding up the detection of blob and tubular objects in images. We identified filtering procedure as being responsible for up to 98% of the global processing time, in the used blob or tubular detector functions. We show that after a certain dimension of the filter it is beneficial to combine frequency-domain techniques with parallel processing to develop faster signal processing algorithms. The proposed framework is applied to medical wireless capsule endoscopy (WCE) images, where blob and/or tubular detectors are useful in distinguishing between abnormal and normal images.