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检索条件"任意字段=IEEE Symposium on Computational Intelligence in Image and Signal Processing"

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Segmentation of color image based on partial differential equations

Segmentation of color image based on partial differential eq...

引用

2011 4th International symposium on computational intelligence and Design, ISCID 2011

作者： Yuan, Chun Liang, Shangli Division of Information Technology Graduate School at Shenzhen Tsinghua University Shenzhen China

ISBN: (纸本)9780769545004

image segmentation is an important task in image processing. A lot of image segmentation methods or models have been proposed. But most of these methods cannot work well with color images, which actually contain more useful information about the objects inside. In this paper, a segmentation model for color images is proposed. The new model is based on the GAC segmentation model and extends the concept of gradient from one channel to three channels. Experiments show that the new model has better performance than the GAC model, especially in the segmentation of color images. © 2011 ieee.

关键词： image segmentation

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The use of interval methods in signal processing and control for systems biology

The use of interval methods in signal processing and control...

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ieee symposium on Foundations of computational intelligence

作者： Edmonson, William Ocloo, Senanu Williams, Cranos Alexander, Winser North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

ISBN: (纸本)9781424407033

The development of approaches for understanding the complex dynamics of biological systems is a growing research area in electrical engineering, particularly in the fields of signal processing and controls. The focus of our research is the exploitation of the parallels between engineering and biology through the development of optimization and identification methods. Specifically, this research consists of developing methods for the estimation of unmeasured states, the identification of parameters of kinetic models and the validation of biochemical models. This work falls under the general research topic of systems biology. We explore the use of interval analysis in developing numerical algorithms for optimization and validation of systems biology problems. A major attribute of this method is that convergence to global minima is guaranteed. This paper includes a development of an adaptive interval optimization method based on the branch-and-bound method known as Smooth Interval Branch-and-Bound. One potential impact of this research is the development of more accurate models of biological systems. This will aid in the design of drugs for cancer and disease treatment and aid in the study of how they propagate.

关键词： Biological systems

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Study and Analysis of Hallucination in HDR images

Study and Analysis of Hallucination in HDR Images

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ieee International Conference on computational intelligence and Computing Research (ICCIC)

作者： Thakur, S. K. Kowsalya, S. Nallaperumal, Krishnan Marappan, Karthikeyan Vishwanath, N. MS Univ Ctr Informat Technol & Engn Tirunelveli Tamil Nadu India Tamilnadu Coll Engn Coimbatore Tamil Nadu India Toc H Inst Sci & Technol Ernakulam Kerala India

ISBN: (纸本)9781479915941;9781479915958

This work presents a study and analysis of Hallucination in HDR images it presents a technique for the MO dynamic range image in over-exposed and underexposed regions, the hallucination image become blur and hence a bilateral filler algorithm is applied to smooth the image and preserve edges more effectively after applying the tone mapping algorithm to map these high dynamic images to produce a high dynamic range image will contents intact, this mapping technique is known hallucination technique or High dynamic range images.

关键词： High Dynamic Range image Hallucination image processing Edge Detection HDR Tone Mapping

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Translational Displacement Estimation with Subpixel Accuracy of DCT-Sign Phase Correlation 9

Translational Displacement Estimation with Subpixel Accuracy...

引用

9th International symposium on image and signal processing and Analysis (ISPA)

作者： Ito, Izumi Tokyo Inst Technol Tokyo 1528552 Japan

ISBN: (纸本)9781467380324

The accuracy of subpixel estimation of Discrete Cosine Transform (DCT) -sign phase correlation (-SPC) is presented using real images involved by actual movement. So far, the accuracy had been reported just using simulated images. Using real images and two types of simulated images, we evaluate the performance with subpixel accuracy of DCT-SPC with curve fitting compared to phase correlation with curve fitting. For real images captured by a commercial-off-the-shelf product camera, the error of estimated value with subpixel accuracy of DCT-SPC with curve fitting is lower than that of phase correlation with curve fitting, although DCT-SPC uses only the signs of DCT coefficients, while phase correlation uses the phase term with complex numbers.

