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An Iterated Local Search-Based Algorithm to Support Cell Nuclei Detection in Pap Smears Test 1

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21st International Conference on Enterprise Information Systems (ICEIS)

作者： Diniz, Debora N. Souza, Marcone J. F. Carneiro, Claudia M. Ushizima, Daniela M. de Medeiros, Fatima N. S. Oliveira, Paulo H. C. Bianchi, Andrea G. C. Univ Fed Ouro Preto Inst Exact Sci & Biol Grad Program Comp Sci Ouro Preto Brazil Univ Fed Ouro Preto Biol Sci Res Ctr Grad Program Biotechnol Ouro Preto Brazil Univ Calif Berkeley Berkeley Inst Data Sci Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Berkeley CA USA Univ Fed Ceara Teleinformat Engn Dept Fortaleza Ceara Brazil

ISBN: (数字)9783030407834

ISBN: (纸本)9783030407834;9783030407827

The focus of this work is on the detection of nuclei in synthetic images of cervical cells. Finding nuclei is an important step in building a computational method to help cytopathologists identify cell changes from Pap smears. The method developed in this work combines both the Multi-Start and the Iterated Local Search metaheuristics and uses the features of a region to identify a nucleus. It aims to improve the assertiveness of the screening and reduce the professional workload. The irace package was used to automatically calibrate all parameter values of the method. The proposed approach was compared with other methods in the literature according to recall, precision, and F1 metrics using the ISBI Overlapping Cytology Image Segmentation Challenge database (2014). The results show that the proposed method has the second-best values of F1 and recall, while the accuracy is still high.

关键词： Nuclei segmentation Cervical cells simple linear iterative clustering Density-based spatial clustering of applications with noise Iterated Local Search Multi-Start Metaheuristic Pap smear images analysis

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Copy Move Forgery Detection Based on Densefield and Sparse-field Method 19

Copy Move Forgery Detection Based on Densefield and Sparse-f...

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6th IEEE Cyber Science and Technology Congress (CyberSciTech)

作者： Zhang, Weiwei Tang, Xinhua Shandong Jianzhu Univ Sch Sci Jinan Peoples R China Shandong Univ Polit Sci & Law Sch Cyberspace Secur Jinan Peoples R China

ISBN: (纸本)9781665421744

Copy move is a common means of digital image forgery. Some regions in an image copied and pasted to other regions of the same image to achieve the purpose of increasing or eliminating some targets. SIFT algorithm has been widely used in the field of copy move forensics because of its robustness, such as rotation, scaling, brightness change and so on. However, SIFT algorithm cannot extract enough keypoints for tamper detection in smooth regions and usually cannot give accurate location of tamper regions. In order to overcome the first problem, we transform the color image to gray, HSV and Lab space before feature extraction. For the second problem, we propose a fusion strategy combining sparse-field method and dense-field method. The proposed algorithm is mainly divided into three stages. In the first stage, the image is preprocessed by color space transformation and adaptive simple linear iterative clustering (SLIC) segmentation. In the second stage, SIFT algorithm is used for keypoint extraction and matching. Then, the SLIC superpixel of the matching keypoints is used as the rough location before block feature extraction. In the third stage, Zernike moments feature extraction and PatchMatch feature matching is performed for the above region. Finally, the tamper region is accurately located by morphological post-processing. Extensive experiments demonstrate the superior performance of the proposed scheme.

关键词： Copy move Forgery detection SIFT simple linear iterative clustering PatchMatch

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A Novel Method of Cervical Cell Image Segmentation via Region Merging and SLIC 16

A Novel Method of Cervical Cell Image Segmentation via Regio...

