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

共 13657 条记录，以下是411-420 订阅

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Effectiveness of image processing and Different Machine Learning Models in Classifying Local Soybean Varieties in the Philippines 12

Effectiveness of Image Processing and Different Machine Lear...

引用

12th IEEE conference on systems, Process and Control, ICSPC 2024

作者： Dioses, Isaac Angelo M. Salum, Romero Dante Nayga, Desiray Asuncion, Mark Joseph Dioses, Jesusimo L. Castillo, Wendell M. MAPUA University School of Information Technology Makati Campus Metro Manila Makati Philippines Isabela State University College of Computing Studies Information and Communication Technology San Fabian Echague Isabela Philippines

ISBN: (纸本)9798350391398

Deceptive providers offering inferior products threaten the integrity of soybean exportation and product quality. To uphold fair trade standards and protect consumer rights, it is essential to classify and trace different soybean varieties accurately. Implementing stringent legislation and reliable technology for crop origin identification is crucial in maintaining order in the soybean trading market. This study uses artificial intelligence, specifically machine learning, image processing, and computer vision, to develop a standard model for soybean classification. Researchers collected two bags of soybeans from CVRC Cagayan at Ilagan Isabela, resulting in a dataset of 1562 soybean images, with 810 images of PSB SY2 and 752 images of CL1 Soy. The study employed Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) algorithms to create models for classifying and predicting soybean varieties. The SVM model achieved an accuracy of 88.2%, while the KNN model, using the FINE KNN preset, outperformed with an accuracy of 94.4%. Although the KNN algorithm exhibited slower training speed and time, it demonstrated superior accuracy over the SVM model, as evidenced by its confusion matrix. Feature selection using the MRMR algorithm identified perimeter and equivalent diameter as the most significant features for soybean classification, while minor axis length was deemed the least important. To improve the precision and dependability of soybean variety classification and safeguard customer interests, this research emphasizes the potential of machine learning approaches in conjunction with image processing and morphology feature extraction. © 2024 IEEE.

关键词： Support vector machines

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Evolution of real-time processing of visual information over four decades: a retrospective as outlook to the future of real-time imaging

Evolution of real-time processing of visual information over...

引用

Real-time processing of image, Depth and Video Information 2023

作者： Carlsohn, Matthias F. Engineering and Consultancy Dr. Carlsohn for Computer Vision and Image Communication Bremen Germany

ISBN: (数字)9781510662636

ISBN: (纸本)9781510662629

The expectations and demands for real-time performance for processing of visual information usually overloaded contemporary computer systems at all times. Increasing hardware performance enabled the use of more complex analysis and vice versa. For a long time, this led to a mutual fertilization of electronics and computer programs or architectures and algorithms with coherent data structures. Appropriate tool sets marked corner stones and boosted some significant developments. However, up to now one got always the impression that the requirements were always heading the possible performance and the open question becoming more serious is: where will it end? This paper provides a short rush through four decades of real-time image processing with a conclusion for an outlook into future opportunities and current developments providing guidelines to host corresponding research results by this conference in the coming years. New concepts, programming paradigms and production technologies are paving the way for future image processing solutions providing real-time capabilities. © 2023 SPIE.

关键词： Parallel architectures

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Effect of image Down-sampling on Detection of Adversarial Examples 29th

Effect of Image Down-sampling on Detection of Adversarial Ex...

引用

29th International conference on Neural Information processing

作者： Peng, Anjie Li, Chenggang Zhu, Ping Wu, Zhiyuan Wang, Kun Zeng, Hui Yu, Wenxin Southwest Univ Sci & Technol Mianyang 621010 Sichuan Peoples R China Sci & Technol Commun Secur Lab Chengdu 610041 Sichuan Peoples R China Chengdu Univ Informat Technol Chengdu 610103 Sichuan Peoples R China

ISBN: (纸本)9789819916382;9789819916399

Detecting adversarial examples and rejecting to input them into a CNN classifier is a crucial defense method to prevent the CNN being fooled by the adversarial examples. Considering that attackers usually utilize down-sampling to match the input size of CNN and the detection methods are commonly evaluated on down-sampled images, we study how the detectability of adversarial examples is affected by the interpolation algorithm if the legitimate image is down-sampled prior to be attacked. Since the down-sampling changes the relationships among neighboring pixels, the steganalysis-based detectors relying on the neighborhood dependencies are probably affected sharply. Experimental results on imageNet verify that the detection accuracy varies among different interpolation kernels dramatically (the largest difference of accuracy is up to about 9%), and such novel phenomena appear valid universally across the tested CNN models and attack algorithms for the steganalysis-based detection method. Our work is of interest to both attackers and defenders for the purpose of benchmarking the attack algorithm and detection method respectively.

关键词： Convolution Neural Network Adversarial image Detection Down-sampling

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Secure Vision: Enhancing Data Security in Wireless Sensor Networks through image processing 8

Secure Vision: Enhancing Data Security in Wireless Sensor Ne...

