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Proceedings of the 37th International Conference on Neural Information processing systems

作者： Xiangning Chen Chen Liang Da Huang Esteban Real Kaiyuan Wang Hieu Pham Xuanyi Dong Thang Luong Cho-Jui Hsieh Yifeng Lu Quoc v. Le Google and UCLA Google UCLA

We present a method to formulate algorithm discovery as program search, and apply it to discover optimization algorithms for deep neural network training. We leverage efficient search techniques to explore an infinite and sparse program space. To bridge the large generalization gap between proxy and target tasks, we also introduce program selection and simplification strategies. Our method discovers a simple and effective optimization algorithm, Lion (EvoLved Sign Momentum). It is more memory-efficient than Adam as it only keeps track of the momentum. Different from adaptive optimizers, its update has the same magnitude for each parameter calculated through the sign operation. We compare Lion with widely used optimizers, such as Adam and Adafactor, for training a variety of models on different tasks. On image classification, Lion boosts the accuracy of viT by up to 2% on imageNet and saves up to 5x the pre-training compute on JFT. On vision-language contrastive learning, we achieve 88.3% zero-shot and 91.1% fine-tuning accuracy on imageNet, surpassing the previous best results by 2% and 0.1%, respectively. On diffusion models, Lion outperforms Adam by achieving a better FID score and reducing the training compute by up to 2.3x. For autoregressive, masked language modeling, and fine-tuning, Lion exhibits a similar or better performance compared to Adam. Our analysis of Lion reveals that its performance gain grows with the training batch size. It also requires a smaller learning rate than Adam due to the larger norm of the update produced by the sign function. Additionally, we examine the limitations of Lion and identify scenarios where its improvements are small or not statistically significant.

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Colour based Object Classification using KNN Algorithm for Industrial Applications

Colour based Object Classification using KNN Algorithm for I...

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Automation, Computing and Renewable systems (ICACRS), International Conference on

作者： N C Santosh Kumar S Uma Maheswari vigneshwari. M P v Pramila Rashmita Khilar Ashok Kumar Department of Computer Science Engineering Kakatiya Institute of Technology and Science Warangal Telangana India Department of Electronics and Communication Engineering Panimalar Engineering College India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science Banasthali Vidyapith Rajasthan India

ISBN: (纸本)9781665460859

Object classification and detection involve numerous applications like image processing, picture retrieval, security and surveillance, video communication, robot vision and observation. They are often classified based on their properties like colour, shape, quality and texture. However, their accuracy is mostly predicted by colour recognition. Colour based detection of objects has a discriminating quality to detect an object of any primary colour. The extraction of two or three-dimensional objects based on colour plays a vital role in real-time image processing technology. This review paper proposes the method for colour-based object classification applied in the lays industry using the ‘K-nearest neighbour algorithm’ which is the simplest form of machine learning classification method. The ML model is designed in such a way that once the colour of the lays pack is detected it also specifies its particular flavour. Hence for efficient machine-based colour identification in industries, this method would be an appropriate one and can be used for many other applications.

关键词： Industries Renewable energy sources Machine learning algorithms image color analysis Shape Surveillance Robot vision systems

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Algorithm for image enhancement and subsequent stitching of combined multichannel images 26

Algorithm for image enhancement and subsequent stitching of ...

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Conference on image and Signal processing for Remote Sensing XXvI

作者： Semenishchev, E. voronin, v. Agaian, S. Zelensky, A. Ilyukhin, U. Zhdanova, M. Moscow State Tech Univ STANKIN 1a Vadkovsky Moscow 127055 Russia CUNY Dept Comp Sci New York NY 10314 USA

