In order to improve the classifier performance in semantic image annotation, we propose a novel method which adopts learning vector quantization (LVQ) technique to optimize low level feature data extracted from given ...
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This paper presents a copy-paste block detection method based on characteristics of double JPEG compress. The JPEG compress will bring JPEG compression characteristics to the DCT coefficients, these characteristics ar...
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Blurred images are caused by many factors such as defocus, motion, and atmospheric turbulence. Due to the unknown various factors that cannot be distinguished in the blurred image, it is necessary to propose a unified...
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A novel authentication watermarking scheme for images is proposed in this paper, which holds accuracy location and high security at the same time. In the scheme, different keys are selected for different host data, an...
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This paper presents a copy-paste block detection method based on characteristics of double JPEG compress. The JPEG compress will bring JPEG compression characteristics to the DCT coefficients, these characteristics ar...
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This paper presents a copy-paste block detection method based on characteristics of double JPEG compress. The JPEG compress will bring JPEG compression characteristics to the DCT coefficients, these characteristics are closely related with the quality factor. Copy-paste tamper between JPEG images will disrupt the JPEG compression characteristics of the final image. The method in this paper is designed to deal with double JPEG compression whose DCT blocks are different during the two compresses, and the experiment shows that our method can work effectively on double JPEG compression with different quality factors and is not subject to the impact of DCT blocks.
The multi-channel image or the video clip has the natural form of tensor. The values of the tensor can be corrupted due to noise in the acquisition process. We consider the problem of recovering a tensor L of visual d...
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The multi-channel image or the video clip has the natural form of tensor. The values of the tensor can be corrupted due to noise in the acquisition process. We consider the problem of recovering a tensor L of visual data from its corrupted observations X = L + S, where the corrupted entries S are unknown and unbounded, but are assumed to be sparse. Our work is built on the recent studies about the recovery of corrupted low-rank matrix via trace norm minimization. We extend the matrix case to the tensor case by the definition of tensor trace norm in. Furthermore, the problem of tensor is formulated as a convex optimization, which is much harder than its matrix form. Thus, we develop a high quality algorithm to efficiently solve the problem. Our experiments show potential applications of our method and indicate a robust and reliable solution.
This paper proposes a graph-based method for segmentation of a text image using a selected colour-channel image. The text colour information usually presents a two polarity trend. According to the observation that the...
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This paper proposes a graph-based method for segmentation of a text image using a selected colour-channel image. The text colour information usually presents a two polarity trend. According to the observation that the histogram distributions of the respective colour channel images are usually different from each other, we select the colour channel image with the histogram having the biggest distance between the two main peaks, which represents the main foreground colour strength and background colour strength respectively. The peak distance is estimated by the mean-shift procedure performed on each individual channel image. Then, a graph model is constructed on a selected channel image to segment the text image into foreground and background. The proposed method is tested on a public database, and its effectiveness is demonstrated by the experimental results.
In order to solve the problem of image degradation caused by dust environments, an image degradation model considering multiple scattering factors caused by dust was first established using the first-order multiple sc...
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In this paper we propose a novel framework for action recognition based on multiple features for improve action recognition in videos. The fusion of multiple features is important for recognizing actions as often a si...
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ISBN:
(纸本)9781424475421
In this paper we propose a novel framework for action recognition based on multiple features for improve action recognition in videos. The fusion of multiple features is important for recognizing actions as often a single feature based representation is not enough to capture the imaging variations (view-point, illumination etc.) and attributes of individuals (size, age, gender etc.). Hence, we use two kinds of features: i) a quantized vocabulary of local spatio-temporal (ST) volumes (cuboids and 2-D SIFT), and ii) the higher-order statistical models of interest points, which aims to capture the global information of the actor. We construct video representation in terms of local space-time features and global features and integrate such representations with hyper-sphere multi-class SVM. Experiments on publicly available datasets show that our proposed approach is effective. An additional experiment shows that using both local and global features provides a richer representation of human action when compared to the use of a single feature type.
Roadmap methods were widely used in route planning fields, both for robots and unmanned aircrafts. Traditional roadmap is constituted by connecting the vertexes of convex obstacle, which is related to the locations of...
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