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Ceramics and defects Infrared thermography and numerical simulations-a wide-ranging view for quantitative analysis

JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY 2016年第1期123卷 43-62页

作者： Sfarra, Stefano Perilli, Stefano Paoletti, Domenica Ambrosini, Dario Univ Aquila Las ER Lab Dept Ind & Informat Engn & Econ DIIIE I-67100 Laquila AQ Italy

The feasibility of square-pulsed thermography nondestructive testing for the detection of defects in one ceramic material sample has been carried out by finite element (FE) analysis. In particular, a ceramic plate containing defects of different diameters, depths, locations, nature, and shapes has been numerically investigated by means of Comsol(A (R)) Multiphysics computer program, taking into account the results coming from both a MATLAB (TM) script and the infrared thermography (IRT) technique. Indeed, the FE method simulates through a 3D model the heat transfer process induced into the ceramic material by two halogen lamps that have been applied in order to provoke an optimum thermal stress. Moreover, further defects like cracks arose beneath the surface of the plate due to the shrinkage process, have been discovered, and contrasted using a non-usual segmentation algorithm that when correlated in the time to IRT data simulates the thermo-elastic effect. Following the non-direct procedure proposed, both the depth of each defect and its main dimensions have been retrieved into a satisfactory accuracy.

关键词： Nondestructive testing Defects Inclusions Modeling Thermal properties of the pottery Silica Infrared thermography segmentation algorithm

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Multilevel framework to handle object occlusions for real-time tracking

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IET IMAGE PROCESSING 2016年第11期10卷 885-892页

作者： Cai, Yingfeng Wang, Hai Chen, Xiaobo Chen, Long Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Peoples R China Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Peoples R China

This study proposes an efficient method to handle the object occlusions seen in monocular traffic image sequences. The motivation of this study is different methods perform differently in occlusion segmentation and the authors' idea is to use a situation-driven approach to aggregate different methods in order to get a good performance. This study classifies occlusion into four categories according to the foreground situation and a multilevel occlusion handling framework is utilised. First, the image segmentation algorithm based on convex hull analysis is utilised for intra-frame level occlusion segmentation. The segmentation algorithm is established by the compactness ratio and interior distance ratio of the foreground. Second, an online sample-based classification algorithm is utilised for tracking level occlusion segmentation. Training samples are extracted from the historical frames before occlusion and testing samples are extracted from the current frame by an adaptive searching strategy. The segmentation of occlusion is transferred into the online classification of testing samples. Such algorithm is established by the similarity and coherence of target's property between continuous frames. Experiments on video sequences illustrate the good performance of the proposed method under different conditions with low computational cost.

关键词： image sequences image segmentation feature extraction object occlusions real-time tracking monocular traffic image sequences multilevel occlusion handling image segmentation convex hull analysis intra-frame level occlusion segmentation segmentation algorithm online sample-based classification algorithm video sequences

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The Research on Image Extraction and segmentation algorithm in License Plate Recognition

The Research on Image Extraction and Segmentation Algorithm ...

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2011 13th IEEE Joint International Computer Science and Information Technology Conference(JICSIT 2011)

作者： Weijian Fang Network Center Chongqing Three Gorges University Chongqing, China Xin Zhou College of Physics and Electronic Engineering Chongqing Three Gorges University Chongqing, China

As a vital part of Intelligent Transportation System, License Plates Recognition System is meaningful in Vehicle Positioning and Traffic Monitoring. It consists of Vehicle License Plate Location, Character segmentation and Character Recognition. In the paper we put forward a new method of segmentation algorithm on the basis of comparison and analysis some common methods of License Plate Recognition, which firstly chooses the self-adaptive thresholds for license images and executes the binarization;then arranges with projection Method and fixed-edge methods on the basis of connected-area methods. The experiment's result shows this new method has better recognition performance.