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7th Symposium on Information and Communication Technology (SoICT)

作者： Xia, Haiying Jin, Kaiyue Jiang, Frank Quang Anh Tran Guangxi Normal Univ Coll Elect Engn Guangxi Guilin Peoples R China Univ Technol Fac Engn & IT Sydney NSW Australia Posts & Telecommun Inst Technol Ho Chi Minh City Vietnam

ISBN: (纸本)9781450348157

Considering that the existing methods of cell segmentation are sensitive to noise, in this paper, a novel method was proposed to obtain the contour of cervical cell accurately and robustly. Firstly, the mean shift algorithm was utilized to smooth the cell image. Then, the initial contour of cervical cell was extracted by an adaptive threshold algorithm. Secondly, SLIC (simple linear iterative clustering, SLIC) was applied to the smoothed cell image to get the superpixels of the whole cell image. Finally, based on the initial contour, we can automatically set some marks for background and foreground in a cell image full of superpixels. Then the superpixels were merged by the rule of maximal similarity. A key property of our superpixel merging is that it does not require a preset threshold, and the non-marker background regions are merged with the marked area automatically, while the non-marker superpixels are identified to avoid from being merged into background. We validate our method via the cervical cell image database and demonstrate that our method can extract the contour of cytoplasm from a single-cell cervical smear image accurately in a relatively short time.

关键词： Image segmentation Region merging simple linear iterative clustering

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Multimodal Decomposable Models by Superpixel Segmentation and Point-in-Time Cheating Detection 8

Multimodal Decomposable Models by Superpixel Segmentation an...

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International Conference on Advanced Computer Science and Information Systems (ICACSIS)

作者： Yohannes Ayumi, Vina Fanany, Mohamad Ivan Univ Indonesia Fac Comp Sci Machine Learning & Comp Vis Lab Depok Indonesia

ISBN: (纸本)9781509046294

This research aims to classify cheating activity during exam from video observation. The method uses Conditional Random Field (CRF) for classifying and detecting some classes of cheating activities. The method used to detect the location of the joints of the body is a Multimodal Decomposable Model (MODEC) with superpixel segmentation. The used joints are head, shoulders, elbows, and wrists. The superpixel method is simple linear iterative clustering (SLIC). Comparison between MODEC and MODEC + SLIC as feature detector for CRF showed that MODEC + SLIC capable of providing a better activity classification. From our experiments, the cheating activities in average can be detected up to 83.9%. Moving beyond ouly detecting the class of motion segments, we also devised point-in-time event detection system also using CRF. The time of occurrences of three consecutive cheating activities are determined from a sequence of video frames.

关键词： simple linear iterative clustering Conditional Random Field Multimodal Decomposable Models Activity Recognition

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ACCURATE AND FAST BUILDING DETECTION USING BINARY BAG-OF-FEATURES

ACCURATE AND FAST BUILDING DETECTION USING BINARY BAG-OF-FEA...

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ISPRS Hannover Workshop / High-Resolution Earth Imaging for Geospatial Information (HRIGI) Workshop / City Models, Roads and Traffic (CMRT) Workshop / Image Sequence Analysis (ISA) Workshop / European Calibration and Orientation (EuroCOW) Workshop

作者： Hu, Y. Li, Z. Li, P. Ding, Y. Liu, Y. Chongqing Geomat Ctr Chongqing 401121 Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Hubei Peoples R China Chongqing Univ Coll Optoelect Engn Chongqing 400044 Peoples R China

This paper presents a non-interactive building detection approach employing binary bag-of-features (BBOF), namely, extracting building roof contours in remote sensing images automatically, rapidly and accurately. The proposed method includes two major stages, i.e., building area detection and building contours extraction. In the first stage, it contains three modules. i.e., over-segmentation, intersection point classification, building area detection. Firstly, the orthophoto is over-segmented by the simple linear iterative clustering (SLIC) superpixel segmentation method, and the intersection points is obtained. Secondly, the oriented FAST and rotated BRIEF (ORB) descriptors are generated in LAB colour space from the patches that centred on the intersection points, and the BBOF classifier is adopted to classify the intersection points into two categories. Thirdly, the area that contains of the building roof are detected through reserving the regions around the intersection points in inner parts of building roof, and eliminating the regions around the intersection points in non-building roof. At last, we can roughly generate the building area. For the second stage, it is similar to the first one while the main difference is that its classifier has three categories. Finally, we provide an evaluation between two different classifiers, including ORB+BBOF and SURF+BOF. This evaluation is conducted on orthophotos with different roof colours, texture, shape, size and orientation. The proposed approach presents several advantages in terms of scalability, suitability and simplicity with respect to the existing methods.

