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Journal articles on the topic 'Neural networks (Computer science) Imaging systems Image processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Kartbayev, Timur, Bahitzhan Akhmetov, Aliya Doszhanova, et al. "DEVELOPMENT OF A COMPUTER SYSTEM FOR IDENTITY AUTHENTICATION USING ARTIFICIAL NEURAL NETWORKS." Image Analysis & Stereology 36, no. 1 (2017): 51. http://dx.doi.org/10.5566/ias.1612.

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The aim of the study is to increase the effectiveness of automated face recognition to authenticate identity, considering features of change of the face parameters over time. The improvement of the recognition accuracy, as well as consideration of the features of temporal changes in a human face can be based on the methodology of artificial neural networks. Hybrid neural networks, combining the advantages of classical neural networks and fuzzy logic systems, allow using the network learnability along with the explanation of the findings. The structural scheme of intelligent system for identifi

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Abedalla, Ayat, Malak Abdullah, Mahmoud Al-Ayyoub, and Elhadj Benkhelifa. "Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures." PeerJ Computer Science 7 (June 29, 2021): e607. http://dx.doi.org/10.7717/peerj-cs.607.

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Medical imaging refers to visualization techniques to provide valuable information about the internal structures of the human body for clinical applications, diagnosis, treatment, and scientific research. Segmentation is one of the primary methods for analyzing and processing medical images, which helps doctors diagnose accurately by providing detailed information on the body’s required part. However, segmenting medical images faces several challenges, such as requiring trained medical experts and being time-consuming and error-prone. Thus, it appears necessary for an automatic medical image s

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Kollia, Ilianna, Nikolaos Simou, Andreas Stafylopatis, and Stefanos Kollias. "SEMANTIC IMAGE ANALYSIS USING A SYMBOLIC NEURAL ARCHITECTURE." Image Analysis & Stereology 29, no. 3 (2010): 159. http://dx.doi.org/10.5566/ias.v29.p159-172.

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Image segmentation and classification are basic operations in image analysis and multimedia search which have gained great attention over the last few years due to the large increase of digital multimedia content. A recent trend in image analysis aims at incorporating symbolic knowledge representation systems and machine learning techniques. In this paper, we examine interweaving of neural network classifiers and fuzzy description logics for the adaptation of a knowledge base for semantic image analysis. The proposed approach includes a formal knowledge component, which, assisted by a reasonin

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Pagani, Luca, Paolo Parenti, Salvatore Cataldo, Paul J. Scott, and Massimiliano Annoni. "Indirect cutting tool wear classification using deep learning and chip colour analysis." International Journal of Advanced Manufacturing Technology 111, no. 3-4 (2020): 1099–114. http://dx.doi.org/10.1007/s00170-020-06055-6.

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Abstract In the growing Industry 4.0 market, there is strong need to implement automatic inspection methods to support manufacturing processes. Tool wear in turning is one of the biggest concerns that most expert operators are able to indirectly infer through the analysis of the removed chips. Automatising this operation would enable developing more efficient cutting processes that turns in easier process planning management toward the Zero Defect Manufacturing paradigm. This paper presents a deep learning approach, based on image processing applied to turning chips for indirectly identifying

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Heizmann, Michael, Alexander Braun, Markus Hüttel, et al. "Artificial intelligence with neural networks in optical measurement and inspection systems." at - Automatisierungstechnik 68, no. 6 (2020): 477–87. http://dx.doi.org/10.1515/auto-2020-0006.

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AbstractOptical measuring and inspection systems play an important role in automation as they allow a comprehensive and non-contact quality assessment of products and processes. In this field, too, systems are increasingly being used that apply artificial intelligence and machine learning, mostly by means of artificial neural networks. Results achieved with this approach are often very promising and require less development effort. However, the supplementation and replacement of classical image processing methods by machine learning methods is not unproblematic, especially in applications with

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Qin, Pinle, Jun Chen, Kai Zhang, and Rui Chai. "Convolutional neural networks and hash learning for feature extraction and of fast retrieval of pulmonary nodules." Computer Science and Information Systems 15, no. 3 (2018): 517–31. http://dx.doi.org/10.2298/csis171210020q.

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With a widespread use of digital imaging data in hospitals, the size of medical image repositories is increasing rapidly. This causes difficulty in managing and querying these large databases leading to the need of content based medical image retrieval (CBMIR) systems. A major challenge in CBMIR systems is the ?semantic gap? that exists between the low level visual information captured by imaging devices and high level semantic information perceived by the human. Using deep convolution neural network (CNN) to construct the CBMIR system can fully characterize the high level semantic features in

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Magro, Daniel, Kristian Zarb Adami, Andrea DeMarco, Simone Riggi, and Eva Sciacca. "A comparative study of convolutional neural networks for the detection of strong gravitational lensing." Monthly Notices of the Royal Astronomical Society 505, no. 4 (2021): 6155–65. http://dx.doi.org/10.1093/mnras/stab1635.

