Готові списки джерел за темами / LINEAR ITERATIVE CLUSTERING

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Добірка наукової літератури з теми "LINEAR ITERATIVE CLUSTERING"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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Статті в журналах з теми "LINEAR ITERATIVE CLUSTERING"

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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Більше джерел

Дисертації з теми "LINEAR ITERATIVE CLUSTERING"

Alexandre, Eduardo Barreto. "IFT-SLIC: geração de superpixels com base em agrupamento iterativo linear simples e transformada imagem-floresta." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-24092017-235915/.

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A representação de imagem baseada em superpixels tem se tornado indispensável na melhoria da eficiência em sistemas de Visão Computacional. Reconhecimento de objetos, segmentação, estimativa de profundidade e estimativa de modelo corporal são alguns importantes problemas nos quais superpixels podem ser aplicados. Porém, superpixels podem influenciar a qualidade dos resultados do sistema positiva ou negativamente, dependendo de quão bem eles respeitam as fronteiras dos objetos na imagem. Neste trabalho, é proposto um método iterativo para geração de superpixels, conhecido por IFT-SLIC, baseado em sequências de Transformadas Imagem-Floresta, começando com uma grade regular de sementes. Um procedimento de recomputação de pixels sementes é aplicado a cada iteração, gerando superpixels conexos com melhor aderência às bordas dos objetos presentes na imagem. Os superpixels obtidos via IFT-SLIC correspondem, estruturalmente, a árvores de espalhamento enraizadas nessas sementes, que naturalmente definem superpixels como regiões de pixels fortemente conexas. Comparadas ao Agrupamento Iterativo Linear Simples (SLIC), o IFT-SLIC considera os custos dos caminhos mínimos entre pixels e os centros dos agrupamentos, em vez de suas distâncias diretas. Funções de conexidade não monotonicamente incrementais são exploradas em neste método resultando em melhor desempenho. Estudos experimentais indicam resultados de extração de superpixels superiores pelo método proposto em comparação com o SLIC. Também é analisada a efetividade do IFT-SLIC, em termos de medidas de eficiência e acurácia, em uma aplicação de segmentação do céu em fotos de paisagens. Os resultados mostram que o IFT-SLIC é competitivo com os melhores métodos do estado da arte e superior a muitos outros, motivando seu desenvolvimento para diferentes aplicações.
Image representation based on superpixels has become indispensable for improving efficiency in Computer Vision systems. Object recognition, segmentation, depth estimation, and body model estimation are some important problems where superpixels can be applied. However, superpixels can influence the quality of the system results in a positive or negative manner, depending on how well they respect the object boundaries in the image. In this work, we propose an iterative method for superpixels generation, known as IFT-SLIC, which is based on sequences of Image Foresting Transforms, starting with a regular grid for seed sampling. A seed pixel recomputation procedure is applied per each iteration, generating connected superpixels with a better adherence to objects borders present in the image. The superpixels obtained by IFT-SLIC structurally correspond to spanning trees rooted at those seeds, that naturally define superpixels as regions of strongly connected pixels. Compared to Simple Linear Iterative Clustering (SLIC), IFT-SLIC considers minimum path costs between pixel and cluster centers rather than their direct distances. Non-monotonically increasing connectivity functions are explored in our IFT-SLIC approach leading to improved performance. Experimental results indicate better superpixel extraction by the proposed approach in comparation to that of SLIC. We also analyze the effectiveness of IFT-SLIC, according to efficiency, and accuracy on an application -- namely sky segmentation. The results show that IFT-SLIC can be competitive to the best state-of-the-art methods and superior to many others, which motivates it\'s further development for different applications.

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Neubert, Peer. "Superpixels and their Application for Visual Place Recognition in Changing Environments." Doctoral thesis, Universitätsbibliothek Chemnitz, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-190241.

