The presence of specular reflections in images can lead many traditional computer vision algorithms to produce erroneous results. To address this problem, we propose a method based on the neutral interface reflection ...
详细信息
ISBN:
(纸本)0769512720
The presence of specular reflections in images can lead many traditional computer vision algorithms to produce erroneous results. To address this problem, we propose a method based on the neutral interface reflection model for separating the diffuse and specular reflection components in color images. From two photometric images without calibrated lighting, the illuminant chromaticity is estimated, and the RGB intensities of the two reflection components are computed for each pixel using a linear model of surface reflectance. Unlike most previous methods, the presented technique does not assume any dependencies among pixels, such as regionally uniform surface reflectance.
Learning-enhanced relevance feedback is one of the most promising and active research directions in content-based image retrieval. However, the existing approaches either require prior knowledge of the data or entail ...
详细信息
ISBN:
(纸本)0769512720
Learning-enhanced relevance feedback is one of the most promising and active research directions in content-based image retrieval. However, the existing approaches either require prior knowledge of the data or entail high computation costs, making them less practical. To overcome these difficulties and motivated by the successful history of optimal adaptive filters, we present a new approach to interactive image retrieval. Specifically, we cast the image retrieval problem in the optimal filtering framework, which does not require prior knowledge of the data, supports incremental learning, is simple to implement and achieves better performance than state-of-the-art approaches. To evaluate the effectiveness and robustness of the proposed approach, extensive experiments have been carried out on a large heterogeneous image collection with 17,000 images. We report promising results on a wide variety of queries.
The purpose of unconstrained handwritten numeral recognition is to assign a numeral to one of ten classes or reject it. The challenge is to maintain a high performance and not to misrecognize confusing patterns. In so...
详细信息
The purpose of unconstrained handwritten numeral recognition is to assign a numeral to one of ten classes or reject it. The challenge is to maintain a high performance and not to misrecognize confusing patterns. In some applications, it is desirable to reject a pattern instead of running the risk of misclassifying it. In order to improve the reliability of a single neural network classifier on confusing numerals, knowledge from five human experts is gathered and analyzed. A new way to construct database and represent the required output values in the output layer of MLP's training process is given in this paper. Experiments on a synthesized confusing database and a real database show that the proposed approach will facilitate the design of a highly reliable single neural network classifier.
Object recognition from a single view fails when the available features are not sufficient to determine the identity of a single object, either because of similarity with another object or because of feature corruptio...
详细信息
ISBN:
(纸本)0769512720
Object recognition from a single view fails when the available features are not sufficient to determine the identity of a single object, either because of similarity with another object or because of feature corruption due to clutter and occlusion. Active object recognition systems have addressed this problem successfully, but they require complicated systems with adjustable viewpoints that are not always available. In this paper we investigate the performance gain available by combining the results of a single view object recognition system applied to imagery obtained from multiple fixed cameras. In particular, we address performance in cluttered scenes with varying degrees of information about relative camera pose. We argue that a property common to many computer vision recognition systems, which we term a weak target error, is responsible for two interesting limitations of multi-view performance enhancement: the lack of significant improvement in systems whose single-view performance is weak, and the plateauing of performance improvement as additional multi-view constraints are added.
This paper presents a new method for automatically separating the motion of multiple independently moving objects in a sequence of images based on the notion of illumination subspace. We show that intensities of obser...
详细信息
ISBN:
(纸本)0769512720
This paper presents a new method for automatically separating the motion of multiple independently moving objects in a sequence of images based on the notion of illumination subspace. We show that intensities of observed trajectories of image features on a single body lie on a linearly independent frame space with three, or fewer, dimensions. We then argue that it is possible in theory to determine the grouping of the feature points by way of separating the illumination subspaces. As a clue for practical separation, we also introduce the surface interaction matrix which is valid for Lambertian reflectance surface. Recently, several authors have presented different algorithms on this task of grouping by factorization-based procedures using the coordinates of image features. While the challenges in their approaches are to realize the robust performance in the presence of noise, we propose to incorporate the above photometric analysis available at given feature points in the conventional schemes of motion segmentation, and show that the performance is indeed stabilized through experiments on real and synthetic image sequences.
Accurate estimation of effective camera focal length is crucial to the success of panoramic image stitching. Fast techniques for estimating the focal length exist, but are dependent upon a close initial approximation ...
