H. Fathi; A.R. Ahmadyfard; H. Khosravi
Abstract
Recently, significant attention has been paid to the development of virtual reality systems in several fields such as commerce. Trying on virtual clothes is becoming a solution for the online clothing industry. In this paper, we propose a method for the problem of virtual clothing using 3D point matching ...
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Recently, significant attention has been paid to the development of virtual reality systems in several fields such as commerce. Trying on virtual clothes is becoming a solution for the online clothing industry. In this paper, we propose a method for the problem of virtual clothing using 3D point matching of a selected cloth and the customer body. For this purpose, we provide a 3D model of the customer and the selected clothes, put up on the mannequin, using a Kinect camera. As the size of the abdominal part of the customer is different from the mannequin, after pre-processing of the two captured point clouds, the 3D point cloud of the selected clothes is deformed to fit the 3D point cloud of the customer’s body. We use Laplacian-Beltrami curvature as a descriptor to find the abdominal part in the two point clouds. Then, the abdominal part of the mannequin is deformed in 3D space to fit the abdominal part of the customer. Finally, the head and neck of the customer are attached to the mannequin point.The proposed method has two main advantages over the existing methods for virtual clothing. First, no need for an expert to design a 3D model for the customer body and the selected clothes in advanced graphical software such as Unity. Second, there is no restriction for the style of the selected clothes and their texture while existing methods have such restrictions. The experimental results justify the ability of the proposed method for virtual clothing.
H.6. Pattern Recognition
Z. Imani; Z. Ahmadyfard; A. Zohrevand
Abstract
In this paper we address the issue of recognizing Farsi handwritten words. Two types of gradient features are extracted from a sliding vertical stripe which sweeps across a word image. These are directional and intensity gradient features. The feature vector extracted from each stripe is then coded using ...
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In this paper we address the issue of recognizing Farsi handwritten words. Two types of gradient features are extracted from a sliding vertical stripe which sweeps across a word image. These are directional and intensity gradient features. The feature vector extracted from each stripe is then coded using the Self Organizing Map (SOM). In this method each word is modeled using the discrete Hidden Markov Model (HMM). To evaluate the performance of the proposed method, FARSA dataset has been used. The experimental results show that the proposed system, applying directional gradient features, has achieved the recognition rate of 69.07% and outperformed all other existing methods.
Ali Harimi; Ali Shahzadi; Alireza Ahmadyfard; Khashayar Yaghmaie
Abstract
Speech Emotion Recognition (SER) is a new and challenging research area with a wide range of applications in man-machine interactions. The aim of a SER system is to recognize human emotion by analyzing the acoustics of speech sound. In this study, we propose Spectral Pattern features (SPs) and Harmonic ...
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Speech Emotion Recognition (SER) is a new and challenging research area with a wide range of applications in man-machine interactions. The aim of a SER system is to recognize human emotion by analyzing the acoustics of speech sound. In this study, we propose Spectral Pattern features (SPs) and Harmonic Energy features (HEs) for emotion recognition. These features extracted from the spectrogram of speech signal using image processing techniques. For this purpose, details in the spectrogram image are firstly highlighted using histogram equalization technique. Then, directional filters are applied to decompose the image into 6 directional components. Finally, binary masking approach is employed to extract SPs from sub-banded images. The proposed HEs are also extracted by implementing the band pass filters on the spectrogram image. The extracted features are reduced in dimensions using a filtering feature selection algorithm based on fisher discriminant ratio. The classification accuracy of the pro-posed SER system has been evaluated using the 10-fold cross-validation technique on the Berlin database. The average recognition rate of 88.37% and 85.04% were achieved for females and males, respectively. By considering the total number of males and females samples, the overall recognition rate of 86.91% was obtained.
