A Multi-Objective Gray Consistency Correction Method for Mosaicking Regional SAR Intensity Images with Brightness Anomalies by Jiaying Wang, Xin Shen, Deren Li, Litao Li, Yonghua Jiang, Jun Pan, Zezhong Lu, Wei Yao

“…In the process of mosaicking regional synthetic aperture radar (SAR) intensity images, multiple images with significant brightness anomalies can cause a considerable number of pixels to exceed the grayscale quantization range. …”
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Algoritmo para classificação de plantas de milho atacadas pela lagarta do cartucho (Spodoptera frugiperda, Smith) em imagens digitais Algorithm for classification of fall armyworm... by Darly G. de Sena Júnior, Francisco de A. de C. Pinto, Daniel M. de Queiroz, Evandro C. Mantovani

“…O algoritmo apresentou índice de exatidão global de 80,74%.<br>An image processing and analysis algorithm was developed to identify the fall armyworm damage on corn plants. …”
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Provenance-Finding of Dust Storms Using AVHRR Sensor Images of NOAA Satellite (Case Study: Southwest of Iran) by Mohammad Hossein Rezaei Moghaddam, Mojtaba Mahdian Boruojeni

“…After preparing the images, in the processing stage, the particles were identified by applying the dust detection algorithms including brightness temperature differences in the thermal infrared bands (band 4&5 AVHRR sensor) and false color combination (R: 1 G: 2 B4). 3- Results and Discussion Detecting the images, a total of 62 origin points were extracted in the southwest of the country which were transferred to the GIS environment. …”
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Advanced retinal disease detection from OCT images using a hybrid squeeze and excitation enhanced model. by Gülcan Gencer, Kerem Gencer

“…<h4>Results</h4>The combined features from EfficientNetB0 and Xception are processed via fully connected layers and categorized using the Softmax algorithm. …”
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Unlocking ecological insights from sub‐seasonal visible‐to‐shortwave infrared imaging spectroscopy: The SHIFT campaign by K. Dana Chadwick, Frank Davis, Kimberley R. Miner, Ryan Pavlick, Mark Reynolds, Philip A. Townsend, Philip G. Brodrick, Christiana Ade, Jean Allen, Leander Anderegg, Yoseline Angel, Indra Boving, Kristin B. Byrd, Petya Campbell, Luke Carberry, Katherine C. Cavanaugh, Kyle C. Cavanaugh, Kelly Easterday, Regina Eckert, Michelle Gierach, Kaitlin Gold, Erin Hestir, Fred Huemmrich, Maggie Klope, Raymond F Kokaly, Piper Lovegreen, Kelly Luis, Conor McMahon, Nicholas Nidzieko, Francisco Ochoa, Anna Jiselle Ongjoco, Elsa Ordway, Madeleine Pascolini‐Campbell, Natalie Queally, Dar A. Roberts, Clare M. Saiki, Fabian D. Schneider, Alexey N. Shiklomanov, Germán D. Silva, Jordan Snyder, Michele Thornton, Anna T. Trugman, Nidhi Vinod, Ting Zheng, Dulcinea M. Avouris, Brianna Baker, Latha Baskaran, Tom Bell, Megan Berg, Michael Bernas, Niklas Bohn, Renato K. Braghiere, Zach Breuer, Andrew J. Brooks, Nolan Burkard, Julia Burmistrova, Kerry Cawse‐Nicholson, John Chapman, Johana Chazaro‐Haraksin, Joel Cryer, K. C. Cushman, Kyla Dahlin, Phuong D. Dao, Athena DiBartolo, Michael Eastwood, Clayton D. Elder, Angela Giordani, Kathleen Grant, Robert O. Green, Alexa Hanson, Brendan Heberlein, Mark Helmlinger, Simon Hook, Daniel Jensen, Emma Johnson, Marie Johnson, Michael Kiper, Christopher Kibler, Jennifer Y. King, Kyle R. Kovach, Aaron Kreisberg, Daniel Lacey, Evan Lang, Christine Lee, Amanda M. Lopez, Brittany Lopez Barreto, Andrew Maguire, Elliott Marsh, Charles Miller, Dieu My T. Nguyen, Cassandra Nickles, Jonathan P. Ocón, Elijah P. Papen, Maria Park, Benjamin Poulter, Ann Raiho, Porter Reim, Timothy H. Robinson, Fernando E. Romero Galvan, Ethan Shafron, Brenen R. Skalitzky, Sydney Stroschein, Niky C. Taylor, David R. Thompson, Kate Thompson, Cecily Tye, Joelie Van Beek, Cecilia Vanden Heuvel, Jonathan Vellanoweth, Evert Vermeer, Claire Villanueva‐Weeks, Kristen Zumdahl, David Schimel

“…Abstract We stand at the threshold of a transformative era in Earth observation, marked by space‐borne visible‐to‐shortwave infrared (VSWIR) imaging spectrometers that promise consistent global observations of ecosystem function, phenology, and inter‐ and intra‐annual change. …”
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YOLO-SAIL: Attention-Enhanced YOLOv5 With Optimized Bi-FPN for Ship Target Detection in SAR Images by Prabu Selvam, P. Shanmuga Sundari, M. Tamilselvi, T. Suresh, M. Murugappan, Muhammad E. H. Chowdhury

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Dynahead-YOLO-Otsu: an efficient DCNN-based landslide semantic segmentation method using remote sensing images by Zheng Han, Bangjie Fu, Zhenxiong Fang, Yange Li, Jiaying Li, Nan Jiang, Guangqi Chen

