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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XII"

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Advances in Database Technology - Proceedings: 27th International conference on Extending Database Technology, EDBT 2024

Advances in Database Technology - Proceedings: 27th Internat...

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27th International conference on Extending Database Technology, EDBT 2024

ISBN: (纸本)9783893180943

The proceedings contain 87 papers. The topics discussed include: Auto-FP: an experimental study of automated feature preprocessing for tabular data;Data-CASE: grounding data regulations for compliant data processing systems;data coverage for detecting representation bias in image datasets: a crowdsourcing approach;balancing utility and fairness in submodular maximization;stateful entities: object-oriented cloud applications as distributed dataflows;learning over sets for databases;a new PET for data collection via forms with data minimization, full accuracy and informed consent;adaptive compression for databases;analysis of open government datasets from a data design and integration perspective;fine-grained geo-obfuscation to protect workers’ location privacy in time-sensitive spatial crowdsourcing;and a framework to evaluate early time-series classification algorithms.

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Applications of Vedic multiplier - A Review

Applications of Vedic multiplier - A Review

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2021 Virtual National conference on Materials for Electronics Applications, ViNCMEA 2021

作者： Khubnani, Rashi Sharma, Tarunika Subramanyam, Chitirala New Horizon College of Engineering Bangalore India

Vedic Multiplier is a key tool in rapidly growing technology especially in the immense domain of image processing, Digital Signal processing, real-time signal. Multipliers are important block in digital systems and play a critical role in digital designs. Along with accuracy demand for minimizing time area, power, and delay of the processor by enhancing speed is the focus point. Vedic mathematics rules and algorithms generate partial products concurrently and save time. This paper is a review of the application and modification of Vedic multiplier in different fields and a comparison of Vedic multiplier with other multipliers for enhancing performance parameters. © Published under licence by IOP Publishing Ltd.

关键词： Digital signal processing

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Synergizing Support Vector Machines and image processing for Superior Skin Cancer Detection : A Crammer-Singer Approach

Synergizing Support Vector Machines and Image Processing for...

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International conference on Communication and Electronics systems (ICCES)

作者： S. Divya S. Thilagavathi Department of Computer Sciene Sri Krishna Adithya College of Arts and Science Coimbatore India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Skin cancer, which primarily impacts skin exposed to ultraviolet (UV) rays against the sun, represents dangerous to the most significant organs in the human body, the skin. Usually, a spot, lump, or mole that appears on the skin is the primary suspicion of skin cancer. However, each of these can undergo changes in coloring or shape as time passes. Recovery for skin cancer is mostly possible if the disease is discovered early. Numerous medical diagnostic methods, such as Dermoscopy, biopsy, and ocular examination of the affected area, are useful in helping anticipate the development of skin cancer. However, these approaches have the disadvantage of delivering erroneous results because it is extremely difficult to distinguish between normal and malignant skin. Therefore, the drawback of these diagnostic procedures is that machine learning algorithms are currently used together with image processing techniques to examine the images for the purpose of precisely identifying skin cancer. The current research employs the ISIC dataset to develop a novel model for skin cancer classification that combines image processing techniques with advanced machine learning methods, including Crammer-Singer Support vector machine learning algorithms. The categorization of skin cancer begins with preprocessing the input image, which includes hair removal using a morphological filter and image enhancement using a median filter to minimize noise and increase image clarity. The ABCD approach is used to segment lesion images by evaluating them for asymmetry, border irregularity, color variability, and diameter. The crammer-Singer SVM algorithm is then used with these images to classify skin lesions into various types such as melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis (AK), benign keratosis (BKL), dermatofibroma (DF), vascular lesion (VASC), and squamous cell carcinoma (SCC), leveraging its robust multi-class handling capabilities. The system achieve

关键词： Support vector machines image segmentation Machine learning algorithms Accuracy Filtering algorithms Skin Classification algorithms Lesions image enhancement Skin cancer

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AINoC: New Interconnect for Future Deep Neural Network Accelerators 16th

AINoC: New Interconnect for Future Deep Neural Network Accel...

