Recently real-time active 3D range cameras based on time-of-flight technology (PMD) have become available. Those cameras can be considered as a competing technique for stereo-vision based surface reconstruction. Since...
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ISBN:
(纸本)9781424411795
Recently real-time active 3D range cameras based on time-of-flight technology (PMD) have become available. Those cameras can be considered as a competing technique for stereo-vision based surface reconstruction. Since those systems directly yield accurate 3d measurements, they can be used for benchmarking vision based approaches, especially in highly dynamic environments. Therefore, a comparative study of the two approaches is relevant. In this work the achievable accuracy of the two techniques, PMD and stereo, is compared on the basis of patchlet estimation. As patchlet we define an oriented small planar 3d patch with associated surface normal. Least-squares estimation schemes for estimating patchlets from PMD range images as well as from a pair of stereo images are derived It is shown, how the achivable accuracy can be estimated for both systems. Experiments under optimal conditions for both systems are performed and the achievable accuracies are compared It has been found that the PMD system outperformed the stereo system in terms of achievable accuracy for distance measurements, while the estimation of normal direction is comparable for both systems.
Presented is a full computervision system for the identification of post-harvest damage in citrus packing houses. The method is based on the combined use of hyperspectral images and the Mahalanobis kernel classifier....
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Presented is a full computervision system for the identification of post-harvest damage in citrus packing houses. The method is based on the combined use of hyperspectral images and the Mahalanobis kernel classifier. More accurate and reliable results compared to other methods are obtained in several scenarios and acquired images.
In this paper, we propose a novel online learning method which can learn appearance models incrementally from a given video stream. The data of each frame in the video can be discarded as soon as it has been processed...
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ISBN:
(纸本)9781424411795
In this paper, we propose a novel online learning method which can learn appearance models incrementally from a given video stream. The data of each frame in the video can be discarded as soon as it has been processed. We only need to maintain a few linear eigenspace models and a transition matrix to approximately construct face appearance manifolds. It is convenient to use these learnt models for video-based face recognition. There are mainly two contributions in this paper First, we propose an algorithm which can learn appearance models online without using a pretrained model. Second, we propose a methodfor eigenspace splitting to prevent that most samples cluster into the same eigenspace. This is useful for clustering and classification. Experimental results show that the proposed method can both learn appearance models online and achieve high recognition rate.
Nonparametric image registration algorithms use deformation fields to define nonrigid transformations relating two images. Typically, these algorithms operate by successively solving linear systems of partial differen...
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ISBN:
(纸本)9781424411795
Nonparametric image registration algorithms use deformation fields to define nonrigid transformations relating two images. Typically, these algorithms operate by successively solving linear systems of partial differential equations. These PDE systems arise by linearizing the Euler-Lagrange equations associated with the minimization of a functional defined to contain an image similarity term and a regularizer. Iterative linear system solvers can be used to solve the linear PDE systems, but they can be extremely slow. Some faster techniques based on Fourier methods, multigrid methods, and additive operator splitting, existfor solving the linear PDE systemsfor specific combinations of regularizers and boundary conditions. In this paper, we show that Fourier methods can be employed to quickly solve the linear PDE systems for every combination of standard regularizers (diffusion, curvature, elastic, and fluid) and boundary conditions (Dirichlet, Neumann, andperiodic).
Biometrics is personal authentication which uses an individual's information. In terms of user authentication, biometric systems have many advantages. However, despite its advantages, they also have some disadvant...
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ISBN:
(纸本)9781424411795
Biometrics is personal authentication which uses an individual's information. In terms of user authentication, biometric systems have many advantages. However, despite its advantages, they also have some disadvantages in the area of privacy problems. Changeable biometrics is solution to problem of privacy protection. In this paper we propose a changeable face biometrics system to overcome this problem. The proposed method uses the PCA and ICA methods and genetic algorithms. PCA and ICA coefficient vectors extracted from an input face image were normalized using their norm. The two normalized vectors were transformed using a weighting matrix which is derived using genetic algorithms and then scrambled randomly. A new transformed face coefficient vector was generated by addition of the two weighted normalized vectors. Through experiments, we see that we can achieve performance accuracy that is better than conventional methods. And, it is also shown that the changeable templates are non-invertible and provide sufficient reproducibility.
