Journal articles: 'LINEAR ITERATIVE CLUSTERING'

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Zhao, Jiaxing, Ren Bo, Qibin Hou, Ming-Ming Cheng, and Paul Rosin. "FLIC: Fast linear iterative clustering with active search." Computational Visual Media 4, no. 4 (October 27, 2018): 333–48. http://dx.doi.org/10.1007/s41095-018-0123-y.

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Yan, Qingan, Long Yang, Chao Liang, Huajun Liu, Ruimin Hu, and Chunxia Xiao. "Geometrically Based Linear Iterative Clustering for Quantitative Feature Correspondence." Computer Graphics Forum 35, no. 7 (October 2016): 1–10. http://dx.doi.org/10.1111/cgf.12998.

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Magaraja, Anousouya Devi, Ezhilarasie Rajapackiyam, Vaitheki Kanagaraj, Suresh Joseph Kanagaraj, Ketan Kotecha, Subramaniyaswamy Vairavasundaram, Mayuri Mehta, and Vasile Palade. "A Hybrid Linear Iterative Clustering and Bayes Classification-Based GrabCut Segmentation Scheme for Dynamic Detection of Cervical Cancer." Applied Sciences 12, no. 20 (October 18, 2022): 10522. http://dx.doi.org/10.3390/app122010522.

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Cervical cancer earlier detection remains indispensable for enhancing the survival rate probability among women patients worldwide. The early detection of cervical cancer is done relatively by using the Pap Smear cell Test. This method of detection is challenged by the degradation phenomenon within the image segmentation task that arises when the superpixel count is minimized. This paper introduces a Hybrid Linear Iterative Clustering and Bayes classification-based GrabCut Segmentation Technique (HLC-BC-GCST) for the dynamic detection of Cervical cancer. In this proposed HLC-BC-GCST approach, the Linear Iterative Clustering process is employed to cluster the potential features of the preprocessed image, which is then combined with GrabCut to prevent the issues that arise when the number of superpixels is minimized. In addition, the proposed HLC-BC-GCST scheme benefits of the advantages of the Gaussian mixture model (GMM) on the extracted features from the iterative clustering method, based on which the mapping is performed to describe the energy function. Then, Bayes classification is used for reconstructing the graph cut model from the extracted energy function derived from the GMM model-based Linear Iterative Clustering features for better computation and implementation. Finally, the boundary optimization method is utilized to considerably minimize the roughness of cervical cells, which contains the cytoplasm and nuclei regions, using the GrabCut algorithm to facilitate improved segmentation accuracy. The results of the proposed HLC-BC-GCST scheme are 6% better than the results obtained by other standard detection approaches of cervical cancer using graph cuts.

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Eun, Hyunjun, Yoonhyung Kim, Chanho Jung, and Changick Kim. "Adaptive Sampling of Initial Cluster Centers for Simple Linear Iterative Clustering." Journal of Korean Institute of Communications and Information Sciences 43, no. 1 (January 31, 2018): 20–23. http://dx.doi.org/10.7840/kics.2018.43.1.20.

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Oh, Ki-Won, and Kang-Sun Choi. "Acceleration of simple linear iterative clustering using early candidate cluster exclusion." Journal of Real-Time Image Processing 16, no. 4 (March 31, 2016): 945–56. http://dx.doi.org/10.1007/s11554-016-0583-1.

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Choi, Kang-Sun, and Ki-Won Oh. "Subsampling-based acceleration of simple linear iterative clustering for superpixel segmentation." Computer Vision and Image Understanding 146 (May 2016): 1–8. http://dx.doi.org/10.1016/j.cviu.2016.02.018.

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Yamamoto, Takeshi, Katsuhiro Honda, Akira Notsu, and Hidetomo Ichihashi. "A Comparative Study on TIBA Imputation Methods in FCMdd-Based Linear Clustering with Relational Data." Advances in Fuzzy Systems 2011 (2011): 1–10. http://dx.doi.org/10.1155/2011/265170.

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Relational fuzzy clustering has been developed for extracting intrinsic cluster structures of relational data and was extended to a linear fuzzy clustering model based on Fuzzyc-Medoids (FCMdd) concept, in which Fuzzyc-Means-(FCM-) like iterative algorithm was performed by defining linear cluster prototypes using two representative medoids for each line prototype. In this paper, the FCMdd-type linear clustering model is further modified in order to handle incomplete data including missing values, and the applicability of several imputation methods is compared. In several numerical experiments, it is demonstrated that some pre-imputation strategies contribute to properly selecting representative medoids of each cluster.

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Huang, Hui-Yu, and Zhe-Hao Liu. "Stereo Matching with Spatiotemporal Disparity Refinement Using Simple Linear Iterative Clustering Segmentation." Electronics 10, no. 6 (March 18, 2021): 717. http://dx.doi.org/10.3390/electronics10060717.

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Stereo matching is a challenging problem, especially for computer vision, e.g., three-dimensional television (3DTV) or 3D visualization. The disparity maps from the video streams must be estimated. However, the estimated disparity sequences may cause undesirable flickering errors. These errors result in poor visual quality for the synthesized video and reduce the video coding information. In order to solve this problem, we here propose a spatiotemporal disparity refinement method for local stereo matching using the simple linear iterative clustering (SLIC) segmentation strategy, outlier detection, and refinements of the temporal and spatial domains. In the outlier detection, the segmented region in the initial disparity is used to distinguish errors in the binocular disparity. Based on the color similarity and disparity difference, we recalculate the aggregated cost to determine adaptive disparities to recover the disparity errors in disparity sequences. The flickering errors are also effectively removed, and the object boundaries are well preserved. Experiments using public datasets demonstrated that our proposed method creates high-quality disparity maps and obtains a high peak signal-to-noise ratio compared to state-of-the-art methods.

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Cong, Jinyu, Benzheng Wei, Yilong Yin, Xiaoming Xi, and Yuanjie Zheng. "Performance evaluation of simple linear iterative clustering algorithm on medical image processing." Bio-Medical Materials and Engineering 24, no. 6 (2014): 3231–38. http://dx.doi.org/10.3233/bme-141145.