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Superpixels are the results of an image oversegmentation. They are an established intermediate level image representation and used for various applications including object detection, 3d reconstruction and semantic segmentation. While there are various approaches to create such segmentations, there is a lack of knowledge about their properties. In particular, there are contradicting results published in the literature. This thesis identifies segmentation quality, stability, compactness and runtime to be important properties of superpixel segmentation algorithms. While for some of these properties there are established evaluation methodologies available, this is not the case for segmentation stability and compactness. Therefore, this thesis presents two novel metrics for their evaluation based on ground truth optical flow. These two metrics are used together with other novel and existing measures to create a standardized benchmark for superpixel algorithms. This benchmark is used for extensive comparison of available algorithms. The evaluation results motivate two novel segmentation algorithms that better balance trade-offs of existing algorithms: The proposed Preemptive SLIC algorithm incorporates a local preemption criterion in the established SLIC algorithm and saves about 80 % of the runtime. The proposed Compact Watershed algorithm combines Seeded Watershed segmentation with compactness constraints to create regularly shaped, compact superpixels at the even higher speed of the plain watershed transformation. Operating autonomous systems over the course of days, weeks or months, based on visual navigation, requires repeated recognition of places despite severe appearance changes as they are for example induced by illumination changes, day-night cycles, changing weather or seasons - a severe problem for existing methods. Therefore, the second part of this thesis presents two novel approaches that incorporate superpixel segmentations in place recognition in changing environments. The first novel approach is the learning of systematic appearance changes. Instead of matching images between, for example, summer and winter directly, an additional prediction step is proposed. Based on superpixel vocabularies, a predicted image is generated that shows, how the summer scene could look like in winter or vice versa. The presented results show that, if certain assumptions on the appearance changes and the available training data are met, existing holistic place recognition approaches can benefit from this additional prediction step. Holistic approaches to place recognition are known to fail in presence of viewpoint changes. Therefore, this thesis presents a new place recognition system based on local landmarks and Star-Hough. Star-Hough is a novel approach to incorporate the spatial arrangement of local image features in the computation of image similarities. It is based on star graph models and Hough voting and particularly suited for local features with low spatial precision and high outlier rates as they are expected in the presence of appearance changes. The novel landmarks are a combination of local region detectors and descriptors based on convolutional neural networks. This thesis presents and evaluates several new approaches to incorporate superpixel segmentations in local region detection. While the proposed system can be used with different types of local regions, in particular the combination with regions obtained from the novel multiscale superpixel grid shows to perform superior to the state of the art methods - a promising basis for practical applications.

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BAGRI, VIKAS. "SIMPLE LINEAR ITERATIVE CLUSTERING AND HAAR WAVELET BASED IMAGE FORGERY DETECTION." Thesis, 2018. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16358.

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The ready availability ofimage-editingsoftwaremakesitimportanttoensuretheauthenticity of images. This thesis concerns the detection and localization of cloning, or Copy-Move Forgery (CMF), which is the most common type of image tampering, inwhichpart(s)ofthe image are copied and pasted back somewhereelseinthesameimage.Post-processingcanbe used to produce more realistic doctored images and thus can increase the difficulty of detecting forgery. The thesis postulates the use of segmentation approach by following the three steps, segmentation of the image by SLIC, then using the Haar Wavelet Transform to extract the features and then using the Dense Depth Reconstruction algorithmforfeaturematching.The experimental results illustrate that our proposed algorithms can detect forgery in images containing copy-move objects with different types of transformation (translation, rotation, scaling, distortion andcombinedtransformation).Moreover,theproposedmethodsarerobust to postprocessing (i.e. blurring, brightness change, color reduction, JPEG compression, variations in contrast and added noise) and can detect multiple duplicated objects.

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Wang, Wei. "Spatially Adaptive Analysis and Segmentation of Polarimetric SAR Data." Doctoral thesis, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-218081.

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In recent years, Polarimetric Synthetic Aperture Radar (PolSAR) has been one of the most important instruments for earth observation, and is increasingly used in various remote sensing applications. Statistical modelling and scattering analysis are two main ways for PolSAR data interpretation, and have been intensively investigated in the past two decades. Moreover, spatial analysis was applied in the analysis of PolSAR data and found to be beneficial to achieve more accurate interpretation results. This thesis focuses on extracting typical spatial information, i.e., edges and regions by exploring the statistical characteristics of PolSAR data. The existing spatial analysing methods are mainly based on the complex Wishart distribution, which well characterizes the inherent statistical features in homogeneous areas. However, the non-Gaussian models can give better representation of the PolSAR statistics, and therefore have the potential to improve the performance of spatial analysis, especially in heterogeneous areas. In addition, the traditional fixed-shape windows cannot accurately estimate the distribution parameter in some complicated areas, leading to the loss of the refined spatial details. Furthermore, many of the existing methods are not spatially adaptive so that the obtained results are promising in some areas whereas unsatisfactory in other areas. Therefore, this thesis is dedicated to extracting spatial information by applying the non-Gaussian statistical models and spatially adaptive strategies. The specific objectives of the thesis include: (1) to develop reliable edge detection method, (2) to develop spatially adaptive superpixel generation method, and (3) to investigate a new framework of region-based segmentation. Automatic edge detection plays a fundamental role in spatial analysis, whereas the performance of classical PolSAR edge detection methods is limited by the fixed-shape windows. Paper 1 investigates an enhanced edge detection method using the proposed directional span-driven adaptive (DSDA) window. The DSDA window has variable sizes and flexible shapes, and can overcome the limitation of fixed-shape windows by adaptively selecting homogeneous samples. The spherically invariant random vector (SIRV) product model is adopted to characterize the PolSAR data, and a span ratio is combined with the SIRV distance to highlight the dissimilarity measure. The experimental results demonstrated that the proposed method can detect not only the obvious edges, but also the tiny and inconspicuous edges in heterogeneous areas. Edge detection and region segmentation are two important aspects of spatial analysis. As to the region segmentation, paper 2 presents an adaptive PolSAR superpixel generation method based on the simple linear iterative clustering (SLIC) framework. In the k-means clustering procedure, multiple cues including polarimetric, spatial, and texture information are considered to measure the distance. Since the constant weighting factor which balances the spectral similarity and spatial proximity may cause over- or under-superpixel segmentation in different areas, the proposed method sets the factor adaptively based on the homogeneity analysis. Then, in heterogeneous areas, the spectral similarity is more significant than the spatial constraint, generating superpixels which better preserved local details and refined structures. Paper 3 investigates another PolSAR superpixel generation method, which is achieved from the global optimization aspect, using the entropy rate method. The distance between neighbouring pixels is calculated based on their corresponding DSDA regions. In addition, the SIRV distance and the Wishart distance are combined together. Therefore, the proposed method makes good use of the entropy rate framework, and also incorporates the merits of the SIRV distance and the Wishart distance. The superpixels are generated in a homogeneity-adaptive manner, resulting in smooth representation of the land covers in homogeneous areas, and well preserved details in heterogeneous areas.