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ABSTRACT As we enter the era of large-scale imaging surveys with the upcoming telescopes such as the Large Synoptic Survey Telescope (LSST) and the Square Kilometre Array (SKA), it is envisaged that the number of known strong gravitational lensing systems will increase dramatically. However, these events are still very rare and require the efficient processing of millions of images. In order to tackle this image processing problem, we present machine learning techniques and apply them to the gravitational lens finding challenge. The convolutional neural networks (CNNs) presented here have been

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Petukhova, N. V., M. P. Farkhadov, M. V. Zamegrad, and S. P. Grachev. "Digital technologies in the diagnosis and treatment of neurological diseases." Neurology, Neuropsychiatry, Psychosomatics 11, no. 4 (2019): 104–10. http://dx.doi.org/10.14412/2074-2711-2019-4-104-110.

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The review considers works devoted to convolutional neural networks as a main method for digital image processing, as well as to the diagnosis of neurological diseases based on computer-aided analysis of magnetic resonance imaging and electroencephalography. It describes approaches to building computer-aided diagnostic systems and gives examples of these systems in neurology. The virtual reality technology used to rehabilitate patients with imbalance, posttraumatic disorders, and consequences of stroke is presented. Digitalization is stated to be one of the priority areas for medicine developm

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Apostolidis, Kyriakos D., and George A. Papakostas. "A Survey on Adversarial Deep Learning Robustness in Medical Image Analysis." Electronics 10, no. 17 (2021): 2132. http://dx.doi.org/10.3390/electronics10172132.

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In the past years, deep neural networks (DNN) have become popular in many disciplines such as computer vision (CV), natural language processing (NLP), etc. The evolution of hardware has helped researchers to develop many powerful Deep Learning (DL) models to face numerous challenging problems. One of the most important challenges in the CV area is Medical Image Analysis in which DL models process medical images—such as magnetic resonance imaging (MRI), X-ray, computed tomography (CT), etc.—using convolutional neural networks (CNN) for diagnosis or detection of several diseases. The proper func

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Gvozdev, O. G., V. A. Kozub, N. V. Kosheleva, A. B. Murynin, and A. A. Richter. "Neural Network Method for Constructing Three-Dimensional Models of Rigid Objects from Satellite Images." Mekhatronika, Avtomatizatsiya, Upravlenie 22, no. 1 (2021): 48–55. http://dx.doi.org/10.17587/mau.22.48-55.

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A method has been developed for constructing three-dimensional models of rigid objects on the earth’s surface using one satellite image using the example of railway infrastructure. The method consists in step-by-step processing of satellite images with sequential application of two convolutional neural networks. In the first processing step, a satellite image is segmented by a neural network to select a plurality of objects of predetermined classes. At the second stage of processing with the help of neural network local analysis of image areas detected by results of the first stage of processi

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Ashok Kumar, K., Vamsi Pulikonda, and Narendarnath Sai. "Road Fault Detection by Using Convolutional Neural Networks." Journal of Computational and Theoretical Nanoscience 17, no. 8 (2020): 3374–77. http://dx.doi.org/10.1166/jctn.2020.9188.

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Bad conditions of road due to the potholes are one of the major cause of road damage and accidents to vehicles. Recently, with the increase in pollution and vehicular traffic, most of roads are being filled with many small and large potholes in most of places in the country. Detecting potholes manually is a time-consuming task and labour-intensive task, automating this process which saves a lot of time and money. Hence, Many different methodologies have been implemented that is from reporting to authorities manually to the use of laser imaging. Though all of these techniques have some disadvan

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Wu, Gangshan, Taihe Lin, Wei Huo, and Ning Cao. "Application of convolutional neural networks and image processing algorithms based on traffic video in vehicle taillight detection." International Journal of Sensor Networks 35, no. 3 (2021): 181. http://dx.doi.org/10.1504/ijsnet.2021.10036664.

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Cao, Ning, Wei Huo, Taihe Lin, and Gangshan Wu. "Application of convolutional neural networks and image processing algorithms based on traffic video in vehicle taillight detection." International Journal of Sensor Networks 35, no. 3 (2021): 181. http://dx.doi.org/10.1504/ijsnet.2021.113842.

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Mohammed, Hind R., and Zahir M. Hussain. "Hybrid Mamdani Fuzzy Rules and Convolutional Neural Networks for Analysis and Identification of Animal Images." Computation 9, no. 3 (2021): 35. http://dx.doi.org/10.3390/computation9030035.

