Linear discriminant analysis (LDA) is a popular face recognition technique. However, an inherent problem with this technique stems from the parametric nature of the scatter matrix, in which the sample distribution in ...
详细信息
ISBN:
(纸本)0769523722
Linear discriminant analysis (LDA) is a popular face recognition technique. However, an inherent problem with this technique stems from the parametric nature of the scatter matrix, in which the sample distribution in each class is assumed to be normal distribution. So it tends to suffer in the case of non-normal distribution. In this paper a nonparametric scatter matrix is defined to replace the traditional parametric scatter matrix in order to overcome this problem. Two kinds of nonparametric subspace analysis (NSA): PNSA and NNSA are proposed for face recognition. The former is based on the principal space of intra-personal scatter matrix, while the latter is based on the null space. In addition, based on the complementary nature of PNSA and NNSA, we further develop a dual NSA-based classifier framework using Gabor images to further improve the recognition performance. Experiments achieve near perfect recognition accuracy (99.7%) on the XM2VTS database.
The problem of finding the closest point in high-dimensional spaces is common in computational vision. Unfortunately, the complexity of most existing search algorithms, such as k-d tree and R-tree, grows exponentially...
详细信息
ISBN:
(纸本)0818672587
The problem of finding the closest point in high-dimensional spaces is common in computational vision. Unfortunately, the complexity of most existing search algorithms, such as k-d tree and R-tree, grows exponentially with dimension, making them impractical for dimensionality above 15. In nearly all applications, the closest point is of interest only if it lies within a user specified distance ε. We present a simple and practical algorithm to efficiently search for the nearest neighbor within Euclidean distance ε. Our algorithm uses a projection search technique along with a novel data structure to dramatically improve performance in high dimensions. A complexity analysis is presented which can help determine ε in structured problems. Benchmarks clearly show the superiority of the proposed algorithm for high dimensional search problems frequently encountered in machine vision, such as real-time object recognition.
The paper presents an analysis of the stability of pose estimation. The investigated pose estimation technique is based on orientations of three edge segments and provides the rotation part of object pose. The specifi...
详细信息
ISBN:
(纸本)0818672587
The paper presents an analysis of the stability of pose estimation. The investigated pose estimation technique is based on orientations of three edge segments and provides the rotation part of object pose. The specific emphasis of the analysis is on determining how the stability varies with view point relative to an object. The stability investigation propagates the uncertainty in edge segment orientations to the resulting effect on the pose parameters. It is shown that there is a very strong variation in noise sensitivity over the range of viewpoints and that exactly what viewpoints offer highest robustness towards noise can be determined in advance. Experiments on real images verify the theoretical results and show that, dependent on viewpoint, pose parameter variance varies from 0.05 to 20 (degrees squared).
This paper proposes a method for detecting obstacles on a runway by controlling their expected disparities. By approximating the runway by a planar surface, the initial model flow field (MFF) corresponding to an obsta...
详细信息
ISBN:
(纸本)0818672587
This paper proposes a method for detecting obstacles on a runway by controlling their expected disparities. By approximating the runway by a planar surface, the initial model flow field (MFF) corresponding to an obstacle-free runway is described by the data from onboard sensors (OBS). The error variance of the initial MFF is computed and used to estimate the MFF. Obstacles are detected by comparing the expected residual flow disparities with the residual flow field (RFF) estimated after warping (or stabilizing) an image using the MFF. Expected temporal and spatial disparities are obtained from the use of the OBS. This allows us to control the residual disparities by increasing the temporal baseline and/or by utilizing the spatial baseline if distant objects cannot be detected for a given temporal baseline. Experimental results for two real flight image sequences are presented.
This paper introduces a new method for object recognition which is based on a recurrent neural network trained in a supervised mode. The RNN inputs 3-dimensional laser scanner data sequentially, in a natural temporal ...
详细信息
ISBN:
(纸本)9781424439942
This paper introduces a new method for object recognition which is based on a recurrent neural network trained in a supervised mode. The RNN inputs 3-dimensional laser scanner data sequentially, in a natural temporal order in which the laser returns arrive to the scanner The method is illustrated on a two-class problem with real data.
In order to overcome several limitations of structured light 3D acquisition methods, the colors, intensities, and shapes of the projected patterns are adapted to the scene. Based on a crude estimate of the scene geome...
详细信息
ISBN:
(纸本)0769523722
In order to overcome several limitations of structured light 3D acquisition methods, the colors, intensities, and shapes of the projected patterns are adapted to the scene. Based on a crude estimate of the scene geometry and reflectance characteristics, the local intensity ranges in the projected patterns are adapted, in order to avoid over- and under-exposure in the image. This avoids the infamous specularity problems and generally increases accuracy. The estimated geometry also helps to limit the effect of aliasing caused by the sampling of foreshortened patterns. Furthermore, the approach also acounts for the adverse effects that small motions during scanning would normally have. Moreover, the approach yields a confidence measure at every pixel of the range image. Last but not least, the scanner consists of consumer products only, and therefore is cheap.
Uncertainty estimates related to the position of image features are seeing increasing use in several computervision problems. Many of these have been recast from standard least squares model fitting to techniques tha...
详细信息
We simultaneously approach two tasks of nonlinear discriminant analysis and kernel selection problem by proposing a unified criterion, Fisher+Kernel Criterion. In addition, an efficient procedure is derived to optimiz...
详细信息
The success of an intelligent robotic system depends on the performance of its vision-system which in turn depends to a great extend upon the quality of its calibration. During the execution of a task the vision-syste...
详细信息
ISBN:
(纸本)0780342364
The success of an intelligent robotic system depends on the performance of its vision-system which in turn depends to a great extend upon the quality of its calibration. During the execution of a task the vision-system is subject to external influences such as vibrations, thermal expansion etc. which affect and possibly render invalid the initial calibration. Moreover it is possible that the parameters of the vision-system like e.g. the zoom or the focus are altered intentionally in order to perform specific vision-tasks. This paper describes a technique for automatically maintaining calibration of stereovision systems over time without using again any particular calibration apparatus. It uses all available information, i.e. both spatial and temporal data. Uncertainty is systematically manipulated and maintained. Synthetical and real data are used to validate the proposed technique, and the results compare very favourably with those given by classical calibration methods.
In many vision problems, we want to infer two (or more) hidden factors which interact to produce our observations. We may want to disentangle illuminant and object colors in color constancy;rendering conditions from s...
详细信息
ISBN:
(纸本)0780342364
In many vision problems, we want to infer two (or more) hidden factors which interact to produce our observations. We may want to disentangle illuminant and object colors in color constancy;rendering conditions from surface shape in shape-from-shading;face identity and head pose in face recognition;or font and letter class in character recognition. We refer to these two factors generically as ''style'' and ''content''. Bilinear models offer a powerful framework for extracting the two-factor structure of a set of observations, and are familiar in computational vision from several well-known lines of research. This paper shows how bilinear models can be used to learn the style-content structure of a pattern analysis or synthesis problem, which can then be generalized to solve related tasks using different styles and/or content. We focus on three tasks: extrapolating the style of data to unseen content classes, classifying data with known content under a novel style, and translating data from novel content classes and style to a known style or content. We show examples from color constancy, face pose estimation, shape-from-shading, typography and speech.
暂无评论