In this paper we formulate the Radon transform as a convolution integral over the Euclidean motion group (SE(2)) and provide a minimum mean square error (MMSE) stochastic deconvolution method for the Radon transform i...
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In this paper we formulate the Radon transform as a convolution integral over the Euclidean motion group (SE(2)) and provide a minimum mean square error (MMSE) stochastic deconvolution method for the Radon transform inversion. Proposed approach provides a fundamentally new formulation that can model nonstationary signal and noise fields. Key components of our development are the Fourier transform over SE(2), stochastic processes indexed by groups and fast implementation of the SE(2) Fourier transform. Numerical studies presented here demonstrate that the method yields image quality that is comparable or better than the filtered backprojection algorithm. Apart from X-ray tomographic image reconstruction, the proposed deconvolution method is directly applicable to inverse radiotherapy, and broad range of science and engineering problems in computer vision, patternrecognition, robotics as well as protein science.
A major obstacle to the broader use of 3D object reconstruction and modeling is the extent of manual intervention needed. Such interventions are currently extensive and exist throughout every phase of a 3D reconstruct...
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Commercial detection plays an important role in various video segmentation and indexing applications. It provides high-level program segmentation so that other algorithms can be applied on the true program material in...
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Commercial detection plays an important role in various video segmentation and indexing applications. It provides high-level program segmentation so that other algorithms can be applied on the true program material in the broadcast. It is a challenge to have robust commercial detection methodology for various platforms, content formats, and broadcast styles that are used all over the world Wide deployment of such an algorithm not only requires the development of new algorithms but also updating and tuning of parameters for existing algorithms. We present visual commercial detectors that rely on features including, luminance, letterbox, and keyframe distance. These detectors were developed after a careful study of the various features that can be extracted during MPEG-encoding process in real time. Due to the intermittent nature of the features, and platform restrictions, the commercial detection relies on a set of thresholds to keep the implementation as simple as possible. We evolved these thresholds using genetic algorithms (GAs) to optimize the performance. We show how a scalar genetic algorithm can locate sets of parameters in a multi-objective space (precision and recall) that outperform the values selected by an expert engineer. We present the results of optimizing a commercial detection algorithm for different data sets and parameter sets. In this paper we show that GAs drastically improved our approach and enabled fast prototyping and performance tuning of commercial detection algorithms.
This paper proposes a unified framework for spatio-temporal segmentation of video sequences. A Bayesian network is presented to model the interactions among the motion vector field, the intensity segmentation field, a...
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This paper proposes a unified framework for spatio-temporal segmentation of video sequences. A Bayesian network is presented to model the interactions among the motion vector field, the intensity segmentation field, and the video segmentation field. The notions of distance transformation and Markov random field are used to express spatio-temporal constraints. Given consecutive frames, an optimization method is proposed to maximize the conditional probability density of the three fields in an iterative way. Experimental results show that the approach is robust and generates spatio-temporally coherent segmentation results.
We present a robust image synthesis method to automatically infer missing information from a damaged 2D image by tensor voting. Our method translates image color and texture information into an adaptive ND tensor, fol...
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We present a robust image synthesis method to automatically infer missing information from a damaged 2D image by tensor voting. Our method translates image color and texture information into an adaptive ND tensor, followed by a voting process that infers non-iteratively the optimal color values in the ND texture space for each defective pixel. ND tensor voting can be applied to images consisting of roughly homogeneous and periodic textures (e.g. a brick wall), as well as difficult images of natural scenes which contain complex color and texture information. To effectively tackle the latter type of difficult images, a two-step method is proposed. First, we perform texture-based segmentation in the input image, and extrapolate partitioning curves to generate a complete segmentation for the image. Then, missing colors are synthesized using ND tensor voting. Automatic tensor scale analysis is used to adapt to different feature scales inherent in the input. We demonstrate the effectiveness of our approach using a difficult set of real images.
This paper introduces a practical approach for superresolution, the process of reconstructing a high-resolution image from the low-resolution input ones. The emphasis of our work is to super-resolve frames from dynami...