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8th International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Swamy, B. Venkata Gopi, S. Sudhakar, Kattupalli Girish, Pechetti Rani, M. Janaki Upadhyay, Shrikant B V Raju Institute of Technology Department of Chemistry Telangana State India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Cse Chennai Avadi India Lakireddy Bali Reddy College of Engineering Department of Ai & Ds Mylavaram India Bonam Venkata Chalamayya Institute of Technology and Science Batlapalem Amalapuram India Dr. Mgr Educational and Research Institute Department of Ece Chennai India Mlr Institute of Technology Department of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350376425

The secure Wireless Sensor Network (WSN) architecture was designed in such a way that the tradeoffs among efficiency, scalability and security were balanced. It was consisted of several sensor nodes, cluster heads, base station as well as the control center all of them played a vital role in functioning and managing of data. This network security was provided with a layered security approach like the physical, data link, network, transport, and application layers. Data security and integrity are secured through advanced image processing algorithms, and reliable communication protocol. Regular maintenance, constant monitoring and hardening solutions through resilience and fault tolerance mechanisms added a layer of trust in securing this WSN. A comparative analysis was made using different algorithms, such as Intrusion Detection systems (IDS) and detection accuracy is 98% and proposed model which has shown a better result in the energy consumption, data transmission latency, encryption time, detection precision as well as efficiency to insure an original data integrity. © 2024 IEEE.

关键词： Data integrity

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Chinese image Description Generation Model Based on Recurrent Fusion Encoding 5th

Chinese Image Description Generation Model Based on Recurren...

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5th International conference on 3D Imaging Technologies-Multidimensional Signal processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Zhou, Shiyu Donghua University Shanghai China

ISBN: (纸本)9789819751839

The image description job is a crucial area of research in the nexus of computer vision and applications for natural language processing. The objective is to produce accurate text descriptions using the picture attributes retrieved by the model. The conventional encoder-decoder model produces an inadequate encoding feature because it does less direct dimensionality reduction on images and is deficient in sequence feature information. This research suggests a recurrent fusion encoding-based Chinese picture description generation model to solve the aforementioned issues. R-CNN is used to extract the image's features first. Then, at the encoding side, gated recurrent units and a soft attention mechanism are used to build a recurrent fusion network for full sequence fusion of the image features. Finally, the sequence feature information is combined with a self-attention mechanism for multi-level interaction before being sent to the encoding side. I carried out enough tests on the Flickr_8k dataset and the Ai_Challenger dataset to confirm the model’s validity. According to the experimental data, the model performs better than some Chinese image description algorithms in each index by 0.2–4.2% points, and the generated statements are more accurate and sufficient for image description. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Encoding (symbols)

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EgpuIP: An Embedded GPU Accelerated Library for image processing 24

EgpuIP: An Embedded GPU Accelerated Library for Image Proces...

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24th IEEE International conference on High Performance Computing and Communications, 8th IEEE International conference on Data Science and systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wang, Luhan Jia, Haipeng Zhang, Yunquan Li, Kun Wei, Cunyang Institute of Computing Technology Chinese Academy of Sciences State Key Lab of Processors Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Microsoft Research China

ISBN: (纸本)9798350319934

With the advances of embedded GPUs' programming models like GLES and OpenCL, the mobile processor has gained more parallel computing capability, which enables real-time image processing on portable devices. GLES is an excellent option to implement high-performance image processing on embedded GPUs due to its low hardware overhead. However, most GLES studies only focus on porting specific algorithms to embedded GPUs without a general optimization guide. In order to address the pending problems, this paper presents effective performance optimization chains to guide the optimization of image processing algorithms by using GLES. The image processing algorithms can be divided into three modes including data-independent, data-sharing and data-related according to their memory access and calculation characteristics. Based on this classification, our optimization chains contain four optimization directions. 1) Optimizing access to off-chip memory for the memory-bound data-independent algorithm;2) Exploiting data locality by utilizing shared memory and cache for the data-sharing algorithm;3) Redesigning the algorithm to optimize the sharing of computational results between threads for the data-related algorithm;4) Making full use of computation resource for the above three algorithms. Based on these optimization chains, we design an embedded GPU accelerated image processing library, EgpuIP. To evaluate specific optimization methods and performance improvements, we employ histogram equalization, Gaussian pyramid and integral filter as the representative algorithm from EgpuIP and adequately optimize them guided by the proposed optimization chains. Compared to the OpenCV, experiments show that the three algorithms in EgpuIP provide up to 19×88×, and 3× speedup respectively. © 2022 IEEE.