QC 20171123

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Частини книг з теми "LINEAR ITERATIVE CLUSTERING"

Liao, Nannan, Hui Liu, Cheng Li, Xia Ren, and Baolong Guo. "Simple Linear Iterative Clustering with Efficiency." In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 109–17. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1057-9_11.

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Zhang, Houwang, and Yuan Zhu. "KSLIC: K-mediods Clustering Based Simple Linear Iterative Clustering." In Pattern Recognition and Computer Vision, 519–29. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31723-2_44.

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Ding, Tianyou, Wentao Zhang, and Chunning Zhou. "Clustering Effect of Iterative Differential and Linear Trails." In Information Security and Cryptology, 252–71. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26553-2_13.

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Choi, Kang-Sun, and Ki-Won Oh. "Fast Simple Linear Iterative Clustering by Early Candidate Cluster Elimination." In Pattern Recognition and Image Analysis, 579–86. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19390-8_65.

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Wang, Jing, Zilan Hu, and Haixian Wang. "Parcellating Whole Brain for Individuals by Simple Linear Iterative Clustering." In Neural Information Processing, 131–39. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46675-0_15.

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Su, Fan, Hui Xu, Guodong Chen, Zhenhua Wang, Lining Sun, and Zheng Wang. "Improved Simple Linear Iterative Clustering Algorithm Using HSL Color Space." In Intelligent Robotics and Applications, 413–25. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27541-9_34.

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Pavithra, G., T. C. Manjunath, and Dharmanna Lamani. "Detection of Primary Glaucoma in Humans Using Simple Linear Iterative Clustering (SLIC) Algorithm." In Lecture Notes on Data Engineering and Communications Technologies, 417–28. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24643-3_50.

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Mathews, Arun B., S. U. Aswathy, and Ajith Abraham. "Lung CT Image Enhancement Using Improved Linear Iterative Clustering for Tumor Detection in the Juxta Vascular Region." In Lecture Notes in Networks and Systems, 463–71. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09176-6_53.

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Chowdhary, Chiranji Lal. "Simple Linear Iterative Clustering (SLIC) and Graph Theory-Based Image Segmentation." In Handbook of Research on Machine Learning Techniques for Pattern Recognition and Information Security, 157–70. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3299-7.ch010.

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Анотація:

With the extensive application of deep acquisition devices, it has become more feasible to access deep data. The accuracy of image segmentation can be improved by depth data as an additional feature. The current research interests in simple linear iterative clustering (SLIC) are because it is a simple and efficient superpixel segmentation method, and it is initially applied for optical images. This mainly comprises three operation steps (i.e., initialization, local k-means clustering, and postprocessing). A scheme to develop the image over-segmentation task is introduced in this chapter. It considers the pixels of an image with simple linear iterative clustering and graph theory-based algorithm. In this regard, the main contribution is to provide a method for extracting superpixels with greater adherence to the edges of the regions. The experimental tests will consider biomedical grayscales. The robustness and effectiveness will be verified by quantitative and qualitative results.

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Wang, Shuliang, Wenyan Gan, Deyi Li, and Deren Li. "Data Field for Hierarchical Clustering." In Developments in Data Extraction, Management, and Analysis, 303–24. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2148-0.ch014.

