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检索条件"任意字段=2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006"

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Affine image registration using a new information metric

Affine image registration using a new information metric

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conference on computer vision and pattern recognition (cvpr)

作者： Jie Zhang A. Rangarajan Department of Computer & Information Science & Engineering University of Florida Gainesville FL USA

We present a new information metric for multimodality image registration. The metric is technically a pseudometric since it satisfies the properties, (i) nonnegativity, (ii) symmetry, (iii) triangle inequality and is (iv) zero if (but not only if) the two image intensities are identical. Information metrics are rarely used in image registration and notably, the widely used mutual information measure is not a metric. Given images A and B, the metric used here is the sum of the conditional entropies H(A/spl bsol/B) and H(B/spl bsol/A). We show that when compared to mutual information which can even become negative in the multiple image case, it is easier to extend our metric to the registration of multiple images. And, after using an upper bound, we show that the sum of the conditional entropies can be efficiently computed even in the multiple image case. We use the metric to simultaneously register multiple 2D slice images obtained from proton density (PD), magnetic resonance (MR) T2 and MR T1 3D volumes and to match human face images obtained wider different illuminations. Our results demonstrate the efficacy of the metric in affine, multiple image registration.

关键词： Image registration Mutual information Entropy Upper bound Registers Protons Magnetic resonance Humans Face Lighting

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Efficient tracking with the Bounded Hough Transform

Efficient tracking with the Bounded Hough Transform

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conference on computer vision and pattern recognition (cvpr)

作者： M. Greenspan Limin Shang P. Jasiobedzki School of Computing Queen's University Canada Department of Electrical & Computer Engineering Queen's University Canada MD Robotics Limited Brampton ONT Canada

The Bounded Hough Transform is introduced to track objects in a sequence of sparse range images. The method is based upon a variation of the General Hough Transform that exploits the coherence across image frames that results from the relationship between known bounds on the object's velocity and the sensor frame rate. It is extremely efficient, running in O(N) for N range data points, and effectively trades off localization precision for runtime efficiency. The method has been implemented and tested on a variety of objects, including freeform surfaces, using both simulated and real data from Lidar and stereovision sensors. The motion bounds allow the inter-frame transformation space to be reduced to a reasonable, and indeed small size, containing only 729 possible states. In a variation, the rotational subspace is projected onto the translational subspace, which further reduces the transformation space to only 54 states. Experimental results confirm that the technique works well with very sparse data, possibly comprising only tens of points per frame, and that it is also robust to measurement error and outliers.

关键词： Feature extraction Runtime Data mining Iterative closest point algorithm Airports Image sensors Testing Laser radar Robustness Measurement errors

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Jitter camera: high resolution video from a low resolution detector

Jitter camera: high resolution video from a low resolution d...

引用

conference on computer vision and pattern recognition (cvpr)

作者： M. Ben-Ezra A. Zomet S.K. Nayar Computer Science Department Columbia University NY USA

Video cameras must produce images at a reasonable frame-rate and with a reasonable depth of field. These requirements impose fundamental physical limits on the spatial resolution of the image detector. As a result, current cameras produce videos with a very low resolution. Moving the camera and applying super-resolution reconstruction algorithms can computationally enhance the resolution of videos. However, a moving camera introduces motion blur, which limits super-resolution quality. We analyze this effect and derive a theoretical result showing that motion blur has a substantial degrading effect on the performance of super resolution. The conclusion is, that in order to achieve the highest resolution, motion blur should be avoided. Sampling the space-time volume of the video in a specific manner can minimize motion blur. We have developed a novel camera, called the "jitter camera" that achieves this sampling. By applying an adaptive super-resolution algorithm to the video produced by the jitter camera, we show that resolution can be notably enhanced for stationary or slowly moving objects, while it is improved slightly or left unchanged for objects with fast and complex motions. The end result is a video that has a significantly higher resolution than the captured one.

关键词： Jitter Cameras Spatial resolution Detectors Sampling methods Reconstruction algorithms Image motion analysis Performance analysis Motion analysis Degradation

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Hybrid textons: modeling surfaces with reflectance and geometry

Hybrid textons: modeling surfaces with reflectance and geome...

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conference on computer vision and pattern recognition (cvpr)

作者： Jing Wang K.J. Dana Electrical and Computer Engineering Department Rutgers University Piscataway NJ USA

The appearance of surface texture as it varies with angular changes of view and illumination is becoming an increasingly important research topic. The bidirectional texture function (BTF) is used in surface modeling because it describes observed image texture as a function of imaging parameters. The BTF has no geometric information, as it is based solely on observed texture appearance. Computational tasks such as recognizing or rendering typically require projecting a sampled BTF to a lower dimensional subspace or clustering to extract representative textons. However, there is a serious drawback to this approach. Specifically, cast shadowing and occlusions are not fully captured. When recovering the full BTF from a sampled BTF with interpolation, the following two characteristics are difficult or impossible to reproduce: (1) the position and contrast of the shadow border, (2) the movement of the shadow border when the imaging parameters are changed continuously. For a textured surface, the nonlinear effects of cast shadows and occlusions are not negligible. On the contrary, these effects occur throughout the surface and are important perceptual cues to infer surface type. In this paper we present a texture representation that integrates appearance-based information from the sampled BTF with concise geometric information inferred from the sampled BTF The model is a hybrid of geometric and image-based models and has key advantages in a wide range of tasks, including texture prediction, recognition, and synthesis.

