In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.
In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.
Violent interaction detection is of vital importance in some video surveillance scenarios like railway stations, prisons or psychiatric centres. Existing vision-based methods are mainly based on hand-crafted features ...
Violent interaction detection is of vital importance in some video surveillance scenarios like railway stations, prisons or psychiatric centres. Existing vision-based methods are mainly based on hand-crafted features such as statistic features between motion regions, leading to a poor adaptability to another dataset. En lightened by the development of convolutional networks on common activity recognition, we construct a FightNet to represent the complicated visual violence interaction. In this paper, a new input modality, image acceleration field is proposed to better extract the motion attributes. Firstly, each video is framed as RGB images. Secondly, optical flow field is computed using the consecutive frames and acceleration field is obtained according to the optical flow field. Thirdly, the FightNet is trained with three kinds of input modalities, i.e., RGB images for spatial networks, optical flow images and acceleration images for temporal networks. By fusing results from different inputs, we conclude whether a video tells a violent event or not. To provide researchers a common ground for comparison, we have collected a violent interaction dataset (VID), containing 2314 videos with 1077 fight ones and 1237 no-fight ones. By comparison with other algorithms, experimental results demonstrate that the proposed model for violent interaction detection shows higher accuracy and better robustness.
It is challenging to capture a high-dynamic range (HDR) scene using a low-dynamic range (LDR) camera. This paper presents an approach for improving the dynamic range of cameras by using multiple exposure images of sam...
It is challenging to capture a high-dynamic range (HDR) scene using a low-dynamic range (LDR) camera. This paper presents an approach for improving the dynamic range of cameras by using multiple exposure images of same scene taken under different exposure times. First, the camera response function (CRF) is recovered by solving a high-order polynomial in which only the ratios of the exposures are used. Then, the HDR radiance image is reconstructed by weighted summation of the each radiance maps. After that, a novel local tone mapping (TM) operator is proposed for the display of the HDR radiance image. By solving the high-order polynomial, the CRF can be recovered quickly and easily. Taken the local image feature and characteristic of histogram statics into consideration, the proposed TM operator could preserve the local details efficiently. Experimental result demonstrates the effectiveness of our method. By comparison, the method outperforms other methods in terms of imaging quality.
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrot...
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This paper deals with surface normal estimation from calibrated stereo images. We show here how the affine transformation between two projections defines the surface normal of a 3D planar patch. We give a formula that...
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ISBN:
(纸本)9789897580918
This paper deals with surface normal estimation from calibrated stereo images. We show here how the affine transformation between two projections defines the surface normal of a 3D planar patch. We give a formula that describes the relationship of surface normals, camera projections, and affine transformations. This formula is general since it works for every kind of cameras. We propose novel methods for estimating the normal of a surface patch if the affine transformation is known between two perspective images. We show here that the normal vector can be optimally estimated if the projective depth of the patch is known. Other non-optimal methods are also introduced for the problem. The proposed methods are tested both on synthesized data and images of real-world 3D objects.
While traditional dental fillings are molded during a dental visit, dental restoration (e.g. inlays and onlays) are fabricated in a dental lab to offer a long lasting reparative solution to tooth decay or similar stru...
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In this work we derive a novel framework rendering measured distributions into approximated distributions of their mean. This is achieved by exploiting constraints imposed by the Gauss-Markov theorem from estimation t...
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In this work we derive a novel framework rendering measured distributions into approximated distributions of their mean. This is achieved by exploiting constraints imposed by the Gauss-Markov theorem from estimation theory, being valid for mono-modal Gaussian distributions. It formulates the relation between the variance of measured samples and the so-called standard error, being the standard deviation of their mean. However, multi-modal distributions are present in numerous imageprocessing scenarios, e.g. local gray value or color distributions at object edges, or orientation or displacement distributions at occlusion boundaries in motion estimation or stereo. Our method not only aims at estimating the modes of these distributions together with their standard error, but at describing the whole multi-modal distribution. We utilize the method of channel representation, a kind of soft histogram also known as population codes, to represent distributions in a non-parametric, generic fashion. Here we apply the proposed scheme to general mono- and multimodal Gaussian distributions to illustrate its effectiveness and compliance with the Gauss-Markov theorem.
Accurate modeling of human teeth is mandatory for many reasons. Some of them are (1) Providing comfort to patients during mold process, and (2) Enhancing the accuracy level for oral orthodontist and dental care person...
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This paper describes a unique method for tubular object visualization. The method involves rendering the exterior of the tube invisible while keeping the interior visible. This 'One-sided-transparency' techniq...
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This paper describes a unique method for tubular object visualization. The method involves rendering the exterior of the tube invisible while keeping the interior visible. This "One-sided-transparency" techn...
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ISBN:
(纸本)9781479957521
This paper describes a unique method for tubular object visualization. The method involves rendering the exterior of the tube invisible while keeping the interior visible. This "One-sided-transparency" technique renders a more complete view of the tube's interior. When applied to virtual colonoscopy (VC), it compares favorably to existing methods. It provides more complete images, reduces computational time, and reduces memory requirements while preserving VCs benefits for patients and practitioners. The approach also has various potential uses outside of VC.
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