This paper considers the image reconstruction problem when the original image is assumed to be sparse and when limited information of the point spread function (PSF) is available. In. particular, we are interested in ...
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ISBN:
(纸本)9781424414833
This paper considers the image reconstruction problem when the original image is assumed to be sparse and when limited information of the point spread function (PSF) is available. In. particular, we are interested in reconstructing the magnetization density given Magnetic Resonance Force Microscopy (MRFM) image data, and an alternating iterative algorithm is presented to solve this problem. Simulations demonstrate its performance not only in the reconstruction of the original image, but also in the recovery of the partially known PSF. In addition, we suggest the introduction of a smoothing penalty on allowable PSFs to improve the reconstruction.
We describe a novel algorithm for visually identifying the font used in a scanned printed document. Our algorithm requires no pre-recognition of characters in the string (i. e. optical character recognition). Gradient...
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ISBN:
(纸本)9781479983391
We describe a novel algorithm for visually identifying the font used in a scanned printed document. Our algorithm requires no pre-recognition of characters in the string (i. e. optical character recognition). Gradient orientation features are collected local the character boundaries, and quantized into a hierarchical Bag of Visual Words representation. Following stop-word analysis, classification via logistic regression (LR) of the codebooked features yields per-character probabilities which are combined across the string to decide the posterior for each font. We achieve 93.4 % accuracy over a 1000 font database of scanned printed text comprising Latin characters.
The paper describes an automatic registration procedure based on a multiresolution analysis of images. The approach is quite general and can be applied to a large variety of images. Furthermore the algorithm is very r...
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ISBN:
(纸本)0780331222
The paper describes an automatic registration procedure based on a multiresolution analysis of images. The approach is quite general and can be applied to a large variety of images. Furthermore the algorithm is very robust and can effectively cope with a considerable range of transformations, since the registration is obtained iteratively at different multiresolution scales. The procedure is completely automatic, and relies on the grey level information content of the images. We have applied the algorithm to test images of banknotes, aerial stereo pairs, multispectral and SAR images. In all the cases we have obtained excellent results, outperforming the best algorithms available at present and used in industrial applications.
image reconstruction problems in radio astronomy and other fields like biomedical imaging are often ill-posed and some form of regularization is required. This imposes user specified constraints to the reconstruction ...
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ISBN:
(纸本)9781457705397
image reconstruction problems in radio astronomy and other fields like biomedical imaging are often ill-posed and some form of regularization is required. This imposes user specified constraints to the reconstruction process that may produce an undesirable bias to the solution. We propose a data driven model based least squares reconstruction method based on the Karhunen-Loeve transform. We show that this constraint stems from intrinsic physical properties of the measurement process and demonstrate the improvement of the method over unregularized least squares reconstruction using actual data from the Low Frequency Array (LOFAR), a phased array radio telescope in the Netherlands.
Camera calibration is a necessary prerequisite in many applications of robotics, especially in robot vision in order to obtain metric reconstruction from a 2D image. In this paper, we address the problem of calibratin...
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ISBN:
(纸本)9781665405409
Camera calibration is a necessary prerequisite in many applications of robotics, especially in robot vision in order to obtain metric reconstruction from a 2D image. In this paper, we address the problem of calibrating from a single image of a surface of revolution (SOR) based on deep learning, in order to determine the camera intrinsic parameters. Geometric constraints based on the symmetry properties of the SOR structure are deployed to our proposed learning-based camera calibration framework. To enable the calibration from a single view, we also propose a learning-based conics detection model fitting the geometric primitive of a cylinder. The calibration from a single view can be completed by minimizing the geometric constraints of two conics detected by the learning-based model with cylinder images as input. Objects with a surface of revolution are commonly visible in daily life, such as cans, bottles, and bowls, making this research both significant and practical. Finally, traditional calibration techniques are compared against our single image calibration. Experiments conducted on newly generated dataset demonstrate the effectiveness and robustness of the proposed method.
Most imageprocessing frameworks are not generic enough to provide true reusability of data structures and algorithms. In fact, genericity allows users to write and experiment virtually any method on any compatible in...
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ISBN:
(纸本)9781424479948
Most imageprocessing frameworks are not generic enough to provide true reusability of data structures and algorithms. In fact, genericity allows users to write and experiment virtually any method on any compatible input(s). In this paper, we advocate the use of generic programming in the design of imageprocessing software, while preserving performances close to dedicated code. The implementation of our proposal, Milena, a generic and efficient library, illustrates the benefits of our approach.
The proceedings contains 165 papers. Topics discussed include telecommunications in medicine, image restoration, image coding, parallel algorithms, multidimensional filtering, edge detection, motion estimation, multim...
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The proceedings contains 165 papers. Topics discussed include telecommunications in medicine, image restoration, image coding, parallel algorithms, multidimensional filtering, edge detection, motion estimation, multimedia applications, image models, signal and imageprocessing, color and printing, stereo matching and shape, low bit rate video coding, tomographic theory and algorithms, video compression, facial imageprocessing, object recognition.
Current digital cameras suffer from poor performance in low-light situations. This paper addresses the problem of improving camera performance by using liveview images to augment a final capture. Attention is given to...
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ISBN:
(纸本)9781424479948
Current digital cameras suffer from poor performance in low-light situations. This paper addresses the problem of improving camera performance by using liveview images to augment a final capture. Attention is given to improving only the low- and mid-range frequency information in the final capture, corresponding to available information in the liveview images. Attention is also given to providing a solution with a small memory and computational footprint. It is shown that liveview images can be used to improve image capture, even in scenes containing significant object motion.
in this paper, an image significance region detection algorithm combining global color clustering and color contrast is proposed. Firstly, by clustering the image color, the color of each pixel in the image is replace...
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ISBN:
(纸本)9781728136608
in this paper, an image significance region detection algorithm combining global color clustering and color contrast is proposed. Firstly, by clustering the image color, the color of each pixel in the image is replaced by the color of the closest clustering center. Then, the clustering color contrast is used to calculate the significance region of the image, which enhances the significance region and reduces the influence of nonsignificant objects. Experimental results show that this method has higher precision and better recall rate, and significantly reduces the influence of complex texture on the calculation of significance region.
Accurate identification of different components of a developing human embryo play crucial roles in assessing the quality of such embryo. One of the most important components of a day-5 human embryo is Inner Cell Mass ...
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ISBN:
(纸本)9781538618424
Accurate identification of different components of a developing human embryo play crucial roles in assessing the quality of such embryo. One of the most important components of a day-5 human embryo is Inner Cell Mass (ICM). ICM is a part of an embryo that will eventually develop into a fetus. In this paper, an automatic coarse-to-fine texture based approach presented to identify regions of an embryo corresponding to the ICM. First, blastocyst area corresponding to the textured regions is recognized using Gabor and DCT features. Next, two ICM localization approaches are introduced to identify a rough estimate of the ICM location. Finally, the boundaries of the ICM region is finalized using a region based level-set. Experimental results on a data set of 220 day-5 human embryo images confirm that the proposed method is capable of identifying ICM with average Precision, Recall, and Jaccard Index of 78.7%, 86.8%, and 70.3%, respectively.
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