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Accurate, fast, and automatic detection and classification of animal images is challenging, but it is much needed for many real-life applications. This paper presents a hybrid model of Mamdani Type-2 fuzzy rules and convolutional neural networks (CNNs) applied to identify and distinguish various animals using different datasets consisting of about 27,307 images. The proposed system utilizes fuzzy rules to detect the image and then apply the CNN model for the object’s predicate category. The CNN model was trained and tested based on more than 21,846 pictures of animals. The experiments’ results

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Matsubara, Teppei, Tomoshiro Ochiai, Morihiro Hayashida, Tatsuya Akutsu, and Jose C. Nacher. "Convolutional neural network approach to lung cancer classification integrating protein interaction network and gene expression profiles." Journal of Bioinformatics and Computational Biology 17, no. 03 (2019): 1940007. http://dx.doi.org/10.1142/s0219720019400079.

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Deep learning technologies are permeating every field from image and speech recognition to computational and systems biology. However, the application of convolutional neural networks (CCNs) to “omics” data poses some difficulties, such as the processing of complex networks structures as well as its integration with transcriptome data. Here, we propose a CNN approach that combines spectral clustering information processing to classify lung cancer. The developed spectral-convolutional neural network based method achieves success in integrating protein interaction network data and gene expressio

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Kalms, Lester, Pedram Amini Rad, Muhammad Ali, Arsany Iskander, and Diana Göhringer. "A Parametrizable High-Level Synthesis Library for Accelerating Neural Networks on FPGAs." Journal of Signal Processing Systems 93, no. 5 (2021): 513–29. http://dx.doi.org/10.1007/s11265-021-01651-5.

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AbstractIn recent years, Convolutional Neural Network CNN have been incorporated in a large number of applications, including multimedia retrieval and image classification. However, CNN based algorithms are computationally and resource intensive and therefore difficult to be used in embedded systems. FPGA based accelerators are becoming more and more popular in research and industry due to their flexibility and energy efficiency. However, the available resources and the size of the on-chip memory can limit the performance of the FPGA accelerator for CNN. This work proposes an High-Level Synthe

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Tamim, Nasser, M. Elshrkawey, Gamil Abdel Azim, and Hamed Nassar. "Retinal Blood Vessel Segmentation Using Hybrid Features and Multi-Layer Perceptron Neural Networks." Symmetry 12, no. 6 (2020): 894. http://dx.doi.org/10.3390/sym12060894.

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Segmentation of retinal blood vessels is the first step for several computer aided-diagnosis systems (CAD), not only for ocular disease diagnosis such as diabetic retinopathy (DR) but also of non-ocular disease, such as hypertension, stroke and cardiovascular diseases. In this paper, a supervised learning-based method, using a multi-layer perceptron neural network and carefully selected vector of features, is proposed. In particular, for each pixel of a retinal fundus image, we construct a 24-D feature vector, encoding information on the local intensity, morphology transformation, principal mo

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Wang, Lingfeng. "Forecast Model of TV Show Rating Based on Convolutional Neural Network." Complexity 2021 (February 24, 2021): 1–10. http://dx.doi.org/10.1155/2021/6694538.

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The TV show rating analysis and prediction system can collect and transmit information more quickly and quickly upload the information to the database. The convolutional neural network is a multilayer neural network structure that simulates the operating mechanism of biological vision systems. It is a neural network composed of multiple convolutional layers and downsampling layers sequentially connected. It can obtain useful feature descriptions from original data and is an effective method to extract features from data. At present, convolutional neural networks have become a research hotspot

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Santhi, Natchimuthu, Chinnaraj Pradeepa, Parthasarathy Subashini, and Senthil Kalaiselvi. "Automatic Identification of Algal Community from Microscopic Images." Bioinformatics and Biology Insights 7 (January 2013): BBI.S12844. http://dx.doi.org/10.4137/bbi.s12844.

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A good understanding of the population dynamics of algal communities is crucial in several ecological and pollution studies of freshwater and oceanic systems. This paper reviews the subsequent introduction to the automatic identification of the algal communities using image processing techniques from microscope images. The diverse techniques of image preprocessing, segmentation, feature extraction and recognition are considered one by one and their parameters are summarized. Automatic identification and classification of algal community are very difficult due to various factors such as change

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Badr, Eman. "Images in Space and Time." ACM Computing Surveys 54, no. 6 (2021): 1–38. http://dx.doi.org/10.1145/3453657.

