We describe a dataset of several thousand calibrated, geo-referenced, high dynamic range color images, acquired under uncontrolled, variable illumination in an outdoor region spanning hundreds of meters. All image, fe...
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ISBN:
(纸本)0769512720
We describe a dataset of several thousand calibrated, geo-referenced, high dynamic range color images, acquired under uncontrolled, variable illumination in an outdoor region spanning hundreds of meters. All image, feature, calibration, and geo-referencing data are available at http://***/data. Calibrated imagery is of fundamental interest in a wide variety of applications. We have made this data available in the belief that researchers in computer graphics, computer vision, photogrammetry and digital cartography will find it useful in several ways: as a test set for their own algorithms;as a calibrated image set for applications such as image-based rendering, metric 3D reconstruction, and appearance recovery;and as controlled imagery for integration into existing GIS systems and applications. The web-based interface to the data provides interactive viewing of high-dynamic-range images and mosaics;extracted edge and point features;intrinsic and extrinsic calibration, along with maps of the ground context in which the images were acquired;the spatial adjacency relationships among images;the epipolar geometry relating adjacent images;, compass and absolute scale overlays;and quantitative consistency measures for the calibration data.
To utilize CT or MRI images for computer aided diagnosis applications, robust features that represent 3-D image data need to be constructed and subsequently used by a classification method. In this paper, we present a...
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To utilize CT or MRI images for computer aided diagnosis applications, robust features that represent 3-D image data need to be constructed and subsequently used by a classification method. In this paper, we present a computer aided diagnosis system for early diagnosis of colon cancer. The system extracts features by a new 3-D patternprocessing.method and processes them using a support vector machine classifier. Our 3-D patternprocessing.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 this 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 so as to obtain better features. We show that the combination of these better features with support vector machines classification provides a good recognition rate.
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 acr...
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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 imageprocessing.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.
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 ...
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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.
The dichromatic reflectance model introduced by Shafer predicts that the colour signals of most materials fall on a plane spanned by a vector due to the material and a vector that represents the scene illuminant. Sinc...
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The dichromatic reflectance model introduced by Shafer predicts that the colour signals of most materials fall on a plane spanned by a vector due to the material and a vector that represents the scene illuminant. Since the illuminant is in the span of all dichromatic planes, colour constancy can be achieved by finding the intersection of two or more planes. Unfortunately, this approach has proven to be hard to get to work in practice. First, segmentation needs to be carried out and second, the actual intersection computation is quite unstable: small changes in the orientation of a dichromatic plane can significantly alter the location of the intersection point. In this paper we propose to ameliorate the instability problem by regularising the intersection. Specifically we introduce a constraint on the colour of the illuminant. We show how the intersection problem in the context of convex and non-convex illuminant constraints, based on the distribution of common light sources, can be solved. This algorithm coupled with the simplest of segmentations results in good estimation results for a large set of real images. Estimation performance is significantly better than for the unconstrained algorithm.
In this paper, we present a segmented linear subspace model for face recognition that is robust under varying illumination conditions. The algorithm generalizes the 3D illumination subspace model by segmenting the ima...
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ISBN:
(纸本)0769512720
In this paper, we present a segmented linear subspace model for face recognition that is robust under varying illumination conditions. The algorithm generalizes the 3D illumination subspace model by segmenting the image into regions that have surface normals whose directions are close to each other. This segmentation is performed using a K-means clustering algorithm and requires only a few training images under different illuminations. When the linear subspace model is applied to the segmented image, recognition is robust to attached and cast shadows, and the recognition rate is equal to that of computationally more complex systems that require constructing the 3D surface of the face.
In this paper, we exploit some previous theoretical results about decision tree pruning to derive a color segmentation algorithm which avoids some of the common drawbacks of region merging techniques. The algorithm ha...
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ISBN:
(纸本)0769512720
In this paper, we exploit some previous theoretical results about decision tree pruning to derive a color segmentation algorithm which avoids some of the common drawbacks of region merging techniques. The algorithm has both statistical and computational advantages over known approaches. It authorizes the processing.of 512/spl times/512 images in less than a second on conventional PC computers. Experiments are reported on thirty-five images of various origins, illustrating the quality of the segmentations obtained.
This paper addresses the problem of calibrating camera lens distortion, which can be significant in medium to wide angle lenses. Our approach is based on the analysis of distorted images of straight lines. We derive n...
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ISBN:
(纸本)0769512720
This paper addresses the problem of calibrating camera lens distortion, which can be significant in medium to wide angle lenses. Our approach is based on the analysis of distorted images of straight lines. We derive new distortion measures that can be optimized using non-linear search techniques to find the best distortion parameters that straighten these lines. Unlike other approaches, we also provide fast, closed-form solutions to the distortion coefficients. Experiments to evaluate the performance of this approach on synthetic and real data are reported.
The paper presents a fast and robust algorithm to identify text in image or video frames with complex backgrounds and compression effects. The algorithm first extracts the candidate text line on the basis of edge anal...
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ISBN:
(纸本)0769512720
The paper presents a fast and robust algorithm to identify text in image or video frames with complex backgrounds and compression effects. The algorithm first extracts the candidate text line on the basis of edge analysis, baseline location and heuristic constraints. Support Vector Machine (SVM) is then used to identify text line from the candidates in edge-based distance map feature space. Experiments based on a large amount of images and video frames from different sources showed the advantages of this algorithm compared to conventional methods in both identification quality and computation time.
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