关键词： License Plate Recognition Binarization Image segmentation segmentation algorithm

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Feasibility of a semi-automated contrast-oriented algorithm for tumor segmentation in retrospectively gated PET images: phantom and clinical validation

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PHYSICS IN MEDICINE AND BIOLOGY 2015年第24期60卷 9227-9251页

作者： Carles, Montserrat Fechter, Tobias Nemer, Ursula Nanko, Norbert Mix, Michael Nestle, Ursula Schaefer, Andrea Univ Hosp Freiburg Dept Radiat Oncol D-79106 Freiburg Germany Univ Hosp Freiburg Dept Nucl Med D-79106 Freiburg Germany Univ Saarland Dept Nucl Med Med Ctr D-66421 Homburg Germany

PET/CT plays an important role in radiotherapy planning for lung tumors. Several segmentation algorithms have been proposed for PET tumor segmentation. However, most of them do not take into account respiratory motion and are not well validated. The aim of this work was to evaluate a semi-automated contrast-oriented algorithm (COA) for PET tumor segmentation adapted to retrospectively gated (4D) images. The evaluation involved a wide set of 4D-PET/CT acquisitions of dynamic experimental phantoms and lung cancer patients. In addition, segmentation accuracy of 4D-COA was compared with four other state-of-the-art algorithms. In phantom evaluation, the physical properties of the objects defined the gold standard. In clinical evaluation, the ground truth was estimated by the STAPLE (Simultaneous Truth and Performance Level Estimation) consensus of three manual PET contours by experts. algorithm evaluation with phantoms resulted in: (i) no statistically significant diameter differences for different targets and movements (Delta phi = 0.3 +/- 1.6 mm);(ii) reproducibility for heterogeneous and irregular targets independent of user initial interaction and (iii) good segmentation agreement for irregular targets compared to manual CT delineation in terms of Dice Similarity Coefficient (DSC = 0.66 +/- 0.04), Positive Predictive Value (PPV = 0.81 +/- 0.06) and Sensitivity (Sen. = 0.49 +/- 0.05). In clinical evaluation, the segmented volume was in reasonable agreement with the consensus volume (difference in volume (% Vol) = 40 +/- 30, DSC = 0.71 +/- 0.07 and PPV = 0.90 +/- 0.13). High accuracy in target tracking position (Delta ME) was obtained for experimental and clinical data (Delta MEexp = 0 +/- 3 mm;Delta MEclin= 0.3 +/- 1.4 mm). In the comparison with other lung segmentation methods, 4D-COA has shown the highest volume accuracy in both experimental and clinical data. In conclusion, the accuracy in volume delineation, position tracking and its robustness on highly irregular

关键词： segmentation algorithm 4D-PET/CT radiotherapy dynamic phantoms lung cancer

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Simulation Model for Three-Channel Luminescent Images of Cancer Cell Populations

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JOURNAL OF APPLIED SPECTROSCOPY 2015年第6期81卷 996-1003页

作者： Lisitsa, E. V. Yatskou, M. M. Apanasovich, V. V. Apanasovich, T. V. Shytsik, M. M. Belorussian State Univ Minsk 220030 BELARUS

We have developed a simulation model for three-channel luminescent images of cancer cells. We do a comparative analysis of two methods for segmentation of objects within three-channel luminescent images, employed in the CellProfiler and CellDataMiner software packages, using simulated and experimental data as examples. We have studied the functional efficiency of the segmentation algorithms under high photoelectron multiplier noise conditions. Our simulation model allows us to simplify selection of the algorithm for analysis of the data during biophysical studies, with the aim of automating procedures for processing luminescent images.

关键词： luminescence microscopy cell simulation model segmentation algorithm

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Hybrid clusteing algorithm and Neural Network classifier for satellite image classification

Hybrid clusteing algorithm and Neural Network classifier for...

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International Conference on Industrial Instrumentation and Control

作者： Praveena, S. Singh, S. P. MGIT ECE Hyderabad Andhra Pradesh India

ISBN: (纸本)9781479971657

This paper presents a hybrid clustering algorithm and feed-forward neural network classifier for land-cover mapping of trees, shade, building and road. It starts with the single step preprocessing procedure to make the image suitable for segmentation. The preprocessed image is segmented using the hybrid genetic-Artificial Bee Colony (ABC) algorithm that is developed by hybridizing the ABC and FCM to obtain the effective segmentation in satellite image and classified using neural network. The performance of the proposed hybrid algorithm is compared with the algorithms like, Artificial Bee Colony(ABC) algorithm, ABC-GA algorithm, Moving KFCM.

