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Colour constancy using the chromagenic constraint

Colour constancy using the chromagenic constraint

引用

conference on computer vision and pattern recognition (cvpr)

作者： G.D. Finlayson S.D. Hordley P. Morovic School of Computing Sciences University of East Anglia Norwich UK

In this paper we propose that two images are captured of every scene: a normal image and an image captured where a coloured filter is placed in front of the camera. This additional information is then used in solving for colour constancy. The novelty of our approach is not that we add a colour filter (this is an old idea) but in how we use the additional information. In contradistinction to previous work we propose that the dimensionality of the 6 measurements per image pixel remains at 3 (not 6): we do not add a filter to increase the number of degrees of freedom but rather as a way of estimating the illuminant. We say that a filter is chromagenic if the relationship between filtered and unfiltered RGBs varies with and depends strongly on illumination. The canonical chromagenic algorithm works by testing the applicability of pre-computed relations in situ in an image. We extend the chromagenic approach to incorporate knowledge of the gamut of colours we expect to see under a given light and so in effect we make a hybrid gamut mapping + chromagenic algorithm. Experiments validate our approach with chromagenic gamut mapping shown to deliver significantly better constancy than all other algorithms tested.

关键词： Layout Lighting Reflectivity Optical polarization Testing computer vision Cameras Information filtering Information filters Pixel

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Boosting saliency in color image features

Boosting saliency in color image features

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conference on computer vision and pattern recognition (cvpr)

作者： J. van de Weijer Th. Gevers Intelligent Sensory Information Systems Faculty of Science University of Amsterdam Amsterdam Netherlands

The aim of salient point detection is to find distinctive events in images. Salient features are generally determined from the local differential structure of images. They focus on the shape saliency of the local neighborhood. The majority of these detectors is luminance based which has the disadvantage that the distinctiveness of the local color information is completely ignored. To fully exploit the possibilities of color image salient point detection, color distinctiveness should be taken into account next to shape distinctiveness. In this paper color distinctiveness is explicitly incorporated into the design of saliency detection. The algorithm, called color saliency boosting, is based on an analysis of the statistics of color image derivatives. Isosalient color derivatives can be closely approximated by ellipsoidal surfaces in color derivative space. Based on this remarkable statistical finding, isosalient derivatives are transformed by color boosting to have equal impact on the saliency. Color saliency boosting is designed as a generic method easily adaptable to existing feature detectors. Results show that substantial improvements in information content are acquired by targeting color salient features. Further, the generality of the method is illustrated by applying color boosting to multiple existing saliency methods.

关键词： Boosting Detectors computer vision Phase detection Shape Data mining Image color analysis Intelligent systems Intelligent sensors Information systems

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Combining object and feature dynamics in probabilistic tracking

Combining object and feature dynamics in probabilistic track...

引用

conference on computer vision and pattern recognition (cvpr)

作者： L. Taycher J.W. Fisher T. Darrell Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA

Objects can exhibit different dynamics at different scales, and this is often exploited by visual tracking algorithms. A local dynamic model is typically used to extract image features that are then used as input to a system for tracking the entire object using a global dynamic model. Approximate local dynamics may be brittle - point trackers drift due to image noise and adaptive background models adapt to foreground objects that become stationary - but constraints from the global model can make them more robust. We propose a probabilistic framework for incorporating global dynamics knowledge into the local feature extraction processes. A global tracking algorithm can be formulated as a generative model and used to predict feature values that are incorporated into an observation process of the feature extractor. We combine such models in a multichain graphical model framework. We show the utility of our framework for improving feature tracking and thus shape and motion estimates in a batch factorization algorithm. We also propose an approximate filtering algorithm appropriate for online applications, and demonstrate its application to background subtraction.

关键词： Feature extraction Hidden Markov models computer science Artificial intelligence Laboratories Background noise Noise robustness Predictive models Graphical models Tracking

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Discriminative learning of Markov random fields for segmentation of 3D scan data

Discriminative learning of Markov random fields for segmenta...

