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...
详细信息
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.
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...
详细信息
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...
详细信息
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...
详细信息
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.
Current computervision systems whose basic methodology is open-loop or filter type typically use image segmentation followed by object recognition algorithms. These systems are not robust for most real-world applicat...
详细信息
ISBN:
(纸本)0818672587
Current computervision systems whose basic methodology is open-loop or filter type typically use image segmentation followed by object recognition algorithms. These systems are not robust for most real-world applications. In contrast, the system presented here achieves robust performance by using reinforcement learning to induce a mapping from input images to corresponding segmentation parameters. This is accomplished by using the confidence level of model matching as a reinforcement signal for a team of learning automata to search for segmentation parameters during training. The use of the recognition algorithm as part of the evaluation function for image segmentation gives rise to significant improvement of the system performance by automatic generation of recognition strategies. The system is verified through experiments on sequences of color images with varying external conditions.
This paper will review the design of a working system that visually recognizes hand gestures for the control of a window based user interface. After an overview of the system, it will explore one aspect of gestural in...
详细信息
ISBN:
(纸本)0780342364
This paper will review the design of a working system that visually recognizes hand gestures for the control of a window based user interface. After an overview of the system, it will explore one aspect of gestural interaction in depth, hand tracking, and what is needed for the user to be able to interact comfortably with on-screen objects. We describe how the location of the hand is mapped to a location on the screen, and how it is both necessary and possible to smooth the camera input using a non-linear physical model of the cursor. The performance of the system is examined, especially with respect to object selection. We show how a standard HCI model of object selection (Fitts' Law) can be extended to model the selection performance of free-hand pointing.
The problem of non-parametric probability density function (PDF) estimation using Radial Basis Function (RBF) Neural Networks is addressed here. We investigate two criteria, based on a modified Kullback-Leibler distan...
详细信息
ISBN:
(纸本)0818672587
The problem of non-parametric probability density function (PDF) estimation using Radial Basis Function (RBF) Neural Networks is addressed here. We investigate two criteria, based on a modified Kullback-Leibler distance, that lead to an appropriate choice of the network architecture complexity. In the first criterion the modification consists in the addition of a term that penalizes complex architectures (MPL criterion). The second strategy involves the regularization of the network through the imposition of lower bounds on the standard deviation derived from conditions of existence of rejection tests (LBSD criterion). Experimental results indicate that the MPL criterion outperforms-the LBSD method.
This paper describes a probabilistic decomposition of human dynamics at multiple abstractions, and shows how to propagate hypotheses across space, time, and abstraction levels. recognition in this framework is the suc...
详细信息
ISBN:
(纸本)0780342364
This paper describes a probabilistic decomposition of human dynamics at multiple abstractions, and shows how to propagate hypotheses across space, time, and abstraction levels. recognition in this framework is the succession of very general low level grouping mechanisms to increased specific and learned model based grouping techniques at higher levels. Hard decision thresholds are delayed and resolved by higher level statistical models and temporal context. Low-level primitives are areas of coherent motion found by EM clustering, mid-level categories are simple movements represented by dynamical systems, and high-level complex gestures are represented by Hidden Markov Models as successive phases of simple movements. We show how such a representation can be learned from training data, and apply It to the example of human gait recognition.
It is widely accepted that textureless surfaces cannot be recovered using passive sensing techniques. The problem is approached by viewing image formation as a Sully three-dimensional mapping. It is shown that the len...
详细信息
ISBN:
(纸本)0780342364
It is widely accepted that textureless surfaces cannot be recovered using passive sensing techniques. The problem is approached by viewing image formation as a Sully three-dimensional mapping. It is shown that the lens encodes structural information of the scene within a compact three-dimensional space behind it. After analyzing the information content of this space and by using its properties we derive necessary and sufficient conditions for the recovery of textureless scenes. Based on these conditions, a simple procedure for recovering textureless scenes is described. We experimentally demonstrate the recovery of three textureless surfaces, namely, a line, a plane, and a paraboloid. Since textureless surfaces represent the worst case recovery scenario, all the results and the recovery procedure are naturally applicable to scenes with texture.
A fundamental problem in depth from defocus is the measurement of relative defocus between images. We propose a class of broadband operators that, when used together, provide invariance to scene texture and produce ac...
详细信息
ISBN:
(纸本)0818672587
A fundamental problem in depth from defocus is the measurement of relative defocus between images. We propose a class of broadband operators that, when used together, provide invariance to scene texture and produce accurate and dense depth maps. Since the operators are broadband, a small number of them are sufficient for depth estimation of scenes with complex textural properties. Experiments are conducted on both synthetic and real scenes to evaluate the performance of the proposed operators. The depth detection gain error is less than 1%, irrespective of texture frequency. Depth accuracy is found to be 0.5 approx. 1.2% of the distance of the object from the imaging optics.
暂无评论