Figure 1: We explore HiddenPursuits, where the trajectory of a moving target is partially hidden while users perform selections via smooth pursuit eye movements. This allows (1) expanding selection areas by enabling targets to move out of the bounds of small displays (e.g., mobile devices and smartwatches), and (2) selection of targets even if they are partially occluded (e.g., in VR). In our user study, we hid 0%, 25%, 50% and 75% of the target's (fish) trajectories, and measured how well eye movements correlate with the anticipated target's trajectory. Arrows are for illustration and were not displayed.

In this paper we use cellular learning automata integrated with a normalized gradient based motion detection algorithm in fuzzy domain to detect and track the moving objects in a video. A sequence of the video frames with a preset interval are first converted to gray scale images and then based on the first frame a first order gradient is calculated in fuzzy domain. Normalization process is then performed and the gray levels are ranged between 0 and 255 for each pixel. A sequence of primary motion detected images are calculated from the normalized first order gradient images subject to a minimum threshold value for difference between every two sequent gradient images. If the condition of a minimum difference is met, each primary motion detected image is therefore computed as the products of the second order gradient of the image by the difference of the current frame with the average. Two terms of the products provide larger values for larger motions while vibrations and slight shakings are avoided. Resulting motion images are normalized to the gray scale range [0 : 255]. Then, cellular learning automata algorithm is used to reconstruct and form the object(s) of interest based on a set of rules. Objects are detected by a contour after the reconstruction. Detected objects in the sequential frames are used for the tracking purpose. This algorithm can be used for controlling and supervising a secured public place viewed by a fixed camera. The objects may be considered as the traversing people, animals, cars or any carrying baggage and the algorithm can be parameterized for the objects of interest. Finally, all parameters of the method as the threshold values, score, penalty and the number of evolution cycles are analyzed to find the optimum values for the dataset under analysis. Comprehensive experiments are performed to show the capability and efficiency of the proposed method while it is stated that developing this code in MATLAB constrains working in offline processing mode.

Diabetic retinopathy is one of the most important causes of visual impairment. In this paper, a supervised automatic lesion detection in digital retina images for diagnosis and screening purposes. The aim of this study is to present a supervised approach for exudate detection in fundus images and also to analyze the method to find the optimum structure. Cellular automata model is used as the base for this task. To improve the adaptability and efficiency of the cellular automata, the rules are updating by a learning process to produce the cellular learning automata. Then, the algorithm is transferred to fuzzy domain for the task of digital retina image analysis. Automaton is created with simple and extended Moore neighborhood. Rule selection and rule updating are performed automatically and the score and penalty assignments are applied to the cells toward a segmentation process. To evaluate the proposed method, statistical parameters of sensitivity, specificity and accuracy are used. A comprehensive experiment is then executed comprising two main phases. First all structural parameters of the automaton are optimized in an investigation study and then a comparison is made between the proposed method with six other well-known methods to verify the results. In the best structure the statistical parameters of sensitivity, specificity and accuracy are computed as 96.3%, 98.7% and 96.1% for STARE retina image dataset.