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Hitting a Major League fastball pitch may be the most difficult task in the sports realm. Anecdotal evidence suggests that certain individuals are able to perform this task reasonably well, perhaps because of superior sensitivity to changes in motion. However, the substantial lack of research investigating detection and assessment of changes in motion renders this conclusion problematic (Kelling, 2008). Two experiments, using expert and novice participants, assessed sensitivity to changes in motion. Experts for these studies were defined as current members of the Georgia Institute of Technology Yellow Jacket softball team. Experimental procedures included assessments of capabilities in batting and motion tracking tasks. Experiment One presented participants with recorded softball pitches thrown from a pitching machine. Experiment Two required participants to predict multiple landing locations for incomplete motion paths resulting from a single main target exploding into additional shrapnel pieces. Results suggest minimal expertise effects in the softball task with high performance by all participants, while distinct expertise effects exist in the shrapnel task. The motion tracking task resulted in fewer errors by experts, while all participants demonstrated a significantly large drop in performance with increasing number of shrapnel pieces. Findings from this work not only have application to the sport of softball, but are critical for identifying the people's capability to detect and assess changes in motion.

Title: Image Deblurring in Demanding Conditions Author: Jan Kotera Department: Institute of Information Theory and Automation, Czech Academy of Sciences Supervisor: Doc. Ing. Filip Šroubek, Ph.D., DSc., Institute of Information Theory and Automation, Czech Academy of Sciences Abstract: Image deblurring is a computer vision task consisting of removing blur from image, the objective is to recover the sharp image corresponding to the blurred input. If the nature and shape of the blur is unknown and must be estimated from the input image, image deblurring is called blind and naturally presents a more difficult problem. This thesis focuses on two primary topics related to blind image deblurring. In the first part we work with the standard image deblurring based on the common convolution blur model and present a method of increasing robustness of the deblur- ring to phenomena violating the linear acquisition model, such as for example inten- sity clipping caused by sensor saturation in overexposed pixels. If not properly taken care of, these effects significantly decrease accuracy of the blur estimation and visual quality of the restored image. Rather than tailoring the deblurring method explicitly for each particular type of acquisition model violation we present a general approach based on flexible automatic...