It is often necessary to handle randomness and geometry is computervision, for instance to match and fuse together noisy geometric features such as points, lines or 3D frames, or to estimate a geometric transformatio...
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ISBN:
(纸本)0818672587
It is often necessary to handle randomness and geometry is computervision, for instance to match and fuse together noisy geometric features such as points, lines or 3D frames, or to estimate a geometric transformation from a set of matched features. However, the proper handling of these geometric features is far more difficult than for points, and a number of paradoxes can arise. We analyse in this article three basic problems: (1) what is a uniform random distribution of features, (2) how to define a distance between features, and (3) what is the 'mean feature' of a number of feature measurements, and we propose generic methods to solve them.
In many multitarget tracking applications in computervision, a detection algorithm provides locations of potential targets. Subsequently, the measurements are associated with previously estimated target trajectories ...
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ISBN:
(纸本)0769523722
In many multitarget tracking applications in computervision, a detection algorithm provides locations of potential targets. Subsequently, the measurements are associated with previously estimated target trajectories in a data association step. The output of the detector is often imperfect and the detection data may include multiple, split measurements from a single target or a single merged measurement from several targets. To address this problem, we introduce a multiple hypothesis tracker for interacting targets that generate split and merged measurements. The tracker is based on an efficient Markov chain Monte Carlo (MCMC) based auxiliary variable particle filter The particle filter is Rao-Blackwellized such that the continuous target state parameters are estimated analytically, and an MCMC sampler generates samples from the large discrete space of data associations. In addition, we include experimental results in a scenario where we track several interacting targets that generate these split and merged measurements.
The Perseus system is a purposive visual architecture that has been used to recognize the pointing gesture. recognition of this gesture is an important part of natural human-machine interfaces. Perseus is modularized ...
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ISBN:
(纸本)0818672587
The Perseus system is a purposive visual architecture that has been used to recognize the pointing gesture. recognition of this gesture is an important part of natural human-machine interfaces. Perseus is modularized into 6 types of components: feature maps, object representations, markers, visual routines, a segmentation map, and a long term visual memory. This structure not only allows Perseus to use knowledge about the task and environment at every stage of processing to more efficiently and accurately solve the pointing task, but also allows it to be extended to tasks other than recognizing pointing.
A systematic methodology is presented for automatic selection of scale levels when detecting one-dimensional features, such as edges and ridges. A novel concept of a scale-space edge is introduced and defined as a con...
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ISBN:
(纸本)0818672587
A systematic methodology is presented for automatic selection of scale levels when detecting one-dimensional features, such as edges and ridges. A novel concept of a scale-space edge is introduced and defined as a connected set of points in scale-space. Two specific measures of edge strength are analyzed in detail. It is shown that by expressing these in terms of γ-normalized derivatives, an immediate consequence of this definition is that fine scales are selected for sharp edges, whereas coarse scales are selected for diffuse edge, such that an edge model constitutes a valid abstraction of the intensity profile across the edge.
recognition ambiguity, due to noisy measurements and uncertain object models, can be quantified and actively used by an autonomous agent to efficiently gather new data and improve its information about the environment...
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ISBN:
(纸本)0818672587
recognition ambiguity, due to noisy measurements and uncertain object models, can be quantified and actively used by an autonomous agent to efficiently gather new data and improve its information about the environment. In this work an information-based utility measure is used to derive from a learned classification of shape models an efficient data collection strategy, specifically aimed at increasing classification confidence when recognizing uncertain shapes. Promising simulation results are presented and discussed.
In many automatic face recognition applications, a set of a person's face images is available rather than a single image. In this paper we describe a novel method for face recognition using image sets. We propose ...
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ISBN:
(纸本)0769523722
In many automatic face recognition applications, a set of a person's face images is available rather than a single image. In this paper we describe a novel method for face recognition using image sets. We propose a flexible, semi-parametric model for learning probability densities confined to highly non-linear but intrinsically low-dimensional manifolds. The model leads to a statistical formulation of the recognition problem in terms of minimizing the divergence between densities estimated on these manifolds. The proposed method is evaluated on a large data set, acquired in realistic imaging conditions with severe illumination variation. Our algorithm is shown to match the best and outperform other state-of-the-art algorithms in the literature, achieving 94% recognition rate on average.
The basic limitations of the current appearance-based matching methods using eigenimages are non-robust estimation of coefficients and inability to cope with problems related to occlusions and segmentation. In this pa...
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ISBN:
(纸本)0818672587
The basic limitations of the current appearance-based matching methods using eigenimages are non-robust estimation of coefficients and inability to cope with problems related to occlusions and segmentation. In this paper we present a new approach which successfully solves these problems. The major novelty of our approach lies in the way how the coefficients of the eigenimages are determined. Instead of computing the coefficients by a projection of the data onto the eigenimages, we extract them by a hypothesize-and-test paradigm using subsets of image points. Competing hypotheses are then subject to a selection procedure based on the Minimum Description Length principle. The approach enables us not only to reject outliers and to deal with occlusions but also to simultaneously use multiple classes of eigenimages.
We address the problem of locating a gray-level pattern in a gray-level image. The pattern can have been transformed formed by an affine transformation, and may have undergone some additional changes. We define a diff...
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ISBN:
(纸本)0780342364
We address the problem of locating a gray-level pattern in a gray-level image. The pattern can have been transformed formed by an affine transformation, and may have undergone some additional changes. We define a difference function based on comparing each pixel of the pattern with a window: in the image, and search efficiently for transformations that minimise the difference function. The search is guaranteed: it will always find the transformation minimising the difference function, and not get fooled by a local minimum;it is also efficient, in that it does not need to examine every transformation in order to achieve this guarantee. This technique can be applied to object location, motion tracking, optical flow, or block-based motion compensation in video image sequence compression (e.g., MPEG).
One of the central problems in stereo matching (and other image registration tasks) is the selection of optimal window sizes for comparing image regions. This paper addresses this problem with some novel algorithms ba...
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ISBN:
(纸本)0818672587
One of the central problems in stereo matching (and other image registration tasks) is the selection of optimal window sizes for comparing image regions. This paper addresses this problem with some novel algorithms based on iteratively diffusing support at different disparity hypotheses, and locally controlling the amount of diffusion based on the current quality of the disparity estimate. It also develops a novel Bayesian estimation technique which significantly outperforms techniques based on area-based matching (SSD) and regular diffusion. We provide experimental results on both synthetic and real stereo image pairs.
Probabilistic models have been previously shown to be efficient and effective for modeling and recognition of human motion. In particular we focus on methods which represent the human motion model as a triangulated gr...
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ISBN:
(纸本)0769523722
Probabilistic models have been previously shown to be efficient and effective for modeling and recognition of human motion. In particular we focus on methods which represent the human motion model as a triangulated graph. Previous approaches learned models based just on positions and velocities of the body parts while ignoring their appearance. Moreover a heuristic approach was commonly used to obtain translation invariance. In this paper we suggest an improved approach for learning such models and using them for human motion recognition. The suggested approach combines multiple cues, i.e., positions, velocities and appearance into both the learning and detection phases. Furthermore, we introduce global variables in the model, which can represent global properties such as translation, scale or view-point. The model is learned in an unsupervised manner from unlabelled data. We show that the suggested hybrid probabilistic model (which combines global variables, like translation, with local variables, like relative positions and appearances of body parts), leads to: (i) faster convergence of learning phase, (ii) robustness to occlusions, and, (iii) higher recognition rate.
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