We propose an iterative method for estimating rigid transformations from point sets using adiabatic quantum computation. Compared to existing quantum approaches, our method relies on an adaptive scheme to solve the pr...
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ISBN:
(纸本)9781665469463
We propose an iterative method for estimating rigid transformations from point sets using adiabatic quantum computation. Compared to existing quantum approaches, our method relies on an adaptive scheme to solve the problem to high precision, and does not suffer from inconsistent rotation matrices. Experimentally, our method performs robustly on several 2D and 3D datasets even with high outlier ratio.
In this paper, we present a new approach to extract characters on a license plate of a moving vehicle given a sequence of perspective distortion corrected license plate images. We model the extraction of characters as...
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ISBN:
(纸本)0780342364
In this paper, we present a new approach to extract characters on a license plate of a moving vehicle given a sequence of perspective distortion corrected license plate images. We model the extraction of characters as a Markov random field (MRF), With the MRF modeling, the extraction of characters is formulated as the problem of maximizing the a posteriori probability based on given prior and observations. A genetic algorithm with local greedy mutation operator is employed do optimize the objective function. Experiments and comparison study were conducted. It is shown that our approach provides better performance than other single frame methods.
This paper introduces a unified approach to the problem of verifying Alignment hypotheses in the presence of substantial amounts of uncertainty in the predicted locations of projected model features. Our approach is i...
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ISBN:
(纸本)0780342364
This paper introduces a unified approach to the problem of verifying Alignment hypotheses in the presence of substantial amounts of uncertainty in the predicted locations of projected model features. Our approach is independent of whether the uncertainty is distributed or bounded, and, moreover, incorporates information about the domain in a formally correct manner. Information which can be incorporated includes the error model, the distribution of background features, and the positions of the data features near each predicted model feature. Experiments are described that demonstrate the improvement over previously used methods. Furthermore, our method is efficient in that the number of operations is on the order of the number of image features that lie nearby the predicted model features.
We present a new approach for resolving occlusions in augmented reality. The main interest is that it does not require 3D reconstruction of the considered scene. Our idea is to use a contour based approach and to labe...
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ISBN:
(纸本)0780342364
We present a new approach for resolving occlusions in augmented reality. The main interest is that it does not require 3D reconstruction of the considered scene. Our idea is to use a contour based approach and to label each contour point as being ''behind'' or ''in front of'', depending on whether it is in front of or behind the virtual object. This labeling step only requires that the contours can be tracked from frame to frame. A proximity graph is then built in order to group the contours that belong to the same occluding object. Finally, we use some kind of active contours to accurately recover the mask of the occluding object.
recognition using only visual evidence cannot always be successful due to limitations of information and resources available during training. Considering relation among lexicon entries is sometimes useful for decision...
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recognition using only visual evidence cannot always be successful due to limitations of information and resources available during training. Considering relation among lexicon entries is sometimes useful for decision making. In this paper, we present a method to capture lexical similarity to lexicon and reliability of a character recognizer which serve to capture the dynamism of the environment. A parameter lexical similarity is defined by measuring these two factors as edit distance between lexicon entries and separability of each character's recognition results. Our experiments show that a utility function considering lexical similarity in a decision stage can enhance the performance of a conventional word recognizer.
Recently active learning has attracted a lot of attention in computervision field, as it is time and cost consuming to prepare a good set of labeled images for vision data analysis. Most existing active learning appr...
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ISBN:
(纸本)9780769549897
Recently active learning has attracted a lot of attention in computervision field, as it is time and cost consuming to prepare a good set of labeled images for vision data analysis. Most existing active learning approaches employed in computervision adopt most uncertainty measures as instance selection criteria. Although most uncertainty query selection strategies are very effective in many circumstances, they fail to take information in the large amount of unlabeled instances into account and are prone to querying outliers. In this paper we present a novel adaptive active learning approach that combines an information density measure and a most uncertainty measure together to select critical instances to label for image classifications. Our experiments on two essential tasks of computervision, object recognition and scene recognition, demonstrate the efficacy of the proposed approach.
This paper addresses the problem of estimating the epipolar geometry from point correspondences between two images taken by uncalibrated perspective cameras. It is shown that Jepson's and Heeger's linear subsp...
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ISBN:
(纸本)0818672587
This paper addresses the problem of estimating the epipolar geometry from point correspondences between two images taken by uncalibrated perspective cameras. It is shown that Jepson's and Heeger's linear subspace technique for infinitesimal motion estimation can be generalized to the finite motion case by choosing an appropriate basis for projective space. This yields a linear method for weak calibration. The proposed algorithm has been implemented and tested on both real and synthetic images, and it is compared to other linear and non-linear approaches to weak calibration.
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.
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.
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).
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