The use of computer aided design requires line drawings and m p s to be digitized and stored in databases. The input of line drawings and nwps into data bases requires vectorization of lines, and recognition of symbol...
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Most camera images are saved as 8-bit standard RGB (sRGB) compressed JPEGs. Even when JPEG compression is set to its highest quality, the encoded sRGB image has been significantly processed in terms of color and tone ...
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ISBN:
(纸本)9781467388511
Most camera images are saved as 8-bit standard RGB (sRGB) compressed JPEGs. Even when JPEG compression is set to its highest quality, the encoded sRGB image has been significantly processed in terms of color and tone manipulation. This makes sRGB-JPEG images undesirable for many computer vision tasks that assume a direct relationship between pixel values and incoming light. For such applications, the RAW image format is preferred, as RAW represents a minimally processed, sensor-specific RGB image with higher dynamic range that is linear with respect to scene radiance. The drawback with RAW images, however, is that they require large amounts of storage and are not well-supported by many imaging applications. To address this issue, we present a method to encode the necessary metadata within an sRGB image to reconstruct a high-quality RAW image. Our approach requires no calibration of the camera and can reconstruct the original RAW to within 0.3% error with only a 64 KB overhead for the additional data. More importantly, our output is a fully self-contained 100% complainant sRGB-JPEG file that can be used as-is, not affecting any existing image workflow - the RAW image can be extracted when needed, or ignored otherwise. We detail our approach and show its effectiveness against competing strategies.
We address the problem of 2D-3D pose estimation in difficult viewing conditions, such as low illumination, cluttered background, and large highlights and shadows that appear on the object of interest. In such challeng...
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Spatially-discrete Markov random fields (MRFs) and spatially-continuous variational approaches are ubiquitous in low-level vision, including image restoration, segmentation, optical flow, and stereo. Even though both ...
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Most effective particular object and image retrieval approaches are based on the bag-of-words (BoW) model. All state-of-the-art retrieval results have been achieved by methods that include a query expansion that bring...
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Silhouettes provide rich information on three-dimensional shape, since the intersection of the associated visual cones generates the "visual hull", which encloses and approximates the original shape. However...
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ISBN:
(纸本)9781467388511
Silhouettes provide rich information on three-dimensional shape, since the intersection of the associated visual cones generates the "visual hull", which encloses and approximates the original shape. However, not all silhouettes can actually be projections of the same object in space: this simple observation has implications in object recognition and multi-view segmentation, and has been (often implicitly) used as a basis for camera calibration. In this paper, we investigate the conditions for multiple silhouettes, or more generally arbitrary closed image sets, to be geometrically "consistent". We present this notion as a natural generalization of traditional multi-view geometry, which deals with consistency for points. After discussing some general results, we present a "dual" formulation for consistency, that gives conditions for a family of planar sets to be sections of the same object. Finally, we introduce a more general notion of silhouette "compatibility" under partial knowledge of the camera projections, and point out some possible directions for future research.
In the advancing fields of computer vision and deep learning, video dynamic object removal technology is an important research area. The goal of this technology is to remove specific dynamic objects from a continuous ...
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Pointwise label and pairwise label are both widely used in computer vision tasks. For example, supervised image classification and annotation approaches use pointwise label, while attribute-based image relative learni...
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ISBN:
(纸本)9781467388511
Pointwise label and pairwise label are both widely used in computer vision tasks. For example, supervised image classification and annotation approaches use pointwise label, while attribute-based image relative learning often adopts pairwise labels. These two types of labels are often considered independently and most existing efforts utilize them separately. However, pointwise labels in image classification and tag annotation are inherently related to the pairwise labels. For example, an image labeled with "coast" and annotated with "beach, sea, sand, sky" is more likely to have a higher ranking score in terms of the attribute "open"; while "men shoes" ranked highly on the attribute "formal" are likely to be annotated with "leather, lace up" than "buckle, fabric". The existence of potential relations between pointwise labels and pairwise labels motivates us to fuse them together for jointly addressing related vision tasks. In particular, we provide a principled way to capture the relations between class labels, tags and attributes; and propose a novel framework PPP(Pointwise and Pairwise image label Prediction), which is based on overlapped group structure extracted from the pointwise-pairwise-label bipartite graph. With experiments on benchmark datasets, we demonstrate that the proposed framework achieves superior performance on three vision tasks compared to the state-of-the-art methods.
The high-level synthesis converting software to hardware automatically is one of the important technologies for significantly reducing the burden caused by developing hardware module. The embedded imageprocessing.pro...
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The parametric Bayesian Feature Enhancement (BFE) and a datadriven Denoising Autoencoder (DA) both bring performance gains in severe single-channel speech recognition conditions. The first can be adjusted to different...
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