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A probabilistic framework for surface reconstruction from multiple images

A probabilistic framework for surface reconstruction from mu...

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conference on computer Vision and pattern recognition (CVPR)

作者： M. Agrawal L.S. Davis Department of Computer Science University of Maryland College Park MD USA

ISBN: (纸本)0769512720

The paper presents a novel probabilistic framework for 3D surface reconstruction from multiple stereo images. The method works on a discrete voxelized representation of the scene. An iterative scheme is used to estimate the probability that a scene point lies on the true 3D surface. The novelty of our approach lies in the ability to model and recover surfaces which may be occluded in some views. This is done by explicitly estimating the probabilities that a 3D scene point is visible in a particular view from the set of given images. This relies on the fact that for a point on a lambertian surface, if the pixel intensities of its projection along two views differ, then the point is necessarily occluded in one of the views. We present results of surface reconstruction from both real and synthetic image sets.

关键词： Surface reconstruction image reconstruction Layout Stereo vision Cameras Stereo image processing computer vision Machine vision computer science Educational institutions

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A new 3-D pattern recognition technique with application to computer aided colonoscopy

A new 3-D pattern recognition technique with application to ...

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conference on computer Vision and pattern recognition (CVPR)

作者： S.B. Gokturk C. Tomasi Electrical Engineering Department University of Stanford Stanford CA USA Computer Science Department University of Stanford Stanford CA USA

ISBN: (纸本)0769512720

To utilize CT or MRI images for computer aided diagnosis applications, robust features that represent 3D image data need to be constructed and subsequently used by a classification method. We present a computer aided diagnosis system for early diagnosis of colon cancer. The system extracts features via a new 3D pattern processing.method and processes them using a support vector machine classifier. Our 3D pattern processing.method, called Random Orthogonal Shape Section (ROSS) mimics the radiologist's way of viewing these images and combines information from many random triples of mutually orthogonal sections going through the volume. Another contribution of the paper is a new feedback framework between the classification algorithm and the definition of the features. This framework, called Distinctive Component Analysis combines support vector samples with linear discriminant analysis to map the features of clustered support vectors to a lower dimensional space where the two classes of objects of interest are optimally separated to obtain better features. We show that the combination of these better features with support vector machine classification provides a good recognition rate.

关键词： pattern recognition Application software computer applications Colonoscopy Cancer Support vector machine classification Computed tomography Magnetic resonance imaging Robustness Colonography

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Reconstruction of specular surfaces using polarization imaging

Reconstruction of specular surfaces using polarization imagi...

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conference on computer Vision and pattern recognition (CVPR)

作者： S. Rahmann N. Canterakis Institute for Pattern Recognition and Image Processing Computer Science Department University of Freiburg Freiburg im Breisgau Germany

ISBN: (纸本)0769512720

Traditional intensity imaging does not offer a general approach for the perception of textureless and specular reflecting surfaces. Intensity based methods for shape reconstruction of specular surfaces rely on virtual (i.e. mirrored) features moving over the surface under viewer motion. We present a novel method based on polarization imaging for shape recovery of specular surfaces. This method overcomes the limitations of the intensity based approach because no virtual features are required. It recovers whole surface patches and not only single curves on the surface. The presented solution is general as it is independent of the illumination. The polarization image encodes the projection of the surface normals onto the image and therefore provides constraints on the surface geometry. Taking polarization images from multiple views produces enough constraints to infer the complete surface shape. The reconstruction problem is solved by an optimization scheme where the surface geometry is modelled by a set of hierarchical basis functions. The optimization algorithm proves to be well converging, accurate and noise resistant. The work is substantiated by experiments on synthetic and real data.

