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Compared to the mouse, as a two-dimensional and precise interface device, hand gestures provide more degrees of freedom for users to interact with computers by employing intelligent computing methods. Leap Motion Controller is gaining more popularity due to its ability to detect and track hand joints in three dimensions. However, in some cases, the Leap Motion Controller measurements are not correct enough. We show that the occlusion, palm angle, and the limited field of view are the main downsides of the Leap Motion Controller. In this paper, a framework is proposed to manipulate and deform a three-dimensional object by hand gestures. We select only a few gestures so that the system instructions can be easily memorized. The gestures are not defined very strictly, so users can do them properly without getting tired. We propose that calculating a reliable space from a Leap Motion Controller can significantly reduce these problems. To deform objects, the Free Form Deformation technique is used, which allows for more local deformation. The selected gestures and determined space for interaction make the deformation framework achieve a balance between the accuracy, user-factors, required tasks for deformation, and limitation of the hand tracking device. The proposed method, compared to related studies, offers more creative methods for deforming objects and more natural movements to interact with the system. According to the conducted user study, a significant difference is observed between hand gesture interaction and mouse in terms of speed and number of attempts. [ABSTRACT FROM AUTHOR]

In this paper, a novel shape descriptor for shape recognition is proposed. An evolutionary process is introduced in which a contour is reconstructed from the bounding circle of the shape. In this evolutionary process, circle points always move toward the shape in normal direction until they arrive at the shape contour. Three different descriptors are extracted from this process: the first descriptor is defined as the number of steps that every circle point should pass from circle to shape contour which is called evolution steps (ES). The second descriptor is considered as the boundary distance (BD) of the sample points at the end of the evolution process. The third descriptor is the mean of curvature of the evolution lines that are created by moving points, (MCEL). In matching stage, dynamic programming is employed to best matching between shapes. Finally, normalizing the features makes them to be invariant to scale. Sparse representation as a new framework for classification is applied in the recognition stage. The proposed descriptors are evaluated for task of shape recognition on several data sets. Experimental results demonstrate the advantaged performance of the proposed method in shape recognition. [ABSTRACT FROM AUTHOR]