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Meenalochani, Manickam, Natarajan Hemavathi, and Selvaraj Sudha. "Performance analysis of iterative linear regression-based clustering in wireless sensor networks." IET Science, Measurement & Technology 14, no. 4 (June 1, 2020): 423–29. http://dx.doi.org/10.1049/iet-smt.2019.0258.

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Tang, Xiaoqing, Junlong Chen, Yazhou Liu, and Quansen Sun. "Hyperspectral image classification by fusing sparse representation and simple linear iterative clustering." Journal of Applied Remote Sensing 9, no. 1 (December 22, 2015): 095977. http://dx.doi.org/10.1117/1.jrs.9.095977.

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Marleny, Finki Dona, Ihdalhubbi Maulida, and Mambang Mambang. "SIMPLE LINEAR ITERATIVE CLUSTERING (SLIC) UNTUK SEGMENTASI MOTIF DASAR CITRA KAIN SASIRANGAN." Jurnal Simantec 11, no. 1 (December 28, 2022): 19–26. http://dx.doi.org/10.21107/simantec.v11i1.14274.

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Nagata, Munehiro, Masatsugu Hada, Masashi Iwasaki, and Yoshimasa Nakamura. "Eigenvalue clustering of coefficient matrices in the iterative stride reductions for linear systems." Computers & Mathematics with Applications 71, no. 1 (January 2016): 349–55. http://dx.doi.org/10.1016/j.camwa.2015.11.022.

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Bommisetty, Reddy Mounika, Om Prakash, and Ashish Khare. "Video superpixels generation through integration of curvelet transform and simple linear iterative clustering." Multimedia Tools and Applications 78, no. 17 (May 21, 2019): 25185–219. http://dx.doi.org/10.1007/s11042-019-7554-z.

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Liu, Tianli, Dongsong Li, Zhiming Jiao, Tao Liang, Hao Zhou, and Guoqing Yang. "A coloured oil level indicator detection method based on simple linear iterative clustering." IOP Conference Series: Earth and Environmental Science 100 (December 2017): 012151. http://dx.doi.org/10.1088/1755-1315/100/1/012151.

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Wang, Yuchan, Baojiu Li, and Marius Cautun. "Iterative removal of redshift-space distortions from galaxy clustering." Monthly Notices of the Royal Astronomical Society 497, no. 3 (July 24, 2020): 3451–71. http://dx.doi.org/10.1093/mnras/staa2136.

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ABSTRACT Observations of galaxy clustering are made in redshift space, which results in distortions to the underlying isotropic distribution of galaxies. These redshift-space distortions (RSDs) not only degrade important features of the matter density field, such as the baryonic acoustic oscillation (BAO) peaks, but also pose challenges for the theoretical modelling of observational probes. Here, we introduce an iterative non-linear reconstruction algorithm to remove RSD effects from galaxy clustering measurements, and assess its performance by using mock galaxy catalogues. The new method is found to be able to recover the real-space galaxy correlation function with an accuracy of $\sim \!1{{\ \rm per\ cent}}$, and restore the quadrupole accurately to 0, on scales $s\gtrsim 20\,h^{-1}\, {\rm Mpc}$. It also leads to an improvement in the reconstruction of the initial density field, which could help to accurately locate the BAO peaks. An ‘internal calibration’ scheme is proposed to determine the values of cosmological parameters, as a part of the reconstruction process, and possibilities to break parameter degeneracies are discussed. RSD reconstruction can offer a potential way to simultaneously extract the cosmological parameters, initial density field, real-space galaxy positions, and large-scale peculiar velocity field (of the real Universe), making it an alternative to standard perturbative approaches in galaxy clustering analysis, bypassing the need for RSD modelling.

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Zhu, Yaguang, Kailu Luo, Chao Ma, Qiong Liu, and Bo Jin. "Superpixel Segmentation Based Synthetic Classifications with Clear Boundary Information for a Legged Robot." Sensors 18, no. 9 (August 25, 2018): 2808. http://dx.doi.org/10.3390/s18092808.

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In view of terrain classification of the autonomous multi-legged walking robots, two synthetic classification methods for terrain classification, Simple Linear Iterative Clustering based Support Vector Machine (SLIC-SVM) and Simple Linear Iterative Clustering based SegNet (SLIC-SegNet), are proposed. SLIC-SVM is proposed to solve the problem that the SVM can only output a single terrain label and fails to identify the mixed terrain. The SLIC-SegNet single-input multi-output terrain classification model is derived to improve the applicability of the terrain classifier. Since terrain classification results of high quality for legged robot use are hard to gain, the SLIC-SegNet obtains the satisfied information without too much effort. A series of experiments on regular terrain, irregular terrain and mixed terrain were conducted to present that both superpixel segmentation based synthetic classification methods can supply reliable mixed terrain classification result with clear boundary information and will put the terrain depending gait selection and path planning of the multi-legged robots into practice.

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Wuttke, S., W. Middelmann, and U. Stilla. "IMPROVING ACTIVE QUERIES WITH A LOCAL SEGMENTATION STEP AND APPLICATION TO LAND COVER CLASSIFICATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-1/W1 (May 30, 2017): 165–73. http://dx.doi.org/10.5194/isprs-annals-iv-1-w1-165-2017.

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Active queries is an active learning method used for classification of remote sensing images. It consists of three steps: hierarchical clustering, dendrogram division, and active label selection. The goal of active learning is to reduce the needed amount of labeled data while preserving classification accuracy. We propose to apply local segmentation as a new step preceding the hierarchical clustering. We are using the SLIC (simple linear iterative clustering) algorithm for dedicated image segmentation. This incorporates spatial knowledge which leads to an increased learning rate and reduces classification error. The proposed method is applied to six different areas of the Vaihingen dataset.