关键词： image reconstruction Surface reconstruction Polarization Shape Geometry Surface texture Lighting Solid modeling image converters Multi-stage noise shaping

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Handling occlusions in dense multi-view stereo

Handling occlusions in dense multi-view stereo

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conference on computer Vision and pattern recognition (CVPR)

作者： Sing Bing Kang R. Szeliski Jinxiang Chai Microsoft Research Microsoft Corporation USA The Robotics Institute Carnegie Mellon University USA

ISBN: (纸本)0769512720

While stereo matching was originally formulated as the recovery of 3D shape from a pair of images, it is now generally recognized that using more than two images can dramatically improve the quality of the reconstruction. Unfortunately, as more images are added, the prevalence of semi-occluded regions (pixels visible in some but not all images) also increases. We propose some novel techniques to deal with this problem. Our first idea is to use a combination of shiftable windows and a dynamically selected subset of the neighboring images to do the matches. Our second idea is to explicitly label occluded pixels within a global energy minimization framework, and to reason about visibility within this framework so that only truly visible pixels are matched. Experimental results show a dramatic improvement using the first idea over conventional multibaseline stereo, especially when used in conjunction with a global energy minimization technique. These results also show that explicit occlusion labeling and visibility reasoning do help, but not significantly, if the spatial and temporal selection is applied first.

关键词： Stereo vision Pixel Robots Stereo image processing Shape image reconstruction image recognition Labeling computer vision Application software

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A model-based road sign identification system

A model-based road sign identification system

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conference on computer Vision and pattern recognition (CVPR)

作者： Y.B. Lauziere D. Gingras F.P. Ferrie Centre for Intelligent Machines CIM McGill University Montreal QUE Canada INO Sainte-Foy QUE Canada Institute des Matériaux et Systèmes Intelligents IMSI Université de Sherbrook Sherbrooke QUE Canada

ISBN: (纸本)0769512720

A road sign (RS) recognition system poses a real challenge for machine vision. It must recognize a wide variety of RSs under considerable variations in illumination and imaging geometry-all in real-time. Such a system is presented, with emphasis on the system architecture and specific model-based techniques used in the different processing.steps. Central to this are a unique physics-based color detection approach and a novel template matching scheme for planar objects. Since the approach strongly relies on modelling for both detection and recognition, it offers the advantage of being reconfigurable by changing only a few parameters. The system is modular with respect to the sensor and the recognition data structure is simple to extend and maintain, and is easily adaptable to different regulations, e.g. North American vs European RSs. The data needed for recognition is computed automatically by modelling image formation with a few geometrical parameters. Experimental results are presented which demonstrate the performance of the system in a real task environment with high overall performance.

关键词： image recognition Roads Lighting Solid modeling image databases Spatial databases Geometry Machine intelligence Machine vision Object detection

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Clustering art

Clustering art

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conference on computer Vision and pattern recognition (CVPR)

作者： K. Barnard P. Duygulu D. Forsyth Computer Division University of California Berkeley USA

ISBN: (纸本)0769512720

We extend a recently developed method (K. Barnard and D. Forsyth, 2001) for learning the semantics of image databases using text and pictures. We incorporate statistical natural language processing.in order to deal with free text. We demonstrate the current system on a difficult dataset, namely 10000 images of work from the Fine Arts Museum of San Francisco. The images include line drawings, paintings, and pictures of sculpture and ceramics. Many of the images have associated free text which varies greatly from physical description to interpretation and mood. We use WordNet to provide semantic grouping information and to help disambiguate word senses, as well as emphasize the hierarchical nature of semantic relationships. This allows us to impose a natural structure on the image collection that reflects semantics to a considerable degree. Our method produces a joint probability distribution for words and picture elements. We demonstrate that this distribution can be used: (a) to provide illustrations for given captions, and (b) to generate words for images outside the training set. Results from this annotation process yield a quantitative study of our method. Finally, the annotation process can be seen as a form of object recognizer that has been learned through a partially supervised process.