关键词： Building Detection simple linear iterative clustering Binary Bag-Of-Features Machine Learning Local Feature Descriptor Classification

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A Novel Profuse clustering Technique for Image Denoising 6

A Novel Profuse Clustering Technique for Image Denoising

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6th International Conference on Smart Computing and Communications (ICSCC)

作者： Thamilselvan, P. Sathiaseelan, J. G. R. Bishop Heber Coll Autonomous Dept Comp Sci Tiruchirappalli 620017 Tamil Nadu India

Digital images assume a vital part both in day by day life applications, for example, satellite TV, computed tomography, magnetic resonance imaging and additionally in ranges of research and innovation, for example, cosmology and geographical information systems. An expansive segment of computerized image preparing incorporates image restoration. Image restoration is a technique for removal or decrease of corruption that are caused amid the image catching. Corruption originates from obscuring as well as commotion due to the electronic and photometric sources. Obscuring is the type of data transfer capacity decrease of images caused by flawed image development process, for example, relative movement amongst camera and unique scene or by an optical framework that is out of core interest. Image Denoising is an important pre-processing task before further processing of the image like segmentation, feature extraction, texture analysis, etc. which removes the noise while retaining the edges and other detailed features as much as possible. This noise gets introduced during acquisition, transmission & reception and storage & retrieval processes. This paper presents a novel pre-processing algorithm which is named as Profuse clustering Technique (PCT) based on the super pixel clustering. K-Means clustering, simple linear iterative clustering, Fusing Optimization algorithms are involved in this proposed Profuse clustering Technique and is further used for denoising the Lung Cancer images to get the more accurate result in the decision making process. (c) 2018 The Authors. Published by Elsevier B.V.

关键词： Superpixel clustering K-Means clustering Profuse clustering Technique Fusing Optimization algorithm simple linear iterative clustering Denoising

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Evaluating the effect of band subset selection in SLIC superpixel segmentation 26

Evaluating the effect of band subset selection in SLIC super...

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Conference on Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXVI

作者： Pochamreddy, Pavithra Alkhatib, Mohammed Q. Velez-Reyes, Miguel Univ Texas El Paso Dept Elect & Comp Engn 500 W Univ Ave El Paso TX 79968 USA Abu Dhabi Polytech POB 111499 Abu Dhabi U Arab Emirates

ISBN: (数字)9781510635623

ISBN: (纸本)9781510635623

The simple linear iterative clustering (SLIC) algorithm is widely used for superpixel segmentation in hyperspectral image processing. In this paper, we study the effect of band-subset selection as a dimensionality reduction pre-processing step for SLIC superpixel segmentation. Column subset selection based band subset selection methods are studied. The quality of the resulting SLIC superpixel segmentation by the homogeneity of the resulting superpixels. A superpixel is considered homogeneous if the matrix resulting from unfolding the spectral signatures in the superpixel is a nearly rank one. The homogeneity ratio (number of homogeneous superpixels over total number of superpixels in the image) is used as a performance metric to compare different SLIC segmentation results. Experiments using the HYDICE Urban hyperspectral image are presented. Results show a slight increase in the homogeneity ratio for small numbers of bands (3-6) over SLIC using all bands.

关键词： Superpixel segmentation simple linear iterative clustering band subset selection dimensionality reduction

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Gas Ash Microscopic Image Segmentation with SLIC and Mean Shift 34

Gas Ash Microscopic Image Segmentation with SLIC and Mean Sh...