关键词： Solid modeling Reflectivity Geometry Surface texture Predictive models Lighting Image texture Rendering (computer graphics) Data mining Shadow mapping

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An algorithm for multiple object trajectory tracking

An algorithm for multiple object trajectory tracking

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conference on computer vision and pattern recognition (cvpr)

作者： Mei Han Wei Xu Hai Tao Yihong Gong NEC Laboratories of America Inc. Cupertino CA USA University of California Santa Cruz Santa Cruz CA USA

Most tracking algorithms are based on the maximum a posteriori (MAP) solution of a probabilistic framework called Hidden Markov Model, where the distribution of the object state at current time instance is estimated based on current and previous observations. However this approach is prone to errors caused by temporal distractions such as occlusion, background clutter and multi-object confusion. In this paper we propose a multiple object tracking algorithm that seeks the optimal state sequence which maximizes the joint state-observation probability. We name this algorithm trajectory tracking since it estimates the state sequence or "trajectory" instead of the current state. The algorithm is capable of tracking multiple objects whose number is unknown and varies during tracking. We introduce an observation model which is composed of the original image, the foreground mask given by background subtraction and the object detection map generated by an object detector The image provides the object appearance information. The foreground mask enables the likelihood computation to consider the multi-object configuration in its entirety. The detection map consists of pixel-wise object detection scores, which drives the tracking algorithm to perform joint inference on both the number of objects and their configurations efficiently.

关键词： Trajectory State estimation Hidden Markov models Inference algorithms Object detection Target tracking computer vision Particle filters National electric code Laboratories

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Effect of colorspace transformation, the illuminance component, and color modeling on skin detection

Effect of colorspace transformation, the illuminance compone...

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conference on computer vision and pattern recognition (cvpr)

作者： S. Jayaram S. Schmugge M.C. Shin L.V. Tsap Department of Computer Science University of North Carolina Charlotte Charlotte NC USA Electronics Engineering Department Lawrence Livemore National Laboratory Livermore CA USA

Skin detection is an important preliminary process in human motion analysis. It is commonly performed in three steps: transforming the pixel color to a non-RGB colorspace, dropping the illuminance component of skin color, and classifying by modeling the skin color distribution. In this paper, we evaluate the effect of these three steps on the skin detection performance. The importance of this study is a new comprehensive colorspace and color modeling testing methodology that would allow for making the best choices for skin detection. Combinations of nine colorspaces, the presence of the absence of the illuminance component, and the two color modeling approaches are compared. The performance is measured by using a receiver operating characteristic (ROC) curve on a large dataset of 805 images with manual ground truth. The results reveal that (1) colorspace transformations can improve performance in certain instances, (2) the absence of the illuminance component decreases performance, and (3) skin color modeling has a greater impact than colorspace transformation. We found that the best performance was obtained by transforming the pixel color to the SCT or HSI colorspaces, keeping the illuminance component, and modeling the color with the histogram approach.

关键词： Skin Motion detection Face detection Color Detectors Laboratories Humans Motion analysis Histograms Lighting

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Error analysis for a navigation algorithm based on optical-flow and a digital terrain map

Error analysis for a navigation algorithm based on optical-f...

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conference on computer vision and pattern recognition (cvpr)

作者： R. Lerner E. Rivlin P.H. Rotstein Department of Computer Science Technion-Israel Institute of Technology Haifa Israel University of Minnesota Minneapolis MN USA

This paper deals with the error analysis of a novel navigation algorithm that uses as input the sequence of images acquired from a moving camera and a digital terrain (or elevation) map (DTM/DEM). More specifically, it has been shown that the optical flow derived from two consecutive camera frames can be used in combination with a DTM to estimate the position, orientation and ego-motion parameters of the moving camera. As opposed to previous works, the proposed approach does not require an intermediate explicit reconstruction of the 3D world. In the present work the sensitivity of the algorithm outlined above is studied. The main sources for errors are identified to be the optical-flow evaluation and computation, the quality of the information about the terrain, the structure of the observed terrain and the trajectory of the camera. By assuming appropriate characterization of these error sources, a closed form expression for the uncertainty of the pose and motion of the camera is first developed and then the influence of these factors is confirmed using extensive numerical simulations. The main conclusion of this paper is to establish that the proposed navigation algorithm generates accurate estimates for reasonable scenarios and error sources, and thus can be effectively used as part of a navigation system of autonomous vehicles.