关键词： Subspace constraints image databases Predictive models Natural language processing Art Painting Ceramics Mood Probability distribution image generation

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3D head tracking using motion adaptive texture-mapping

3D head tracking using motion adaptive texture-mapping

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conference on computer Vision and pattern recognition (CVPR)

作者： L.M. Brown IBM Thomas J. Watson Research Center Hawthorne NY USA

ISBN: (纸本)0769512720

We have developed a fast robust 3D head tracking system based on rendering a texture-mapped cylinder. In order to handle the variable frame-to-frame motion changes, the system uses motion templates, which adapt to the current size of the motion increment. The relationship between measurable pixel energy and tracking error is used to design the parameters of the adaptive algorithm. To speedup processing.and decouple rotational and translational motion, 2D positional information of the neckline is utilize. The confidence at each point is computed based on the amount of information used in creating the texture map and re-rendering the face. The system can also handle large out-of-plane rotations via additional templates. If the tracker fails, it can recover using an independent face detection routine. We compare the results of our approach with the extensive results of a closely related technique.

关键词： Face recognition Head Humans image databases Robustness Face detection Facial animation Testing Energy measurement Algorithm design and analysis

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Rapid object detection using a boosted cascade of simple features

Rapid object detection using a boosted cascade of simple fea...

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conference on computer Vision and pattern recognition (CVPR)

作者： P. Viola M. Jones Mitsubishi Electric Research Laboratories Inc. Cambridge MA USA Compaq Cambridge Research Laboratory Cambridge MA USA

ISBN: (纸本)0769512720

This paper describes a machine learning approach for visual object detection which is capable of processing.images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the "integral image" which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers. The third contribution is a method for combining increasingly more complex classifiers in a "cascade" which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.

关键词： Object detection Face detection Pixel Detectors Filters Machine learning image representation Focusing Skin Robustness

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Removing weather effects from monochrome images

Removing weather effects from monochrome images

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conference on computer Vision and pattern recognition (CVPR)

作者： S.G. Narasimhan S.K. Nayar Department of Computer Sciences Columbia University NY USA

ISBN: (纸本)0769512720

images of outdoor scenes captured in bad weather suffer from poor contrast. Under bad weather conditions, the light reaching a camera is severely scattered by the atmosphere. The resulting decay in contrast varies across the scene and is exponential in the depths of scene points. Therefore, traditional space invariant image processing.techniques are not sufficient to remove weather effects from images. In this paper, we present a fast physics-based method to compute scene structure and hence restore contrast of the scene from two or more images taken in bad weather In contrast to previous techniques, our method does not require any a priori weather-specific or scene information, and is effective under a wide range of weather conditions including haze, mist, fog and other aerosols. Further, our method can be applied to gray-scale, RGB color, multi-spectral and even IR images. We also extend the technique to restore contrast of scenes with moving objects, captured using a video camera.

关键词： Layout image restoration Atmospheric modeling Light scattering Weather forecasting Cameras Aerosols Filtering Physics Atmospheric waves

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3D model generation for cities using aerial photographs and ground level laser scans

3D model generation for cities using aerial photographs and ...

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conference on computer Vision and pattern recognition (CVPR)

作者： C. Fruh A. Zakhor Video and Image Processing Laboratory University of California Berkeley USA Army Research Office USA

ISBN: (纸本)0769512720

In this paper we describe techniques for 3D textured model construction of urban areas using acquisition devices such as intensity cameras, as well as 2D laser scanner. Our experimental set up consists of a truck equipped with one camera and two fast, inexpensive 2D laser scanner, traveling on city streets under normal traffic conditions. The horizontal laser scans are used to determine the approximate component of motion along the movement of the acquisition vehicle. The vertical scanner is used to build 3D models of the facade of the buildings. To improve the accuracy of localization of the truck and hence our resulting 3D models of the city, two different methods are developed and compared: the first method employs a correlation technique and the second method is based on Markov Monte Carlo localization. Both techniques use digital road maps and aerial photographs in conjunction with laser scans. A fairly accurate textured, 3D model of downtown area has been acquired in a matter of few minutes, limited only by traffic conditions during the data acquisition phase.

关键词： Cities and towns Laser modes Cameras Data acquisition Traffic control Vehicles Roads Remote monitoring Global Positioning System image processing

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