Segmentation of monochrome images to obtain boundaries of the object is an important problem in scene analysis. An algorithm is described for locating object boundaries from an image of objects. The algorithm, called ...
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Segmentation of monochrome images to obtain boundaries of the object is an important problem in scene analysis. An algorithm is described for locating object boundaries from an image of objects. The algorithm, called Global-Local-Edge-Coincidence (GLEC) uses both local and global edge information to select a stable set of object boundaries. Significantly improved results are shown in several examples including blocks, building and aerial photograph. The significance of this algorithm is that the boundaries of objects can often be located from a single image.
This paper presents a new method for extracting the 3-D shape and texture of an object undergoing translational motion from image sequences captured through a monocular extra-wide picture viewing angle. The feature of...
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ISBN:
(纸本)0818658258
This paper presents a new method for extracting the 3-D shape and texture of an object undergoing translational motion from image sequences captured through a monocular extra-wide picture viewing angle. The feature of this work is extracting this information from image sequences without requiring rigid environmental conditions. In this method, the relative positions between target and view position are estimated based on spatio-temporal image analysis, and shape is reconstructed from the multiple silhouette information. After reconstructing the 3-D shape, the voxel value of a surface point is determined by statistically analyzing those images that contain the surface point. The proposed method can extract 3-D shape and surface texture at a stroke from outdoor scenes. An experiment using real outdoor scenes confirms the effectiveness of the method.
In smartphones and mobile camera devices, the image Signal Processor(ISP) is applied to reconstruct the RAW image into a sRGB image for human reading by a series of signal modules. Due to the non-linear ISP transforma...
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ISBN:
(纸本)9798350365474
In smartphones and mobile camera devices, the image Signal Processor(ISP) is applied to reconstruct the RAW image into a sRGB image for human reading by a series of signal modules. Due to the non-linear ISP transformation, it is complicated to model the degradation in the sRGB domain. Most existing super-resolution methods directly handle the sRGB image processed by the ISP, introducing more difficult degradation patterns. To address this challenge, we propose an enhanced transformer network named RBSFormer. Unlike other methods that operate on sRGB images, RBSFormer takes RAW images as input, thus avoiding the complex degradation introduced by ISP processing. We design two enhanced core components, i.e., Enhanced Cross-Covairance Attention(EXCA) and Enhanced Gated Feedforward Network(EGFN), in the RBSFormer, and we further introduce data augmentation in the RAW domain and hybrid ensemble strategies to enhance our results. Experimental results demonstrate superior performance against the majority of methods both qualitatively and quantitatively. Our RBSFormer achieves 3rd place in terms of all the evaluation metrics both on the official validation and testing set with fewer parameters in the NTIRE 2024 challenge on Raw image Super Resolution.
We present a novel, generic image classification method based on a recent machine learning algorithm (ensembles of extremely randomized decision trees). images are classified using randomly extracted subwindows that a...
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ISBN:
(纸本)0769523722
We present a novel, generic image classification method based on a recent machine learning algorithm (ensembles of extremely randomized decision trees). images are classified using randomly extracted subwindows that are suitably normalized to yield robustness to certain image transformations. Our method is evaluated on four very different, publicly available datasets (COIL-100, ZuBuD, ETH80, WANG). Our results show that our automatic approach is generic and robust to illumination, scale, and viewpoint changes. An extension of the method is proposed to improve its robustness with respect to rotation changes.
Linear filters have two major drawbacks. First, edges in the image are smoothed with increasing filter size. Second, by extending the filters to multi-channel data, correlation between the channels is lost. Only a few...
