This paper proposes a parallel image thinning algorithm and a skeletonization algorithm based on cellular automaton (CA). Cellular automaton is a parallel computation model and a non-linear dynamical system. In this p...
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This paper proposes a parallel image thinning algorithm and a skeletonization algorithm based on cellular automaton (CA). Cellular automaton is a parallel computation model and a non-linear dynamical system. In this paper, each image pixel is identified as a cell of CA and the change of cell depends on the current state of itself and the state of its neighbors. In a binary image, this paper assumes that the objects (white pixel) are preys which are surrounded by many ants (every black pixels). The movement of ants is controlled by cellular automation. The ants gnaw preys until the preys (objects) become skeleton. The proposed parallel skeletonization algorithm can produce a traditional skeleton with a thin line located in the center of object, and the proposed thinning algorithm can produce a new kind of skeleton which is named as the ants-gnawing skeleton. The computation of ants-gnawing skeleton is faster than the traditional skeleton while it contains more the structural features of image. Benefiting from the properties of cellular automation, the proposed thinning algorithm does not change the basic geometry structure of image, and it is invariant for image rotation.
In the field of human-computer interaction, vision-based gesture recognition methods are widely studied. However, its recognition effect depends to a large extent on the performance of the recognition algorithm. The s...
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In the field of human-computer interaction, vision-based gesture recognition methods are widely studied. However, its recognition effect depends to a large extent on the performance of the recognition algorithm. The skeletonization algorithm and convolutional neural network (CNN) for the recognition algorithm reduce the impact of shooting angle and environment on recognition effect, and improve the accuracy of gesture recognition in complex environments. According to the influence of the shooting angle on the same gesture recognition, the skeletonization algorithm is optimized based on the layer-by-layer stripping concept, so that the key node information in the hand skeleton diagram is extracted. The gesture direction is determined by the spatial coordinate axis of the hand. Based on this, gesture segmentation is implemented to overcome the influence of the environment on the recognition effect. In order to further improve the accuracy of gesture recognition, the ASK gesture database is used to train the convolutional neural network model. The experimental results show that compared with SVM method, dictionary learning + sparse representation, CNN method and other methods, the recognition rate reaches 96.01%.
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