详细信息
ISBN:
(纸本)0769512720
Accurate estimation of effective camera focal length is crucial to the success of panoramic image stitching. Fast techniques for estimating the focal length exist, but are dependent upon a close initial approximation or the existence of a full circle panoramic image sequence. Numerical solutions of the focal length demonstrate strong coupling between the focal length and the angles used to position each component image about the common spherical center. This paper demonstrates that parameterizing panoramic image positions using spherical arc length instead of angles effectively decouples the focal length from the image position. This new parameterization does not require an initial focal length estimate for quick convergence, nor does it require a full circle panorama in order to refine the focal length. Experiments with synthetic and real image sets demonstrate the robustness of the method and a speedup of 5 to 20 times over angle based positioning.
Perceptual popout is defined by both feature similarity and local feature contrast. We identify these two measures with attraction and repulsion, and unify the dual processes of association by attraction and segregati...
详细信息
ISBN:
(纸本)0769512720
Perceptual popout is defined by both feature similarity and local feature contrast. We identify these two measures with attraction and repulsion, and unify the dual processes of association by attraction and segregation by repulsion in a single grouping framework. We generalize normalized cuts to multi-way partitioning with these dual measures. We expand graph partitioning approaches to weight matrices with negative entries, and provide a theoretical basis for solution regularization in such algorithms. We show that attraction, repulsion and regularization each contributes in a unique way to popout. Their roles are demonstrated in various salience detection and visual search scenarios. This work opens up the possibilities of encoding negative correlations in constraint satisfaction problems, where solutions by simple and robust eigendecomposition become possible.
image rectification is the process of warping a pair of stereo images in order to align the epipolar lines with the scan-lines of the images. Once a pair of images is rectified, stereo matching can be implemented in a...
详细信息
ISBN:
(纸本)0769512720
image rectification is the process of warping a pair of stereo images in order to align the epipolar lines with the scan-lines of the images. Once a pair of images is rectified, stereo matching can be implemented in an efficient manner. Given the epipolar geometry, it is straightforward to define a rectifying transformation, however, many transformations will lead to unwanted image distortions. In this paper, we present a novel method for stereo rectification that determines the transformation that minimizes the effects of resampling that can impede stereo matching. The effects we seek to minimize are the loss of pixels due to under-sampling and the creation of new pixels due to over-sampling. To minimize these effects we parameterize the family of rectification transformations and solve for the one that minimizes the change in local area integrated over the area of the images.
Segmentation in noisy images is an important and difficult problem in patternrecognition. Edge detection is a crucial step in this process. Current subjective and objective methods for evaluation and comparison of se...
详细信息
Segmentation in noisy images is an important and difficult problem in patternrecognition. Edge detection is a crucial step in this process. Current subjective and objective methods for evaluation and comparison of segmentation techniques are inadequate or not applicable to edge detection techniques. A general framework for segmentation evaluation in noisy images is introduced after a brief review of previous work. Several measures based on similarity between true and result segmented images are defined. These measures are, then, combined in a unique criterion as a proposed global measure of performance. The results indicate that this global measure can be helpful in the evaluation and comparison of segmentation techniques applied to noisy images.
The paper presents an improvement of the classical Non-negative Matrix Factorization (NMF) approach for dealing with local representations of image objects. NHF, when applied to global data representations such as fac...
详细信息
ISBN:
(纸本)0769512720
The paper presents an improvement of the classical Non-negative Matrix Factorization (NMF) approach for dealing with local representations of image objects. NHF, when applied to global data representations such as faces presents a high ability to represent local features of the original data in an unsupervised way. However, when applied to local representations, NMF generates redundant basis. This work implements an improvement on the original NMF approach by incorporating prior knowledge in the form of a weight matrix extracted from the training data. A detailed mathematical description of the inclusion of this weight matrix is provided, and results demonstrating its advantages are included. Furthermore, the original NMF approach lacks a hierarchy of the elements of the estimated basis. A technique to determine an ordered set of discriminant basis is also presented. Finally, the effectiveness of the weighted approach with respect to the classical approach is experimentally compared. This is done by implementing a clustering algorithm that automatically extracts object parts from the NMF representation of an image database corresponding to newspapers.
暂无评论