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Medical imaging diagnosis is mostly subjective, as it depends on medical experts. Hence, the service provided is limited by expert opinion variations and image complexity as well. However, with the increasing advancements in deep learning field, techniques are developed to help in the diagnosis and risk assessment processes. In this article, we survey different types of images in healthcare. A review of the concept and research methodology of Radiomics will highlight the potentials of integrated diagnostics. Convolutional neural networks can play an important role in next generations of automa

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Slyusar, Vadym, Mykhailo Protsenko, Anton Chernukha, et al. "Construction of an advanced method for recognizing monitored objects by a convolutional neural network using a discrete wavelet transform." Eastern-European Journal of Enterprise Technologies 4, no. 9(112) (2021): 65–77. http://dx.doi.org/10.15587/1729-4061.2021.238601.

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The tasks that unmanned aircraft systems solve include the detection of objects and determining their state. This paper reports an analysis of image recognition methods in order to automate the specified process. Based on the analysis, an improved method for recognizing images of monitored objects by a convolutional neural network using a discrete wavelet transform has been devised. Underlying the method is the task of automating image processing in unmanned aircraft systems. The operability of the proposed method was tested using an example of processing an image (aircraft, tanks, helicopters

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Sineglazov, Victor, and Anatoly Kot. "Design of hybrid neural networks of the ensemble structure." Eastern-European Journal of Enterprise Technologies 1, no. 4 (109) (2021): 31–45. http://dx.doi.org/10.15587/1729-4061.2021.225301.

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This paper considers the structural-parametric synthesis (SPS) of neural networks (NNs) of deep learning, in particular convolutional neural networks (CNNs), which are used in image processing. It has been shown that modern neural networks may possess a variety of topologies. That is ensured by using unique blocks that determine their essential features, namely, the compression and excitation unit, the attention module convolution unit, the channel attention module, the spatial attention module, the residual unit, the ResNeXt block. This, first of all, is due to the need to increase their effi

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Gavrilov, D. A., E. I. Zakirov, E. V. Gameeva, V. Yu Semenov, and O. Yu Aleksandrova. "Automated skin melanoma diagnostics based on mathematical model of artificial convolutional neural network." Research'n Practical Medicine Journal 5, no. 3 (2018): 110–16. http://dx.doi.org/10.17709/2409-2231-2018-5-3-11.

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In the last 10 years there has been a revolu on in the ﬁ eld of computer image analysis and pa ern recogni on. Modern algorithms of computer vision equaled and even in some problems surpassed human capabili es. This jerk is largely due to the emergence and development of the technology of deep convolu onal neural networks.Recent developments in the ﬁ eld of image processing and machine learning open up the prospect of crea ng systems based on ar ﬁ cial neural convolu onal networks, superior to humans in problems of image classiﬁ ca on, in par cular, in solving problems of analysis of various m

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Butt, Muhammad Atif, Asad Masood Khattak, Sarmad Shafique, et al. "Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems." Complexity 2021 (February 12, 2021): 1–11. http://dx.doi.org/10.1155/2021/6644861.

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In step with rapid advancements in computer vision, vehicle classification demonstrates a considerable potential to reshape intelligent transportation systems. In the last couple of decades, image processing and pattern recognition-based vehicle classification systems have been used to improve the effectiveness of automated highway toll collection and traffic monitoring systems. However, these methods are trained on limited handcrafted features extracted from small datasets, which do not cater the real-time road traffic conditions. Deep learning-based classification systems have been proposed

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Cui, Yibo, Chi Zhang, Kai Qiao, Linyuan Wang, Bin Yan, and Li Tong. "Study on Representation Invariances of CNNs and Human Visual Information Processing Based on Data Augmentation." Brain Sciences 10, no. 9 (2020): 602. http://dx.doi.org/10.3390/brainsci10090602.

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Representation invariance plays a significant role in the performance of deep convolutional neural networks (CNNs) and human visual information processing in various complicated image-based tasks. However, there has been abounding confusion concerning the representation invariance mechanisms of the two sophisticated systems. To investigate their relationship under common conditions, we proposed a representation invariance analysis approach based on data augmentation technology. Firstly, the original image library was expanded by data augmentation. The representation invariances of CNNs and the

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Kushwah, Chandra Pal, and Kuruna Markam. "Semantic Segmentation of Satellite Images using Deep Learning." Regular issue 10, no. 8 (2021): 33–37. http://dx.doi.org/10.35940/ijitee.h9186.0610821.

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Bidirectional in recent years, Deep learning performance in natural scene image processing has improved its use in remote sensing image analysis. In this paper, we used the semantic segmentation of remote sensing images for deep neural networks (DNN). To make it ideal for multi-target semantic segmentation of remote sensing image systems, we boost the Seg Net encoder-decoder CNN structures with index pooling & U-net. The findings reveal that the segmentation of various objects has its benefits and drawbacks for both models. Furthermore, we provide an integrated algorithm that incorporates

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Yasutomi, Suguru, Tatsuya Arakaki, Ryu Matsuoka, et al. "Shadow Estimation for Ultrasound Images Using Auto-Encoding Structures and Synthetic Shadows." Applied Sciences 11, no. 3 (2021): 1127. http://dx.doi.org/10.3390/app11031127.

