A wide-spread use of 3D models in archeology application requires low cost equipment and technically simple modeling procedures. In this context methods for automatic 3D modeling based on fully automatic techniques fo...
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A wide-spread use of 3D models in archeology application requires low cost equipment and technically simple modeling procedures. In this context methods for automatic 3D modeling based on fully automatic techniques for 3D views registration will play a central role. This paper proposes a very robust procedure which does not require special equipment or skill in order to make 3D models. The results of this paper, originally conceived to address the costs issues of heritage's modeling, can be profitably exploited also in other modeling applications.
This paper overviews our research on digital preservation of cultural assets and digital restoration of their original appearance. Geometric models are digitally achieved through a pipeline consisting of scanning, reg...
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This paper overviews our research on digital preservation of cultural assets and digital restoration of their original appearance. Geometric models are digitally achieved through a pipeline consisting of scanning, registering and merging multiple range images. We have developed a robust simultaneous registration method and an efficient and robust voxel-based integration method. On the geometric models created, we have to align texture images acquired from a color camera. We have developed two texture mapping methods. In an attempt to restore the original appearance of historical heritage objects, we have synthesized several buildings and statues using scanned data and literature survey with advice from experts.
This paper discusses how stereo vision achieved through the use of omnidirectional sensors can help mobile robot navigation providing advantages, in terms of both versatility and performance, with respect to the class...
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This paper discusses how stereo vision achieved through the use of omnidirectional sensors can help mobile robot navigation providing advantages, in terms of both versatility and performance, with respect to the classical stereo system based on two horizontally-displaced traditional cameras. The paper also describes an automatic calibration strategy for catadioptric omnidirectional sensors and results obtained using a stereo obstacle detection algorithm devised within a general framework in which, with some limitations, many existing algorithm designed for traditional cameras can be adapted for use with omnidirectional sensors.
Feature weighting algorithms assign weights to features according to their relevance to a particular task. Unfortunately, the best-known feature weighting algorithm, ReliefF, is biased. It decreases the relevance of s...
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Feature weighting algorithms assign weights to features according to their relevance to a particular task. Unfortunately, the best-known feature weighting algorithm, ReliefF, is biased. It decreases the relevance of some features and increases the relevance of others when irrelevant attributes are added to the data set. This paper presents an improved version of the algorithm, Iterative Relief, and shows on synthetic data that it removes the bias found in ReliefF. This paper also shows that Iterative Relief outperforms ReliefF on the task of cat and dog discrimination, using real images.
In this paper, we present a probabilistic tracking framework that combines sound and vision to achieve more robust and accurate tracking of multiple objects. In a cluttered or noisy scene, our measurements have a non-...
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In this paper, we present a probabilistic tracking framework that combines sound and vision to achieve more robust and accurate tracking of multiple objects. In a cluttered or noisy scene, our measurements have a non-Gaussian, multi-modal distribution. We apply a particle filter to track multiple people using combined audio and video observations. We have applied our algorithm to the domain of tracking people with a stereo-based visual foreground detection algorithm and audio localization using a beamforming technique. Our model also accurately reflects the number of people present. We test the efficacy of our system on a sequence of multiple people moving and speaking in an indoor environment.
In the recent past, fragment matching has been treated in two different approaches, one using curve matching methods and one that compares whole surfaces or volumes, depending on the nature of the broken artefacts. Pr...
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In the recent past, fragment matching has been treated in two different approaches, one using curve matching methods and one that compares whole surfaces or volumes, depending on the nature of the broken artefacts. Presented here is a fast, unified method that combines curve matching techniques with a surface matching algorithm to estimate the positioning and respective matching error for the joining of three-dimensional fragmented objects. Combining both aspects of fragment matching, essentially eliminates most of the ambiguities present in each one of the matching problem categories and helps provide more accurate results with low computational cost.
A method of word extraction based on the area Voronoi diagram is presented in this paper. Firstly, connected components are generated from the input image. Secondly, noise removal is performed including a special symb...
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A method of word extraction based on the area Voronoi diagram is presented in this paper. Firstly, connected components are generated from the input image. Secondly, noise removal is performed including a special symbol detection technique to find some types of special symbols lying between words. Thirdly, base on the area Voronoi diagram, we select appropriate Voronoi edges which separate two neighboring connected components. Finally, words are extracted by merging the connected components based on the Voronoi edge between them. The result generated by this method is satisfactory with the ability to correctly group words of different size, font and arrangement. Experiments show that the proposed method achieves a high accuracy.
While the importance of representations for recognition has been widely recognized, in practice the choice of representations is often limited and applications are forced to choose relatively the best one among the av...
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While the importance of representations for recognition has been widely recognized, in practice the choice of representations is often limited and applications are forced to choose relatively the best one among the available. In this paper, we advocate an integrated learning framework where the representation is learned with respect to a chosen performance criterion. For linear representations, this problem is posed as an optimization one on the underlying manifold determined by the constraints of the application; manifolds related to typical computervision applications are given. To develop computationally effective algorithms, the underlying geometric structures are exploited. We demonstrate the feasibility and effectiveness of the proposed framework by finding optimal linear filters for recognition with other additional properties.
We study the use of kernel subspace methods for learning low-dimensional representations for classification. We propose a kernel pooled local discriminant subspace method and compare it against several competing techn...
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We study the use of kernel subspace methods for learning low-dimensional representations for classification. We propose a kernel pooled local discriminant subspace method and compare it against several competing techniques: Principal Component Analysis (PCA), Kernel PCA (KPCA), and linear local pooling in classification problems. We evaluate the classification performance of the nearest-neighbor rule with each subspace representation. The experimental results demonstrate the effectiveness and performance superiority of the kernel pooled subspace method over competing methods such as PCA and KPCA in some classification problems.
We aim to define an event ontology that allows natural representation of complex spatio-temporal events common in the physical world by a composition of simpler events. The events are abstracted into three hierarchies...
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We aim to define an event ontology that allows natural representation of complex spatio-temporal events common in the physical world by a composition of simpler events. The events are abstracted into three hierarchies. Primitive events are defined directly from the mobile object properties. Single-thread composite events are a number of primitive events with temporal sequencing. Multi-thread composite events are a number of single-thread events with temporal/ spatial/logical relationships. This hierarchical event representation naturally leads to a language description of the events. We define an Event recognition Language (ERL) which allows the users to define the events of interest conveniently without interacting with the low level processing in the program. We will also briefly mention some approaches to compute the proposed representation.
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