Environment
H. Hassanpour; Z. Mortezaie; A. Beghdadi
Abstract
Video surveillance systems are widely used in the public and private sectors for maintaining security and healthcare purposes. Performance of surveillance systems directly depends on their accuracy in re-identification. There are three regions in a camera view, including person’s body, background, ...
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Video surveillance systems are widely used in the public and private sectors for maintaining security and healthcare purposes. Performance of surveillance systems directly depends on their accuracy in re-identification. There are three regions in a camera view, including person’s body, background, and possible carried object by the person. Background, in existing approaches, is either overlooked or treated like a person’s body in re-identification. In this paper, these three regions are considered in re-identification but with different importance. In our proposed technique, first, the input image is semantically segmented into the three regions using a deep semantic segmentation approach. Then, the effect of each region on characteristic features of people is tuned depending on the region’s importance in re-identification. The proposed technique, leveraging robust descriptors, such as the Gaussian of Gaussian (GOG) and Hierarchical Gaussian Descriptors (HGD), can enhance existing methods in dealing with the challenging issues such as partial occlusion caused by carried objects and background in re-identification. Experimental results on commonly used people re-identification datasets demonstrate effectiveness of the proposed technique in improving performance of existing re-identification methods.
H. Hassanpour; R. Ebadi; A. Zehtabian; Z. Amiri
Abstract
In X-ray computed tomography (CT), existence of metallic implants in the subject may corrupt images and produce dark and bright streaking artifacts. In this paper a new method for reducing metal artifact from dental X-ray CT images is introduced. In the proposed method, the Radon transform is used in ...
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In X-ray computed tomography (CT), existence of metallic implants in the subject may corrupt images and produce dark and bright streaking artifacts. In this paper a new method for reducing metal artifact from dental X-ray CT images is introduced. In the proposed method, the Radon transform is used in order to project the CT data into the sinogram domain. The sinogram of data can be decomposed into its illumination and reflectance components by using the homomorphic wavelet filtering. The investigation of the CT images shows that the degradations caused by metallic artifacts appear mainly in the illumination component. Therefore, in our approach the corrupted illumination component is restored by using the apriori information driven from the previous artifact-free sections. The results show that the metal artifacts are considerably reduced without eliminating the important details of the CT images. The proposed method is also compared with other existing methods on a set of dental CT images. Comparisons show the superiority of the proposed method over other existing methods.