ISBN: (纸本)9781510638808

Modern systems of parameter control and decision making are most often based on the analysis of images or video data. Most often, images are distorted by the presence of a noise component. Another interfering factor may be the incorrect white balance or color contrast settings. This problem arises with a sharp change in the luminous of the scene or with insufficient lighting. These problems can occur in various directions of using photo and video data, such as: autonomous control systems;unmanned aerial vehicles;safety systems;art photography;medicine and telemedicine;satellite images, etc. Ways to improve the quality of images can be as of developing sensors or new electronic components, as preprocessing algorithms. The development of programs does not require changes to existing equipment, and therefore is a more urgent task. In parallel processing of multichannel images, the task of primary processing is complicated. Such images may include channel: RGB;infrared;ultraviolet;x-ray;3D or combined image. Most often, the methods used for one type of image require adjustment for another and may not be optimal for solving this problem. The article considers the issue of using the multi-criteria smoothing method, with the possibility of adaptive parameter changes for various types of images. As an approach to implement the improvement of the group of images, the work proposes phased processing for each multi-channel image. As a first step, an algorithm for changing the color space is applied, in which multiple adaptive compression of the range occurs, based on a change in the size of the clusters. This algorithm allows adaptive absorption of adjacent pixel regions by analysis of histograms of the gradients. The application of this approach allows performing primary localization and simplification of the image. In the next step, we search for areas of significance (maximum number of transitions or complexity of an object). We check the coincidence of areas in a multi-cha

关键词： local features image IR denoising multichannel images

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Techniques of image processing and Segmentation in Predicting Hydrocephalus using Magnetic Resonance image

Techniques of Image Processing and Segmentation in Predictin...

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International Conference on Advanced Computing and Communication systems (ICACCS)

作者： S.v. Rajiga M. Gunasekaran Government Arts College (Autonomous) Salem -7. Government Arts College Dharmapuri

Children are most commonly affected by many neurological disorders now a days. One of the common disease is hydrocephalus occurring 1/1000 in infantile age group and also in adults as a result of congenital, acquired, tumors, spina bifida, bleeding or infection. Hydrocephalus may cause disability and even death when it is left untreated. Hydrocephalus occurs when, due to previous causes in the brain, excessive cerebrospinal fluid builds up in the brain. When diagnosing tumours and hydrocephalus, image processing plays a vital role. The gap between the visual representation of data captured by MRI and the information relevant to the individual is a major challenge in the medical field. The latest image processing and data mining technologies are used to classify images with high precision. This research paper suggest a definition of image processing and segmentation algorithms for the evaluation of hydrocephalus in children and the determination of its MRI volume. Earlier research work are intended for identifying Hydrocephalus from CT brain images. But in the proposed work MRI images are used for diagnosis. This paper presents techniques and algorithms of image processing and Segmentation which will be very useful in diagnosing Hydrocephalus.

关键词： Neurological diseases image segmentation Brain visualization Pediatrics Magnetic resonance imaging Magnetic resonance

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Technology of establishment of terrestrial objects aerospace survey-based object-oriented monitoring systems

Technology of establishment of terrestrial objects aerospace...

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Conference on Regional Problems of Earth Remote Sensing (RPERS)

作者： Maglinets, Yurii A. Brezhnev, Ruslan, v Siberian Fed Univ Inst Space & Informat Technol Kirenskogo St 26 Krasnoyarsk 660074 Russia

The paper studies a technology for the establishment of Earth remote sensing systems based on the object-oriented approach to the complex system analysis and design. The technology relies upon the system of knowledge combining the knowledge of a certain subject domain, the knowledge of image processing algorithms and the typology of the problems in question. Another important element of the technology is a set of human-computer interaction methods that make it possible to arrange a task setting and solving cycle without or with little involvement of image processing experts. The paper presents the input conditions and the main stages of the technology, as well as examples of possible use of the system in agricultural monitoring.

