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Detecting decayed peach using a rotating hyperspectral imaging testbed

LWT-FOOD SCIENCE AND TECHNOLOGY 2018年 87卷 326-332页

作者： Sun, Ye Xiao, Hui Tu, Sicong Sun, Ke Pan, Leiqing Tu, Kang Nanjing Agr Univ Coll Food Sci & Technol 1 Weigang Rd Nanjing 210095 Jiangsu Peoples R China Univ Oxford Med Sci Div Oxford OX3 7BN England Univ Sydney Sydney Med Sch Sydney NSW 2006 Australia

A hyperspectral imaging system with a moving testbed has been developed for detection of the disease caused by Rhizopus stolonifera in peaches. The all-around hyperspectral imaging of the whole peach was obtained, which can identify the decayed area fully and is suitable for online monitoring. Three single band images (709, 807, and 874 nm) which were selected by statistical methods and an image segmentation algorithm were applied to locate the decayed area of peaches was developed based on band ratio image coupled with a simple thresholding method. The performance of image segmentation algorithm of the single-band images was evaluated. The detection accuracy of peaches classified as 'sound', 'slight-decayed', 'moderate-decayed' and 'severe-decayed' were 95%, 66.29%, 100% and 100%, respectively. The spectral information was extracted from the decayed area to improve the detection accuracy. The six optical wavelengths were selected by SPA (successive projections algorithm) from the full spectral range. The classification accuracy of sound and rotten peaches was 100% if only these two categories were applied. Our results demonstrated that the hyperspectral imaging method offers the potential to be used to automatically detect fungal infection in peaches. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Band ratio Rhizopus stolonifera image segmentation algorithm PLS-DA

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Visualization and Quantification of Broken Wires in Steel Wire Ropes Based on Induction Thermography

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IEEE SENSORS JOURNAL 2021年第17期21卷 18497-18503页

作者： Xia, Hui Yan, Rong Wu, Jianbo He, Sha Zhang, Muchao Qiu, Qiao Zhu, Junzhen Wang, Jie Sichuan Univ Sch Mech Engn Chengdu 610065 Peoples R China CNPC Chuanqing Drilling & Exploration Corp Safety Environm Qual Surveillance & Inspect Res I Guanghan 618300 Peoples R China Army Acad Armored Forces Dept Vehicle Engn Beijing 10072 Peoples R China

Wire ropes (WRs) are widely used in various areas, such as tourist cables, mining, and elevators. Thus, it is very important to detect broken wires (BWs) to prevent safety issues. To guarantee the structural health of WRs, this paper proposes the use of induction thermography (IT) to detect the BWs. An image segmentation algorithm based on scale morphology is proposed to eliminate the background influence of non-uniform heating and visualize the BWs. Further, the area of the BW region, after the image binarization, subtraction, and filtering, is extracted as a feature to quantify the number of BWs. Results show that the proposed methods can effectively visualize and quantify the broken region within 1 BWs.

关键词： Visualization Wires Sensors Heating systems Cameras Spirals Safety Broken wires induction thermography image segmentation algorithm visualization binarization quantification

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Optimal region growing segmentation and its effect on classification accuracy

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2011年第13期32卷 3747-3763页

作者： Gao, Yan Francois Mas, Jean Kerle, Norman Navarrete Pacheco, Jose Antonio Univ Nacl Autonoma Mexico Ctr Invest Geog Ambiental Morelia Michoacan Mexico Int Inst Geoinformat Sci & Earth Observat ITC NL-7500 AA Enschede Netherlands

image segmentation is a preliminary and critical step in object-based image classification. Its proper evaluation ensures that the best segmentation is used in image classification. In this article, image segmentations with nine different parameter settings were carried out with a multi-spectral Landsat imagery and the segmentation results were evaluated with an objective function that aims at maximizing homogeneity within segments and separability between neighbouring segments. The segmented images were classified into eight land-cover classes and the classifications were evaluated with independent ground data comprising 600 randomly distributed points. The accuracy assessment results presented similar distribution as that of the objective function values, that is segmentations with the highest objective function values also resulted in the highest classification accuracies. This result shows that image segmentation has a direct effect on the classification accuracy;the objective function not only worked on a single band image as proved by Espindola et al. (2006, Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation. International Journal of Remote Sensing, 27, pp. 3035-3040) but also on multi-spectral imagery as tested in this, and is indeed an effective way to determine the optimal segmentation parameters. McNemar's test (z(2) = 10.27) shows that with the optimal segmentation, object-based classification achieved accuracy significantly higher than that of the pixel-based classification, with 99% significance level.

关键词： Taxonomy image segmentation algorithm object orientation image segmentation image classification region growing Significance level Objective function Neighbor images MULTISPECTRAL parameter setting

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Unsupervised image segmentation based on multidimensional particle swarm optimization

Unsupervised image segmentation based on multidimensional pa...