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conference on computer vision and pattern recognition (cvpr)

作者： D. Anguelov B. Taskarf V. Chatalbashev D. Koller D. Gupta G. Heitz A. Ng Computer Science Department Stanford University Stanford CA USA

We address the problem of segmenting 3D scan data into objects or object classes. Our segmentation framework is based on a subclass of Markov random fields (MRFs) which support efficient graph-cut inference. The MRF models incorporate a large set of diverse features and enforce the preference that adjacent scan points have the same classification label. We use a recently proposed maximum-margin framework to discriminatively train the model from a set of labeled scans; as a result we automatically learn the relative importance of the features for the segmentation task. Performing graph-cut inference in the trained MRF can then be used to segment new scenes very efficiently. We test our approach on three large-scale datasets produced by different kinds of 3D sensors, showing its applicability to both outdoor and indoor environments containing diverse objects.

关键词： Markov random fields Layout Image segmentation computer science Testing Large-scale systems Indoor environments Mobile robots Robot sensing systems Robot localization

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Complex 3D shape recovery using a dual-space approach

Complex 3D shape recovery using a dual-space approach

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conference on computer vision and pattern recognition (cvpr)

作者： Chen Liang K.-Y.K. Wong Department of Computer Science University of Hong Kong Hong Kong China

ISBN: (纸本)0769523722

This paper presents a novel method for reconstructing complex 3D objects with unknown topology using silhouettes extracted from image sequences. This method exploits the duality principle governing surface points and their corresponding tangent planes, and enables a direct estimation of points on the contour generators. A major problem in other related works concerns with the search for a tangent basis at singularities in the dual tangent space. This problem is addressed here by utilizing the epipolar parameterization for identifying a well-defined basis at each point, and thus avoids any form of search. For the degenerate cases where epipolar parameterization breaks up, a fast on-the-fly validation is performed for each computed surface point, which consequently leads to a significant improvement in robustness. As the resulting contour generator points are not suitable for direct triangulation, a topologically correct surface extracting method based on slicing plane is presented. Both experiments on synthetic and real world data show that the proposed method has comparable robustness as those existing volumetric methods regarding surface of complex topology, whilst producing more accurate estimation of surface points.

关键词： Shape Robustness Surface reconstruction Cameras Topology Data mining Surface treatment computer science Image reconstruction Image sequences

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patterns of gene deletion following genome duplication in yeast

Patterns of gene deletion following genome duplication in ye...

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2005 ieee Computational Systems Bioinformatics conference, Workshops and Poster Abstracts

作者： Byrnes, Jake K. Li, Wen-Hsiung Department of Ecology and Evolution University of Chicago

ISBN: (纸本)0769524427

Whole genome duplication (WGD) is followed by massive duplicate deletion that reorganizes gene adjacencies. We compare the deletion patterns and adjacency reorganization following WGD in yeast with simulations. We find that deletion events alternate between paralogous chromosomes more often than expected under a random duplicate deletion model. © 2005 ieee.

关键词： pattern recognition

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Blob segmentation using joint space-intensity likelihood ratio test: application to 3D tumor segmentation

Blob segmentation using joint space-intensity likelihood rat...

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conference on computer vision and pattern recognition (cvpr)

作者： K. Okada U. Akdemir Siemens Corporate Research Inc. Princeton NJ USA

We propose a novel semi-automatic figure-ground segmentation solution for blob-like objects in multi-dimensional images. The blob-like structure constitutes various objects of interest that are hard to segment in many application domains, such as tumor lesions in 3D medical data. The proposed solution is motivated towards computer-aided diagnosis medical applications, justifying our semi-automatic and figure-ground approach. The efficient segmentation is realized by combining the robust anisotropic Gaussian model fitting and the likelihood ratio test (LRT)-based non-parametric segmentation in joint space-intensity domain. The robustly fitted Gaussian is exploited to estimate the foreground and background likelihoods for both spatial and intensity variables. We demonstrate that the LRT with the bootstrapped likelihoods is assured to be the optimal Bayesian classification while automatically determining the LRT threshold. A 3D implementation of the proposed algorithm is applied to the lung nodule segmentation in CT data and validated with 1310 cases. Our efficient solution segments a target nodule in less than 3 seconds in average.