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Acoustic shadows are common artifacts in medical ultrasound imaging. The shadows are caused by objects that reflect ultrasound such as bones, and they are shown as dark areas in ultrasound images. Detecting such shadows is crucial for assessing the quality of images. This will be a pre-processing for further image processing or recognition aiming computer-aided diagnosis. In this paper, we propose an auto-encoding structure that estimates the shadowed areas and their intensities. The model once splits an input image into an estimated shadow image and an estimated shadow-free image through its

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Thanh, Dang N. H., Nguyen Hoang Hai, Le Minh Hieu, Prayag Tiwari, and V. B. Surya Prasath. "Skin lesion segmentation method for dermoscopic images with convolutional neural networks and semantic segmentation." Computer Optics 45, no. 1 (2021): 122–29. http://dx.doi.org/10.18287/2412-6179-co-748.

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Melanoma skin cancer is one of the most dangerous forms of skin cancer because it grows fast and causes most of the skin cancer deaths. Hence, early detection is a very important task to treat melanoma. In this article, we propose a skin lesion segmentation method for dermoscopic images based on the U-Net architecture with VGG-16 encoder and the semantic segmentation. Base on the segmented skin lesion, diagnostic imaging systems can evaluate skin lesion features to classify them. The proposed method requires fewer resources for training, and it is suitable for computing systems without powerfu

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Ribeiro, David Augusto, Juan Casavílca Silva, Renata Lopes Rosa, et al. "Light Field Image Quality Enhancement by a Lightweight Deformable Deep Learning Framework for Intelligent Transportation Systems." Electronics 10, no. 10 (2021): 1136. http://dx.doi.org/10.3390/electronics10101136.

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Light field (LF) imaging has multi-view properties that help to create many applications that include auto-refocusing, depth estimation and 3D reconstruction of images, which are required particularly for intelligent transportation systems (ITSs). However, cameras can present a limited angular resolution, becoming a bottleneck in vision applications. Thus, there is a challenge to incorporate angular data due to disparities in the LF images. In recent years, different machine learning algorithms have been applied to both image processing and ITS research areas for different purposes. In this wo

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Mehrer, Johannes, Courtney J. Spoerer, Emer C. Jones, Nikolaus Kriegeskorte, and Tim C. Kietzmann. "An ecologically motivated image dataset for deep learning yields better models of human vision." Proceedings of the National Academy of Sciences 118, no. 8 (2021): e2011417118. http://dx.doi.org/10.1073/pnas.2011417118.

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Deep neural networks provide the current best models of visual information processing in the primate brain. Drawing on work from computer vision, the most commonly used networks are pretrained on data from the ImageNet Large Scale Visual Recognition Challenge. This dataset comprises images from 1,000 categories, selected to provide a challenging testbed for automated visual object recognition systems. Moving beyond this common practice, we here introduce ecoset, a collection of >1.5 million images from 565 basic-level categories selected to better capture the distribution of objects relevan

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González, Pablo, Alberto Castaño, Emily E. Peacock, Jorge Díez, Juan José Del Coz, and Heidi M. Sosik. "Automatic plankton quantification using deep features." Journal of Plankton Research 41, no. 4 (2019): 449–63. http://dx.doi.org/10.1093/plankt/fbz023.

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Abstract The study of marine plankton data is vital to monitor the health of the world’s oceans. In recent decades, automatic plankton recognition systems have proved useful to address the vast amount of data collected by specially engineered in situ digital imaging systems. At the beginning, these systems were developed and put into operation using traditional automatic classification techniques, which were fed with hand-designed local image descriptors (such as Fourier features), obtaining quite successful results. In the past few years, there have been many advances in the computer vision c

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Gupta, Lokesh, and Dr Saroj Hiranwal. "Numerical Simulation and Design of Computer Aided Diabetic Retinopathy Using Improved Convolutional Neural Network." International Journal on Recent and Innovation Trends in Computing and Communication 9, no. 4 (2021): 23–27. http://dx.doi.org/10.17762/ijritcc.v9i4.5477.