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ISBN:
(纸本)0769512720
Linear filters have two major drawbacks. First, edges in the image are smoothed with increasing filter size. Second, by extending the filters to multi-channel data, correlation between the channels is lost. Only a few researchers have explored the possibilities of mode filtering to overcome these problems. In this article mode filtering will be motivated from both a local histogram with tonal scale and a robust statistics point of view. The tonal scale is proved to be equal to the scale of the error norm function within the robust statistics framework. Instead of the more commonly studied global mode, our focus is on the local mode. It preserves edges and details and is easily extensible to multi-channel data. A generalization of the spatial Gaussian filtering to a spatial and tonal Gaussian filter is used to iterate to the local mode. Results on color images include successful noise attenuation while preserving edges and detail by local mode filtering.
A description is given of a versatile imageprocessing.system developed at Pratt & Whitney Aircraft (P&WA) and the highly efficient spatial domain filtering techniques which are its computational heart. By exp...
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A description is given of a versatile imageprocessing.system developed at Pratt & Whitney Aircraft (P&WA) and the highly efficient spatial domain filtering techniques which are its computational heart. By exploiting block average filtering techniques requiring only local storage, each block filtering operation uses only four additions and one multiplication per pixel, regardless of image or block size. As a consequence, extremely rapid imageprocessing.is possible;for example, deconvolution of a 512 multiplied by 512 pixel image can be performed in just five seconds on an IBM 3033 with Fortran code.
image data compression can be achieved by a number of techniques such as DPCM and Transform Coding. Adaptive image compression can be done with any of these techniques. Adaptive image data compression is a procedure i...
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image data compression can be achieved by a number of techniques such as DPCM and Transform Coding. Adaptive image compression can be done with any of these techniques. Adaptive image data compression is a procedure in which the number of bits allocated to each image block changes from block to block depending on the block complexity. This variable input bit rate must be converted to a constant output bit rate by a rate buffer. A study is made of some causal and noncausal approaches to adaptively allocating bits under the constraint of a fixed size buffer. A discussion is also presented of the optimal noncausal approach whose performance is a least upper bound on any causal approach.
We present an image indexing method based on a hierarchical description of the density of each of the image classes in a given database. The method is similar in spirit to traditional agglomerative clustering procedur...
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ISBN:
(纸本)0769512720
We present an image indexing method based on a hierarchical description of the density of each of the image classes in a given database. The method is similar in spirit to traditional agglomerative clustering procedures but produces a complete mixture density, instead of a representative point, at each node of the indexing tree. Estimation of the density at a given node only requires knowledge of the mixture parameters of the children nodes, not the original data. The process is very flexible and efficient, therefore suited to problems involving large databases where existing groupings may have to be combined, or new groupings created, frequently. Experimental results show that the new indexing structure consistently outperforms a linear search when both efficiency and retrieval accuracy are taken into account.
We present a semantic segmentation algorithm for RGB remote sensing images. Our method is based on the Dilated Stacked U-Nets architecture. This state-of-the-art method has been shown to have good performance in other...
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ISBN:
(数字)9781538661000
ISBN:
(纸本)9781538661000
We present a semantic segmentation algorithm for RGB remote sensing images. Our method is based on the Dilated Stacked U-Nets architecture. This state-of-the-art method has been shown to have good performance in other applications. We perform additional post-processing.by blending image tiles and degridding the result. Our method gives competitive results on the DeepGlobe dataset.
We present a method to locate an `object' in a color image, or more precisely, to select a set of likely locations for the object. The model is assumed to be of known color, which permits the use of color-space pr...
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ISBN:
(纸本)081863880X
We present a method to locate an `object' in a color image, or more precisely, to select a set of likely locations for the object. The model is assumed to be of known color, which permits the use of color-space processing. A new method is presented, which exploits more information than the previous Backprojection Algorithm of Swain and Ballard at a competitive complexity. Precisely, the new algorithm is based on matching local histograms with the model, instead of directly replacing pixels with a confidence that they belong to the object. We prove that a simple version of this algorithm degenerates into Backprojection in the worst case. In addition, we show how to estimate the scale of the model. We also propose the use of co-occurrence histograms to deal with cases where important color variations can be expected. Results are shown on pictures digitized from the famous `Waldo' books.
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