Computer vision approaches have been applied to a wide variety of domains. These approaches are able to replicate human processes as well as provide new insights. Once developed, computer vision approaches also permit high throughput analysis of processes that would otherwise be tedious. In this thesis, we focus on developing tools that replicate and go above human analysis for two applications.
We aim at developing tools to track infants` head motions in a non-intrusive manner during an autism assessment. We propose a method to track facial features from a single camera and use their changing locations to sequentially update the motions.
We also aim at developing an elliptical-cell and multiple-fluorescence segmentation program to allow for the high throughput analysis of C.albicans and S.cerevisiae yeast cultures. We propose to segment pseudohyphal cells based on an extension of the circular Hough transform; as well as, segment multiple types of fluorescence labeling.