关键词： Human computer interaction

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Typical values of z-resolution for different Digital Breast Tomosynthesis systems evaluated in a multicenter study

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PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS 2024年 119卷 103300-103300页

作者： Dalmonte, S. Golinelli, P. Oberhofer, N. Strocchi, S. Rossetti, v. Berta, L. Porzio, M. Angelini, L. Paruccini, N. villa, R. Bertolini, M. Delle Canne, S. Cavallari, M. D'Ercole, L. Guerra, G. Rosasco, R. Cannillo, B. D'Alessio, A. Di Nicola, E. Origgi, D. De Marco, P. Maldera, A. Scabbio, C. Rottoli, F. Castriconi, R. Lorenzini, E. Pasquali, G. Pietrobon, F. Bregant, P. Giovannini, G. Favuzza, v. Bruschi, A. D'Urso, D. Maestri, D. De Novellis, S. Fracassi, A. Boschiroli, L. Quattrocchi, M. Gilio, M. A. Roberto, E. Altabella, L. Califano, G. Cimmino, M. C. Bortoli, E. Deiana, E. Pagan, L. Berardi, P. Ardu, v. Azzeroni, R. Campoleoni, M. Ravaglia, v. Univ Bologna Med Phys Specializat Sch Bologna Italy AUSL Romagna Med Phys Unit Ravenna Italy Azienda USL Modena Med Phys Unit Modena Italy SABES Med Phys Unit Bolzano Italy ASST Sette Laghi Med Phys Unit Varese Italy Citta Salute & Sci Med Phys Unit Turin Italy ASL CN1 Med Phys Unit Cuneo Italy ASST Monza Med Phys Unit Monza Italy Azienda AUSL IRCCS Reggio Emilia Med Phys Unit Reggio Emilia Italy Fatebenefratelli Isola Tiberina Gemelli Isola Med Phys Unit Rome Italy IRCCS Fdn Policlin San Matteo Med Phys Unit Pavia Italy Studio Associato Fisici Sanit Med Phys Unit Lugo Italy ASL3 Sistema Sanit Reg Liguria Med Phys Unit Genoa Italy AOU Maggiore Carita Med Phys Unit Novara Italy ASUR Marche Area Vasta3 Med Phys Unit Macerata Italy European Inst Oncol IRCCS Med Phys Unit IEO Milan Italy PO Dimiccoli ASL BT Med Phys Barletta Italy ASST Santi Paolo & Carlo Presidio San Paolo Med Phys Unit Milan Italy IRCCS Osped San Raffaele Med Phys Unit Grp San Donato Milan Italy Osped Civ Carrara Med Phys Unit Carrara Italy ASST Bergamo Ovest Med Phys Unit Treviglio Italy Osped Belluno Med Phys Unit Belluno Italy Osped Cattinara Med Phys Unit Trieste Italy ASL2 Osped Santa Corona Med Phys Unit Pietra Ligure Italy USL Toscana Ctr Med Phys Unit Empoli Italy ULSS 2 Marca Trevigiana Med Phys Unit Treviso Italy ASL Pescara Med Phys Unit Pescara Italy ASST Nord Milano Med Phys Unit Milan Italy Azienda Toscana Nord Ovest Med Phys Unit Lucca Italy ASL CN2 Cuneo Med Phys Unit Cuneo Italy AOUI VR Med Phys Unit Verona Italy AOR San Carlo Potenza Med Phys Unit Potenza Italy USL Toscana Sud Est Med Phys Unit Siena Italy USL Toscana Sud Est Med Phys Unit Grosseto Italy ASL Cagliari Med Phys Unit Cagliari Italy Azienda USL Bologna Med Phys Unit Bologna Italy Fdn IRCCS Ca Granda Med Phys Unit Milan Italy

