Research on Virtual Color Restoration of Complex Building System Based on Discrete Wavelet Transform by Chunqiao Song, Xutong Wu

“…We find all the required partitions according to the user’s needs and use the center points in these partitions as the initial clustering centers of the fuzzy C-means (FCM) algorithm to complete the automatic clustering of the two images. …”
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Microarray Core Detection by Geometric Restoration by Jimmy C. Azar, Christer Busch, Ingrid B. Carlbom

“…The algorithm’s simplicity, accuracy, and computational efficiency allow for automated high-throughput analysis of microarray images.…”
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Automated quantitative analysis of coat and skin coloration in laboratory animals by V. A. Prikhodko, U. V. Nogaeva, D. Yu. Ivkin, S. V. Okovityi

“…The algorithm for colorimetric analysis included 3 steps: 1) preprocessing images and masking the background; 2) hierarchical color clustering and reclustering; 3) calculating absolute and relative color cluster areas. …”
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An Object Feature-Based Recognition and Localization Method for Wolfberry by Renwei Wang, Dingzhong Tan, Xuerui Ju, Jianing Wang

“…To improve the object recognition and localization capabilities of wolfberry harvesting robots, this study introduces an object feature-based image segmentation algorithm designed for the segmentation and localization of wolfberry fruits and branches in unstructured lighting environments. …”
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A multitasking ant system for multi-depot pick-up and delivery location routing problem with time window by Haoyuan Lv, Ruochen Liu, Jianxia Li

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Potentiality of Landsat-9 for early-season mapping of winter garlic and winter wheat by Haifeng Tian, Mengdan Yang, Fangli Wu, Yaochen Qin, Xiwang Zhang, Jiayi Liu, Weiyang Yan

“…According to J-M (Jeffries-Matusita) distances, we evaluated the separability of winter garlic, winter wheat, and other ground objects based on the two Landsat-9 images. Then, winter garlic and winter wheat were extracted by using unsupervised classification method, i.e. the IsoData and K-means clustering algorithms, and supervised classification method, i.e. the Random Forest (RF), and Support Vector Machine (SVM) algorithms. …”
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Real-Time Analysis of Athletes’ Physical Condition in Training Based on Video Monitoring Technology of Optical Flow Equation by Cuijuan Wang

“…We densely sampled feature points contain as much motion information as possible in the video and then express this motion information through trajectory description and finally achieve segmentation of moving targets through clustering of motion trajectories. At the same time, the basic concepts of image segmentation and video motion target segmentation are described, and the division standards of different video motion segmentation algorithms and their respective advantages and disadvantages are analyzed. …”
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VPL geometry processing for open multilevel and multiscalar databases by E. Lanzara, G. Fatigati, S. Guardascione, M. T. Rapicano

“…The system allows semi-automatic clustering of areas, cells, colours attribution and labelling of text and image from link data list. …”
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PDCNN: FRAMEWORK for Potato Diseases Classification Based on Feed Foreword Neural Network by Israa Mohammed Hassoon, Samar Amil Qassir, Musaab Riyadh

“…The objective of this work  is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. …”
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Summaries of certain spatial patterns retrieved from multidate remote-sensing data by Hema Nair

“…Techniques, such as clustering and genetic algorithms, are used to mine images. …”
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EPCNet: Implementing an ‘Artificial Fovea’ for More Efficient Monitoring Using the Sensor Fusion of an Event-Based and a Frame-Based Camera by Orla Sealy Phelan, Dara Molloy, Roshan George, Edward Jones, Martin Glavin, Brian Deegan

“…The cameras are calibrated to create a multi-modal stereo vision system where pixel coordinates can be projected between the event and RGB camera image planes. This calibration is used to project bounding boxes detected by clustering of events into the RGB image plane, thereby cropping each RGB frame instead of down-sampling to meet the requirements of the CNN. …”
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Akuisisi Foreground dan Background Berbasis Fitur DTC pada Matting Citra secara Otomatis by Meidya Koeshardianto, Eko Mulyanto Yuniarno, Mochamad Hariadi

