Combining Global Features and Local Interoperability Optimization Method for Extracting and Connecting Fine Rivers by Jian Xu, Xianjun Gao, Zaiai Wang, Guozhong Li, Hualong Luan, Xuejun Cheng, Shiming Yao, Lihua Wang, Sunan Shi, Xiao Xiao, Xudong Xie

“…Due to the inherent limitations in remote sensing image quality, seasonal variations, and radiometric inconsistencies, river extraction based on remote sensing image classification often results in omissions. …”
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Stability Evaluation of Fault Diagnosis Model Based on Elliptic Fourier Descriptor by YanZhu Hu, Yu Hu, XinBo Ai, HuiYang Zhao, Zhen Meng, WenJia Tian, Jiao Wang

“…Aiming at the stability evaluation of the fault diagnosis model based on the characteristic clustering, an image edge detection method based on the Elliptic Fourier Descriptor (EFDSE) is proposed to evaluate the stability of the fault diagnosis model, which applies similarity measurement of image to effective evaluation of faulty diagnosis algorithm. …”
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Unsupervised Classification Method for Polarimetric Synthetic Aperture Radar Imagery Based on Yamaguchi Four-Component Decomposition Model by Sheng Sun, Renfeng Liu, Wen Wen

“…Therewith, the four-component model is combined with the Wishart distance model. The new proposed algorithm of clustering is rolled out thereafter and the procedure of this new method is listed. …”
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Study of spectral overlap and heterogeneity in agriculture based on soft classification techniques by Shubham Rana, Salvatore Gerbino, Petronia Carillo

“…The analysis involves preprocessing the image data, calculating the vegetation indices, and applying the MPCM algorithm to perform soft classification, allowing pixels to belong to multiple classes with varying degrees of membership. …”
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Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web by Shiwei Zhu, Guanwen Fang, Chichun Zhou, Jie Song, Zesen Lin, Yao Dai, Xu Kong

“…The upgraded UML method primarily consists of the following three aspects. (1) We employ a convolutional autoencoder to denoise galaxy images and adaptive polar coordinate transformation to enhance the model’s rotational invariance. (2) A pretrained dual-encoder convolutional neural network based on ConvNeXt and a vision transformer is used to encode the image data, while contrastive learning is then applied to reduce the dimension of the features. (3) We adopt a bagging-based clustering model to cluster galaxies with similar features into distinct groups. …”
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Severity Scale of Diabetic Macular Ischemia Based on the Distribution of Capillary Nonperfusion in OCT Angiography by Miyo Yoshida, MD, Tomoaki Murakami, MD, PhD, Keiichi Nishikawa, MD, Kenji Ishihara, MD, PhD, Yuki Mori, MD, PhD, Akitaka Tsujikawa, MD, PhD

“…Conclusions: The application of dimensionality reduction and clustering has facilitated the development of a novel severity scale for DMI based on the distribution of capillary nonperfusion in OCTA images. …”
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Optimasi Fuzzy C-Means dan K-Means Menggunakan Algoritma Genetika untuk Pengklasteran Dataset Diabetic Retinopathy by Muhammad Ezar Al Rivan, Steven Steven, William Tanzil

“…Genetic Algorithm-Fuzzy-C-Means has smallest inter cluster distance and biggest intra cluster distance. …”
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On the Impact of Labeled Sample Selection in Semisupervised Learning for Complex Visual Recognition Tasks by Eftychios Protopapadakis, Athanasios Voulodimos, Anastasios Doulamis

“…We propose and explore a variety of combinatory sampling approaches that are based on sparse representative instances selection (SMRS), OPTICS algorithm, k-means clustering algorithm, and random selection. …”
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Mining behavior pattern of mobile malware with convolutional neural network by Xin ZHANG, Weizhong QIANG, Yueming WU, Deqing ZOU, Hai JIN

“…The features extracted by existing malicious Android application detection methods are redundant and too abstract to reflect the behavior patterns of malicious applications in high-level semantics.In order to solve this problem,an interpretable detection method was proposed.Suspicious system call combinations clustering by social network analysis was converted to a single channel image.Convolution neural network was applied to classify Android application.The model trained was used to find the most suspicious system call combinations by convolution layer gradient weight classification activation mapping algorithm,thus mining and understanding malicious application behavior.The experimental results show that the method can correctly discover the behavior patterns of malicious applications on the basis of efficient detection.…”
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An Improved Machine Learning-Based Method for Unsupervised Characterisation for Coral Reef Monitoring in Earth Observation Time-Series Data by Zayad AlZayer, Philippa Mason, Robert Platt, Cédric M. John