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This paper introduces a practical approach for superresolution, the process of reconstructing a high-resolution image from the low-resolution input ones. The emphasis of our work is to super-resolve frames from dynamic video sequences which may contain significant object occlusion or scene changes. As the quality of super-resolved images highly relies on the correctness of image alignment between consecutive frames, we employ the robust optical flow method to accurately estimate motion between the image pair. An efficient and reliable scheme is designed to detect and discard incorrect matchings which may degrade the output quality. We also introduce the usage of elliptical weighted average (EWA) filter to model the spatially-variant point spread function (PSF) of acquisition system in order to improve accuracy of the model. A number of complex and dynamic video sequences are tested to demonstrate the applicability and reliability of our algorithm.
Accurate and robust registration of multiple three-dimensional (3D) views is crucial for creating digital 3D models of real-world scenes. In this paper, we present a framework for evaluating the quality of model hypot...
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Accurate and robust registration of multiple three-dimensional (3D) views is crucial for creating digital 3D models of real-world scenes. In this paper, we present a framework for evaluating the quality of model hypotheses during the registration phase. We use maximum likelihood estimation to learn a probabilistic model of registration success. This method provides a principled way to combine multiple measures of registration accuracy. Also, we describe a stochastic algorithm for robustly searching the large space of possible models for the best model hypothesis. This new approach can detect situations in which no solution exists, outputting a set of model parts if a single model using all the views cannot be found. We show results for a large collection of automatically modeled scenes and demonstrate that our algorithm works independently of scene size and the type of range sensor. This work is part of a system we have developed to automate the 3D modeling process for a set of 3D views obtained from unknown sensor viewpoints.
Surrounding neighbor blocks dependent fragile watermark scheme has its disadvantages, which couldn't successfully distinguish where the image has been tampered. In this paper, we propose a random block dependant f...
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Local feature methods suitable for image feature based object recognition and for the estimation of motion and structure are composed of two steps, namely the 'where' and 'what' steps. The 'where&#...
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Local feature methods suitable for image feature based object recognition and for the estimation of motion and structure are composed of two steps, namely the 'where' and 'what' steps. The 'where' step (e.g., interest point detector) must select image points that are robustly localizable under common image deformations and whose neighborhoods are relatively informative. The 'what' step (e.g., local feature extractor) then provides a representation of the image neighborhood that is semi-invariant to image deformations, but distinctive enough to provide model identification. We present a quantitative evaluation of both the 'where' and the 'what' steps for three recent local feature methods: a) phase-based local features [2], b) differential invariants [14], and c) the scale invariant feature transform (SIFT) [9]. Moreover, in order to make the phase-based approach more comparable to the other two approaches, we also introduce a new form of multi-scale interest point detector to be used for its 'where' step. The results show that the phase-based local features lead to better performance than the other two approaches when dealing with common illumination changes, 2D rotation, and sub-pixel translation. On the other hand, the phase-based local features are somewhat more sensitive to scale and large shear changes than the other two methods. Finally, we demonstrate the viability of the phase-based local feature in a simple object recognition system.
Previous studies on face detection in video footages show that segmenting faces accurately and reliably is often hard to succeed, leading instead to laborious and tedious interactive manipulation. This paper presents ...
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ISBN:
(纸本)0780377508
Previous studies on face detection in video footages show that segmenting faces accurately and reliably is often hard to succeed, leading instead to laborious and tedious interactive manipulation. This paper presents a segmentation method using controlled weights on the three HSV components and constructs a face detection and image retrieval system. First, it is shown that HSV color has advantages over RGB or YCbCr one when segmenting a face and generating a binary pattern that retains as many features of the face as possible in the original color picture. Then, a face detection and image retrieval system is constructed using HSV color, where each time a significant scene change is detected segmentation is carried out for the beginning frame using a few sets of the weights on the HSV components, and resulting patterns are checked in some requirements and correlated with a typical face pattern. computer experiments show that the successful detection rate is more than 95 percent and that images can be retrieved from an input face image in a short time.
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