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Angulakshmi, M., and G. G. Lakshmi Priya. "Walsh Hadamard Transform for Simple Linear Iterative Clustering (SLIC) Superpixel Based Spectral Clustering of Multimodal MRI Brain Tumor Segmentation." IRBM 40, no. 5 (October 2019): 253–62. http://dx.doi.org/10.1016/j.irbm.2019.04.005.

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Snehalatha, Snehalatha. "Brain Mri Image Segmentation Using Simple Linear Iterative Clustering (SLIC) Segmentation With Superpixel Fusion." Bioscience Biotechnology Research Communications 14, no. 5 (June 15, 2021): 358–64. http://dx.doi.org/10.21786/bbrc/14.5/62.

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Prasad Kondisetty, Durga, and Mohammed Ali Hussain. "A novel approach for cDNA image segmentation using SLIC based SOM methodology." International Journal of Engineering & Technology 7, no. 2.8 (March 19, 2018): 52. http://dx.doi.org/10.14419/ijet.v7i2.8.10323.

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In the segmentation of computer vision images, Super pixels are act as key role from last decade. There are multiple algorithms and techniques to analyze the Super pixels but amount all of them the best super pixel analyzing method is Simple Linear Iterative Clustering (SLIC) have come to pivot increasingly in recent years. The studying of micro array gene expression from MRI imaging is more useful to detect tumors or any other cancer diseases, so that the complementary DNA (cDNA) microarray is a well established tool for studying the same. The segmentation of microarray images is the main step in a microarray analysis. In this paper, we proposed an algorithm to segmenting the cDNA micro array image using Simple Linear Iterative Clustering (SLIC) based Self Organizing Maps (SOM) methodology. However, the proposed algorithm is taken up a challenging task to study the poor quality of images also. There are two steps to analyze the image, first, a pre-processing the applied image to reduce noise levels and second, to segment the image using SLIC based SOM methodology.

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Liu, Bowen, Ting Zhang, Yujian Li, Zhaoying Liu, and Zhilin Zhang. "Kernel Probabilistic K-Means Clustering." Sensors 21, no. 5 (March 8, 2021): 1892. http://dx.doi.org/10.3390/s21051892.

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Kernel fuzzy c-means (KFCM) is a significantly improved version of fuzzy c-means (FCM) for processing linearly inseparable datasets. However, for fuzzification parameter m=1, the problem of KFCM (kernel fuzzy c-means) cannot be solved by Lagrangian optimization. To solve this problem, an equivalent model, called kernel probabilistic k-means (KPKM), is proposed here. The novel model relates KFCM to kernel k-means (KKM) in a unified mathematic framework. Moreover, the proposed KPKM can be addressed by the active gradient projection (AGP) method, which is a nonlinear programming technique with constraints of linear equalities and linear inequalities. To accelerate the AGP method, a fast AGP (FAGP) algorithm was designed. The proposed FAGP uses a maximum-step strategy to estimate the step length, and uses an iterative method to update the projection matrix. Experiments demonstrated the effectiveness of the proposed method through a performance comparison of KPKM with KFCM, KKM, FCM and k-means. Experiments showed that the proposed KPKM is able to find nonlinearly separable structures in synthetic datasets. Ten real UCI datasets were used in this study, and KPKM had better clustering performance on at least six datsets. The proposed fast AGP requires less running time than the original AGP, and it reduced running time by 76–95% on real datasets.

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Tang, Wei, Yang Yang, Lanling Zeng, and Yongzhao Zhan. "Optimizing MSE for Clustering with Balanced Size Constraints." Symmetry 11, no. 3 (March 6, 2019): 338. http://dx.doi.org/10.3390/sym11030338.

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Clustering is to group data so that the observations in the same group are more similar to each other than to those in other groups. k-means is a popular clustering algorithm in data mining. Its objective is to optimize the mean squared error (MSE). The traditional k-means algorithm is not suitable for applications where the sizes of clusters need to be balanced. Given n observations, our objective is to optimize the MSE under the constraint that the observations need to be evenly divided into k clusters. In this paper, we propose an iterative method for the task of clustering with balanced size constraints. Each iteration can be split into two steps, namely an assignment step and an update step. In the assignment step, the data are evenly assigned to each cluster. The balanced assignment task here is formulated as an integer linear program (ILP), and we prove that the constraint matrix of this ILP is totally unimodular. Thus the ILP is relaxed as a linear program (LP) which can be efficiently solved with the simplex algorithm. In the update step, the new centers are updated as the centroids of the observations in the clusters. Assuming that there are n observations and the algorithm needs m iterations to converge, we show that the average time complexity of the proposed algorithm is O ( m n 1 . 65 ) – O ( m n 1 . 70 ) . Experimental results indicate that, comparing with state-of-the-art methods, the proposed algorithm is efficient in deriving more accurate clustering.

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NARAZAKI, HIROSHI, and ANCA L. RALESCU. "ITERATIVE INDUCTION OF A CATEGORY MEMBERSHIP FUNCTION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 02, no. 01 (March 1994): 91–100. http://dx.doi.org/10.1142/s0218488594000080.

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We propose a new iterative method for inducing classification knowledge from symbolic data. Our method generates a hierarchy of clusters and does not assume a particular knowledge representation formula (e.g., a conjunctive formula, a linear discriminant function). Our method consists of two stages, i.e., the optimization and clustering stages. The first stage maps the symbolic problem into the numerical domain based on an optimization approach. In the second stage, the examples are clustered into positive, negative, and fuzzy zones using induced membership degrees. This learning procedure is iterated until the fuzzy zone becomes empty. Out method learns "topological knowledge" which is found to be useful for the evaluation of the training data quality. Further, we show that our method is useful using real world data of an industrial knowledge acquisition problem.

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Ren Xinlei, 任欣磊, and 王阳萍 Wang Yangping. "Super-Pixel Segmentation of Remote Sensing Image Based on Improved Simple Linear Iterative Clustering Algorithm." Laser & Optoelectronics Progress 57, no. 22 (2020): 222801. http://dx.doi.org/10.3788/lop57.222801.