关键词： Testing Neoplasms Image segmentation Application software Robustness Light rail systems Lesions Biomedical imaging Medical diagnostic imaging computer aided diagnosis

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Using the KL-center for efficient and accurate retrieval of distributions arising from texture images

Using the KL-center for efficient and accurate retrieval of ...

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conference on computer vision and pattern recognition (cvpr)

作者： E. Spellman B.C. Vemuri M. Rao Department of CISE University of Florida Gainesville FL USA

Image retrieval is a common problem in many computer vision applications and the literature abounds with techniques with impressive retrieval accuracies. Several of these techniques use probability distributions to represent the "objects" they intend to retrieve. We present a novel approach to search such collections of distributions efficiently. Like many standard data structures, our method uses an "average" to represent a large set (Le., cluster) of objects, thus allowing the search to disregard an unpromising subset with only one comparison to its average. Our contribution lies in choosing the average: Inspired by information theory, we choose a representative that is optimally "close" in a minimax sense to all members of its set when measured using the Kullback-Liebler (KL) divergence. We present a texture retrieval system and test it on the CUReT database, measuring accuracy and efficiency. We find that using the KL-center yields speed ups of more than a factor of three over an exhaustive search while guaranteeing identical accuracy. The KL-center also out-performs other commonly used representatives such as the arithmetic mean. Although we present results only in texture retrieval, our approach will likely aid image and shape retrieval as well.

关键词： Image retrieval computer vision Application software Probability distribution Data structures Information theory Minimax techniques Information retrieval System testing Databases

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Face recognition based on frontal views generated from non-frontal images

Face recognition based on frontal views generated from non-f...

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conference on computer vision and pattern recognition (cvpr)

作者： V. Blanz P. Grother P.J. Phillips T. Vetter Max Planck Institut für Informatik Saarbrucken Germany National Institute for Standards and Technology Gaithersburg MD USA Department Informatik University of Basel Basel Switzerland

This paper presents a method for face recognition across large changes in viewpoint. Our method is based on a morphable model of 3D faces that represents face-specific information extracted from a dataset of 3D scans. For non-frontal face recognition in 2D still images, the morphable model can be incorporated in two different approaches: in the first, it serves as a preprocessing step by estimating the 3D shape of novel faces from the non-frontal input images, and generating frontal views of the reconstructed faces at a standard illumination using 3D computer graphics. The transformed images are then fed into state-of-the-art face recognition systems that are optimized for frontal views. This method was shown to be extremely effective in the Face recognition Vendor Test FRVT 2002. In the process of estimating the 3D shape of a face from an image, a set of model coefficients are estimated. In the second method, face recognition is performed directly from these coefficients. In this paper we explain the algorithm used to preprocess the images in FRVT 2002, present additional FRVT 2002 results, and compare these results to recognition from the model coefficients.

关键词： Face recognition Image generation Shape Data mining Data preprocessing Image reconstruction Lighting computer graphics Testing Image recognition

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Beyond Lambert: reconstructing specular surfaces using color

Beyond Lambert: reconstructing specular surfaces using color

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conference on computer vision and pattern recognition (cvpr)

作者： S.P. Mallick T.E. Zickler D.J. Kriegman P.N. Belhumeur Computer Science and Engineering University of California San Diego La Jolla CA USA Engineering and Applied Sciences Harvard University Cambridge USA Computer Science Columbia University New York NY USA

We present a photometric stereo method for non-diffuse materials that does not require an explicit reflectance model or reference object. By computing a data-dependent rotation of RGB color space, we show that the specular reflection effects can be separated from the much simpler, diffuse (approximately Lambertian) reflection effects for surfaces that can be modeled with dichromatic reflectance. Images in this transformed color space are used to obtain photometric reconstructions that are independent of the specular reflectance. In contrast to other methods for highlight removal based on dichromatic color separation (e.g., color histogram analysis and/or polarization), we do not explicitly recover the specular and diffuse components of an image. Instead, we simply find a transformation of color space that yields more direct access to shape information. The method is purely local and is able to handle surfaces with arbitrary texture.

关键词： Surface reconstruction Reflectivity Image color analysis Image reconstruction Photometry Reflection Histograms Image analysis Independent component analysis Polarization

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