“…This study presents an innovative approach to automated coral reef monitoring using satellite imagery, addressing challenges in image quality assessment and correction. The method employs Principal Component Analysis (PCA) coupled with clustering for efficient image selection and quality evaluation, followed by a machine learning-based cloud removal technique using an XGBoost model trained to detect land and cloudy pixels over water. …”
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Three-Dimensional Phenotyping Pipeline of Potted Plants Based on Neural Radiation Fields and Path Segmentation by Xinghui Zhu, Zhongrui Huang, Bin Li

“…An indoor collection system was constructed to obtain multi-view image sequences of potted plants. The structure from motion and neural radiance fields (SFM-NeRF) algorithm was then utilized to reconstruct 3D point clouds, which were subsequently denoised and calibrated. …”
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A Shield Segment Bolt Detection and Localization Method Based on Ellipse Detection by Jie Liu, Shuaishuai Liu, Zhendong He, Yanqiu Xiao, Guangzhen Cui, Pengpeng Wang

“…In response to the issue of decreased elliptical detection accuracy due to circular arc defects, an arc-cluster-based elliptical detection algorithm is further proposed, which encompasses key steps such as image preprocessing, arc clustering, and least squares fitting. …”
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Entropy-based Variational Learning of Finite Inverted Beta-Liouville Mixture Model by Mohammad Sadegh Ahmadzadeh, Narges Manouchehri, Hafsa Ennajari, Nizar Bouguila, Manar Amayri, Wentao Fan

“…To deploy the proposed model, we introduce an entropy-based variational inference algorithm. The performance of the proposed model is evaluated on two real-world applications, namely, human activity recognition and image categorization.…”
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ADAPTIVE VISION AI by V. Vodyanitskyi, V. Yuskovych-Zhukovska

“…An untrained computer vision algorithm is unable to understand the relationship between the shapes in the image and the objects. …”
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Real-time crop row detection using computer vision- application in agricultural robots by Md. Nazmuzzaman Khan, Adibuzzaman Rahi, Veera P. Rajendran, Mohammad Al Hasan, Sohel Anwar

“…Secondly, a clustering algorithm is used to differentiate between the crop and weed pixels. …”
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Microcrack feature extraction method for chaotic optical surface of silicon nitride ceramic bearing roller based on multi-scale wavelet transform enhancement and optimized PSO-FCM... by Yi Jiang, Mengtao Tang, Wenjie Dong, Guoxing Tang, Weiwen Hu, Jiao Li

“…Through the db4 basis function of multi-scale decomposition of microcrack features, the soft threshold function is constructed to deeply denoise the image. The normalized fusion features of microcracks after multi-scale vector decomposition are enhanced, and the gradient information is enhanced while retaining the microcrack features on the chaotic optical surface. …”
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Bi-modal ultrasound radiomics and habitat analysis enhanced the pre-operative prediction of axillary lymph node burden in patients with early-stage breast cancer by Jing Xu, Pan Qi, Xiaoyan Ou, Qiaoxin Zhong, Zhen-Wen Chen, Yin Wang, Aijiao Yi, Bin Wang

“…The feature extraction was manually delineated with ITK-SNAP software, while a K-means clustering algorithm was employed for the segmentation of sub-regions, with the number of clusters ranging from 2 to 10. …”
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Benchmark dataset on feeding intensity of the pearl gentian grouper(Epinephelus fuscoguttatus♀×E. lanceolatus♂) by Haijing Qin, Yunchen Tian, Jianing Quan, Xueqi Cong, Qingfei Li, Jinzhu Sui

“…In order to solve these problems, this study constructs a benchmark dataset of the feeding intensity of pearl gentian groupers in a factory circulating water environment, which is divided into feeding fish groups and fish aggregation areas by training Unet semantic segmentation network and compares standard clustering algorithms through clustering evaluation indexes to maximally select the optimal clustering method and the number of clusters that are suitable for this paper's dataset. …”
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Transductive zero-shot learning via knowledge graph and graph convolutional networks by Qiong Li, Xin Sun, Junyu Dong

“…With a shallow graph convolutional network having a small number of layers, we learn the classifier for each category, supervised by the visual classifiers of the seen categories. During testing, a clustering strategy, the Double Filter Module with Hungarian algorithm, is applied to the unseen samples, and then, the learned classifiers are used to predict their categories. …”
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A digital measurement approach for structural condition assessment of sewers by Martins Luís Lages, Ribeiro Álvaro Silva, Almeida Maria do Céu, Brito Rita Salgado

“…The acquisition of multiple images of the observation scenario and application of the bundle adjustment technique, supported by computational algorithms dedicated to image parallel computation and clustering, is the base for the approach adopted. …”
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