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6th International Conference on Wireless, Mobile and Multi-Media (ICWMMN 2015)

作者： Lin Wang Wanxu Zhang Dong Wang Bo Jiang School of Information Science and Technology Northwest University Xi'an 710127 China School of Computer and Information Technology Beijing Jiaotong University 100044 China

ISBN: (纸本)9781510822030

An unsupervised image segmentation method based on multidimensional (MD) particle swarm optimization (PSO) is proposed in this paper. Firstly, a clustering-based nonlinear objective function of unsupervised image segmentation is established according to Turi's validity index. Secondly, MD PSO algorithm is adopted to minimize the objective function to seek the optimal number and cluster centers of segmented regions simultaneously. Finally, global best (GB) position of swam in each dimension is modified to avoid being trapped in local optima. Experimental results valid the performance of the proposed image segmentation algorithm.

关键词： Unsupervised image segmentation Turi's validity index Multidimensional PSO image segmentation Particle swarm optimization image segmentation algorithm MULTIDIMENSIONAL Objective function parametric subharmonic oscillator dimensions

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Interactive image segmentation via Backpropagating Refinement Scheme

Interactive Image Segmentation via Backpropagating Refinemen...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Won-Dong Jang Chang-Su Kim Harvard University Korea University

ISBN: (纸本)9781728132945

An interactive image segmentation algorithm, which accepts user-annotations about a target object and the background, is proposed in this work. We convert user-annotations into interaction maps by measuring distances of each pixel to the annotated locations. Then, we perform the forward pass in a convolutional neural network, which outputs an initial segmentation map. However, the user-annotated locations can be mislabeled in the initial result. Therefore, we develop the backpropagating refinement scheme (BRS), which corrects the mislabeled pixels. Experimental results demonstrate that the proposed algorithm outperforms the conventional algorithms on four challenging datasets. Furthermore, we demonstrate the generality and applicability of BRS in other computer vision tasks, by transforming existing convolutional neural networks into user-interactive ones.

关键词： image segmentation image segmentation algorithm pixel Interactive Refining images computer vision Neural network maps measured distance Scheme

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Hierarchical Clustering Linkage for Region Merging in Interactive image segmentation on Dental Cone Beam Computed Tomography

Hierarchical Clustering Linkage for Region Merging in Intera...

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International Conference on Applied Information Technology and Innovation (ICAITI)

作者： Agus Zainal Arifin Maryamah Siska Arifiani Arna Fariza Dini Adni Navastara Rarasmaya Indraswari Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9781538667279;9781538667262

Interactive image segmentation has a better result than the automatic and manual image segmentation because a user can help the image segmentation algorithm by marking the sample of background and object in the image. The algorithm will merge the regions in the image based on the user marking. In interactive image segmentation, the calculation of the distance between regions and the sequence of the merging process is important to obtain an accurate segmentation result. In this paper, we proposed a new region merging strategy using hierarchical clustering based on interclass and intra-class variances for each region and neighborhood relationship. This research aims to improve the region merging strategy and it is expected to result better than the previous research that did not implement the hierarchical clustering. The process to segment an image concludes splitting the image into several regions, user marking to mark the sample of background and object, merging the region that is not marked by the user using the hierarchical clustering until the image fully segmented. The experimental results on dental cone beam computed tomography data show that the proposed method gives a more effective and efficient result in the segmentation process.

关键词： computerised tomography image segmentation medical image processing pattern clustering tomography image segmentation X-Ray Computed Tomography Cluster Analysis Medical image processing Tomography merger imagery (Psychotherapy) images cone beam image segmentation algorithm Dental Cone Dosage Form target image Hierarchical

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Shadowed C-Means for image segmentation Using Local and Non-local Spatial Information

Shadowed C-Means for Image Segmentation Using Local and Non-...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Jing Zou Long Chen C. L. Philip Chen Faculty of Science and Technology University of Macau Macau China

ISBN: (纸本)9781479906505

This paper introduces some new image segmentation methods in the framework of shadowed c-means clustering. By implanting the local and non-local spatial information in the membership value estimation procedure, we propose the Local Spatial Shadowed C-Means (LSSCM) algorithm, Non-local Spatial Shadowed C-Means (NLSSCM) algorithm and their combination - L+NLSSCM. Compared to traditional fuzzy c-means and shadowed c-means based approaches, the proposed image segmentation algorithms can obtain better segmentation results on test images. It is observed the proposed algorithms can effectively tackle the overlapping among segments and the noise problem in images.

关键词： Fuzzy clustering image segmentation Shadowed c-means Non-local Spatial information Spatial information image segmentation image segmentation algorithm images

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IMU-Compensated image segmentation for Improved Vision-Based Control Performance

IMU-Compensated Image Segmentation for Improved Vision-Based...