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Ren, Dayong, Zhenhong Jia, Jie Yang, and Nikola K. Kasabov. "A Practical GrabCut Color Image Segmentation Based on Bayes Classification and Simple Linear Iterative Clustering." IEEE Access 5 (2017): 18480–87. http://dx.doi.org/10.1109/access.2017.2752221.

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Farmaha, Ihor, Marian Banaś, Vasyl Savchyn, Bohdan Lukashchuk, and Taras Farmaha. "Wound image segmentation using clustering based algorithms." New Trends in Production Engineering 2, no. 1 (October 1, 2019): 570–78. http://dx.doi.org/10.2478/ntpe-2019-0062.

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Abstract Classic methods of measurement and analysis of the wounds on the images are very time consuming and inaccurate. Automation of this process will improve measurement accuracy and speed up the process. Research is aimed to create an algorithm based on machine learning for automated segmentation based on clustering algorithms Methods. Algorithms used: SLIC (Simple Linear Iterative Clustering), Deep Embedded Clustering (that is based on artificial neural networks and k-means). Because of insufficient amount of labeled data, classification with artificial neural networks can't reach good results. Clustering, on the other hand is an unsupervised learning technique and doesn't need human interaction. Combination of traditional clustering methods for image segmentation with artificial neural networks leads to combination of advantages of both of them. Preliminary step to adapt Deep Embedded Clustering to work with bio-medical images is introduced and is based on SLIC algorithm for image segmentation. Segmentation with this method, after model training, leads to better results than with traditional SLIC.

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Kim, Yong Hwi, and Kwan H. Lee. "Data Driven SVBRDF Estimation Using Deep Embedded Clustering." Electronics 11, no. 19 (October 9, 2022): 3239. http://dx.doi.org/10.3390/electronics11193239.

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Photo-realistic representation in user-specified view and lighting conditions is a challenging but high-demand technology in the digital transformation of cultural heritages. Despite recent advances in neural renderings, it is still necessary to capture high-quality surface reflectance from photography in a controlled environment for real-time applications such as VR/AR and digital arts. In this paper, we present a deep embedding clustering network for spatially-varying bidirectional reflectance distribution function (SVBRDF) estimation. Our network is designed to simultaneously update the reflectance basis and its linear manifold in the spatial domain of SVBRDF. We show that our dual update scheme excels in optimizing the rendering loss in terms of the convergence speed and visual quality compared to the current iterative SVBRDF update methods.

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Hermawan, Andy, Ilham Zaeni, Aji Wibawa, Gunawan Gunawan, Yosi Kristian, and Shandy Darmawan. "Pengenalan Varietas Ikan Koi Berdasarkan Foto Menggunakan Simple Linear Iterative Clustering Superpixel Segmentation dan Convolutional Neural." Jurnal Inovasi Teknologi dan Edukasi Teknik 1, no. 11 (November 24, 2021): 806–14. http://dx.doi.org/10.17977/um068v1i112021p806-814.

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Object segmentation and image recognition are two computer vision tasks which are still being developed until today. Simple Linear Iterative Clustering is an algorithm which is very popular to help with object segmentation tasks because it is the best in terms of result and speed. In image recognition, Convolutional Neural Networks are also one of the best approaches for any kind of recognition tasks because of their efficiency and the ability to recognize objects like animals do. Koi fish have become a very interesting object to be researched because they are difficult to segment and distinguished between their varieties. The dataset consists of 600 images of Koi fish from 10 different varieties. The Koi fish’s recognition process begins with generating super pixels for the input image. The next step is to merge all neighborhood super pixels by their color similarities. After this step, almost all the background pixels should be detected so that the actual object, the Koi fish, can be segmented. The segmented image is then given to a Convolutional Neural Networks, to learn any important features which distinguish every Koi fish variety from one another. A trained Convolutional Neural Networks can then give a Koi fish variety prediction for an input image. Based on a series of segmentation and model tests performed, it is proven that the segmentation technique, which uses Simple Linear Iterative Clustering in this project, performs exceptionally well across almost all the images in the dataset. The model produced from this project is also able to classify a wide range of Koi fish varieties accurately at 90 percent accuracy with segmentation and 87 percent without segmentation. Segmentasi dan pengenalan objek pada gambar masih merupakan dua buah masalah pada computer vision yang masih terus diteliti dan dikembangkan hingga saat ini. Simple Linear Iterative Clustering merupakan salah satu algoritma segmentasi superpixel yang cukup populer untuk membantu melakukan segmentasi objek karena memiliki hasil superpixel yang baik dan dapat berjalan dengan cepat. Untuk pengenalan objek, Convolutional Neural Networks masih merupakan salah satu yang terbaik untuk berbagai masalah karena efisien dan mampu mengenali objek pada gambar layaknya hewan mengenali objek yang dilihatnya. Ikan koi menjadi sebuah objek yang menarik untuk diteliti karena sulit untuk disegmentasi dan dikenali jenisnya bahkan oleh manusia. Dataset yang digunakan berisi 600 gambar yang terdiri dari 10 varietas ikan koi. Pengenalan ikan koi diawali dengan melakukan generate superpixel pada gambar input, kemudian menggabungkan superpixel-superpixel terdekat yang memiliki warna yang mirip. Dengan cara ini, maka hampir seluruh pixel background dapat dideteksi sehingga objek ikan koi dapat disegmentasi. Gambar hasil segmentasi kemudian dilatihkan ke Convolutional Neural Networks yang akan mempelajari fitur-fitur penting pada setiap jenis ikan koi yang diteliti. Convolutional Neural Networks yang telah dilatih kemudian dapat memberikan prediksi varietas ikan koi dari sebuah input gambar. Berdasarkan hasil uji coba segmentasi dan model yang digunakan, dibuktikan bahwa teknik segmentasi yang memanfaatkan Simple Linear Iterative Clustering yang dilakukan berhasil untuk hampir seluruh gambar pada dataset. Model yang dibuat mampu mengklasifikasikan varietas ikan koi dengan akurasi 90 persen dengan segmentasi dan 87 persen tanpa segmentasi.