关键词： Graphics processing unit

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Braille-to-Voice Converter Using image processing 2nd

Braille-to-Voice Converter Using Image Processing

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2nd International conference on Advances in Computational Intelligence and Informatics, ICACII 2023

作者： Prasad, P. Rajendra Ahmed, Roshan Zameer Lingamurthy, Chethan Raju Chaitra, A.M. Deepak, H.R. Ramaiah Institute of Technology Bangalore560054 India Presidency University Bangalore India

ISBN: (纸本)9789819747269

Approximately 2.2 billion people in the world are visually impaired and rely on assistance to meet their basic needs. Education is a fundamental human need and plays a crucial role in empowering individuals. For visually impaired individuals, education can not only integrate them into society but also enhance their professional and academic abilities. To facilitate better education for the visually impaired, specialized books, educational materials, and devices are necessary. Braille is a tactile system designed for people who are blind, enabling them to read and write through touch. Proposed work aims to improve the education of visually impaired individuals by developing a braille-to-voice converter. The system will use image processing techniques to capture an image of braille text, which will then be analyzed using computer vision algorithms to extract the text and convert it to speech. The ultimate goal is to create a cost-effective, accurate, and user-friendly tool that can be easily integrated into the daily lives of visually impaired individuals. With this system, visually impaired individuals will be able to easily and quickly convert braille text to speech in real time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image enhancement

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Intelligent Frame and Table Segmentation in Blueprint Documents: Method and Implementation 6th

Intelligent Frame and Table Segmentation in Blueprint Docume...

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6th conference on Computational Methods in systems and Software (CoMeSySo)

作者： Hasan, Fudail Kashevnik, Alexey ITMO Univ St Petersburg Russia SPC RAS St Petersburg Russia Petrozavodsk State Univ PetrSU Petrozavodsk Russia

ISBN: (纸本)9783031214370;9783031214387

Blueprints are documents that contain the drawing of a design and the information that explains this design. Recently, the task to automatically recognize the information in the blueprint documents has become required. Segmenting the frame and tables in the blueprint is the first step of understanding the blueprint document. In this paper, we explain an automated method for frame and table segmentation. The proposed method processes the blueprint as an image and defines all the parts (pixels) of the blueprint that belongs to the frame or tables. It finds all the lines in the blueprint's image and decide which combinations of lines construct frame and tables. It can process blueprints that have high resolution. The proposed method allows to isolate frame and tables in the blueprint document. We achieved an accuracy of 99% for excellent quality documents.

关键词： Frame and table segmentation Blueprint processing Table detection Lines segmentation

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The Impact of Preprocessing on Face Recognition using Pseudorandom Pixel Placement 29

The Impact of Preprocessing on Face Recognition using Pseudo...

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29th International conference on systems, Signals and image processing (IWSSIP)

作者： Hadis, Muhammad Sabirin Akita, Junichi Toda, Masashi Nurzaenab Kanazawa Univ Dept Elect Engn & Comp Sci Kanazawa Japan Kumamoto Univ Ctr Management Informat Technol Kumamoto Japan STMIK AKBA Study Program Informat Engn Makassar Indonesia

ISBN: (数字)9781665495783

ISBN: (纸本)9781665495783

The condition of face images, data processing algorithms, and hardware capabilities can influence the accuracy of face recognition. Several studies have been conducted to increase performance of face recognition. One of the steps is to create or even improve the methods in preprocessing as one of the essential steps that can affect accuracy. This paper proposed a pseudorandom pixel placement method applied to the preprocessing step in face recognition to know the impact on accuracy. Eight face objects were used in this study. One face image for one object as training data was taken via a single-lens digital reflex camera and a smartphone. One video for one object was taken from Closed Circuit Television with two different placement angle conditions for testing data. The experiment was carried out with four variations of the basic resolution size of the face image in the testing data to see the performance of the proposed method. The result is five of eight face objects have improved accuracy than without using pseudorandom pixel placement. The best average accuracy result using the proposed method is 63.76% higher than without using the proposed method with a value of 60.09%, so preprocessing using the pseudorandom pixel placement on face recognition can increase accuracy.

关键词： Face Recognition Pseudorandom Pixel Placement One image Training Data image processing Computer Vision

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Post-processing of Thresholding or Deep Learning Methods for Enhanced Tissue Segmentation of Whole-Slide Histopathological images

Post-Processing of Thresholding or Deep Learning Methods for...

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18th International Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2025

作者： Marczyk, Michal Wrobel, Agata Merta, Julia Polanska, Joanna Department of Data Science and Engineering Silesian University of Technology Gliwice44-100 Poland Yale Cancer Center Yale School of Medicine New HavenCT06511 United States Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

Digital pathology allows for the efficient storage and advanced computational analysis of stained histopathological slides of various tissues. Tissue segmentation is a crucial first step of digital pathology aimed at eliminating background, pen markings, and other artifacts, reducing image size, and increasing the efficiency of further analysis. In most cases, color thresholding or deep learning models are used, but their effectiveness is reduced due to complex artifacts and huge color variations between slides. We propose a post processing method to increase the tissue segmentation performance of any initial segmentation algorithm. Using a set of 197 manually annotated histopathological images of breast cancer patients and 63 images of endometrial cancer patients, we tested our method with 3 thresholding techniques and 3 deep learning-based algorithms by calculating the Dice index, Jaccard index, precision, and recall. In both datasets, applying post processing increased precision and recall for thresholding methods and mostly precision for deep learning models. Overall, applying post-processing gave better tissue segmentation performance than initial segmentation methods, significantly increasing Dice and Jaccard indices. Our results proved that thanks to post-processing, the tissue segmentation pipeline is more robust to noises and artifacts commonly present in histopathological images. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： image segmentation

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