In this thesis, we propose a framework for automated assessment of surgical skills to expedite the manual assessment process and to provide unbiased evaluations with possible dexterity feedback. Evaluation of surgical skills is an important aspect in training of medical students. Current practices rely on manual evaluations from faculty and residents and are time consuming. Proposed solutions in literature involve retrospective evaluations such as watching the offline videos. It requires precious time and attention of expert surgeons and may vary from one surgeon to another. With recent advancements in computer vision and machine learning techniques, the retrospective video evaluation can be best delegated to the computer algorithms. Skill assessment is a challenging task requiring expert domain knowledge that may be difficult to translate into algorithms. To emulate this human observation process, an appropriate data collection mechanism is required to track motion of the surgeon's hand in an unrestricted manner. In addition, it is essential to identify skill defining motion dynamics and skill relevant hand locations. This Ph.D. research aims to address the limitations of manual skill assessment by developing an automated motion analysis framework. Specifically, we propose (1) to design and implement quantitative features to capture fine motion details from surgical video data, (2) to identify and test the efficacy of a core subset of features in classifying the surgical students into different expertise levels, (3) to derive absolute skill scores using regression methods and (4) to perform dexterity analysis using motion data from different hand locations.

Textured motion - generally known as dynamic or temporal texture - is a popular research area for synthesis, segmentation and recognition. Dynamic texture is a spatially repetitive, time-varying visual pattern that forms an image sequence with certain temporal stationarity. In dynamic texture, the notion of self-similarity central to conventional image texture is extended to the spatiotemporal domain. Dynamic textures are typically videos of processes, such as waves, smoke, fire, a flag blowing in the wind, a moving escalator, or a walking crowd. Creation of synthetic frames is a key issue especially for movie screen industry to enrich their scenes from a white screen into a shining reality. In robotics world, for example an autonomous vehicle must decide what is traversable terrain (e.g. grass) and what is not (e.g. water). This problem can be addressed by classifying portions of the image into a number of categories, for instance grass, dirt, bushes or water. If these parts are identifiable, then segmentation and recognition of these textures results with an efficient path planning for the autonomous vehicle. In this thesis, we aimed to characterize these textured motions like mentioned above. We tried to implement several known techniques and compared the results.