关键词： ABC-FCM segmentation algorithm Neural Networ Feature Extraction Satellite Image Classification

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Lung segmentation on standard and mobile chest radiographs using oriented Gaussian derivatives filter

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BIOMEDICAL ENGINEERING ONLINE 2015年第1期14卷 1-26页

作者： Ahmad, Wan Siti Halimatul Munirah Wan Zaki, W. Mimi Diyana W. Fauzi, Mohammad Faizal Ahmad Multimedia Univ Fac Engn Persiaran Multimedia Cyberjaya Selangor Malaysia Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Elect Elect & Syst Engn Bangi 43600 Selangor Malaysia

Background: Unsupervised lung segmentation method is one of the mandatory processes in order to develop a Content Based Medical Image Retrieval System (CBMIRS) of CXR. The purpose of the study is to present a robust solution for lung segmentation of standard and mobile chest radiographs using fully automated unsupervised method. Methods: The novel method is based on oriented Gaussian derivatives filter with seven orientations, combined with Fuzzy C-Means (FCM) clustering and thresholding to refine the lung region. In addition, a new algorithm to automatically generate a threshold value for each Gaussian response is also proposed. The algorithms are applied to both PA and AP chest radiographs from both public JSRT dataset and our private datasets from collaborative hospital. Two pre-processing blocks are introduced to standardize the images from different machines. Comparisons with the previous works found in the literature on JSRT dataset shows that our method gives a reasonably good result. We also compare our algorithm with other unsupervised methods to provide fairly comparative measures on the performances for all datasets. Results: Performance measures (accuracy, F-score, precision, sensitivity and specificity) for the segmentation of lung in public JSRT dataset are above 0.90 except for the overlap measure is 0.87. The standard deviations for all measures are very low, from 0.01 to 0.06. The overlap measure for the private image database is 0.81 (images from standard machine) and 0.69 (images from two mobile machines). The algorithm is fully automated and fast, with the average execution time of 12.5 s for 512 by 512 pixels resolution. Conclusions: Our proposed method is fully automated, unsupervised, with no training or learning stage is necessary to segment the lungs taken using both a standard machine and two different mobile machines. The proposed pre-processing blocks are significantly useful to standardize the radiographs from mobile machines. The algori

关键词： Chest radiograph Unsupervised lung segmentation Fuzzy C-means Thresholding Gaussian derivatives Medical image processing segmentation algorithm

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Automatic initialization for segmentation of medical images based on active contour 3

Automatic initialization for segmentation of medical images ...

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IEEE INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING AND SCIENCES

作者： Athertya, Jiyo S. Kumar, G. Saravana Indian Inst Technol Madras 600036 Tamil Nadu India Indian Inst Technol Dept Engn Design Madras 600036 Tamil Nadu India

ISBN: (纸本)9781479940844

Image segmentation forms the crux of an image processing module. Its main goal is to divide the image into foreground and background with region of interest occupying the former. To achieve quality segmentation particularly in medical imaging, algorithms require user interaction with domain knowledge. Active contours are well established techniques which are prolifically used for segmenting medical images. It falls under the semi automatic method owing to the manual contour initialization. In this paper, we propose a simple, global approach of segmentation using an automatic initialization for active contour methods which has the potential to completely eliminate the human interaction.