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The health sector is entirely different from other sectors. It is a high priority department with the highest quality of care and quality, regardless of cost. It does not meet social standards even though it absorbs a lot of budget. Health specialists interpret much of the medical evidence. Due to its subjectivity, complexity of images, broad differences among various interpreters and exhaustion, the image interpretation of human experts is very restricted. It also offers an exciting solution with good medical imaging accuracy following in-depth learning in other practical applications and is

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Reggiani, Enrico, Emanuele DEL Sozzo, Davide Conficconi, Giuseppe Natale, Carlo Moroni, and Marco D. Santambrogio. "Enhancing the Scalability of Multi-FPGA Stencil Computations via Highly Optimized HDL Components." ACM Transactions on Reconfigurable Technology and Systems 14, no. 3 (2021): 1–33. http://dx.doi.org/10.1145/3461478.

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Stencil-based algorithms are a relevant class of computational kernels in high-performance systems, as they appear in a plethora of fields, from image processing to seismic simulations, from numerical methods to physical modeling. Among the various incarnations of stencil-based computations, Iterative Stencil Loops (ISLs) and Convolutional Neural Networks (CNNs) represent two well-known examples of kernels belonging to the stencil class. Indeed, ISLs apply the same stencil several times until convergence, while CNN layers leverage stencils to extract features from an image. The computationally

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Chen, Zhuoran, Gege Ma, Yandan Jiang, Baoliang Wang, and Manuchehr Soleimani. "Application of Deep Neural Network to the Reconstruction of Two-Phase Material Imaging by Capacitively Coupled Electrical Resistance Tomography." Electronics 10, no. 9 (2021): 1058. http://dx.doi.org/10.3390/electronics10091058.

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A convolutional neural network (CNN)-based image reconstruction algorithm for two-phase material imaging is presented and verified with experimental data from a capacitively coupled electrical resistance tomography (CCERT) sensor. As a contactless version of electrical resistance tomography (ERT), CCERT has advantages such as no invasion, low cost, no radiation, and rapid response for two-phase material imaging. Besides that, CCERT avoids contact error of ERT by imaging from outside of the pipe. Forward modeling was implemented based on the practical circular array sensor, and the inverse imag

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Guo, Liang, Guanfeng Song, and Hongsheng Wu. "Complex-Valued Pix2pix—Deep Neural Network for Nonlinear Electromagnetic Inverse Scattering." Electronics 10, no. 6 (2021): 752. http://dx.doi.org/10.3390/electronics10060752.

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Nonlinear electromagnetic inverse scattering is an imaging technique with quantitative reconstruction and high resolution. Compared with conventional tomography, it takes into account the more realistic interaction between the internal structure of the scene and the electromagnetic waves. However, there are still open issues and challenges due to its inherent strong non-linearity, ill-posedness and computational cost. To overcome these shortcomings, we apply an image translation network, named as Complex-Valued Pix2pix, on the inverse scattering problem of electromagnetic field. Complex-Valued

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Mizginov, V. A., V. V. Kniaz, and N. A. Fomin. "A METHOD FOR SYNTHESIZING THERMAL IMAGES USING GAN MULTI-LAYERED APPROACH." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-2/W1-2021 (April 15, 2021): 155–62. http://dx.doi.org/10.5194/isprs-archives-xliv-2-w1-2021-155-2021.

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Abstract. The active development of neural network technologies and optoelectronic systems has led to the introduction of computer vision technologies in various fields of science and technology. Deep learning made it possible to solve complex problems that a person had not been able to solve before. The use of multi-spectral optical systems has significantly expanded the field of application of video systems. Tasks such as image recognition, object re-identification, video surveillance require high accuracy, speed and reliability. These qualities are provided by algorithms based on deep convo

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Sun, Zengguo, Mingmin Zhao, and Bai Jia. "A GF-3 SAR Image Dataset of Road Segmentation." Information Technology and Control 50, no. 1 (2021): 89–101. http://dx.doi.org/10.5755/j01.itc.50.1.27987.

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We constructed a GF-3 SAR image dataset based on road segmentation to boost the development of GF-3 synthetic aperture radar (SAR) image road segmentation technology and make GF-3 SAR images be applied to practice better. We selected 23 scenes of GF-3 SAR images in Shaanxi, China, cut them into road chips with 512 × 512 pixels, and then labeled the dataset using LabelMe labeling tool. The dataset consists of 10026 road chips, and these road images are from different GF-3 imaging modes, so there is diversity in resolution and polarization. Three segmentation algorithms such as Multi-task Networ

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Mao, Yiqing, Tianxiang Wu, Yong Chen, and Shunli Ma. "A 0.2-Terahertz Ceramic Relic Detection System Based on Iterative Threshold Filtering Imaging and Neural Network." Electronics 10, no. 18 (2021): 2213. http://dx.doi.org/10.3390/electronics10182213.

