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Spectral bidirectional texture function reconstruction by fusing multiple-color and spectral images

APPLIED OPTICS 2016年第36期55卷 10400-10408页

作者： Dong, Wei Shen, Hui-Liang Du, Xin Shao, Si-Jie Xin, John H. Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Zhejiang Peoples R China Hong Kong Polytech Univ Inst Text & Clothing Hong Kong Hong Kong Peoples R China

Spectral bidirectional texture function (BTF) is essential for accurate reproduction of material appearance due to its nature of conveying both spatial and spectral information. A practical issue is that the acquisition of raw spectral BTFs is time-consuming. To resolve the limitation, this paper proposes a novel framework for efficient spectral BTF acquisition and reconstruction. The framework acquires red-green-blue (RGB) BTF images and just one spectral image. The full spectral BTFs are reconstructed by fusing the RGB and spectral images based on nonnegative matrix factorization (NMF). Experimental results indicate that the accuracy of spectral reflectance reconstruction is higher than that of existing algorithms. With the reconstructed spectral BTFs, the material appearance can be reproduced with high fidelity under various illumination conditions. (C) 2016 Optical Society of America

关键词： image fusion image processing Imaging systems Multispectral imaging Spectral discrimination Spectral imaging

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Surface reconstruction with Wavelet transformation 20

Surface reconstruction with Wavelet transformation

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20th Jubilee IEEE International Conference on Intelligent Engineering systems (INES)

作者： Balla, Petra Kocsis, Peter Eigner, Gyorgy Antal, Akos BUTE Dept Mechatron Opt & Mech Engn Informat Budapest Hungary Obuda Univ Res & Innovat Ctr Physiol Controls Grp Budapest Hungary

ISBN: (纸本)9781509012169

The development in computing power highlights some forgotten algorithms, which were neglected because of their complexity and slowness on early computers. One example is the Wavelet-Transformation Profilometry (WTP) of which successful application is demonstrated in the paper. WTP is a high level signal processing method using orthogonal algorithms for huge datasets. The high performance in quality and running speed makes the described method suitable for medical image processing applications.

关键词： processing

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A review on automatic analysis techniques for color fundus photographs

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COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2016年 14卷 371-384页

作者： Besenczi, Renato Toth, Janos Hajdu, Andras Univ Debrecen Fac Informat POB 400 H-4002 Debrecen Hungary

In this paper, we give a review on automatic image processing tools to recognize diseases causing specific distortions in the human retina. After a brief summary of the biology of the retina, we give an overview of the types of lesions that may appear as biomarkers of both eye and non-eye diseases. We present several state-of the-art procedures to extract the anatomic components and lesions in color fundus photographs and decision support methods to help clinical diagnosis. We list publicly available databases and appropriate measurement techniques to compare quantitatively the performance of these approaches. Furthermore, we discuss on how the performance of image processing-based systems can be improved by fusing the output of individual detector algorithms. Retinal image analysis using mobile phones is also addressed as an expected future trend in this field. (C) 2015 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

关键词： Biomedical imaging Retinal diseases Fundus image analysis Clinical decision support

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A GPU-based Ultrasound Phased-Array Research System for Non-destructive Testing

A GPU-based Ultrasound Phased-Array Research System for Non-...

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IEEE International Ultrasonics Symposium (IUS)

作者： Lewandowski, Marcin Walczak, Mateusz Witek, Beata Steifer, Tomasz Inst Fundamental Technol Res PAS Dept Strength Mat Warsaw Poland

ISBN: (纸本)9781467398978

ultrasound Phased-Array (PA) systems for nondestructive testing (NDT) use standard beamforming for line-byline image creation. New methods utilizing full-matrix capture (FMC) enable the application of advance processing algorithms, such as the total focusing method and multi-pass adaptive techniques for enhanced flaw visualization. The effective FMC data acquisition and its real-time processing require a very high data throughput and powerful computational resources. Most commercial PA systems support some form of FMC acquisition, but the limited external data bandwidth prevents this mode of operation from being useful. We have developed a fully programmable ultrasound research system capable of performing FMC data acquisition and image reconstruction with a high framerate. The ultrasound platform is supporting up to 192 parallel TX/RX electronics channels integrated with an embedded control PC and a GPU cluster for parallel processing. The implemented software libraries give the end-user control over TX/RX schemes, the acquisition process and signal processing algorithms. This all-in-one system is a fully flexible tool for the research and evaluation of novel Phased-Array FMC imaging methods and complex signal processing algorithms.