Purpose: The aim of the present study, conducted by a working group of the Italian Association of Medical Physics (AIFM), was to define typical z -resolution values for different digital breast tomosynthesis (DBT) models to be used as a reference for quality control (QC). Currently, there are no typical values published in internationally agreed QC protocols. Methods: To characterize the z -resolution of the DBT models, the full width at half maximum (FWHM) of the artifact spread function (ASF), a technical parameter that quantifies the signal intensity of a detail along reconstructed planes, was analyzed. Five different commercial phantoms, CIRS Model 011, CIRS Model 015, Modular DBT phantom, Pixmam 3-D, and Tomophan, were evaluated on reconstructed DBT images and 82 DBT systems (6 vendors, 9 models) in use at 39 centers in Italy were involved. Results: The ASF was found to be dependent on the detail size, the DBT angular acquisition range, the reconstruction algorithm and applied image processing. In particular, a progressively greater signal spread was observed as the detail size increased and the acquisition angle decreased. However, a clear correlation between signal spread and angular range width was not observed due to the different signal reconstruction and image processing strategies implemented in the algorithms developed by the vendors studied. Conclusions: The analysis led to the identification of typical z -resolution values for different DBT model -phantom configurations that could be used as a reference during a QC program.

关键词： Digital Breast Tomosynthesis Z-resolution Artifact spread function Quality control

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Portable uncooled shutterless camera operating in the long-wavelength infrared range. Part I: Camera calibration 7

Portable uncooled shutterless camera operating in the long-w...

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Saratov Fall Meeting (SFM) / 7th International Symposium on Optics and Biophotonics - Optical and Nano-Technologies for Biology and Medicine

作者： Litvinov, I. S. Efimova, D. O. Chernomyrdin, N., v Zaytsev, K., I Perov, A. N. Bauman Moscow State Tech Univ Moscow Russia Russian Acad Sci Prokhorov Gen Phys Inst Moscow Russia Sechenov First Moscow State Med Univ Sechenov Uni Inst Regenerat Med Moscow Russia

ISBN: (纸本)9781510637191

Nowadays, imaging and spectroscopy systems operating in the long-wavelength infrared range (LWIR) are rapidly developed and extensively applied in numerous demanding branches of science and technology. This pushes further developments into the realms of improving the sensitivity and performance of the LWIR systems, as well as reducing their dimensions and cost. Among modern LWIR technologies, uncooled shutterless bolometric matrices form a favorable platform for addressing these challenging problems, being technologically reliable, compact, and cost-effective. Nevertheless, such detectors features high noises and require real-time digital signal processing. In this work, consisted of two parts, we developed a portable LWIR camera, which relies on a commercial uncooled bolometric matrix, and proposed few approaches aimed at the image acquisition improvement. The first part describes algorithms for image calibration. These algorithms were implemented experimentally in a processing module relying on the Field-Programmable Gate Array (FPGA) and the high-speed double data rate Synchronous Dynamic Random Access Memory (SDRAM). The developed LWIR camera holds strong potential in such applications, as non-destructive sensing and medical imaging.

关键词： long-wavelength infrared range (LWIR) optoelectronic system thermal imaging uncooled bolometric sensor digital image processing

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Distributed algorithms based on proximity for self-organizing fog computing systems

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PERvASIvE AND MOBILE COMPUTING 2021年 71卷 101316-101316页

作者： Karagiannis, vasileios Schulte, Stefan TU Wien Distributed Syst Grp Vienna Austria

various performance benefits such as low latency and high bandwidth have turned fog computing into a well-accepted extension of the cloud computing paradigm. Many fog computing systems have been proposed so far, consisting of distributed compute nodes which are often organized hierarchically in layers. To achieve low latency, these systems commonly rely on the assumption that the nodes of adjacent layers reside close to each other. However, this assumption may not hold in fog computing systems that span over large geographical areas, due to the wide distribution of the nodes. To avoid relying on this assumption, in this paper we design distributed algorithms whereby the compute nodes measure the network proximity to each other, and self-organize into a hierarchical or a flat structure accordingly. Moreover, we implement these algorithms on geographically distributed compute nodes, and we experiment with image processing and smart city use cases. Our results show that compared to alternative methods, the proposed algorithms decrease the communication latency of latency-sensitive processes by 27%-43%, and increase the available network bandwidth by 36%-86%. Furthermore, we analyze the scalability of our algorithms, and we show that a flat structure (i.e., without layers) scales better than the commonly used layered hierarchy due to generating less overhead when the size of the system grows. (C) 2020 The Authors. Published by Elsevier B.v.