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Ceramic cultural relics have an important cultural value, and their detection technology plays a pivotal role in the protection of cultural relics. The traditional detection method of ceramic relics is based on X-ray technology, resulting in damage to the cultural relics. The terahertz-wave transmission is nondestructive; yet, the terahertz imaging has several technical problems, such as complex algorithms and unclear imaging. In this paper, we propose a terahertz-wave imaging system of a 0.2-terahertz operating frequency with a single input and single output and perform the transmission imagi

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Scholl, Victoria M., Joseph McGlinchy, Teo Price-Broncucia, Jennifer K. Balch, and Maxwell B. Joseph. "Fusion neural networks for plant classification: learning to combine RGB, hyperspectral, and lidar data." PeerJ 9 (July 29, 2021): e11790. http://dx.doi.org/10.7717/peerj.11790.

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Airborne remote sensing offers unprecedented opportunities to efficiently monitor vegetation, but methods to delineate and classify individual plant species using the collected data are still actively being developed and improved. The Integrating Data science with Trees and Remote Sensing (IDTReeS) plant identification competition openly invited scientists to create and compare individual tree mapping methods. Participants were tasked with training taxon identification algorithms based on two sites, to then transfer their methods to a third unseen site, using field-based plant observations in

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Langford, Michael Austin, and Betty H. C. Cheng. "Enki." ACM Transactions on Autonomous and Adaptive Systems 15, no. 2 (2021): 1–32. http://dx.doi.org/10.1145/3460959.

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Data-driven Learning-enabled Systems are limited by the quality of available training data, particularly when trained offline. For systems that must operate in real-world environments, the space of possible conditions that can occur is vast and difficult to comprehensively predict at design time. Environmental uncertainty arises when run-time conditions diverge from design-time training conditions. To address this problem, automated methods can generate synthetic data to fill in gaps for training and test data coverage. We propose an evolution-based technique to assist developers with uncoveri

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Shimoda, Masayuki, Youki Sada, and Hiroki Nakahara. "FPGA-Based Inter-layer Pipelined Accelerators for Filter-Wise Weight-Balanced Sparse Fully Convolutional Networks with Overlapped Tiling." Journal of Signal Processing Systems 93, no. 5 (2021): 499–512. http://dx.doi.org/10.1007/s11265-021-01642-6.

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AbstractConvolutional neural networks (CNNs) exhibit state-of-the-art performance while performing computer-vision tasks. CNNs require high-speed, low-power, and high-accuracy hardware for various scenarios, such as edge environments. However, the number of weights is so large that embedded systems cannot store them owing to their limited on-chip memory. A different method is used to minimize the input image size, for real-time processing, but it causes a considerable drop in accuracy. Although pruned sparse CNNs and special accelerators are proposed, the requirement of random access incurs a

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Kanoh, Masayoshi. "Special Issue on Selected Papers from SCIS & ISIS 2008 No.2." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 4 (2009): 351. http://dx.doi.org/10.20965/jaciii.2009.p0351.

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Welcome to the second special issue on selected papers from SCIS & ISIS 2008, a joint conference combining the 4th Soft Computing and Intelligent Systems (SCIS) and the 9th International Symposium on advances Intelligent Systems (ISIS) held at Nagoya University, Japan, in September 2008. smallskip Three earlier conferences were held in: the National Institute of Advanced Industrial Science and Technology (AIST), Japan (2002); Keio University, Japan (2004); and Tokyo Institute of Technology, Japan (2006). smallskip Conference topics include fuzzy logic, clustering, evolutionary computation,

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Arora*, Mayank, Sarthak Garg, and Srivani A. "Face Mask Detection System using Mobilenetv2." International Journal of Engineering and Advanced Technology 10, no. 4 (2021): 127–29. http://dx.doi.org/10.35940/ijeat.d2404.0410421.

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In this pandemic, it is getting more and more difficult to keep a track of people who are wearing masks regularly or not. It cannot solely depend on human efforts to take care of this task and therefore there is a need to develop software that can automatically detect whether any given person is wearing a mask or not. Face Detection has evolved as a really popular problem in image processing and computer vision. Many new algorithms are being devised using convolutional architectures to form the algorithm as accurately as possible. These convolutional architectures have made it possible to extr

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Jung, Sung-Woon, Hyuk-Ju Kwon, and Sung-Hak Lee. "Enhanced Tone Mapping Using Regional Fused GAN Training with a Gamma-Shift Dataset." Applied Sciences 11, no. 16 (2021): 7754. http://dx.doi.org/10.3390/app11167754.

