This paper describes substantial advances in the analysis (parsing) of diagrams using constraint grammars. The addition of set types to the grammar and spatial indexing of the data make it possible to efficiently pars...
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The thematic knowledge can bridge the gap between semantic entities and syntactic constituents. In document understanding, the correctness and the efficiency could be improved ifthe thematic knowledge is available. In...
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We describe experimental tests of a spatial extension to the CIELAB color metric for measuring color reproduction errors of digital images. The standard CIELAB ΔE metric is suitable for use on large uniform color tar...
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We describe experimental tests of a spatial extension to the CIELAB color metric for measuring color reproduction errors of digital images. The standard CIELAB ΔE metric is suitable for use on large uniform color targets, but not on images, because color sensitivity changes as a function of spatial pattern. The S-CIELAB extension includes a spatial processing.step, prior to the CIELAB ΔE calculation, so that the results correspond better to color difference perception by the human eye. The S-CIELAB metric was used to predict texture visibility of printed halftone patterns. The results correlate with perceptual data better than standard CIELAB and point the way to various improvements.
The indexing of inaccurately recognized OCR text yields unsatisfactory results, where the quality of the index terms decreases rapidly when the quality of the documents get worse. Index terms of OCR processed document...
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Aiming at automatically discovering the common objects contained in a set of relevant images and segmenting them as foreground simultaneously, object co-segmentation has become an active research topic in recent years...
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ISBN:
(纸本)9781467388511
Aiming at automatically discovering the common objects contained in a set of relevant images and segmenting them as foreground simultaneously, object co-segmentation has become an active research topic in recent years. Although a number of approaches have been proposed to address this problem, many of them are designed with the misleading assumption, unscalable prior, or low flexibility and thus still suffer from certain limitations, which reduces their capability in the real-world scenarios. To alleviate these limitations, we propose a novel two-stage co-segmentation framework, which introduces the weak background prior to establish a globally close-loop graph to represent the common object and union background separately. Then a novel graph optimized-flexible manifold ranking algorithm is proposed to flexibly optimize the graph connection and node labels to co-segment the common objects. Experiments on three image datasets demonstrate that our method outperforms other state-of-the-art methods.
The goal of this paper is to perform 3D object detection from a single monocular image in the domain of autonomous driving. Our method first aims to generate a set of candidate class-specific object proposals, which a...
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ISBN:
(纸本)9781467388511
The goal of this paper is to perform 3D object detection from a single monocular image in the domain of autonomous driving. Our method first aims to generate a set of candidate class-specific object proposals, which are then run through a standard CNN pipeline to obtain highquality object detections. The focus of this paper is on proposal generation. In particular, we propose an energy minimization approach that places object candidates in 3D using the fact that objects should be on the ground-plane. We then score each candidate box projected to the image plane via several intuitive potentials encoding semantic segmentation, contextual information, size and location priors and typical object shape. Our experimental evaluation demonstrates that our object proposal generation approach significantly outperforms all monocular approaches, and achieves the best detection performance on the challenging KITTI benchmark, among published monocular competitors.
The three-dimensional (3-D) motion of an object is recovered from its 2-D perspective projection images without using any knowledge of point-to-point correspondence. The principle is to express the variation of certai...
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ISBN:
(纸本)081860777X
The three-dimensional (3-D) motion of an object is recovered from its 2-D perspective projection images without using any knowledge of point-to-point correspondence. The principle is to express the variation of certain image features as functionals in terms of motion parameters. image features may be outstanding points, line segments, or surface patches on the object. Given images of an object before and after motion, together with the depth information of the object before motion, a heuristic estimate of the motion is first computed. The image before motion is transformed, according to this estimate, so that its position will be close to the other image. Then, the motion that accounts for the remaining small discrepancy is estimated by measuring numerical features on the images. The derivation is based on the optical flow due to infinitesimal motion;and the estimation is done by solving a set of simultaneous linear equations in terms of feature values and motion parameters. This process is iterated;after each iteration of estimation, one image is transformed according to the estimated motion so that it is positioned closer and closer to the other image.
Model-based column segmentation is described. Sequences of horizontal white space across a column are used as the basic features. Structures of columns in a specific publication are described by two levels of regular ...
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This paper introduces an effective decolorization algorithm that preserves the appearance of the original color image. Guided by the original saliency, the method blends the luminance and the chrominance information i...
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The research discussed in this paper focuses on the emergence of a multimedia language for humans and technology to communicate highly subjective concepts (such as impressions, feelings, and emotions). Nowadays techno...
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