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Liu, Yiting, Lianjie Sui, Peijuan Li, Lei Zhang, Qingzheng Wu, Junfeng Du, Yawen Liu, and Hanqi Yu. "A Radar Linear Feature Fitting Algorithm Combining Adaptive Clustering and Corner Detection Operator." Journal of Sensors 2023 (February 24, 2023): 1–17. http://dx.doi.org/10.1155/2023/6991467.

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The precise environmental parameters derived from laser radar scan data can significantly accelerate the process of real-time localization and map-matching technique. One of the research directions is autonomous navigation algorithm based on LiDAR slam. LiDAR has the advantage of having a wide range of accuracy and distance. However, due to the limited amount of LiDAR data available and the influence of sensor noise, it is easy to run into issues such as low accuracy of robot map construction or large positioning errors. At the moment, most of feature extraction algorithms employ an iterative calculation method with high computational complexity and a large amount of computation. Furthermore, due to the dependence of the fixed separation threshold, the algorithms for extracting the linear features of laser radar data are typically undersegment and oversegment. As a result, this paper proposes a radar linear feature fitting algorithm that combines adaptive clustering and corner detection operators. First, bilateral filtering is used to reduce noise and remove invalid data points. Second, the LiDAR data points are classified using adaptive threshold clustering of distance and density. The corner detection operator is applied to the classified data points to determine all possible corners then. Finally, the least square method is used to linearly fit each class and the identified corners within each class. The simulation and experimental results demonstrate that this method avoids the influence of noise points and a fixed segmentation threshold on corner point extraction effectively. The standard variance of length is 9.41 × 10 − 5 m 2 for corner feature extraction and localization in the dataset Cartographer ROS 2D Laser SLAM at Deutsches Museum. When compared to PDBS (point distance based methods) and IEPF (iterative end point fit), only about half the time is used, the accuracy of partition processing is improved by 11.6%, and the accuracy of corner detection is improved by 20.1%. The proposed algorithm can extract the corner features of data frames and linear positioning through experimental verification accurately. The features of the laser scan data that fit are more realistic. It has higher calculation efficiency and position accuracy. It ensures real-time mobile robot map construction and is appropriate for autonomous robot map algorithms developed in embedded systems.

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ZHANG, CHONG, XUANJING SHEN, and HAIPENG CHEN. "BRAIN TUMOR SEGMENTATION BASED ON SUPERPIXELS AND HYBRID CLUSTERING WITH FAST GUIDED FILTER." Journal of Mechanics in Medicine and Biology 20, no. 06 (August 2020): 2050032. http://dx.doi.org/10.1142/s0219519420500323.

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Brain tumor segmentation from magnetic resonance (MR) image is vital for both the diagnosis and treatment of brain cancers. To alleviate noise sensitivity and improve stability of segmentation, an effective hybrid clustering algorithm combined with fast guided filter is proposed for brain tumor segmentation in this paper. Preprocessing is performed using adaptive Wiener filtering combined with a fast guided filter. Then simple linear iterative clustering (SLIC) is utilized for pre-segmentation to effectively remove scatter. During the clustering, K-means[Formula: see text] and Gaussian kernel-based fuzzy C-means (K[Formula: see text]GKFCM) clustering algorithm are combined to segment, and the fast-guided filter is introduced into the clustering. The proposed algorithm not only improves the robustness of the algorithm to noise, but also improves the stability of the segmentation. In addition, the proposed algorithm is compared with other current segmentation algorithms. The results show that the proposed algorithm performs better in terms of accuracy, sensitivity, specificity and recall.

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DIMAGGIO, PETER A., SCOTT R. MCALLISTER, CHRISTODOULOS A. FLOUDAS, XIAO-JIANG FENG, JOSHUA D. RABINOWITZ, and HERSCHEL A. RABITZ. "OPTIMAL METHODS FOR RE-ORDERING DATA MATRICES IN SYSTEMS BIOLOGY AND DRUG DISCOVERY APPLICATIONS." Biophysical Reviews and Letters 03, no. 01n02 (April 2008): 19–42. http://dx.doi.org/10.1142/s1793048008000605.

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The analysis of large-scale data sets via clustering techniques is utilized in a number of applications. Many of the methods developed employ local search or heuristic strategies for identifying the "best" arrangement of features according to some metric. In this article, we present rigorous clustering methods based on the optimal re-ordering of data matrices. Distinct mixed-integer linear programming (MILP) models are utilized for the clustering of (a) dense data matrices, such as gene expression data, and (b) sparse data matrices, which are commonly encountered in the field of drug discovery. Both methods can be used in an iterative framework to bicluster data and assist in the synthesis of drug compounds, respectively. We demonstrate the capability of the proposed optimal re-ordering methods on several data sets from both systems biology and molecular discovery studies and compare our results to other clustering techniques when applicable.

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Lee, Jeong Hwan. "A Comparison of Superpixel Characteristics based on SLIC(Simple Linear Iterative Clustering) for Color Feature Spaces." Journal of the Korea Society of Digital Industry and Information Management 10, no. 4 (December 30, 2014): 151–60. http://dx.doi.org/10.17662/ksdim.2014.10.1.151.

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Chang, Kaiwen, and Bruno Figliuzzi. "Fast marching based superpixels." Mathematical Morphology - Theory and Applications 4, no. 1 (December 17, 2020): 127–42. http://dx.doi.org/10.1515/mathm-2020-0105.