关键词： Correlation Accuracy Estimation Discrete cosine transforms Cameras Discrete Fourier transforms computational modeling

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UUnet: An effective cascade Unet for automatic segmentation of renal parenchyma

UUnet: An effective cascade Unet for automatic segmentation ...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Cao, Gaoyu Sun, Zhanquan Pan, Minlan Pang, Jiangfei He, Zhiqiang Shen, Jiayu Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai Peoples R China

ISBN: (纸本)9781728190488

Although deep learning image segmentation technology has achieved good results in medical image processing, it is still challenging to segment renal parenchyma from diuretic renography. The diuretic nephrogram has the characteristics of obvious noise, poor image quality, unclear boundary and serious redundant information. It is difficult to accurately segment renal parenchyma directly using the classical Unet network. Therefore, we propose a cascaded network, i.e. a segment network that realize segmentation from coarse to fine. The coarse segmentation model is used to obtain the suggested area of the kidney in the diuretic renal image. The cascaded fine segmentation model is to segment the renal parenchyma from the suggested region of the kidney. Compared with the original Unet, the cascade network can reduce the noise interference to a large extent and get better segmentation performance of the renal parenchyma. The experiment showed that the dice coefficient increased by 9.78%, and the proposed network is efficient in the renal parenchyma segmentation.

关键词： Medical image segmentation deep learning cascaded network renal parenchyma

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A novel neural network optimized by quantum genetic algorithm for signal detection in MIMO-OFDM systems

A novel neural network optimized by quantum genetic algorith...

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ieee symposium on computational intelligence in Control and Automation

作者： Li, Fei Zhou, Min Li, Haibo Institute of Signal Processing and Transmission Key Lab. of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Nanjing China Department of Applied Physics and Electronics Umeå University Umeå Sweden

ISBN: (纸本)9781424499038

Neural networks can easily fall into a local extremum and have slow convergence rate. Quantum Genetic Algorithm (QGA) has features of small population size and fast convergence. Based on the investigation of QGA, we propose a novel neural network model, Radial Basis Function (RBF) networks optimized by Quantum Genetic Algorithm (QGA-RBF model). Then we investigate the performance of the proposed QGA-RBF on solving MIMO-OFDM signal detection problem. A novel signal detector based on QGA-RBF for MIMO-OFDM system is also proposed. The simulation results show that the proposed detector has more powerful properties in bit error rate than QGA based detector, RBF based detector and MMSE algorithm based detector, namely a 4-6 dB gain in performance can be achieved. The performance of the proposed detector is closer to optimal, compared with the other detectors. © 2011 ieee.

关键词： Orthogonal frequency division multiplexing

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3D reconstruction of archaeological contexts using image segmentation and Structure from Motion 22

3D reconstruction of archaeological contexts using image seg...

引用

23rd symposium on image, signal processing and Artificial Vision (STSIVA)

作者： Melo Barrera, Luis Enrique Ceron Correa, Alexander Univ Mil Nueva Granada Fac Ingn Bogota Colombia

ISBN: (纸本)9781665416696

Into the large set of techniques to elaborate 3D reconstructions, the Structure from Motion algorithms stand out because of its low cost and versatility, being these algorithms a series of computational procedures to the automated generation of dense point clouds from a set of photographs. The goal of this paper is to propose a workflow for the 3D reconstruction of archaeological objects and contexts employing a low cost UAV (Unmanned aerial vehicle) for the data capture phase, searching to evaluate and problematize the need of use expensive and specific hardware. After this data capture stage the 3D reconstruction process begins. This workflow is composed by three great phases: The generation of binary masks to select regions of interest, the production of dense point clouds and its transformation into a triangular mesh. Finally, sketch out the challenge that means aspire to determine which is the best of the generated reconstructions under quantitative frames.