关键词： ultrasonic imaging full-matrix capture total focusing method GPU processing

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HARP²: An X-Scale Reconfigurable Accelerator-Rich Platform for Massively-Parallel Signal processing algorithms

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JOURNAL OF SIGNAL processing systems FOR SIGNAL image AND VIDEO TECHNOLOGY 2016年第3期85卷 341-353页

作者： Hussain, Waqar Airoldi, Roberto Hoffmann, Henry Ahonen, Tapani Nurmi, Jari Tampere Univ Technol Dept Elect & Commun Engn POB 527 FI-33101 Tampere Finland Univ Chicago Dept Comp Sci Ryerson Hall 2501100 E 58th St Chicago IL 60637 USA

This paper presents design, development and evaluation of an eXtra-large Scale, Homogeneous and a Heterogeneous Accelerator-Rich Platform (HARP(2)) for massively parallel signal processing algorithms. HARP is an integrated platform of multiple Coarse-Grained Reconfigurable Arrays (CGRAs) over a Network-on-Chip (NoC) where each CGRA is scaled and tailored for a specific application. The architecture of the NoC consists of nine nodes in a topology of 3-rowsx3-columns and acts as backbone of communication between different CGRAs. In this experimental work, the HARP template is used to instantiate a homogeneous (HARP-hom) and a heterogeneous (HARP-het) platform. The HARP-het is generated for a proof-of-concept test to verify the design and functionality of HARP. It also provides insight to many features of the design and evaluation in terms of different performance metrics. The other version (HARP-hom) is instantiated for a relatively realistic design problem, i.e., satisfying the execution-time constraints imposed on Fast Fourier Transform processing in IEEE-802.11n demodulators. Both of the versions of HARP are treated for comparative analysis using different performance metrics against some of the existing state-of-the-art platforms. The HARP versions are designed to illustrate large-scale homogeneous/heterogeneous multicore architectures while presenting the advantages of maximizing the number of reconfigurable processing resources on a single chip.

关键词： Reconfigurable CGRA Network-on-Chip Heterogeneous Homogeneous Accelerator-Rich Multicore

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image Sharpness Assessment Based on Local Phase Coherence and LAD Criterion 23

Image Sharpness Assessment Based on Local Phase Coherence an...

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23rd International Conference on systems, Signals and image processing (IWSSIP)

作者： Krzic, Ana Sovic Donlic, Matea Pejcinovic, Mihaela Sersic, Damir Univ Zagreb Fac Elect Engn & Comp Unska 3 Zagreb 10000 Croatia Poslovna Inteligencija Zelinska 4 Zagreb 10000 Croatia

ISBN: (纸本)9781467395557

Edge preservation and precise restoration of details and sharp parts of images is an important issue in modern multimedia systems, 3D imaging, medical imaging and laser microscopy. Objective evaluation of image restoration algorithms in the sense of sharpness is crucial for choice of method to be used. Moreover, automated focus stacking, selective deblurring and other smart restoration methods rely on the proper sharpness measure. In this paper, a novel approach for image sharpness assessment is proposed. The approach is based on local phase coherence (LPC) in the complex wavelet domain. Using maximum pooling of eight LPC maps in eight spatial directions around each pixel, an image sharpness map is generated. Least absolute deviation criterion applied on sorted non-zero elements of the sharpness map results in the proposed global image sharpness measure. The proposed measure is better suited for applications, in comparison with competitive measures.

关键词： Sharpness Assessment Sharpness Measure Blurriness Local Phase Coherence

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Computer Vision System for Defects Detection in PET Preform 21

Computer Vision System for Defects Detection in PET Preform

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21st International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Laucka, Andrius Andriukaitis, Darius Valinevicius, Algimantas Navikas, Dangirutis Kaunas Univ Technol Dept Elect Engn LT-51368 Kaunas Lithuania

ISBN: (纸本)9781509018666

The computer vision systems are mainly devoted for production monitoring in quality inspection systems. It is the fastest growing and most popular non-invasive product defects detection method. The productivity of electronic components growth and their prices decline creates favorable conditions for the development of image processing systems for industrial production. The food industry is one of the main industries. Production volumes grow along with human population growth. Containers for food industry are made in very large quantities and demand on quality inspection system plays important role. An automated computer vision system was developed for the control of PET preparation quality. The implementation of the designed system was presented in this article. The system used for image processing algorithms to inspect the lateral and upper parts of the workpiece. The system is designed according to its operating parameters. Reached throughput is 10,000 workpieces per hour.