关键词： Fog computing Edge computing Hierarchical structures Flat structures Self-organization Internet of Things IoT

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Fast dehazing method for improving the image quality in pellet size measurement

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COMPUTERS IN INDUSTRY 2021年 128卷 103438-103438页

作者： Wu, Xin Liu, Xiao-Yan Yuan, Fei Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Natl Engn Lab Robot Visual Percept & Control Tech Changsha 410082 Hunan Peoples R China Hunan Key Lab Intelligent Robot Technol Elect Mfg Changsha 410082 Hunan Peoples R China

images captured during the manufacturing of iron green pellets may be seriously degraded by haze, which greatly affects the performance of machine vision systems for pellet size measurement. Hence, to improve the quality of green pellet images and pellet size measuring accuracy, a fast image dehazing method is proposed in this study. By analyzing the different features between haze and haze-free image patches, a new and simple prior, which is that haze-free image patches have the maximal local image entropy, is proposed. The transmission map is then calculated using the proposed prior and the haze free image is obtained. The dehazing effect of the proposed algorithm was assessed using qualitative and quantitative criteria, and compared with six current image dehazing algorithms on two datasets of hazy green pellet images established by ourselves. The results demonstrate that the proposed algorithm achieves better dehazing performance than the other algorithms and can improve the accuracy of pellet size measurement. Moreover, the computing efficiency of the proposed method meets the requirements for real-time industrial applications when used as a pre-processing step. This proposed dehazing method will enable machine vision monitoring systems to operate in industrial environments where dust and steam are common. ? 2021 Elsevier B.v. All rights reserved.

关键词： image dehazing Green pellets Linear transformation Local image entropy

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Ant Colony Optimization based Medical image Preservation and Segmentation

Ant Colony Optimization based Medical Image Preservation and...

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Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), International Conference on

作者： Priya Nandihal Piyush Kumar Pareek victor Hugo Costa De Albuquerque Madhumala R B Ashish Khanna v Sunil Kumar Dept of Computer Science and Design Dayananda Sagar Academy of Technology and Management Bangalore Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceara Fortaleza Brazil Department of Teleinformatics Engineering Federal University of Ceara Brazil Department of ISE Dayananda Sagar Academy of Technology and Management Bangalore CSE Dept Maharaja Agrasen Institute of Technology GGSIPU Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru

ISBN: (纸本)9781665493970

image segmentation is an essential component in many different types of computer vision systems. image segmentation is used in order to identify objects and boundaries within pictures. It is important to note that the success of recognition algorithms is heavily dependent on the quality of the picture segmentation, and there is yet no technique that can universally segment images. Boundary detection has a long history of being recognised as one of the most difficult challenges in the fields of image processing and pattern analysis, in particular, when it comes to applications in medical imaging. The clarity of the picture is being muddled by more information, which is the source of the problem's complexity. The creation of segmentation algorithms for medical pictures is an active field of study, and segmentation algorithms are meant to account for a variety of factors, including noise, intensity inhomogeneity, poor contrast between the lesion and the surrounding skin, and so on. There is currently no active contour model that combines the locally statistical property of being more noise-resistant, in which the intensities in the transformed domain have less overlapping in the statistics, and the locally image fitting property of extracting the local image information in order to be able to segment images with intensity inhomogeneity. This is because there is no such model. So the proposed work includes Ant colony optimization based double threshold segmentation in medical images. Performance of the proposed algorithm is more efficient, reliable and accurate than the existing algorithm. Performance parameters such as segmentation accuracy, error rate, and Dice co-efficient are used in order to conduct the evaluation of the proposed work.

关键词： Integrated optics image segmentation Ant colony optimization Biomedical optical imaging Optical design image edge detection Metaheuristics

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