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AbstractIn this article, we present a fast-marching based algorithm for generating superpixel (FMS) partitions of images. The idea behind the algorithm is to draw an analogy between waves propagating in a heterogeneous medium and regions growing on an image at a rate depending on the local color and texture. The FMS algorithm is evaluated on the Berkeley Segmentation Dataset 500. It yields results in terms of boundary adherence that are slightly better than the ones obtained with similar approaches including the Simple Linear Iterative Clustering, the Eikonal-based region growing for efficient clustering and the Iterative Spanning Forest framework for superpixel segmentation algorithms. An interesting feature of the proposed algorithm is that it can take into account texture information to compute the superpixel partition. We illustrate the interest of adding texture information on a specific set of images obtained by recombining textures patches extracted from images representing stripes, originally constructed by Giraud et al. [20]. On this dataset, our approach works significantly better than color based superpixel algorithms.

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Kondisetty, Durga Prasad, and Mohammed Ali Hussain. "SLIC Superpixel Based Self Organizing Maps Algorithm for Segmentation of Microarray Images." International Journal of Advances in Applied Sciences 7, no. 1 (March 1, 2018): 78. http://dx.doi.org/10.11591/ijaas.v7.i1.pp78-85.

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We can find the simultaneous monitoring of thousands of genes in parallel Microarray technology. As per these measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, Intensity extraction, Enhancement and Segmentation are important steps in microarray image analysis. This paper gives simple linear iterative clustering (SLIC) based self organizing maps (SOM) algorithm for segmentation of microarray image. The clusters of pixels which share similar features are called Superpixels, thus they can be used as mid-level units to decrease the computational cost in many vision applications. The proposed algorithm utilizes superpixels as clustering objects instead of pixels. The qualitative and quantitative analysis shows that the proposed method produces better segmentation quality than k-means, fuzzy c-means and self organizing maps clustering methods.

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Gopalakrishnan, Vithya. "Enhancement of Sales promotion using Clustering Techniques in Data Mart." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 2 (December 4, 2015): 6534–40. http://dx.doi.org/10.24297/ijct.v15i2.6934.

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Clustering is an important research topic in wide range of unsupervised classification application. Clustering is a technique, which divides a data into meaningful groups. K-means algorithm is one of the popular clustering algorithms. It belongs to partition based grouping techniques, which are based on the iterative relocation of data points between clusters. It does not support global clustering and it has linear time complexity of O(n2). The existing and conventional data clustering algorithms were nâ€™t designed to handle the huge amount of data. So, to overcome these issues Golay code clustering algorithm is selected. Golay code based system used to facilitate the identification of the set of codeword incarnate similar object behaviors. The time complexity associated with Golay code-clustering algorithm is O(n). In this work, the collected sales data is pre processed by removing all null and empty attributes, then eliminating redundant, and noise data. To enhance the sales promotion, K-means and Golay code clustering algorithms are used to cluster the sales data in terms of place and item. Performances of these algorithms are analyzed in terms of accuracy and execution time. Our results show that the Golay code algorithm outperforms than K-mean algorithm in all factors.

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Liu, Xinwang, Xinzhong Zhu, Miaomiao Li, Chang Tang, En Zhu, Jianping Yin, and Wen Gao. "Efficient and Effective Incomplete Multi-View Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4392–99. http://dx.doi.org/10.1609/aaai.v33i01.33014392.

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Incomplete multi-view clustering (IMVC) optimally fuses multiple pre-specified incomplete views to improve clustering performance. Among various excellent solutions, the recently proposed multiple kernel k-means with incomplete kernels (MKKM-IK) forms a benchmark, which redefines IMVC as a joint optimization problem where the clustering and kernel matrix imputation tasks are alternately performed until convergence. Though demonstrating promising performance in various applications, we observe that the manner of kernel matrix imputation in MKKM-IK would incur intensive computational and storage complexities, overcomplicated optimization and limitedly improved clustering performance. In this paper, we propose an Efficient and Effective Incomplete Multi-view Clustering (EE-IMVC) algorithm to address these issues. Instead of completing the incomplete kernel matrices, EE-IMVC proposes to impute each incomplete base matrix generated by incomplete views with a learned consensus clustering matrix. We carefully develop a three-step iterative algorithm to solve the resultant optimization problem with linear computational complexity and theoretically prove its convergence. Further, we conduct comprehensive experiments to study the proposed EE-IMVC in terms of clustering accuracy, running time, evolution of the learned consensus clustering matrix and the convergence. As indicated, our algorithm significantly and consistently outperforms some state-of-the-art algorithms with much less running time and memory.

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Sumithra, S., K. R. Remya, and Dr M. N. Giri Prasad. "Automatic Detection and Localization of Macular Edema." Volume 5 - 2020, Issue 9 - September 5, no. 9 (September 25, 2020): 552–58. http://dx.doi.org/10.38124/ijisrt20sep342.

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Diabetic retinopathy is an eye disease and causes vision loss to the people who are suffering longer from the diabetes. Exudates, bright and red lesions are identified in the diabetic retinal eye. Automatic detection and localization of macular edema is a challenging issue since exudates have non uniform illumination and are low contrasted. Proposed algorithm to detect macular edema encompasses Simple Linear Iterative Clustering, Fisher linear discriminant and Support vector machine classifer. Optic Disc extraction prior to exudates extraction is also introduced. Performance of the proposed detection algorithm is tested on easily available databases: Diaretdb1, Messidor and E_optha Ex. Proposed method shows an accuracy of 97.81%, specificity 98.65 and Sensitivity 82.71%.

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Li, Qiuxia, Tingkui Mu, Hang Gong, Haishan Dai, Chunlai Li, Zhiping He, Wenjing Wang, et al. "A Superpixel-by-Superpixel Clustering Framework for Hyperspectral Change Detection." Remote Sensing 14, no. 12 (June 13, 2022): 2838. http://dx.doi.org/10.3390/rs14122838.

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Hyperspectral image change detection (HSI-CD) is an interesting task in the Earth’s remote sensing community. However, current HSI-CD methods are feeble at detecting subtle changes from bitemporal HSIs, because the decision boundary is partially stretched by strong changes so that subtle changes are ignored. In this paper, we propose a superpixel-by-superpixel clustering framework (SSCF), which avoids the confusion of different changes and thus reduces the impact on decision boundaries. Wherein the simple linear iterative clustering (SLIC) is employed to spatially segment the different images (DI) of the bitemporal HSIs into superpixels. Meanwhile, the Gaussian mixture model (GMM) extracts uncertain pixels from the DI as a rough threshold for clustering. The final CD results are obtained by passing the determined superpixels and uncertain pixels through K-means. The experimental results of two spaceborne bitemporal HSIs datasets demonstrate competitive efficiency and accuracy in the proposed SSCF.

