hamidreza kanan Articles List

List of Articles hamidreza kanan

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1 - A Novel Approach to Background Subtraction Using Visual Saliency Map
Soheil Tehranipour Hamidreza Rashidy Kanan

Generally human vision system searches for salient regions and movements in video scenes to lessen the search space and effort. Using visual saliency map for modelling gives important information for understanding in many applications. In this paper we present a simple More

Generally human vision system searches for salient regions and movements in video scenes to lessen the search space and effort. Using visual saliency map for modelling gives important information for understanding in many applications. In this paper we present a simple method with low computation load using visual saliency map for background subtraction in video stream. The proposed technique is based on finding image segments whose intensity values can be distinguished accurate. The practical implementation uses a sliding window approach, where the distributions of the objects and surroundings are estimated using semi-local intensity histograms. This introduced method requires no training so it can be used in embedded systems like cameras due to low load in calculation. So with our background subtraction algorithm we can detect pre-defined targets. Also the automatically video regions detected by proposed model are consistent with the ground truth saliency maps of eye movement data. Comparisons with state-of-the-art background subtraction techniques indicate that the introduced approach results in high performance and accuracy. Manuscript profile
Open Access Article
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2 - Pseudo Zernike Moment-based Multi-frame Super Resolution
Sara Salkhordeh Hamidreza Rashidy Kanan

The goal of multi-frame Super Resolution (SR) is to fuse multiple Low Resolution (LR) images to produce one High Resolution (HR) image. The major challenge of classic SR approaches is accurate motion estimation between the frames. To handle this challenge, fuzzy motion More

The goal of multi-frame Super Resolution (SR) is to fuse multiple Low Resolution (LR) images to produce one High Resolution (HR) image. The major challenge of classic SR approaches is accurate motion estimation between the frames. To handle this challenge, fuzzy motion estimation method has been proposed that replaces value of each pixel using the weighted averaging all its neighboring pixels in all LR images which carries the degree of similarity between image blocks centered on two pixels. Since in case of rotation between LR images, comparing the gray level of blocks around the pixels is not a suitable criterion for calculating weight, so, magnitude of Zernike Moments (ZM) has been used as a rotation invariant feature. Due to the lower sensitivity of Pseudo Zernike Moments (PZM) to noise and the higher discrimination capability of it for the same order compared to ZM, in this paper, we propose a new method based on magnitude of PZM of the blocks as a rotation invariant descriptor for representation of pixels in weight calculation. Experimental results on several image sequences show that the performance of the proposed algorithm is better than the existing and new techniques from the aspect of PSNR and visual image quality. Manuscript profile
Open Access Article
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3 - Disguised Face Recognition by Using Local Phase Quantization and Singular Value Decomposition
Fatemeh Jafari Hamidreza Rashidy Kanan

Disguised face recognition is a major challenge in the field of face recognition which has been taken less attention. Therefore, in this paper a disguised face recognition algorithm based on Local Phase Quantization (LPQ) method and Singular Value Decomposition (SVD) is More

Disguised face recognition is a major challenge in the field of face recognition which has been taken less attention. Therefore, in this paper a disguised face recognition algorithm based on Local Phase Quantization (LPQ) method and Singular Value Decomposition (SVD) is presented which deals with two main challenges. The first challenge is when an individual intentionally alters the appearance by using disguise accessories, and the second one is when gallery images are limited for recognition. LPQ has been used for extraction of the statistical feature of the phase in windows with different sizes for each pixel of the image. SVD is used to cope with the challenge of the gallery images limitation and also with the help of original images extracted from that, every single image turns to three renovated images. In this study, disguise is intended as a blur in the image and Local phase quantization method is robust against the disguised mode, due to the use of the statistical feature of the Fourier transform phase. Also the use of different-sized window instead of fixed window in feature extraction stage, the performance of the proposed method has increased. The distance of images from each other is computed by using Manhattan and Euclidean distance for recognition in the proposed method. The Performance of the proposed algorithm has been evaluated by using three series of experiments on two real and synthesized databases. The first test has been performed by evaluating all the possible combinations of the different-sized windows created in the feature extraction stage, and the second experiment has been done by reducing the number of gallery images and then the third one has been carried out in different disguise. In all cases, the proposed method is competitive with to several existing well-known algorithms and when there is only an image of the person it even outperforms them. Manuscript profile

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List of Articles hamidreza kanan