The application of series multi-pooling convolutional neural networks for medical image segmentation by Feng Wang, Siwei Huang, Lei Shi, Weiguo Fan

“…The main contents of this article were studied as follows: the principle and operating approach of convolutional neural network on image processing was first introduced, and then 12-layer convolutions were skillfully set up for local pathways based on two-way convolutional neural network architectures; considering the inter-label dependency in pixel areas, the situation of conditional random field was simulated to design the input series connection structure; multi-pooling input series connection model was designed to solve the problem that the input pixel area is limited; finally, the classification accuracy upon experiments reached 83%, which has verified the effectiveness of model to improve.…”
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PYTHON INTERFACE DESIGN FOR MICROPHOTOGRAPHY ANALYSIS:APPLICATION TO EVALUATE THE HEMORHEOLOGICAL ACTIVITY OF QUERCETIN by P.Buszniez, M. Gómez, H. V. Castellini, B.D. Riquelme

“…The usability criteria of the GUI based on the TkInter library are aimed at non-expert users. The image processing algorithms are contained in the OpenCV2 library, which uses pre-trained neural networks. …”
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Intelligent Computer Technology-Driven Mural Pattern Recognition Method by Wenqing Wei, Lei Gao

“…In recent years, as a new image processing technology, deep learning based on a convolutional neural network is widely used in many fields. …”
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Particle Swarm Optimization Algorithm and Its Application in Image Segmentation by Yu Liyang

“…Meanwhile, classical segmentation methods such as OSTU, Watershed and K-means are also widely used in medical image processing due to their simplicity and effectiveness. …”
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Revealing Traces of Image Resampling and Resampling Antiforensics by Anjie Peng, Yadong Wu, Xiangui Kang

“…Image resampling is a common manipulation in image processing. The forensics of resampling plays an important role in image tampering detection, steganography, and steganalysis. …”
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ChatGPT and CLT: Investigating differences in multimodal processing by Michael Cahalane, Samuel N. Kirshner

“…Furthermore, while humans generally perceive ChatGPT to be more concrete in image processing, there is a notable discrepancy between this perception and the actual level of concreteness exhibited by ChatGPT in handling image-based tasks. …”
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Design and Implementation of Brain Tumor Segmentation and Detection Using a Novel Woelfel Filter and Morphological Segmentation by M Venu Gopalachari, Morarjee Kolla, Rupesh Kumar Mishra, Zarin Tasneem

“…The tumor will be located and identified using morphological image processing. Image denoising refers to the process of removing artefacts such as noise and aliasing from digital images. …”
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Stochastic Risk Assessment with a Lagrangian Solution for the Optimal Cost Allocation in High-Speed Rail Networks by Jing Zuo, Jianwu Dang, Min Lyu

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SACU-Net: Shape-Aware U-Net for Biomedical Image Segmentation With Attention Mechanism and Context Extraction by Yinuo Cao, Yong Cheng

“…With the rapid development of convolutional neural networks in image processing, deep learning has been widely applied to medical image segmentation tasks, including liver, retinal vessels, nuclei, and COVID-19 lesion segmentation. …”
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Portable motorized telescope system for wind turbine blades damage detection by Alejandro Carnero, Cristian Martín, Manuel Díaz

“…The system consists of a stand‐alone Android application that makes use of convolutional neural networks for image processing, a portable telescope to take precise photographs of the turbine blades, and a motorized mount that allows the movement of the telescope. …”
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Micro- and Nanoscale Pore Structure Characterization and Mineral Composition Analysis of Clayey-Silt Hydrate Reservoir in South China Sea by Cheng Lu, Yuxuan Xia, Xuwen Qin, Chao Ma, Hang Bian, Donghui Xing, Hongfeng Lu

“…In this study, computed tomography and scanning electron microscope are used to obtain digital images of three clayey-silt natural gas hydrate reservoir samples in the Shenhu area of South China Sea, and then, the pore structure and the mineral composition of the samples are obtained after image processing. The result indicates that the clayey-silt samples show strong hydrophilic characteristics, small particles, good sorting properties, variable pore distribution, small average pore and throat radius, large porosity, and a large content of submicron pores. …”
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Bridging Disciplines with Photogrammetry: A Coastal Exploration Approach for 3D Mapping and Underwater Positioning by Ali Alakbar Karaki, Ilaria Ferrando, Bianca Federici, Domenico Sguerso

