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3D human pose estimation is one of the most significant tasks in computer vision with wide range of applications. The works for estimating human pose initialized from 2D skeletal estimation from multiple data and has proceeded toward 3D skeletal estimation from minimum input information. In this paper, 3D human pose estimation from a single RGB image is investigated. The proposed work is considered as the ones which firstly estimate 2D pose and then lift the estimated 2D configuration to 3D space. Since most of the errors in this attitude are originated by inaccurate 2D pose inference, we have proposed a method for predicting more accurate 2D poses to obtain 3D poses with less errors. The proposed approach for estimating 2D pose has leveraged deep learning along with the information of the edge map. In other words, we have made use of edge features, which are hand-designed features, in order to guide the deep neural network in training and in learning the features in accordance with the defined objective. Experimental results have demonstrated less errors in 2D and consequently 3D pose estimation in Human3.6M and HumanEva-I benchmarks.

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Human facial generation of example image is used as a requirement for biometric applications for the purpose of identifying individuals. In this paper, face generation consists of three main steps. In the first step, detection of significant lines and edges of the example image are carried out using nonlinear grayscale morphology. Then, hair areas are identified from the face of sample. The final step combines images from previous steps. Similarity and matching between synthesized face sketch and artistic sketch are compared with two methods of extracting features, Principle Component Analysis and Linear Discriminant Analysis, and time of the process is calculated. The experiments on the pair of CUHK database images show that the proposed method compared with state of the art methods such as: Eigen transformation, LLE, and MRF, has no computational complexity and creates a person's face with good quality and much less time. Matching of synthesized face sketch of the proposed method is achieved with a maximum value of 90% when Linear Discriminant Analysis is used to extract feature. The proposed method is also resistant to background effects and brightness of example images.

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Sign language recognition systems help deaf people to access various media. In this paper, the Leap Motion Controller (LMC) and the image of the hand are exploited for sign language recognition. The LMC provides 3D position of the hand joints. The first set of features are extracted from the data provided by the LMC. When the hand is not located in vertical view of the LMC or when the hand posed like a fist, the precise position of the hand joints is not recognizable. The second feature extracted from hand image helps most hand gestures be recognized precisely. The second feature includes histogram of oriented gradients and the distance of the hand contour form the center of the hand. Also, a dataset composed of variant American sign language gestures is created which includes 64000 samples. In recognition stage, random forest is applied which is a good option for large datasets. The experimental results show that the proposed method performs better than similar methods.