“…This is achieved by locating potential landslide regions in advance using the ODD-based Dynahead-YOLO model, which enhances the capacity for detecting landslides with variable proportions and complex background in the images. The preliminary results are then processed using the Otsu binarization algorithm to cluster pixels belonging to landslides from the images of potential regions for semantic segmentation. …”
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Advanced Deep Learning Fusion Model for Early Multi-Classification of Lung and Colon Cancer Using Histopathological Images by A. A. Abd El-Aziz, Mahmood A. Mahmood, Sameh Abd El-Ghany

“…Digital image processing (DIP) and deep learning (DL) algorithms can be employed to analyze the HIs of five different types of lung and colon tissues. …”
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Apollon Mark: bounce mark visualization system for ball sports judgement using prediction-based preceding mirror control by Himari Tochioka, Tomohiro Sueishi, Masatoshi Ishikawa

“…The system then enables bounce mark visualization by simple differential processing between the before and after images with a template-matching image processing algorithm. …”
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Efficient Malaria Parasite Detection From Diverse Images of Thick Blood Smears for Cross-Regional Model Accuracy by Yuming Zhong, Ying Dan, Yin Cai, Jiamin Lin, Xiaoyao Huang, Omnia Mahmoud, Eric S. Hald, Akshay Kumar, Qiang Fang, Seedahmed S. Mahmoud

“…This integration involves image acquisition and algorithmic detection of malaria parasites in various thick blood smear (TBS) datasets sourced from different global regions, including low-quality images from Sub-Saharan Africa. …”
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Using Convolutional Neural Networks for Segmentation of Multiple Sclerosis Lesions in 3D Magnetic Resonance Imaging by Abdulsattar Abdullah Hamad, Mustafa Musa Jaber, Mohammed Altaf Ahmed, Ghaida Muttashar Abdulsahib, Osamah Ibrahim Khalaf, Zelalem Meraf

“…Magnetic Resonance Imaging to detect its lesions is used to diagnose multiple sclerosis. …”
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A comprehensive image dataset for carambola leaf and fruit disease classification and quality assessmentMendeley Data by S.M. Abdullah Al Muhib, Rejowan Arifin Nayeem, Noman Mezi, Nafiz Ahmed Emon

“…The dataset comprises 2,618 original images, an equal number of processed images, and 15,000 augmented images generated from the original images. …”
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Estimation of potato leaf area index based on spectral information and Haralick textures from UAV hyperspectral images by Jiejie Fan, Jiejie Fan, Yang Liu, Yang Liu, Yiguang Fan, Yihan Yao, Riqiang Chen, Mingbo Bian, Yanpeng Ma, Huifang Wang, Haikuan Feng, Haikuan Feng, Haikuan Feng

“…A model for LAI estimation in potato at multiple growth stages based on spectral and textural features screened by the successive projection algorithm (SPA) was constructed using partial least squares regression (PLSR), random forest regression (RFR) and gaussian process regression (GPR) machine learning methods. …”
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Vision Systems for a UR5 Cobot on a Quality Control Robotic Station by Piotr Kohut, Kamil Skop

“…Image processing and analysis algorithms are described, the method of control and communication between the station components is discussed, and operating scenarios are presented as a single robotic station and as a part of the robotic line. …”
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Three Machine Learning Techniques for Melanoma Cancer Detection by Hadi Naghavipour, GholamReza Zandi, Abdulaziz Al-Nahari

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An Industrial Add-On for Precision Die-Centering in Catheter Extrusion Using Visual Servoing by Han Chang Lee, Byeong Hak Kim, Jong Pil Yun, Gyu Man Kim, Woojin Kim, Hong-In Won

“…This paper presents a real-time visual servoing system for automating die-centering in catheter extrusion processes. By leveraging state-of-the-art image processing algorithms, the system dynamically adjusts the positions of the extrusion head and tip in real time, ensuring precise alignment and significantly enhancing product consistency and quality. …”
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MsDC-DEQ-Net: Deep Equilibrium Model (DEQ) with Multiscale Dilated Convolution for Image Compressive Sensing (CS) by Youhao Yu, Richard M. Dansereau

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Adaptive Refinement of Segmented Object Contour Based on the Brightness of Neighboring Pixels Using the Ensemble Method by Vladyslav D. Koniukhov

“…Improving the accuracy of computer vision algorithms plays a significant role in the tasks of medical image segmentation. …”

Airport-FOD3S: A Three-Stage Detection-Driven Framework for Realistic Foreign Object Debris Synthesis by Hanglin Cheng, Yihao Li, Ruiheng Zhang, Weiguang Zhang

“…Traditional Foreign Object Debris (FOD) detection methods face challenges such as difficulties in large-size data acquisition and the ineffective application of detection algorithms with high accuracy. In this paper, image data augmentation was performed using generative adversarial networks and diffusion models, generating images of monitoring areas under different environmental conditions and FOD images of varied types. …”
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Deep convolutional neural network (DCNN)-based model for pneumonia detection using chest x-ray images by S. I. Ele, U. R. Alo, H. F. Nweke, A. H. Okemiri, E. O. Uche-Nwachi

“…This study focuses on developing and implementing a machine learning model tailored specifically for medical diagnosis, leveraging advancements in computer vision and deep learning algorithms. This research aims to design an efficient and accurate model capable of classifying medical images into distinct categories, enabling automated diagnosis and identification of various ailments and conditions. …”
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