关键词： active vision edge detection image segmentation medical image processing automatic active contour method initialization automatic medical image segmentation initialization background image domain knowledge foreground image global segmentation human interaction elimination image processing module manual contour initialization segmentation algorithm segmentation quality semi-automatic method user interaction Active contours Biomedical imaging Feature extraction High definition video Image edge detection Image segmentation Level set

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Quantification of double-layer Ni/YSZ fuel cell anodes from focused ion beam tomography data

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JOURNAL OF POWER SOURCES 2014年 246卷 819-830页

作者： Joos, Jochen Ender, Moses Rotscholl, Ingo Menzler, Norbert H. Ivers-Tiffee, Ellen Karlsruhe Inst Technol IWE D-76131 Karlsruhe Germany Forschungszentrum Julich GmbH Inst Energy & Climate Res IEK 1 D-52425 Julich Germany

Three-dimensional microstructure reconstructions of Ni yttria-stabilized zirconia (Ni/YSZ) anodes are presented, all of which are based on focused ion beam tomography data. The reconstruction procedure, starting from a series of 2D scanning electron micrographs, is investigated step by step and potential sources of error are identified. The distinction between Ni phase, YSZ phase and pore phase is solved by an advanced algorithm, which belongs to the region-growing image segmentation methods. This improves the accuracy of automated grayscale segmentation especially for images with low contrast, which is characteristic of both solid phases in Ni/YSZ anodes. Critical microstructure parameters like material fractions, surface areas, particle size and distribution of Ni, YSZ, and pore phase, as well as phase connectivity and triple-phase boundary density, are evaluated and discussed. In this contribution, two types of high-performance Ni/YSZ anodes differing in thickness of both the anode functional layer and the anode substrate are reconstructed and compared to each other. For the first time, the anode functional layer adjacent to the thin film electrolyte is separately quantified. The presented methods are qualified to quantitatively compare different anode microstructures and relate the result to their electrochemical performance. (C) 2013 Elsevier B.V. All rights reserved.

关键词： SOFC Ni/YSZ anode Focused ion beam (FIB) Microstructure reconstruction segmentation algorithm Voxel

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An Intelligent Approach for Robust Detection and Recognition of Multiple Color and Font Styles Automobiles License Plates: A Feature-Based algorithm 4

An Intelligent Approach for Robust Detection and Recognition...

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International Conference on Audio, Language and Image Processing

作者： Bhutta, M. Usman Maqbool Mahmood, Hasan Malik, Hafiz Quaid I Azam Univ Dept Elect Islamabad Pakistan Univ Michigan Dept Elect & Comp Engn Dearborn MI 48128 USA

ISBN: (纸本)9781479939039

This paper presents a robust real time feature based algorithm for localization and recognition of multiple color and font style license plates. The algorithm uses digitized license plate images for robust detection and recognition. The resolution of the captured image is adjusted and the brightness and contrast are set to appropriate levels. An optimal threshold is applied to detect white regions of the enhanced image. The algorithm uses segmentation and component labeling to create candidate regions intelligently. For localization of the license plate, features of each region are extracted and analyzed by rectangularity percentage of the automobile's license plate. The use of color filter renders the algorithm more robust on license plate localization. Moreover, we use HSV color space to find the specific color regions. After localization of license plate in small sized image, the proposed algorithm uses rapid intelligent method for localization of license plate form the original image. The localized license plate image is cropped and processed by the recognition algorithm. Each character's region is identified by using segmentation algorithm based on the profile which includes character dilation and resizing for subsequent operations. In this paper, correlation optical character recognition and multi-layer perception neural networks techniques are used for recognizing the characters. The performance of the proposed algorithm is acceptable even in low-quality or bad light images.

关键词： correlation methods image colour analysis image enhancement image resolution image segmentation multilayer perceptrons optical character recognition traffic engineering computing HSV color space character dilation color detection color filter color localization color recognition component labeling correlation optical character recognition digitized license plate images feature-based algorithm font style license plate localization font styles automobile license plate recognition intelligent approach multilayer perception neural networks optimal threshold region extraction segmentation algorithm white region detection Correlation Feature extraction Image color analysis Image segmentation Licenses Neural networks Vehicles License plates image colour analysis image resolution image enhancement image segmentation COLOR FILTER optical character recognition Licence licensing Multilayer perceptrons correlation methods color spaces Neural network Feature extraction vehicle

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