This paper presents a novel spatio-temporal Markov random field (MRF) for video denoising. Two main issues are addressed in this paper, namely, the estimation of noise model and the proper use of motion estimation in ...
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ISBN:
(纸本)9781424411795
This paper presents a novel spatio-temporal Markov random field (MRF) for video denoising. Two main issues are addressed in this paper, namely, the estimation of noise model and the proper use of motion estimation in the denoising process. Unlike previous algorithms which estimate the level of noise, our method learns the full noise distribution nonparametrically which serves as the likelihood model in the MRF. Instead of using deterministic motion estimation to align pixels, we set up a temporal likelihood by combining a probabilistic motion field with the learned noise model. The prior of this MRF is modeled by piece-wise smoothness. The main advantage of the proposed spatio-temporal MRF is that it integrates spatial and temporal information adoptively into a statistical inference framework, where the posteriori is optimized using graph cuts with alpha expansion. We demonstrate the performance of the proposed approach on benchmark data sets and real videos to show the advantages of our algorithm compared with previous single frame and multi-frame algorithms.
We introduce a theoretical framework and practical algorithms for replacing time-coded structured light patterns with viewpoint codes, in the form of additional camera locations. Current structured light methods typic...
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ISBN:
(纸本)9781424411795
We introduce a theoretical framework and practical algorithms for replacing time-coded structured light patterns with viewpoint codes, in the form of additional camera locations. Current structured light methods typically use log(N) light patterns, encoded overtime, to unambiguously reconstruct N unique depths. We demonstrate that each additional camera location may replace one frame in a temporal binary code. Our theoretical viewpoint coding analysis shows that, by using a high frequency stripe pattern and placing cameras in carefully selected locations, the epipolar projection in each camera can be made to mimic the binary encoding patterns normally projected over time. Results from our practical implementation demonstrate reliable depth reconstruction that makes neither temporal nor spatial continuity assumptions about the scene being captured.
Peaks extraction is a kind of post-process in many image application or vision tasks that can be used for finding the optimum solution in the solution space. In this paper a real time method is proposed. A candidate q...
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ISBN:
(纸本)9780819469502
Peaks extraction is a kind of post-process in many image application or vision tasks that can be used for finding the optimum solution in the solution space. In this paper a real time method is proposed. A candidate queue is first build for containing highest peaks in the image in ascending order. Then the image is scanned in sequence. At each scanning position every candidate in the queue is updated respectively by some criterions given in this paper. After the image is scanned over, the highest peaks in the image is achieved in the queue. All the process can be accomplished by logic circuit, so the method is very suitable for hardware system such as FPGA and so on.
Most shape matching methods are either fast but too simplistic to give the desired performance or promising as far as performance is concerned but computationally demanding. In this paper, we present a very simple and...
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ISBN:
(纸本)9781424411795
Most shape matching methods are either fast but too simplistic to give the desired performance or promising as far as performance is concerned but computationally demanding. In this paper, we present a very simple and efficient approach that not only performs almost as good as many state-of-the-art techniques but also scales up to large databases. In the proposed approach, each shape is indexed based on a variety of simple and easily computable features which are invariant to articulations and rigid transformations. The features characterize pairwise geometric relationships between interest points on the shape, thereby providing robustness to the approach. Shapes are retrieved using an efficient scheme which does not involve costly operations like shape-wise alignment or establishing correspondences. Even for a moderate size database of 1000 shapes, the retrieval process is several times faster than most techniques with similar performance. Extensive experimental results are presented to illustrate the advantages of our approach as compared to the best in the field.
A target description model based on hierarchical saliency feature and target detection based on the model are proposed, which integrates bottom-up and top-down vision attention mechanisms together. Some saliency featu...
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ISBN:
(纸本)9780819469502
A target description model based on hierarchical saliency feature and target detection based on the model are proposed, which integrates bottom-up and top-down vision attention mechanisms together. Some saliency features of target are extracted on multi-scale, such as local symmetry, corner and so on, which are then processed hierarchically. This makes detection process much simpler and robust. Experiments demonstrate that the approach proposed is effective.
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