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IEEE International Systems Conference

作者： Peter Karasev Patricio Vela Allen Tannenbaum Georgia Tech ECE Univ. Alabama ECE Industrial Perception Inc. School of ECE Georgia Tech Department of ECE University of Alabama

ISBN: (纸本)9781467331074

Traditionally, image segmentation algorithms are viewed as black-box measurement sources. In this work, the system-level formulation of closed-loop tracking is reformulated under the assumption of access to physical sensor data in addition to the video stream. The scope is restricted to algorithms that iteratively minimize a functional to localize the target in each image frame. A nominal iterative algorithm is an open-loop system;IMU data is then used to generate a control to modify the algorithm's behavior and thus create a closed-loop system. Compensating using IMU data in a feedback setting creates the possibility of induced track loss, whereby the compensation effectively undoes the action of the motion control. This issue is overcome by solving an optimization program that exploits known control intent to separate ego-motion disturbances from the control. As a result, the reformulation proposed in this paper enables improved tracking of a fast-moving target even in the presence of strong disturbances/jitter in the camera platform.

关键词： control performance image segmentation image segmentation algorithm Standing Orders physical sensor Targets optimization program Video stream fast-moving

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Fast and reliable iris segmentation algorithm

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IET image PROCESSING 2013年第1期7卷 42-49页

作者： Radman, Abduljalil Jumari, Kasmiran Zainal, Nasharuddin Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Elect Elect & Syst Engn Bangi 43600 Malaysia

Design a fast and reliable iris segmentation algorithm for less constrained iris images is essential to build a robust iris recognition system. Daugman's integrodifferential operator (IDO) is one of powerful iris segmentation mechanisms, but in contrast consumes a large portion of the computational time for localising the rough position of the iris centre and eyelid boundaries. To address this problem, a fast iris segmentation algorithm is proposed. First, the circular Gabor filter is adopted to find the rough position of the pupil centre. Second, the iris and pupil circles are localised using the IDO taken into account that the real centres of the iris and pupil are in the small area around the rough position of the pupil centre. Third, the upper and lower eyelid boundaries are extracted using the live-wire technique. Experimental results demonstrate that the proposed iris segmentation algorithm significantly minimises the required time to segment the iris without affecting the segmentation accuracy. Moreover, the comparison results with state-of-the-art iris segmentation algorithms show the superiority of the proposed algorithm in terms of segmentation accuracy and recognition performance. The challenging UBIRIS.v1 iris image database is utilised to evaluate the performance of the proposed algorithm.

关键词： Fasting Pupil image segmentation algorithm

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Evaluation of Neonatal Cerebral Circulation Under Hypoxic Ischemic Risk Factors Based on Quantitative Analysis of Cerebral Veins with Magnetic Resonance Susceptibility Weighted Imaging

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CLINICAL NEURORADIOLOGY 2024年第4期34卷 859-869页

作者： Xie, Qi Liao, Yan-Hui He, Wen-juan Han, Peng-peng Wu, Jun South China Univ Technol Guangzhou Peoples Hosp 1 Sch Med Med Imaging Dept Nansha Guangzhou 511457 Peoples R China Inst Software Applicat Technol Guangzhou 511458 Peoples R China Meizhou Peoples Hosp Dept Nucl Med Meizhou 514031 Peoples R China

Purpose To observe the regulation of cerebral circulation in vivo based on image segmentation algorithms for deep learning in medical imaging to automatically detect and quantify the neonatal deep medullary veins (DMVs) on susceptibility weighted imaging (SWI) images. To evaluate early cerebral circulation self-rescue for neonates undergoing risk of cerebral hypoxia-ischaemia in vivo. Methods SWI images and clinical data of 317 neonates with or without risk of cerebral hypoxia-ischaemia were analyzed. Quantitative parameters showing the number, width, and curvature of DMVs were obtained using an image segmentation algorithm. Results The number of DMVs was greater in males than in females (p < 0.01), and in term than in preterm infants (p = 0.001). The width of DMVs was greater in term than in preterm infants (p < 0.01), in low-risk than in high-risk group (p < 0.01), and in neonates without intracranial extracerebral haemorrhage (ICECH) than with ICECH (p < 0.05). The curvature of DMVs was greater in term than in preterm infants (P < 0.05). The width of both bilateral thalamic veins and anterior caudate nucleus veins were positively correlated with the number of DMVs;the width of bilateral thalamic veins was positively correlated with the width of DMVs. Conclusion The DMVs quantification based on image segmentation algorithm may provide more detailed and stable quantitative information in neonate. SWI vein quantification may be an observable indicator for in vivo assessment of cerebral circulation self-regulation in neonatal hypoxic-ischemic brain injury.

关键词： MRI SWI Neonatal Hypoxic-Ischemia DMVs image segmentation algorithm

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