Face Identification Based on K-Nearest Neighbor by Ni Kadek Ayu Wirdiani, Praba Hridayami, Ni Putu Ayu Widiari, Komang Diva Rismawan, Putu Bagus Candradinata, I Putu Deva Jayantha

“…The stages of face identification research using the KNN method are pre-processing in the input image. Preprocessing used in this research are contrass stretching, grayscale, and segmentation used haar cascade. …”
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MMG-Based Motion Segmentation and Recognition of Upper Limb Rehabilitation Using the YOLOv5s-SE by Gangsheng Cao, Shen Jia, Qing Wu, Chunming Xia

“…In this paper, the collected MMG signals were transformed into one-dimensional time-series images. After image processing, the training set and test set were divided for the training and testing of the YOLOv5s-SE model. …”
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COMPARISON OF THE METHOD OF ELECTROMETRIC DETERMINATION OF ROOT CANAL PARAMETERS AND THE METHOD OF THRESHOLD SEGMENTATION OF RADIOGRAPHS by Oleksii Perepelytsia, Oleg Avrunin

“…Radiological is the most promising for research because of its painlessness to the patient, low radiation dose during intraoral radiography and the possibility of using image processing algorithms to refine the measurement results. …”
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Optimal Poisson Cognitive System with Markov Learning Model by A. A. Solodov

“…Thus, the following assumptions are accepted in the work, apparently corresponding to the behavior of the system assuming human reactions, i.e. the cognitive system.The images analyzed by the system arise at random moments of time, while the duration of time between neighboring appearances of images is distributed exponentially.The system analyzes the resulting images and makes a decision about the presence or absence of an image at its input in accordance with the optimal Neуman-Pearson algorithm that maximizes the probability of correct identification of the image with a fixed probability of false identification.The system is trainable in the sense that decisions about the presence or absence of an image are made sequentially on a set of identical situations, and the probability of making a decision depends on the previous decision of the system.The new results of the study are analytical expressions for the probabilities of the system staying in each of the possible states, depending on the number of steps of the learning process and the intensities of useful and interfering stimuli at the input of the system. …”
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DETECTION OF KERATOCONUS DISEASE DEPENDING ON CORNEAL TOPOGRAPHY USING DEEP LEARNING by Aseel Abdulhasan Hashim, Mahdi Mazinani

“…The pre-processing stage involves cropping images to retain the relevant maps, which were subjected to contrast enhancement to improve image quality. …”
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Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning. by Jiayi Wu, Yong-Bei Ma, Charles Congdon, Bevin Brett, Shuobing Chen, Yaofang Xu, Qi Ouyang, Youdong Mao

“…However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. …”
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TECHNOLOGIES FOR DEVELOPING DECISION SUPPORT SYSTEMS FOR THE DIAGNOSIS OF BLOOD DISORDERS USING CONVOLUTIONAL NEURAL NETWORKS by U. V. Maslikova, A. A. Supilnikov

“…We selected the most promising machine learning algorithms optimal for the processing of medical images, investigated the technologies of analyzing medical texts, studied the aspects of using the Watson neural network for analyzing the semantics of medical images, as well as the aspect of using the unified medical language UMLS for the needs of syndromic diagnostics for the evaluation of medical texts from medical histories in natural language. …”
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Local Pattern Feature Extraction and Ｒecognition Based on Sparse Ｒepresentation by ZHANG Xue-qin, LIN Ke-zheng, LI Ao

“…Firstly，the image is divided into several sub-images and the Dynamic Threshold Central-symmetric Local Binary Pattern ( DTCLBP) algorithm is used to extract features by thresholding the pixels of each sub-block and encoding the results of comparison with the central pixel values into the Central Symmetric Local Binary Pattern ( CSLBP) ; and then second-order features are extracted from the processed image by the former step using the Central Symmetric Local Derivative Pattern ( CSLDP) ; finally，the sparse representation classification algorithm is used to classify and identify the extracted features. …”
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Towards an automated protocol for wildlife density estimation using camera‐traps by Andrea Zampetti, Davide Mirante, Pablo Palencia, Luca Santini

“…Here, we assessed the capability of two camera‐trap based models to provide robust density estimates when image classification is carried out by machine learning algorithms. …”
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Metallic Artifacts’ Reduction in Microtomography Using the Bone- and Soft-Tissue Decomposition Method by Jan Juszczyk, Jakub Pałachniak, Ewa Piętka

“…Artifacts in computed tomography and X-ray microtomography are image distortions caused by various factors. Some can be reduced before or during the examination, while others are removed algorithmically after image acquisition. …”
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Generating Automatically Print/Scan Textures for Morphing Attack Detection Applications by Juan E. Tapia, Maximilian Russo, Christoph Busch

“…One common scenario involves creating altered images and using them in passport applications. Currently, there are limited datasets available for training the MAD algorithm due to privacy concerns and the challenges of obtaining and processing a large number of printed and scanned images. …”
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A context aware multiclass loss function for semantic segmentation with a focus on intricate areas and class imbalances by Zahra Ghanaei, Modjtaba Rouhani

“…This was achieved by utilizing the SLIC algorithm and analyzing each superpixel to detect key regions in images, followed by implementing a weighting scheme to control the influence of each area in the loss calculation. …”
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High-Precision Stored-Grain Insect Pest Detection Method Based on PDA-YOLO by Fuyan Sun, Zhizhong Guan, Zongwang Lyu, Shanshan Liu

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A new split based searching for exact pattern matching for natural texts. by Saqib Hakak, Saqib Hakak, Amirrudin Kamsin, Palaiahnakote Shivakumara, Mohd Yamani Idna Idris, Gulshan Amin Gilkar

“…Exact pattern matching algorithms are popular and used widely in several applications, such as molecular biology, text processing, image processing, web search engines, network intrusion detection systems and operating systems. …”
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Drone-assisted adaptive object detection and privacy-preserving surveillance in smart cities using whale-optimized deep reinforcement learning techniques by Ahmed Abu-Khadrah, Ahmad Al-Qerem, Mohammad R. Hassan, Ali Mohd Ali, Muath Jarrah

“…Artificial intelligence algorithms and computer-aided processing will handle the images extracted from the surveillance videos to reveal the object. …”
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Computationally Efficient Artifact Suppression for Optical Coherence Tomography Angiography by Wenxin Zhang, Jun Pan, Shiqi Tang, Ning Liu

“…To address this, we propose a computationally efficient image processing technique that simultaneously reduces artifacts to advance clinical OCTA applications. …”
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Spatial identification of manipulable objects for a bionic hand prosthesis by Yurii LOBUR, Kostiantyn VONSEVYCH, Natalia BEZUGLA

“…This article presents a method for the spatial identification of objects for bionic upper limb prostheses, utilizing the analysis of digital images captured by an optoelectronic module based on the ESP32-CAM and classified using neural network algorithms, specifically FOMO (MobileNetV2). …”
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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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A Deep Learning Framework for Damage Assessment of Composite Sandwich Structures by Viviana Meruane, Diego Aichele, Rafael Ruiz, Enrique López Droguett

“…The damage indices, which are represented as grayscale images, are processed using a convolutional-neural-network-based algorithm to automatically identify damaged regions. …”
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An effective method for the abnormal monitoring of stage performance based on visual sensor network by Fuquan Zhang, Gangyi Ding, Lin Xu, Bo Chen, Zuoyong Li

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