“…The innovative aspect of the proposed approach relies on detecting the snorkeler positions on orthorectified images as an alternative to the use of GNSS (Global Navigation Satellite System) positioning, thanks to an image processing tool. Underwater camera positions are estimated through precise time synchronization with the UAV frames, producing a georeferenced 3D model that seamlessly joins terrestrial and submerged landscapes. …”
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Variations in EUV Irradiance: Comparison between LYRA, ESP, and SWAP Integrated Flux by Mehmet Sarp Yalim, Stefaan Poedts

“…The Sun Watcher Using Active Pixel System Detector and Image Processing (SWAP) telescope and Large Yield Radiometer (LYRA) are the two Sun observation instruments on-board PROBA2. …”
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Image denoising algorithm based on multi-channel GAN by Hongyan WANG, Xiao YANG, Yanchao JIANG, Zumin WANG

“…Aiming at the issue that the noise generated during image acquisition and transmission would degrade the ability of subsequent image processing, a generative adversarial network (GAN) based multi-channel image denoising algorithm was developed.The noisy color image could be separated into red-green-blue (RGB) three channels via the proposed approach, and then the denoising could be implemented in each channel on the basis of an end-to-end trainable GAN with the same architecture.The generator module of GAN was constructed based on the U-net derivative network and residual blocks such that the high-level feature information could be extracted effectively via referring to the low-level feature information to avoid the loss of the detail information.In the meantime, the discriminator module could be demonstrated on the basis of fully convolutional neural network such that the pixel-level classification could be achieved to improve the discrimination accuracy.Besides, in order to improve the denoising ability and retain the image detail as much as possible, the composite loss function could be depicted by the illustrated denoising network based on the following three loss measures, adversarial loss, visual perception loss, and mean square error (MSE).Finally, the resultant three-channel output information could be fused by exploiting the arithmetic mean method to obtain the final denoised image.Compared with the state-of-the-art algorithms, experimental results show that the proposed algorithm can remove the image noise effectively and restore the original image details considerably.…”
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Attention-based deep learning for accurate cell image analysis by Xiangrui Gao, Fan Zhang, Xueyu Guo, Mengcheng Yao, Xiaoxiao Wang, Dong Chen, Genwei Zhang, Xiaodong Wang, Lipeng Lai

“…To address these issues, we introduce X-Profiler, a novel HCA method that combines cellular experiments, image processing, and deep learning modeling. X-Profiler combines the convolutional neural network and Transformer to encode high-content images, effectively filtering out noisy signals and precisely characterizing cell phenotypes. …”
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Real Flight Application of a Monocular Image-Based Aircraft Collision Decision Method by Peter Bauer, Antal Hiba, Tamas Zsedrovits

“…Second, it introduces the UAVs and flight test scenarios together with the camera system and the steps of image processing used in flight testing. A brief analysis about intruder detectability is also provided referencing a more exhaustive work of the authors. …”
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Computed Tomography Image Segmentation of Lung Corona Virus Infection Region Based on Combination of Grayscale Morphological Reconstruction and Fast Marching Method by Aws Alazawi, Abbas Fadhal Humadi, Huda Farooq Jameel, Huda Ali Hashim, John Soraghan

“…In worldwide, infectious patients increase rapidly that lead to weariness in health services staff, as well as instant treatment required to avoid patients’ health deterioration due to infection development. Image processing would be reinforcing health services by considering computer-based segmentation. …”
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Medical Images Segmentation Based on Unsupervised Algorithms: A Review by Revella E. A. Armya, Adnan Mohsin Abdulazeez

“…(Magnetic Resonance Imaging), So segmentation of medical images is considered one of the most important medical imaging processes because it extracts the field of interest from the Return on investment (ROI) through an automatic or semi-automatic process. …”
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Applying MLP-Mixer and gMLP to Human Activity Recognition by Takeru Miyoshi, Makoto Koshino, Hidetaka Nambo

“…When applying these methods to sensor data, we often initialize hyperparameters with values optimized for image processing tasks as a starting point. We suggest that comparable accuracy could be achieved with fewer parameters for sensor data, which typically have lower dimensionality than image data. …”
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FUZZY REGION MERGING WITH HIERARCHICAL CLUSTERING TO FIND OPTIMAL INITIALIZATION OF FUZZY REGION IN IMAGE SEGMENTATION by Wawan GUNAWAN

“…Image segmentation is also a fundamental stage in the development of other image applications such as object recognition, target tracking, computer vision, and biomedical image processing. Interactive image segmentation methods with additional user interaction are still popular in research. …”
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