The main focus of this paper is the shape representation and registration using vector level set functions. This powerful representation is more flexible than conventional signed distance level sets since it enables u...
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The main focus of this paper is the shape representation and registration using vector level set functions. This powerful representation is more flexible than conventional signed distance level sets since it enables us to control the shapes registration process by using more complicated transforms. Based on this model, a variational frame work is proposed for the rigid and non-rigid registration of shapes which can be extended to the higher dimensional case in a straightforward manner and also does not need any point correspondences. The optimization criterion presented can handle efficiently both the rigid and the non-rigid operations together. Experimental results in 2D for real shapes registration are discussed to show the efficiency of the approach with small and large global deformations of shapes.
In this paper, we propose a general and robust robotic path planning framework for both planar and terrain environments using level set methods. The framework is general in the sense that it can be used for both 2D an...
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In this paper, we propose a general and robust robotic path planning framework for both planar and terrain environments using level set methods. The framework is general in the sense that it can be used for both 2D and 3D environments. It generates a collision-free optimum paths for the entire or a portion of the configuration space. The optimum planned path can be controlled to follow the safest, shortest, or hybrid path. We have demonstrated the robustness of the proposed framework by correctly extracting all planned paths of complex maps with several obstacles.
We propose an extension of RBF networks which includes a mechanism for optimizing the complexity of the network. The approach involves two procedures: adaptation (training) and selection. The first procedure adaptivel...
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We present a parallel implementation of an MPEG encoder on the Intel Paragon supercomputer. In our approach, both spatial and temporal parallelism have been exploited, While the Paragon has the computation capacity to...
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The Euler-Lagrange (EL) framework is the most widely-used strategy for solving variational optic flow methods. We present the first approach that solves the EL equations of state-of-the-art methods on sequences with p...
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A number of approaches have been proposed in the literature for reconstruction of 3-D objects from sequence of images. Yet, very few studies have been reported on the quantification/validation of the accuracy of these...
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A number of approaches have been proposed in the literature for reconstruction of 3-D objects from sequence of images. Yet, very few studies have been reported on the quantification/validation of the accuracy of these reconstructions. In addition, no design has been reported for a generic vision platform that can allow various modality imaging. The purpose of this paper is two folds: 1) propose a vision platform that lend itself for acquisition of calibrated sequence of images, and concurrently obtain a direct 3-D reconstruction by laser scanning; and 2) develop and implement different approaches for 3-D reconstructions from sequence of images. 3-D reconstructions will be evaluated against the 3-D scanning generated from a laser scanner. Validation of the reconstructions is made by pairwise comparison with the 3-D scanning results. Preliminary studies with the proposed vision platform show a good promise for its use in the validation of various 3-D reconstructions, registration and data fusion.
In this paper we propose a novel weakly-supervised feature learning approach, learning discriminative local features from image-level labelled data for image classification. Unlike existing feature learning approaches...
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In this paper we propose a novel weakly-supervised feature learning approach, learning discriminative local features from image-level labelled data for image classification. Unlike existing feature learning approaches which assume that a set of additional data in the form of matching/non-matching pairs of local patches are given for learning the features, our approach only uses the image-level labels which are much easier to obtain. Experiments on a colonoscopy image dataset with 2100 images shows that the learned local features outperforms other hand-crafted features and gives a state-or-the-art classification accuracy of 93.5%.
The goal of this paper is twofold. First, we present a supervised fuzzy c-mean (SFCM) classifier for the classification of high dimensional data. Comparisons of the conventional FCM clustering technique and Bayesian c...
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ISBN:
(纸本)0769507506
The goal of this paper is twofold. First, we present a supervised fuzzy c-mean (SFCM) classifier for the classification of high dimensional data. Comparisons of the conventional FCM clustering technique and Bayesian classification technique are also presented. Next, we present a two-step classifier in which the proposed SFCM and Bayesian algorithms are used in a cooperative way such that classification results of the SFCM algorithm are used to compute the prior probabilities required for the Bayesian classifier. Classification results of the three algorithms are presented on simulated and real remote sensing multispectral data. The results obtained show improvements in the classification accuracy and reliability using the two-step algorithm.
Ambient reflection is widely present in many applications of computer graphics and imageprocessing, which is traditionally modelled as a constant free from environmental factors. This paper reconsiders ambient reflec...
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Ambient reflection is widely present in many applications of computer graphics and imageprocessing, which is traditionally modelled as a constant free from environmental factors. This paper reconsiders ambient reflection modelling of Lambertian and Phong surfaces and calculates it as reflection integrations of infinitesimal incident beams from the environment. It reveals that ambient reflection exists in variable forms for Lambertian surfaces of non-convex objects and Phong surfaces of all objects. For convex objects with Lambertian surfaces, ambient reflectance coefficient is actually the diffuse reflectance coefficient. Generalised ambient reflection models are proposed to calculate ambient reflection using the same reflection model as used in calculation of other reflections. Based on this analysis, new ambient reflection formulations of Lambertian and Phong surfaces are derived to enable efficient computations in computer graphics and imageprocessing.
Human object classification is an important problem for smart video surveillance applications. In this paper we have proposed a method for human object classification, which classify the objects into two classes: huma...
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