In this paper we present a new cross-platform approach for video game delivery in wired and wireless local networks. The developed 3D streaming and video streaming approaches enable users to access video games on set ...
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Local image descriptors have proved themselves as useful tools for many computer vision tasks such as matching points between multiple images of a scene and object recognition. Current descriptors, such as SIFT, are d...
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Local image descriptors have proved themselves as useful tools for many computer vision tasks such as matching points between multiple images of a scene and object recognition. Current descriptors, such as SIFT, are designed to match image features with unique local neighborhoods. However, the interest point detectors used with SIFT often fail to select perceptible local structures in the image, and the SIFT descriptor does not directly encode the local neighborhood shape. In this paper we propose a symmetry based interest point detector and radial local structure descriptor which consistently captures the majority of basic local image structures and provides a geometrical description of the structure boundaries. This approach concentrates on the extraction of shape properties in image patches, which are an intuitive way to represent local appearance for matching and classification. We explore the specificity and sensitivity of this local descriptor in the context of classification of natural patterns. The implications of the performance comparison with standard approaches like SIFT are discussed.
In this paper, we present a packet-loss resilient 3-D graphics transmission system that is scalable with respect to both channel bandwidth and channel error characteristics. The algorithm trades off source coding effi...
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In this paper, we present a packet-loss resilient 3-D graphics transmission system that is scalable with respect to both channel bandwidth and channel error characteristics. The algorithm trades off source coding efficiency for increased bit-stream error resilience to optimize the decoded mesh quality on the client side. It uses the Compressed Progressive Mesh (CPM) algorithm to generate a hierarchical bit-stream representing different levels of details (LODs). We assign optimal forward error correction (FEC) code rates to protect different parts of the bit-stream differently. These optimal FEC code rates are determined theoretically via a distortion function that accounts for: the channel packet loss rate, the nature of the encoded 3-D mesh and the error protection bit-budget. We present experimental results, which show that with our unequal error protection (UEP) optimal approach, the decoded mesh quality degrades more gracefully (compared to either no error protection (NEP) or equal error protection (EEP) methods) as the packet loss rate increases.
Thresholding-based techniques have been widely used in image segmentation. The selection of appropriate threshold is a very significant issue for image thresholding. In this paper, a new image histogram thresholding m...
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Thresholding-based techniques have been widely used in image segmentation. The selection of appropriate threshold is a very significant issue for image thresholding. In this paper, a new image histogram thresholding method based on fuzzy partition and maximum correlation criterion is presented. In the proposed approach, the regions, i.e. object and background, are considered ambiguous in nature, and hence the regions are transformed into fuzzy domain with membership functions. Then, the fuzzy correlations about regions are constructed and the optimal threshold is determined by searching an optimal parameter combination of the membership functions such that the correlation of the fuzzy partitions is maximized. Since the exhaustive search for all fuzzy parameter combinations is too costly, the differential evolution algorithm is introduced into fuzzy correlation image segmentation to solve this optimal problem adaptively. Experimental results on general images and infrared images demonstrate the effectiveness of the proposed method.
This paper proposes a method for robustly matching active appearance models (AAMs) on images with gross disturbances (outliers). The method consists of two steps. First, an initial residual is calculated by comparing ...
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ISBN:
(数字)9783540264316
ISBN:
(纸本)3540250522
This paper proposes a method for robustly matching active appearance models (AAMs) on images with gross disturbances (outliers). The method consists of two steps. First, an initial residual is calculated by comparing model and image appearance, and modes of the residual are analyzed. Second, all possible mode combinations are tested by evaluating an objective function. The objective function allows the selection of an outlier-free mode combination. Experiments demonstrate the ability of the robust matching method to successfully cope with outliers - compared to standard AAM matching, no degeneration of the model during matching occurs.
We propose a high capacity reversible ternary embedding-watermarking algorithm based on a modification of full-context-prediction-errors(MFCPE) wherein the binary bit stream is converted to the ternary stream then err...
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We propose a high capacity reversible ternary embedding-watermarking algorithm based on a modification of full-context-prediction-errors(MFCPE) wherein the binary bit stream is converted to the ternary stream then error histogram shifting is utilized to embed the ternary *** the existing predictor methods, we provide a full context prediction with a modification of each pixel at most by 1, which significantly reduces *** results confirm that the proposed algorithm achieves high PSNR while providing a higher embedding ***, results indicate that MFCPE outperforms the existing methods in terms of payload and the watermarked image quality.
Head detection is an important, but difficult task, if no restrictions such as static illumination, frontal face appearance or uniform background can be assumed. We present a system that is able to perform head detect...
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Point sets obtained from computer vision techniques are often noisy and non-uniform. We present a new method of surface reconstruction that can handle such data sets using anisotropic basis functions. Our reconstructi...
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ISBN:
(纸本)0769511430
Point sets obtained from computer vision techniques are often noisy and non-uniform. We present a new method of surface reconstruction that can handle such data sets using anisotropic basis functions. Our reconstruction algorithm draws upon the work in variational implicit surfaces for constructing smooth and seamless 3D surfaces. Implicit functions are often formulated as a sum of weighted basis functions that are radially symmetric. Using radially symmetric basis functions inherently assumes, however that the surface to be reconstructed is, everywhere, locally symmetric. Such an assumption is true only at planar regions, and hence, reconstruction using isotropic basis is insufficient to recover objects that exhibit sharp features. We preserve sharp features using anisotropic basis that allow the surface to vary locally. The reconstructed surface is sharper along edges and at corner points. We determine the direction of anisotropy at a point by performing principal component analysis of the data points in a small neighborhood. The resulting field of principle directions across the surface is smoothed through tensor filtering. We have applied the anisotropic basis functions to reconstruct surfaces from noisy synthetic 3D data and from real range data obtained from space carving.
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