关键词： PET computer vision systems digital image processing electronic systems

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processing of data from the camera of structured light for algorithms of image analysis in control systems of mobile robots

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International Conference on Modern Engineering: Science and Education, MESE 2011

作者： Skakunov, Vladimir Belikov, Victor Zhoga, Victor Nesmiynov, Ivan Volgograd State Technical University Volgograd Russia Volgograd State Agrarian University Volgograd Russia

ISBN: (纸本)9783319295787

The article discusses the preprocessing algorithms of three-dimensional clouds of points from structured light cameras, methods of image recognition, algorithms for constructing maps and route planning for robot motion. Various methods and algorithms for the transformation of a three-dimensional cloud of points leads to reduction of computational complexity of all stages of data processing for implementation of the algorithm on an onboard navigation system with limited computing resources. Based on the proposed sequence of image conversion, software has been developed for implementation of the algorithm of spatial orientation of a robot. image processing methods and functions of PCL and ROS metapackage libraries were used. Experimental study of the possibilities of the developed image processing and route construction algorithm was made using the prototype of a mobile robot on a wheeled platform. © Springer International Publishing Switzerland 2016.

关键词： 3D point clouds Mobile robot Navigation algorithms Pattern recognition algorithms Structured-light camera Supervisory management

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Brain MRI Tumor Segmentation with 3D Intracranial Structure Deformation Features

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IEEE INTELLIGENT systems 2016年第2期31卷 66-76页

作者： Jui, Shang-Ling Zhang, Shichen Xiong, Weilun Yu, Fangxiaoqi Fu, Mingjian Wang, Dongmei Hassanien, Aboul Ella Xiao, Kai Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Sch Software Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Inst Biomed Mfg & Life Qual Engn Shanghai 200030 Peoples R China Cairo Univ IT Dept Fac Comp & Informat Cairo Egypt

Extraction of relevant features is of significant importance for brain tumor segmentation systems. To improve brain tumor segmentation accuracy, the authors present an improved feature extraction component that takes advantage of the correlation between intracranial structure deformation and the compression resulting from brain tumor growth. Using 3D nonrigid registration and deformation modeling techniques, the component measures lateral ventricular (LaV) deformation in volumetric magnetic resonance images. By verifying the location of the extracted LaV deformation feature data and applying the features on brain tumor segmentation with widely used classification algorithms, the authors evaluate the proposed component qualitatively and quantitatively with promising results on 11 datasets comprising real and simulated patient images.

关键词： biomedical MRI brain feature extraction image registration image segmentation 3D intracranial structure deformation features 3D nonrigid registration brain MRI tumor segmentation deformation modeling techniques feature extraction lateral ventricular deformation volumetric magnetic resonance images Biomedical image processing Deformable models Feature extraction image coding image segmentation Pattern recognition Tumors MRI deformation feature extraction intelligent systems medical image pattern recognition segmentation

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Research on the Novel Pattern Clustering Algorithm based on Particle Swarm Optimized Adaptive Wavelet Neural Network Model

Research on the Novel Pattern Clustering Algorithm based on ...

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International Conference on Communication and Electronics systems (ICCES)

作者： Zheng, Zhaoxi Wu, Zeling Yu, Miao Sun Yat Sen Univ Kaohsiung Taiwan East China Univ Sci & Technol Shanghai Peoples R China Lubeck Univ Appl Sci Lubeck Germany Shandong Univ Jinan Peoples R China

ISBN: (纸本)9781509010660

image segmentation is an integral part of critical image processing applications. Segmentation involves removal of a region of interest from the background. Recent researches in segmentation incorporate clustering algorithms for separation or removal of regions of interest. Prominent segmentation algorithms include K - means which segment the region from the background and further median filtering could be utilized to remove the unwanted regions in the segmented image. This research paper utilizes an adaptive wavelet neural network model with training or learning process optimized by the particle swarm optimization algorithm. The proposed algorithm has been tested and experimental results indicate a high precision of segmentation when compared with the conventional techniques.

关键词： image segmentation neural network particle swarm optimization wavelet approximation

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