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High-dynamic-range (HDR) imaging is a digital image processing technique that enhances an image’s visibility by modifying its color and contrast ranges. Generative adversarial networks (GANs) have proven to be potent deep learning models for HDR imaging. However, obtaining a sufficient volume of training image pairs is difficult. This problem has been solved using CycleGAN, but the result of the use of CycleGAN for converting a low-dynamic-range (LDR) image to an HDR image exhibits problematic color distortion, and the intensity of the output image only slightly changes. Therefore, we propose

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Militello, Carmelo, Leonardo Rundo, Salvatore Vitabile, and Vincenzo Conti. "Fingerprint Classification Based on Deep Learning Approaches: Experimental Findings and Comparisons." Symmetry 13, no. 5 (2021): 750. http://dx.doi.org/10.3390/sym13050750.

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Biometric classification plays a key role in fingerprint characterization, especially in the identification process. In fact, reducing the number of comparisons in biometric recognition systems is essential when dealing with large-scale databases. The classification of fingerprints aims to achieve this target by splitting fingerprints into different categories. The general approach of fingerprint classification requires pre-processing techniques that are usually computationally expensive. Deep Learning is emerging as the leading field that has been successfully applied to many areas, such as i

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Benito-Picazo, Jesus, Enrique Domínguez, Esteban J. Palomo, and Ezequiel López-Rubio. "Deep learning-based video surveillance system managed by low cost hardware and panoramic cameras." Integrated Computer-Aided Engineering 27, no. 4 (2020): 373–87. http://dx.doi.org/10.3233/ica-200632.

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The design of automated video surveillance systems often involves the detection of agents which exhibit anomalous or dangerous behavior in the scene under analysis. Models aimed to enhance the video pattern recognition abilities of the system are commonly integrated in order to increase its performance. Deep learning neural networks are found among the most popular models employed for this purpose. Nevertheless, the large computational demands of deep networks mean that exhaustive scans of the full video frame make the system perform rather poorly in terms of execution speed when implemented o

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Ma, Andong, and Anthony M. Filippi. "A novel spatial recurrent neural network for hyperspectral imagery classification." Abstracts of the ICA 1 (July 15, 2019): 1. http://dx.doi.org/10.5194/ica-abs-1-233-2019.

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<p><strong>Abstract.</strong> Hyperspectral images (HSIs) contain hundreds of spectral bands, providing high-resolution spectral information pertaining to the Earth’s surface. Additionally, abundant spatial contextual information can also be obtained simultaneously from a HSI. To characterize the properties of ground objects, classification is the most widely-used technology in the field of remote sensing, where each pixel in a HSI is assigned to a pre-defined class. Over the past decade, deep learning has attracted increasing attention in the machine-learning and computer-vi

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Bruhn, Fredrik C., Nandinbaatar Tsog, Fabian Kunkel, Oskar Flordal, and Ian Troxel. "Enabling radiation tolerant heterogeneous GPU-based onboard data processing in space." CEAS Space Journal 12, no. 4 (2020): 551–64. http://dx.doi.org/10.1007/s12567-020-00321-9.

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Abstract The last decade has seen a dramatic increase in small satellite missions for commercial, public, and government intelligence applications. Given the rapid commercialization of constellation-driven services in Earth Observation, situational domain awareness, communications including machine-to-machine interface, exploration etc., small satellites represent an enabling technology for a large growth market generating truly Big Data. Examples of modern sensors that can generate very large amounts of data are optical sensing, hyperspectral, Synthetic Aperture Radar (SAR), and Infrared imag

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Chen, Yuren, Xinyi Xie, Bo Yu, Yi Li, and Kunhui Lin. "Multitarget Vehicle Tracking and Motion State Estimation Using a Novel Driving Environment Perception System of Intelligent Vehicles." Journal of Advanced Transportation 2021 (September 15, 2021): 1–16. http://dx.doi.org/10.1155/2021/6251399.

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The multitarget vehicle tracking and motion state estimation are crucial for controlling the host vehicle accurately and preventing collisions. However, current multitarget tracking methods are inconvenient to deal with multivehicle issues due to the dynamically complex driving environment. Driving environment perception systems, as an indispensable component of intelligent vehicles, have the potential to solve this problem from the perspective of image processing. Thus, this study proposes a novel driving environment perception system of intelligent vehicles by using deep learning methods to

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Przybysz, Artur, Krystian Grzesiak, and Ireneusz Kubiak. "Electromagnetic Safety of Remote Communication Devices—Videoconference." Symmetry 13, no. 2 (2021): 323. http://dx.doi.org/10.3390/sym13020323.

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Devices powered by electricity become sources of electromagnetic emissions in the course of their operation. In the case of devices oriented to process information, these emissions can have a character of revealing emissions, i.e., those whose reception and analysis allow for remote reconstruction of related data. The best known example of this phenomenon is the formation of revealing emissions during the operation of imaging devices: monitors, projectors or printers. Increasingly more often, these components are used for communication in the form of videoconferences with other network users.

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