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In this paper, data field is proposed to group data objects via simulating their mutual interactions and opposite movements for hierarchical clustering. Enlightened by the field in physical space, data field to simulate nuclear field is presented to illuminate the interaction between objects in data space. In the data field, the self-organized process of equipotential lines on many data objects discovers their hierarchical clustering-characteristics. During the clustering process, a random sample is first generated to optimize the impact factor. The masses of data objects are then estimated to select core data object with nonzero masses. Taking the core data objects as the initial clusters, the clusters are iteratively merged hierarchy by hierarchy with good performance. The results of a case study show that the data field is capable of hierarchical clustering on objects varying size, shape or granularity without user-specified parameters, as well as considering the object features inside the clusters and removing the outliers from noisy data. The comparisons illustrate that the data field clustering performs better than K-means, BIRCH, CURE, and CHAMELEON.

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Тези доповідей конференцій з теми "LINEAR ITERATIVE CLUSTERING"

Kim, Kwang-Shik, Dongni Zhang, Mun-Cheon Kang, and Sung-Jea Ko. "Improved simple linear iterative clustering superpixels." In 2013 IEEE 17th International Symposium on Consumer Electronics (ISCE). IEEE, 2013. http://dx.doi.org/10.1109/isce.2013.6570216.

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Li, Shiren, Junwei Huang, Jiayu Shang, and Xiongyi Wei. "A robust simple linear iterative clustering algorithm." In 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP). IEEE, 2017. http://dx.doi.org/10.1109/siprocess.2017.8124557.

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Kang-Sun Choi and Ki-Won Oh. "Fast simple linear iterative clustering for superpixel segmentation." In 2015 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2015. http://dx.doi.org/10.1109/icce.2015.7066521.

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Wei, Zhifei, Baolong Guo, Cheng Li, and Zhijie Chen. "Speeded-up Simple Linear Iterative Clustering Based on Region Homogeneity." In 2019 2nd International Conference on Safety Produce Informatization (IICSPI). IEEE, 2019. http://dx.doi.org/10.1109/iicspi48186.2019.9096051.

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Al-Azawi, Razi J., Qussay S. Al-Jubouri, and Yousra Abd Mohammed. "Enhanced Algorithm of Superpixel Segmentation Using Simple Linear Iterative Clustering." In 2019 12th International Conference on Developments in eSystems Engineering (DeSE). IEEE, 2019. http://dx.doi.org/10.1109/dese.2019.00038.

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Margapuri, Venkat, Trevor Rife, Chaney Courtney, Brandon Schlautman, Kai Zhao, and Mitchell Neilsen. "Fractional Vegetation Cover Estimation using Hough Lines and Linear Iterative Clustering." In 2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS). IEEE, 2022. http://dx.doi.org/10.1109/ipas55744.2022.10052996.

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Doğan, Çağdaş. "Seaweed Growth Detection in Aquaculture Environment Using Simple Linear Iterative Clustering Method." In The 8th International Conference of Biotechnology, Environment and Engineering Sciences. SRO media, 2020. http://dx.doi.org/10.46617/icbe8001.

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Estimating the total biomass of cultivates in aquaculture plantations (fisheries, mussel plants, seaweed farms and compound sites) remains to be an issue for the industry and the researchers alike. There has been a diverse array of approaches towards this issue, like using markers, manually stapling the leaflets, weighting the actual mass of the organism and calculating the total mass by extrapolation. Seaweed growth detection is a subset of this problem. Our goal is to introduce a solution by automatically detecting the ratio of the target object in images of seaweed taken from an underwater environment. Researchers/operators then can evaluate the total mass of seaweed. This study aimed to function as a decision support system. The system is built based on an image segmentation algorithm named Simple Linear Iterative Clustering (SLIC) which is a kind of superpixel segmentation. This paper conveys the results obtained from our approach towards the seaweed growth detection, elaborates on the usage and feasibility of our solution in seaweed sites and showcase the economic impact in the industry. Other dimensions of the growth detection methods in current practice for seaweed growth is also discussed, such as lack of automation in the current best-practices while focusing on the difficulties accompanying this status-quo.

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Junliang, Ma, Wang Xili, and Xiao Bing. "Semi-supervised image segmentation with globalized probability of boundary and simple linear iterative clustering." In 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2017. http://dx.doi.org/10.1109/fskd.2017.8393374.

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Aravinda, H. L., and M. V. Sudhamani. "Simple Linear Iterative Clustering Based Tumor Segmentation in Liver Region of Abdominal CT-scan." In 2017 International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT). IEEE, 2017. http://dx.doi.org/10.1109/icraect.2017.18.

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Chen, Yen-Wei, Akira Furukawa, Ayako Taniguchi, Tomoko Tateyama, and Shuzo Kanasaki. "Automated assessment of small bowel motility function based on simple linear iterative clustering (SLIC)." In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2015. http://dx.doi.org/10.1109/fskd.2015.7382209.

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