关键词： Archaeological reconstruction digital heritage triangular mesh point cloud image segmentation SfM

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An Interactive City Framework: Mobile Cloud Computing Approach

An Interactive City Framework: Mobile Cloud Computing Approa...

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ieee International symposium on signal processing and Information Technology (ISSPIT)

作者： Abd El Aty, Heba M. Hassan, Hanan A. Sheta, Walaa M. Informatics Research Institute MuCSAT Egypt

ISBN: (纸本)9781467307536

This paper presents a mobile augmented reality framework for an interactive three-dimensional city. One of the challenging issues in the design of mobile scalable augmented reality systems is the limitation of computational power. The proposed framework provides a scalable, service-based and open source approach to integrate the real and virtual scene. The key strength of the proposed framework is adopting an emerging and challenging approach: Mobile Cloud Computing. The main advantage of this approach is reducing mobile constraints: limited battery life and limited memory size. The paper focus on the proposed framework architecture and rationale behind designing its infrastructure to provide interactivity and scalability features.

关键词： Virtual Reality Augmented Reality image Retrieval Location-Based Services Software as a service Mobile Computing Virtualization Three-dimensional Maps Mobile Guides Navigation Three-dimensional laser scanning

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Collective network of evolutionary binary classifiers for content-based image retrieval

Collective network of evolutionary binary classifiers for co...

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symposium Series on computational intelligence, ieee SSCI 2011 - 2011 ieee 5th Workshop on Evolving and Adaptive Intelligent Systems, EAIS 2011

作者： Kiranyaz, Serkan Uhlmann, Stefan Pulkkinen, Jenni Gabbouj, Moncef Ince, Turker Dept. of Signal Processing Tampere University of Technology Tampere Finland Faculty of Computer Science Izmir University of Economics Izmir Turkey

ISBN: (纸本)9781424499793

The content-based image retrieval (CBIR) has been an active research field for which several feature extraction, classification and retrieval techniques have been proposed up to date. However, when the database size grows larger, it is a common fact that the overall retrieval performance significantly deteriorates. In this paper, we propose collective network of (evolutionary) binary classifiers (CNBC) framework to achieve a high retrieval performance even though the training (ground truth) data may not be entirely present from the beginning and thus the system can only be trained incrementally. The CNBC framework basically adopts a "Divide and Conquer" type approach by allocating several networks of binary classifiers (NBCs) to discriminate each class and performs evolutionary search to find the optimal binary classifier (BC) in each NBC. In such an evolution session, the CNBC body can further dynamically adapt itself with each new incoming class/feature set without a full-scale re-training or re-configuration. Both visual and numerical performance evaluations of the proposed framework over benchmark image databases demonstrate its scalability;and a significant performance improvement is achieved over traditional retrieval techniques. © 2011 ieee.

关键词： Benchmarking

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Just Noticeable Depth Difference of Human During Viewing of Dynamic Random Dot Stereograms 9

Just Noticeable Depth Difference of Human During Viewing of ...

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9th International symposium on computational intelligence and Design (ISCID)

作者： Yang, Yu Lu, Chaohui Li, Jianzeng Yao, Huanqi Commun Univ China Beijing Peoples R China Jiangsu Broadcasting Corp Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781509035588

Just Noticeable Depth Difference (JNDD) describes the sensitivity of human for depth difference to Stereoscopic three Dimensional (S3D) viewing, which is an important cue in research on Human Visual System (HVS). Understanding the factors of human depth perception is helpful to the research on stereo image processing and optimization, which is heavily based on exploitation of HVS. The traditional JNDD measurement test cannot avoid the impact of monocular stimulation and psychological depth cue. In this paper, a new JNDD measurement method for only binocular stimulation and pure physiological depth cue based on dynamic Random Dot Stereogram (RDS) technique is proposed. Then subjective test are performed on a S3D display with two JNDD measurement methods: the proposed one and a traditional one for binocular disparity depth cue. The results show that observers are more sensitive to the depth difference in the proposed methods.

关键词： just noticeable depth difference percieved depth random dot stereogram stereo image

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