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34th Chinese Control and Decision Conference (CCDC)

作者： Wang, Peizhen Ji, Jinxin Zhang, Ke Zhai, Yujia Anhui Univ Technol Sch Elect Engn & Informat Maanshan 243032 Peoples R China Northeastern Univ Coll Comp Sci & Engn Shenyang 110819 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore Singapore Anhui Univ Technol Engn Practice & Innovat Ctr Maanshan 243032 Peoples R China

ISBN: (纸本)9781665478960

For the problems of long processing time, fragmentary clustering regions and poor segmentation accuracy of gas ash microscopic image segmentation with traditional mean shift algorithm, this paper proposes an improved image segmentation method combining simple linear iterative clustering (SLIC) and mean shift. Firstly, SLIC algorithm is employed to generate a certain number of superpixels of gas ash microscopic images. Then, color, texture, and position information of each superpixel are extracted, and mapped into feature space for mean shift clustering. Finally, an adaptive region merging algorithm is used to further merge similar regions. The experimental results indicate that as a sample mode, the generated superpixels are more effective than original pixels in mean shift clustering, that is, the processing time is greatly shorten and clustering regions are more complete. Owing to the addition of texture features, the generated target areas are more accurate, especially for mosaic structure. This study can offer a support for further automatic component recognition, which is the foundation of industrial robot in analysis of gas ash.

关键词： Mean Shift simple linear iterative clustering Gas Ash Microscopic Image Image Segmentation

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EVALUATION OF SEGMENTATION METHODS FOR SPOIL PILE DELINEATION USING UAV IMAGES

EVALUATION OF SEGMENTATION METHODS FOR SPOIL PILE DELINEATIO...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Thiruchittampalam, S. Banerjee, B. P. Singh, S. K. Glenn, N. F. Raval, S. Univ New South Wales Sch Minerals & Energy Resources Engn Sydney NSW 2052 Australia Univ Moratuwa Dept Earth Resources Engn Moratuwa Sri Lanka Agr Victoria Horsham Vic 3400 Australia Boise State Univ Dept Geosci Boise ID USA

ISBN: (纸本)9798350320107

Mine dump stability depends on the correct placement of fundamental units - i.e., spoil piles, based on their geological and geotechnical characteristics. In-field characterization of individual piles is strenuous. To this end, image-based characterization of spoil piles using remote data obtained through unpiloted aerial system is a potential complementary solution. Several high-level image processing techniques such as object-based classification and feature extraction, inherently rely on effective segmentation. However, studies on segmentation algorithms for spoil pile delineation are lacking. This study provides insight on colour-based and morphology-based segmentation approaches to progress avenues for object-based analysis for spoil characterization in the mine environment.

关键词： Mean-shift segmentation simple linear iterative clustering Voronoi-based segmentation Mine dumps Spoil piles

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A Unified Approach for Copy-Move Forgery Detection Using CNN and SIFT Localization 3

A Unified Approach for Copy-Move Forgery Detection Using CNN...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Khan, Sameer Ali Sravya, Bandi Rupa Debnath, Saswati Soni, Badal Alliance University Department of Computer Science & Engineering Karnataka Bengaluru562106 India Silchar Department of Computer Science & Engineering Assam Cachar788010 India

ISBN: (纸本)9798350319125

In the digital age, images and videos serve as crucial information-sharing tools. Advanced editing techniques have facilitated widespread digital image manipulation. Copy-move forgery, a prevalent method, involves copying image segments to conceal or duplicate content. This study introduces a unified strategy that combines Convolutional Neural Networks (CNN) and Scale-Invariant Feature Transform (SIFT) localization. This integrated approach offers a robust solution for detecting copy-move forgery, effectively countering contemporary image tampering techniques. By merging CNN's feature learning and SIFT's distinctive feature identification, this method shows promise for enhancing image forensics and authenticity verification. © 2023 IEEE.

关键词： CNN Forgery Detection Image Forensics SIFT simple linear iterative clustering

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