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Wang, Shuopeng, Peng Yang, and Hao Sun. "Fingerprinting Acoustic Localization Indoor Based on Cluster Analysis and Iterative Interpolation." Applied Sciences 8, no. 10 (October 10, 2018): 1862. http://dx.doi.org/10.3390/app8101862.

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Fingerprinting acoustic localization usually requires tremendous time and effort for database construction in sampling phase and reference points (RPs) matching in positioning phase. To improve the efficiency of this acoustic localization process, an iterative interpolation method is proposed to reduce the initial RPs needed for the required positioning accuracy by generating virtual RPs in positioning phase. Meanwhile, a two-stage matching method based on cluster analysis is proposed for computation reduction of RPs matching. Results reported show that, on the premise of ensuring positioning accuracy, two-stage matching method based on feature clustering partition can reduce the average RPs matching amount to 30.14% of the global linear matching method taken. Meanwhile, the iterative interpolation method can guarantee the positioning accuracy with only 27.77% initial RPs of the traditional method needed.

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Liu, Qingbing. "New Preconditioners for Nonsymmetric Saddle Point Systems with Singular (1,1) Block." ISRN Computational Mathematics 2013 (August 27, 2013): 1–8. http://dx.doi.org/10.1155/2013/507817.

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We investigate the solution of large linear systems of saddle point type with singular (1,1) block by preconditioned iterative methods and consider two parameterized block triangular preconditioners used with Krylov subspace methods which have the attractive property of improved eigenvalue clustering with increased ill-conditioning of the (1,1) block of the saddle point matrix, including the choice of the parameter. Meanwhile, we analyze the spectral characteristics of two preconditioners and give the optimal parameter in practice. Numerical experiments that validate the analysis are presented.

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Carrilho, A. C., and M. Galo. "AUTOMATIC OBJECT EXTRACTION FROM HIGH RESOLUTION AERIAL IMAGERY WITH SIMPLE LINEAR ITERATIVE CLUSTERING AND CONVOLUTIONAL NEURAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W16 (September 17, 2019): 61–66. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w16-61-2019.

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<p><strong>Abstract.</strong> Recent advances in machine learning techniques for image classification have led to the development of robust approaches to both object detection and extraction. Traditional CNN architectures, such as LeNet, AlexNet and CaffeNet, usually use as input images of fixed sizes taken from objects and attempt to assign labels to those images. Another possible approach is the Fast Region-based CNN (or Fast R-CNN), which works by using two models: (i) a Region Proposal Network (RPN) which generates a set of potential Regions of Interest (RoI) in the image; and (ii) a traditional CNN which assigns labels to the proposed RoI. As an alternative, this study proposes an approach to automatic object extraction from aerial images similar to the Fast R-CNN architecture, the main difference being the use of the Simple Linear Iterative Clustering (SLIC) algorithm instead of an RPN to generate the RoI. The dataset used is composed of high-resolution aerial images and the following classes were considered: house, sport court, hangar, building, swimming pool, tree, and street/road. The proposed method can generate RoI with different sizes by running a multi-scale SLIC approach. The overall accuracy obtained for object detection was 89% and the major advantage is that the proposed method is capable of semantic segmentation by assigning a label to each selected RoI. Some of the problems encountered are related to object proximity, in which different instances appeared merged in the results.</p>

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Mohamed, Nur Ayuni, Mohd Asyraf Zulkifley, Wan Mimi Diyana Wan Zaki, and Aini Hussain. "An automated glaucoma screening system using cup-to-disc ratio via Simple Linear Iterative Clustering superpixel approach." Biomedical Signal Processing and Control 53 (August 2019): 101454. http://dx.doi.org/10.1016/j.bspc.2019.01.003.

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Wu, Rouwan, Zhiyong Xu, Jianlin Zhang, and Lihong Zhang. "Robust Global Motion Estimation for Video Stabilization Based on Improved K-Means Clustering and Superpixel." Sensors 21, no. 7 (April 3, 2021): 2505. http://dx.doi.org/10.3390/s21072505.

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Obtaining accurate global motion is a crucial step for video stabilization. This paper proposes a robust and simple method to implement global motion estimation. We don’t extend the framework of 2D video stabilization but add a “plug and play” module to motion estimation based on feature points. Firstly, simple linear iterative clustering (SLIC) pre-segmentation is used to obtain superpixels of the video frame, clustering is performed according to the superpixel centroid motion vector and cluster center with large value is eliminated. Secondly, in order to obtain accurate global motion estimation, an improved K-means clustering is proposed. We match the feature points of the remaining superpixels between two adjacent frames, establish a feature points’ motion vector space, and use improved K-means clustering for clustering. Finally, the richest cluster is being retained, and the global motion is obtained by homography transformation. Our proposed method has been verified on different types of videos and has efficient performance than traditional approaches. The stabilization video has an average improvement of 0.24 in the structural similarity index than the original video and 0.1 higher than the traditional method.

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Wang, Yu, Qi Qi, and Xuanjing Shen. "Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC." Brain Sciences 10, no. 2 (February 20, 2020): 116. http://dx.doi.org/10.3390/brainsci10020116.

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Non-uniform gray distribution and blurred edges often result in bias during the superpixel segmentation of medical images of magnetic resonance imaging (MRI). To this end, we propose a novel superpixel segmentation algorithm by integrating texture features and improved simple linear iterative clustering (SLIC). First, a 3D histogram reconstruction model is used to reconstruct the input image, which is further enhanced by gamma transformation. Next, the local tri-directional pattern descriptor is used to extract texture features of the image; this is followed by an improved SLIC superpixel segmentation. Finally, a novel clustering-center updating rule is proposed, using pixels with gray difference with original clustering centers smaller than a predefined threshold. The experiments on the Whole Brain Atlas (WBA) image database showed that, compared to existing state-of-the-art methods, our superpixel segmentation algorithm generated significantly more uniform superpixels, and demonstrated the performance accuracy of the superpixel segmentation in both fuzzy boundaries and fuzzy regions.