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16th International Workshop on Design and Architecture for Signal and image processing (DASIP) held jointly with the 18th HiPEAC conference

作者： Krichene, Hana Prasad, Rohit Mouhagir, Ayoub Univ Paris Saclay List CEA F-91120 Palaiseau France

ISBN: (纸本)9783031299698;9783031299704

Data exchanges can be significant in the Deep Neural Network (DNN) algorithms. The interconnection between computing resources can quickly have a substantial impact on the overall performance of the architecture and its energy efficiency. Similarly, access to the different memories of the system, with potentially high data sharing, is a critical point. To overcome these problems, in this paper, we propose a new interconnect network, called AINoC, for future DNN accelerators, which require more flexibility and less power consumption to facilitate their integration into artificial intelligence (AI) edge systems. AINoC is based on (1) parallel routing that ensures communication/computation overlap to improve performance and (2) data reuse (filters, image inputs, and partial sums) to reduce multiple memory accesses. In the experiment section, AINoC can speedup LeNet5 convolution layers by up to 71.74x in latency performance w.r.t. a RISC-V-based CPU and also speedup MobileNetV2 convolution layers by up to 2.35x in latency performance w.r.t. a dataflow architecture featuring row-stationary execution style. AINoC provides any-to-any data exchange with wide interfaces (up to 51.2 GB/s) to support long bursts (up to 384 flits/cycle with packed data, i.e., 3 * 8-bit data in a 32-bit wide datapath) while executing LeNet5 and MobileNetV2. AINoC supports flexible communication with many multicast/broadcast requests with non-blocking transfers. Parallel communication in AINoC can provide up to 128x more throughput (flits/cycle) and bandwidth (GB/s), using parallel routing with respect to single-path routing while executing convolution layers of LeNet5 and MobiletNetV2.

关键词： On-Chip Interconnect Network-on-Chip DNN CNN dataflow execution parallel processing

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Efficiency of deep neural networks for reinforced concrete shear walls failure mode detection 27

Efficiency of deep neural networks for reinforced concrete s...

引用

27th International Computer conference, Computer Society of Iran, CSICC 2022

作者： Barkhordari, Mohammad Sadegh Massone, Leonardo M. Department Of Civil And Environmental Engineering Iran University Of Chile Department Of Civil Engineering Santiago Chile

ISBN: (纸本)9781665480277

After a severe earthquake, it is critical to check the health of hospitals, buildings, fire stations, to name but a few. New technologies such as deep learning algorithms, image processing, and signal processing methods have become more appealing in such quick applications because of their higher reliabilities and efficiency than traditional visual methods. On the other hand, reinforced concrete shear walls are the key structural elements of the most buildings as lateral force resisting systems in seismic areas. Determining the failure mode of reinforced concrete shear walls is a crucial task that can help engineers and designers choose the best retrofitting options. The goal of this research is to assess the ability of deep neural networks for predicting the failure mode of the reinforced concrete shear walls. Furthermore, the SHapley Additive exPlanation approach is used to demonstrate the relevance and contribution of the factors that influence the reinforced concrete shear walls failure mode determination. © 2022 IEEE.

关键词： Deep learning Visualization Buildings Neurons Signal processing algorithms Signal processing Concrete

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A Review on the Applications of Machine Learning and Deep Learning algorithms for image Recognition

A Review on the Applications of Machine Learning and Deep Le...

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Sentiment Analysis and Deep Learning (ICSADL), International conference on

作者： Ketan Wanjari Prateek Verma Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Sawangi (Meghe) Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Sawangi (Meghe) Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest image processing techniques include ViTs alongside GANs and Few-Shot Learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition.

关键词： Deep learning Ethics Data privacy image recognition Machine learning algorithms Reviews Social networking (online) Vectors Security Stakeholders

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A Comparative Analysis of Branch-Cut and Quality-Guided algorithms For inSAR Interferogram

A Comparative Analysis of Branch-Cut and Quality-Guided Algo...

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International conference on image and Signal processing and their Applications (ISPA)

作者： Saoussen Djeddi Tarek Bentahar Atef Bentahar Riad Saidi Hanane Djellab Laboratory of Mathematics Informatics and Systems (LAMIS) Echahid Cheikh Larbi Tebessi University Tebessa Algeria Laboratory of Electrical Engineering-Telecommunications (LABGET) Echahid Cheikh Larbi Tebessi University Tebessa Algeria Dept Commun Base Science and Technology Mustapha Ben Boulaid Batna2 University Batna Algeria LTI Laboratory of Guelma Echahid Cheikh Larbi Tebessi University Tebessa Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Interferometric synthetic aperture radar (inSAR) phase image is a key for the digital elevation model of earth mapping. To achieve this three-dimensional reconstruction, phase unwrapping process must be performed whitch is used to calculate the accurate elevations from the wrapped phase map. For the noise-free images, this process is just a simple integration of the wrapped gradient. But in reality, there is no phase image without noise, therefore the phase unwrapping have to be adaptive with a strong immunity to noise. Several adaptive algorithms have been proposed in such area where Goldstein’s branch-cut and Flynn’s quality-guided are the widely known in the path-following category, they are the most used methods and all other propositions are just enhancements or hybridizations. In this paper, we analyze the performance of each one and provide the substantial difference between them. Both algorithms are implemented using simulated and real inSAR data of different patterns and are analyzed under several relevant criteria.