“…Background acquisition is determined from the variant value of the DCT feature that is clustered using the k-means algorithm. Foreground acquisition is determined by a subset resulting from clustering DCT values with edge detection features. …”
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Integration of Deep Learning and Machine Learning Techniques for Advancing the Detection of Plant Diseases by Abdul Karim, Touqeer Ahmed Jumani, Muhammad Amir Raza, Shadi Khan Baloch, Nouman Qadeer Soomro, Muhammad Masud, Muhammad Salman Saeed, Mouaaz Nahas, Aamir Ali Patoli

“…For the detection of the unknown kind of diseases in plants, the Density‐Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm was utilized for clustering the similar captured images and for disease classification; then CNN XGBoost was applied on the newly developed dataset. …”
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Smart Edge Computing Framework for Real-Time Brinjal Harvest Decision Optimization by T. Tamilarasi, P. Muthulakshmi, Seyed-Hassan Miraei Ashtiani

“…The brinjal detection algorithm employs pixel-based methods, including background elimination, K-means clustering, and symmetry testing for precise identification. …”
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A Spiderless Arachnophobia Therapy: Comparison between Placebo and Treatment Groups and Six-Month Follow-Up Study by Laura Carmilo Granado, Ronald Ranvaud, Javier Ropero Peláez

“…A k-means clustering algorithm revealed that, after 4 weeks of treatment, 42% of the patients moved from the arachnophobic to the nonarachnophobic cluster. …”
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Enhancing High-Temperature Prediction via Sixfold Strategy Consensus-Reaching Processes: A Case Study Using FY-3E Spatiotemporal Remote Sensing Satellite Data by Chao Zhang, Haonan Hou, Arun Kumar Sangaiah, Deyu Li, Wentao Li

“…Initially, the concept of adjustable MG PF PRSs is introduced by integrating picture fuzzy sets with PRSs. Subsequently, a clustering algorithm based on trust similarity analysis is employed as the clustering method within the proposed framework, alongside the incorporation of 6FS-CRP. …”
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Research on Simultaneous Localization and Intrusion Detection Methodology for Intelligent Trains Based on Multisource Fusion by ZENG Xiang, JIANG Guotao, LYU Yu, LI Cheng, PAN Wenbo, LUO Ziqi

“…Following this, object detection is performed within these clearances based on depth map segmentation and a point cloud clustering algorithm to acquire the positions and sizes of intrusion objects. …”
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Cheetah optimized CNN: A bio-inspired neural network for automated diabetic retinopathy detection by V. K. U. Ahamed Gani, N. Shanmugasundaram

“…Preprocessing steps involve median filter and contrast limited adaptive histogram equalization to enhance image quality. The segmented output is clustered using the cascaded fuzzy C-means algorithm and features are extracted with the speeded-up robust features algorithm. …”
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Tree Roots Reconstruction Framework for Accurate Positioning in Heterogeneous Soil by Wenhao Luo, Yee Hui Lee, Hai-Han Sun, Genevieve Ow, Mohamed Lokman Mohd Yusof, Abdulkadir C. Yucel

“…The proposed framework combines four techniques to be applied consecutively: First, a hyperbola extraction method based on a column-connection clustering algorithm is used to extract individual hyperbolae in B-scans, eliminate mutual influence in the process, and suppress noise. …”
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Large-Scale Video Retrieval via Deep Local Convolutional Features by Chen Zhang, Bin Hu, Yucong Suo, Zhiqiang Zou, Yimu Ji

“…Our framework consists of the key-frame extraction algorithm and the feature aggregation strategy. Specifically, the key-frame extraction algorithm takes advantage of the clustering idea so that redundant information is removed in video data and storage cost is greatly reduced. …”
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