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He, Wangpeng, Cheng Li, Yanzong Guo, Zhifei Wei, and Baolong Guo. "A Two-Stage Gradient Ascent-Based Superpixel Framework for Adaptive Segmentation." Applied Sciences 9, no. 12 (June 13, 2019): 2421. http://dx.doi.org/10.3390/app9122421.

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Superpixel segmentation usually over-segments an image into fragments to extract regional features, thus linking up advanced computer vision tasks. In this work, a novel coarse-to-fine gradient ascent framework is proposed for superpixel-based color image adaptive segmentation. In the first stage, a speeded-up Simple Linear Iterative Clustering (sSLIC) method is adopted to generate uniform superpixels efficiently, which assumes that homogeneous regions preserve high consistence during clustering, consequently, much redundant computation for updating can be avoided. Then a simple criterion is introduced to evaluate the uniformity in each superpixel region, once a superpixel region is under-segmented, an adaptive marker-controlled watershed algorithm processes a finer subdivision. Experimental results show that the framework achieves better performance on detail-rich regions than previous superpixel approaches with satisfactory efficiency.

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Saab, Youssef. "A New 2-way Multi-level Partitioning Algorithm." VLSI Design 11, no. 3 (January 1, 2000): 301–10. http://dx.doi.org/10.1155/2000/65821.

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Partitioning is a fundamental problem in the design of VLSI circuits. In recent years, the multi-level partitioning approach has been used with success by a number of researchers. This paper describes a new multi-level partitioning algorithm (PART) that combines a blend of iterative improvement and clustering, biasing of node gains, and local uphill climbs. PART is competitive with recent state-of-the-art partitioning algorithms. PART was able to find new lower cuts for many benchmark circuits. Under suitably mild assumptions, PART also runs in linear time.

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Zhou, H., and H. A. A. Tchelepi. "Two-Stage Algebraic Multiscale Linear Solver for Highly Heterogeneous Reservoir Models." SPE Journal 17, no. 02 (February 6, 2012): 523–39. http://dx.doi.org/10.2118/141473-pa.

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Summary An efficient Two-Stage Algebraic Multiscale Solver (TAMS) that converges to the fine-scale solution is described. The first (global) stage is a multiscale solution obtained algebraically for the given fine-scale problem. In the second stage, a local preconditioner, such as the Block ILU (BILU), or the Additive Schwarz (AS) method is used. Spectral analysis shows that the multiscale solution step captures the low-frequency parts of the error spectrum quite well, while the local preconditioner represents the high-frequency components accurately. Combining the two stages in an iterative scheme results in efficient treatment of all the error components associated with the fine-scale problem. TAMS is shown to converge to the reference fine-scale solution. Moreover, the eigenvalues of the TAMS iteration matrix show significant clustering, which is favorable for Krylov-based methods. Accurate solution of the nonlinear saturation equations (i.e., transport problem) requires having locally conservative velocity fields. TAMS guarantees local mass conservation by concluding the iterations with a multiscale finite-volume step. We demonstrate the performance of TAMS using several test cases with strong permeability heterogeneity and large-grid aspect ratios. Different choices in the TAMS algorithm are investigated, including the Galerkin and finite-volume restriction operators, as well as the BILU and AS preconditioners for the second stage. TAMS for the elliptic flow problem is comparable to state-of-the-art algebraic multigrid methods, which are in wide use. Moreover, the computational time of TAMS grows nearly linearly with problem size.

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H.L, Aravinda, and M. V. Sudhamani. "Liver tumour classification using average correction higher order local autocorrelation coefficient and legendre moments." International Journal of Engineering & Technology 7, no. 2.6 (March 11, 2018): 306. http://dx.doi.org/10.14419/ijet.v7i2.6.11269.

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The major reasons for liver carcinoma are cirrhosis and hepatitis. In order to identify carcinoma in the liver abdominal CT images are used. From abdominal CT images, segmentation of liver portion using adaptive region growing, tumor segmentation from extracted liver using Simple Linear Iterative Clustering is already implemented. In this paper, classification of tumors as benign or malignant is accomplished using Rough-set classifier based on texture feature extracted using Average Correction Higher Order Local Autocorrelation Coefficients and Legendre moments. Classification accuracy achieved in proposed scheme is 90%. The results obtained are promising and have been compared with existing methods.

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Patil, Sumangali, A. Nagaraja Rao, and C. Shoba Bindu. "Class level software fault prediction using step wise linear regression." International Journal of Engineering & Technology 7, no. 4 (September 24, 2018): 2552. http://dx.doi.org/10.14419/ijet.v7i2.17.14881.

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Programming measurements was utilized for foreseeing issue in modules of programming ventures. Convenient forecast of flaws enhances programming quality and subsequently its dependability. In this paper, a framework towards subspace grouping of large data set was pro-posed at class level to minimize the error. We composed an iterative calculation for grouping of high dimensional datasets for improvement of a target work. At that point the bunched data sets were examined utilizing Step-Wise Linear Regression to investigate the relationship among a structure variable and the autonomous factors in order to anticipate of damaged and non-faulty classes. To evaluate the supportive-ness of the model, we drove a practical learning on the Attitude Survey Data. The proposed strategy specifically managed blunder variables and consequently gave precise fault prediction least standard error (0.003) when contrasted with the current technique (4.687). Root mean square error which measures the distinction between the assessed error and the real error was (0.8) in the proposed technique. The results demonstrated that the forecast models based on subspace clustering were essentially predominant to the current techniques.

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Journal articles on the topic 'LINEAR ITERATIVE CLUSTERING'

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