image reconstruction algorithms for computerized tomography are of interest for both medical and industrial applications. The special geometry of these image reconstruction applications can be used to implement optima...
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image reconstruction algorithms for computerized tomography are of interest for both medical and industrial applications. The special geometry of these image reconstruction applications can be used to implement optimal reconstruction algorithms, such as the minimum variance estimator, in a computationally efficient manner which could become competitive with popular approximate algorithms. As a result of measurement acquisition procedures of typical CT configurations, both fan-beam and parallel-beam arrangements lead to circulant matrix forms which can be used as the basis for fast algorithms. The general minimum variance estimator for this application is reviewed, and a fast algorithm is presented which uses Fourier Transform techniques.
Quantification of phenotypes in high-content screening experiments depends on the accuracy of single cell analysis. In such analysis workflows, cell nuclei segmentation is typically the first step and is followed by c...
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ISBN:
(纸本)9780819494283
Quantification of phenotypes in high-content screening experiments depends on the accuracy of single cell analysis. In such analysis workflows, cell nuclei segmentation is typically the first step and is followed by cell body segmentation, feature extraction, and subsequent data analysis workflows. Therefore, it is of utmost importance that the first steps of high-content analysis are done accurately in order to guarantee correctness of the final analysis results. In this paper, we present a novel cell nuclei image segmentation framework which exploits robustness of graph cut to obtain initial segmentation for image intensity-based clump splitting method to deliver the accurate overall segmentation. By using quantitative benchmarks and qualitative comparison with real images from high-content screening experiments with complicated multinucleate cells, we show that our method outperforms other state-of-the-art nuclei segmentation methods. Moreover, we provide a modular and easy-to-use implementation of the method for a widely used platform.
Aim at classification-oriented feature-level fusion of remote sensing image, MRF is firstly introduced in this paper to build prior probability models for multiple object classes, and EM-HMRF scheme is introduced into...
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Aim at classification-oriented feature-level fusion of remote sensing image, MRF is firstly introduced in this paper to build prior probability models for multiple object classes, and EM-HMRF scheme is introduced into image fusion by taking advantage of the equivalence relation between EM-HMRF and fuzzy classification algorithms. Secondly, focusing on how to select model parameter b with self-adaptive character, a new selecting method is dirived. Then a self-adaptive EM-HMRF fusion algorithm is proposed basing non-homogeneous class and direction, which includes 2 centric and distributed-based fusion schemes. Theory analysis and experiment results show that our proposed algorithms with 2 fusion schemes can not only improve the classification accuracy but also enhance the ability to anti-interference, and they have the different advantages in various fusion systems for different applications, and thus improve the effectiveness of classification basing feature-level fusion of remote sensing image.
The rapid preparation of computing resources is an important step to realize efficient remote sensing imageprocessing. It takes a lot of time for researchers to prepare hardware resources and deploy computing framewo...
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In this paper we examine the problem of bit allocation in lossy image set compression. Instead of treating each image independently, image set compression algorithms examine the relationships among similar images and ...
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ISBN:
(纸本)9789531841917
In this paper we examine the problem of bit allocation in lossy image set compression. Instead of treating each image independently, image set compression algorithms examine the relationships among similar images and remove inter-image redundancies to improve compression performance. These algorithms map the original image set into a number of prediction residual images to be coded. Typically the same bit rate is used to encode each residual. We show that a rate-distortion approach based on Lagrangian optimization can lead to further improvement in image set compression algorithms.
The goal of this project is to improve the performance of the parallel computer SYMPATI2. This SIMD processor based system performs with a good efficiency the low level imageprocessing operations, but this efficiency...
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The goal of this project is to improve the performance of the parallel computer SYMPATI2. This SIMD processor based system performs with a good efficiency the low level imageprocessing operations, but this efficiency is drastically cut when handling an intermediate level class of algorithms. This study emphasis the drawbacks encountered to perform such operations. The main one is the interconnection between processors. So, a new interconnection network, called the open intelligent network, is proposed and added to SYMPATI2 to form SYMPATIX. This network detailed below allows irregular transfers of data between the different processing elements of the new system. Furthermore, this network allows the efficient interconnection of specific modules. In this paper, the architecture is evaluated on representative algorithms of imageprocessing. Then, a behavioural model of SYMPATIX is described using a hardware description language, the VHDL. Our SIMD computer efficiency has been considerably upgraded for the low and intermediate levels of imageprocessing. Furthermore, its application area was extended. The last part of the paper describes the performance obtained with simulations.
Imaging scenarios commonly involve erratic, unpredictable camera behavior or subjects that are prone to movement, complicating multi-frame imageprocessing techniques. To address these issues, we developed three techn...
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ISBN:
(纸本)9781628410136
Imaging scenarios commonly involve erratic, unpredictable camera behavior or subjects that are prone to movement, complicating multi-frame imageprocessing techniques. To address these issues, we developed three techniques that can be applied to multi-frame imageprocessingalgorithms in order to mitigate the adverse effects observed when cameras are panning or subjects within the scene are moving. We provide a detailed overview of the techniques and discuss the applicability of each to various movement types. In addition to this, we evaluated algorithm efficacy with demonstrated benefits using field test video, which has been processed using our commercially available surveillance product. Our results show that algorithm efficacy is significantly improved in common scenarios, expanding our software's operational scope. Our methods introduce little computational burden, enabling their use in real-time and low-power solutions, and are appropriate for long observation periods. Our test cases focus on imaging through turbulence, a common use case for multi-frame techniques.
FPGA components are widely used today to perform various algorithms (digital filtering) in real time. The emergence of Dynamically Reconfigurable (DR) FPGAs made it possible to reduce the number of necessary resources...
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ISBN:
(纸本)0819441880
FPGA components are widely used today to perform various algorithms (digital filtering) in real time. The emergence of Dynamically Reconfigurable (DR) FPGAs made it possible to reduce the number of necessary resources to carry out an imageprocessing application (tasks chain). We present in this article an imageprocessing application (image rotation) that exploits the FPGA's dynamic reconfiguration feature. A comparison is undertaken between the dynamic and static reconfiguration by using two criteria, cost and performance criteria. For the sake of testing the validity of our approach in terms of Algorithm and Architecture Adequacy, we realized an AT40K40 based board ARDOISE.
image fusion is the process of combining multiple images into a single image which retains the most pertinent information from each original image source. More recently, multi-scale image fusion approaches have emerge...
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ISBN:
(纸本)9781424465880
image fusion is the process of combining multiple images into a single image which retains the most pertinent information from each original image source. More recently, multi-scale image fusion approaches have emerged as a means of providing a more meaningful fusion which better reflects the human visual system. In this paper, multi-scale decomposition techniques and image fusion algorithms are adapted using the Parameterized Logarithmic imageprocessing (PLIP) model, a nonlinear imageprocessing framework which more accurately processes images. Experimental results via computer simulations illustrate the improved performance of the proposed algorithms by both qualitative and quantitative means.
As a cross task of natural language processing and computer vision, image captioning is a key technology to explore the transformation of artificial intelligence visual perception to high-level semantic understanding....
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