关键词： image quality Earth Noise Signal processing algorithms Adaptive algorithms Noise measurement Synthetic aperture radar

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An Efficient image-Based Virtual Scene Generation and Stitching Algorithm

An Efficient Image-Based Virtual Scene Generation and Stitch...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Luo Wei Chongqing Three Gorges Vocational College Chongqing Wanzhou China

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the development of computer graphics and image processing, the virtual scene generation and splicing technology has been widely used in various fields of computer-aided image analysis. In this context, this study proposes a new and efficient image stitching algorithm to solve the image stitching artifacts caused by perspective changes and exposure differences in multi-camera systems, and expands it to the field of virtual images. First, as a pre-processing part, the model analyzes the image through nonlinear filtering and mesh deformation optimization to ensure image quality. Subsequently, the model presents a new image stitching algorithm based on the image matching method of minimizing projection deviation and thin plate spline theory. This algorithm further proposes an improved EIEOWF algorithm based on circular weighted fusion to achieve the task of smooth transition stitching of images. Finally, the stitching technology was extended to virtual scene generation, and OpenGL technology was used to achieve real-time 3D texture mapping. The test part is verified through experiments on the UDIS-D data set. The results show that the average splicing accuracy of the proposed algorithm in 100 sets of tests is 95%, with a standard deviation of 2%, which is significantly better than the existing comparison algorithm.

关键词： Three-dimensional displays Accuracy Splicing Heuristic algorithms Reliability theory Real-time systems Splines (mathematics) image stitching Standards Optimization

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Abnormal Behavior Detection in Electronic-Exam Videos Using BeatGAN 8

Abnormal Behavior Detection in Electronic-Exam Videos Using ...

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8th International Iranian conference on Signal processing and Intelligent systems, ICSPIS 2022

作者： Atabay, Habibollah Agh Hassanpour, Hamid Shahrood University of Technology Image Processing & Data Mining Lab Shahrood Iran

ISBN: (纸本)9781665476232

Abnormal Behavior Detection is a core ability to recognize cheating in electronic exams (e-exams), especially in the scenes where a fraudulent candidate hides unauthorized resources from the view of the proctor. This work utilizes the BeatGAN algorithm to detect behavioral abnormalities in the skeleton-based motion information of candidates during remote e-exams. We examine the algorithm on the previously published dataset of skeleton features extracted from e-exam videos. The results show that BeatGAN outperforms previously investigated algorithms in sequence anomaly detection using the raw joint-coordinate, implicit bone-length-and-angle and explicit bone-length feature types. But, in event anomaly detection, BeatGAN does not improve the state-of-the-art results on the dataset. © 2022 IEEE.

关键词： Anomaly detection

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Contrast Enhancement and ROI detection in satellite images Application to the environment and sustainable development

Contrast Enhancement and ROI detection in satellite images A...

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International conference on image and Signal processing and their Applications (ISPA)

作者： Aicha Mehidi Malika Mimi Abdallah Mehidi Soraya Zehani Electrical Engineering Department Laboratory of Signals and Systems Faculty of Sciences and Technology University of Abdelhamid Ibn Badis Mostaganem Algeria Laboratory of Signals and Systems Faculty of Sciences and Technology University of Abdelhamid Ibn Badis Mostaganem Algeria Electrical Engineering Department Laboratory of Vision and Communication Systems Faculty of Sciences and Technology University of Biskra

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

During the last few years, remote sensing is considerably used for Earth observation for the environment and sustainable development. The temporal classes of satellite images provide better information for monitoring the earth's surface at different scales; therefore these images are becoming a very relevant opportunity of investigating. The numerical analyses of image processing are based on the artificial intelligence (AI) tool rather than the traditional methods. The environmental indicators from these images offer very important statistics, the carried out work of dynamic phenomena, the observation event and interpretation of evolving circumstances like: atmospheric conditions, crises and natural disasters taking the opportunity to be studied, several problems come out of deforestation and monitoring of water resources (water bodies). The purpose of this study is focused on drawing out details from the database used and then to give an offer for a texture analysis strategy. Firstly, using specific development and then developing the existing software in the second hand. This work is to establish an algorithm for the detection of region of interest (water body) which is based on the theory of fuzzy logic and the hypothesis of Fuzzy C-Means.

关键词： Earth image segmentation Software algorithms Signal processing algorithms Satellite images Sustainable development Artificial intelligence

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