关键词： Error analysis Navigation Optical sensors Optical computing Digital cameras Image motion analysis Image reconstruction Uncertainty Numerical simulation Remotely operated vehicles

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A non-parametric approach for independent component analysis using kernel density estimation

A non-parametric approach for independent component analysis...

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conference on computer vision and pattern recognition (cvpr)

作者： K. Sengupta P. Burman R. Sharma Algorithms Group AuthenTec Melbourne FL USA Department of Statistics University of California Davis Davis CA USA Department of CSE Pennsylvania State University University Park PA USA

Learning using independent component analysis (ICA) has found a wide range of applications in the area of computer vision and pattern analysis, ranging from face recognition to speech separation. This paper presents a non-parametric approach to the ICA problem that is robust towards outlier effects. The algorithm, for the first time in the field of ICA, adopts an intuitive and direct approach, focusing on the very definition of independence itself; i.e. the joint probability density function (pdf) of independent sources is factorial over the marginal distributions. In the proposed algorithm, kernel density estimation is employed to approximate the underlying distributions. There are two major advantages of our algorithm. First, existing algorithms focus on learning the independent components by attempting to fulfill necessary conditions (but not sufficient) for independence. For example, the Jade algorithm attempts to approximate independence by minimizing higher order statistics, which are not robust to outliers. Comparatively, our technique is inherently robust towards outlier effects. Second, since the learning employs kernel density estimation, it is naturally free from the assumptions of source distributions (unlike the Infomax algorithm). Experimental results show that the algorithm is able to perform separation of sources in the presence of outliers, whereas existing algorithms like Jade and Infomax break down under such conditions. The results have also shown that the proposed non-parametric approach is generally source distribution independent. In addition, it is able to separate non-Gaussian zero-kurtotic signals unlike the traditional ICA algorithms like Jade and Infomax.

关键词： Independent component analysis Kernel Robustness Application software computer vision pattern analysis Face recognition Speech analysis Probability density function Higher order statistics

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Learning methods for generic object recognition with invariance to pose and lighting

Learning methods for generic object recognition with invaria...

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conference on computer vision and pattern recognition (cvpr)

作者： Y. LeCun Fu Jie Huang L. Bottou The Courant Institute New York University NY USA NEC Laboratories of America Inc. Princeton NJ USA

We assess the applicability of several popular learning methods for the problem of recognizing generic visual categories with invariance to pose, lighting, and surrounding clutter. A large dataset comprising stereo image pairs of 50 uniform-colored toys under 36 azimuths, 9 elevations, and 6 lighting conditions was collected (for a total of 194,400 individual images). The objects were 10 instances of 5 generic categories: four-legged animals, human figures, airplanes, trucks, and cars. Five instances of each category were used for training, and the other five for testing. Low-resolution grayscale images of the objects with various amounts of variability and surrounding clutter were used for training and testing. Nearest neighbor methods, support vector machines, and convolutional networks, operating on raw pixels or on PCA-derived features were tested. Test error rates for unseen object instances placed on uniform backgrounds were around 13% for SVM and 7% for convolutional nets. On a segmentation/recognition task with highly cluttered images, SVM proved impractical, while convolutional nets yielded 16/7% error. A real-time version of the system was implemented that can detect and classify objects in natural scenes at around 10 frames per second.

关键词： Learning systems Object recognition Testing Support vector machines Support vector machine classification Azimuth Animals Humans Airplanes Gray-scale

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Spatially coherent clustering using graph cuts

Spatially coherent clustering using graph cuts

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conference on computer vision and pattern recognition (cvpr)

作者： R. Zabih V. Kolmogorov Cornell University Ithaca NY USA Microsoft Research Limited Cambridge UK

Feature space clustering is a popular approach to image segmentation, in which a feature vector of local properties (such as intensity, texture or motion) is computed at each pixel. The feature space is then clustered, and each pixel is labeled with the cluster that contains its feature vector. A major limitation of this approach is that feature space clusters generally lack spatial coherence (i.e., they do not correspond to a compact grouping of pixels). In this paper, we propose a segmentation algorithm that operates simultaneously in feature space and in image space. We define an energy function over both a set of clusters and a labeling of pixels with clusters. In our framework, a pixel is labeled with a single cluster (rather than, for example, a distribution over clusters). Our energy function penalizes clusters that are a poor fit to the data in feature space, and also penalizes clusters whose pixels lack spatial coherence. The energy function can be efficiently minimized using graph cuts. Our algorithm can incorporate both parametric and non-parametric clustering methods. It can be applied to many optimization-based clustering methods, including k-means and k-medians, and can handle models, which are very close in feature space. Preliminary results are presented on segmenting real and synthetic images, using both parametric and non-parametric clustering.

关键词： Spatial coherence Image segmentation Clustering algorithms Labeling Pixel Clustering methods Optimization methods Image motion analysis Image texture analysis computer vision

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