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检索条件"任意字段=16th Image Processing: Algorithms and Systems Symposium 2018"

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Classification of Plants Leave Using image processing and Neural Network 8th

Classification of Plants Leave Using Image Processing and Ne...

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8th International Workshop on Soft Computing Applications, SOFA 2018

作者： Bagherinezhad, Hashem Kuchaki Rafsanjani, Marjan Balas, Valentina Emilia Koles, Ioan E. Department of Computer Science Faculty of Mathematics and Computer Shahid Bahonar University of Kerman Kerman Iran Department of Automatics and Applied Software Faculty of Engineering Aurel Vlaicu University of Arad Arad Romania

ISBN: (纸本)9783030521899

Plants are one of the most widely used resources for humans in different fields. therefore, the distinction between the plant species is important and it is referred to as the plant detection system. Until now, this task has been done by the expert botanists which is an overwhelming and time consuming task. Moreover, there is a lack of the memory and the human fault, so the researchers endeavored to solve these disadvantages using the AI algorithms. For this goal, in this paper, a system is proposed that includes four phases: pre-processing, feature extraction, training, and test. In this method, we use the combination of the useful features of the leave shape, the leave texture, the leave color, and we provide a method for the classification of a number of the plant species. Finally, the feature vectors will be created and then, the classification is performed by using feed-forward back-propagation multi-layer perceptron artificial neural network algorithm. then, the results of this method compare with other methods. the obtained results show the high accuracy of this method for a large number of the species in different conditions (such as pests, season changes and lighting). © 2021, Springer Nature Switzerland AG.

关键词： Feature extraction

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Floor extraction and door detection for visually impaired guidance 16

Floor extraction and door detection for visually impaired gu...

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16th IEEE International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Berenguel-Baeta, B. Guerrero-Viu, M. Nova, A. Bermudez-Cameo, J. Perez-Yus, A. Guerrero, J. J. Univ Zaragoza Inst Invest Ingn Aragon Zaragoza 50018 Spain

ISBN: (纸本)9781728177090

Finding obstacle-free paths in unknown environments is a big navigation issue for visually impaired people and autonomous robots. Previous works focus on obstacle avoidance, however they do not have a general view of the environment they are moving in. New devices based on computer vision systems can help impaired people to overcome the difficulties of navigating in unknown environments in safe conditions. In this work it is proposed a combination of sensors and algorithms that can lead to the building of a navigation system for visually impaired people. Based on traditional systems that use RGB-D cameras for obstacle avoidance, it is included and combined the information of a fish-eye camera, which will give a better understanding of the user's surroundings. the combination gives robustness and reliability to the system as well as a wide field of view that allows to obtain many information from the environment. this combination of sensors is inspired by human vision where the center of the retina (fovea) provides more accurate information than the periphery, where humans have a wider field of view. the proposed system is mounted on a wearable device that provides the obstacle-free zones of the scene, allowing the planning of trajectories for people guidance.

关键词： Computer vision omnidirectional camera image processing floor extraction door detector RGBD camera fish-eye camera visually impaired guidance

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Revelio: A Lightweight Captcha Solver Using a Dictionary Based Approach 16th

Revelio: A Lightweight Captcha Solver Using a Dictionary Bas...

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16th International Conference on Information systems Security

作者： Chougule, Abhijeet Tupsamudre, Harshal Lodha, Sachin TCS Res Pune Maharashtra India

ISBN: (纸本)9783030656096;9783030656102

Captcha is an important security measure used by many websites to defend against malicious bot programs. However, with the advancement in the field of computer vision, seemingly complex Captcha schemes have been broken. Although Captcha solving techniques have improved significantly, we observed that many major banking and government websites are still relying on a relatively simple class of text Captchas to counter bot attacks. In this paper, we demonstrate that Captcha schemes deployed on State Bank of India (SBI), Axis bank and Indian Railways (IRCTC) websites can be easily broken using a repertoire of standard image processing techniques. We develop a Captcha solver tool called Revelio which is lightweight, automatic, efficient, and requires minimal labeled data and works in real-time. We evaluate the performance of our tool with the state-of-the-art CNN model on diverse Captcha schemes from 14 major Indian websites. the proposed solver achieves at least 90% accuracy on 10/14 Captcha schemes. Further, we found that for the targeted class of Captcha schemes and a given amount of labeled data, our solver outperforms the CNN based solver.

关键词： Captcha image processing Bot attacks algorithms

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Automatically Designing CNN Architectures for Acoustic Scene Classification

Automatically Designing CNN Architectures for Acoustic Scene...

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International Conference on Computer Engineering and systems

作者： Noha W. Hasan Ali S. Saudi Mahmoud I. Khalil Hazem M. Abbas Ain Shams University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Qalubiya Egypt

ISBN: (纸本)9781665408684

Deep convolutional neural networks (CNNs) have proven their effectiveness in the acoustic scene classification (ASC) task, becoming the state-of-the-art solution to build pow-erful ASC models. Usually, the architectures of these networks and their hyper-parameters have been manually designed by expert researchers in both the investigated problem and CNN architecture design, which can be a challenging task. In this work, we propose a genetic algorithm (GA) that benefits from the characteristics of state-of-the-art CNN architectures in the field of ASC to specifically optimize CNN architectures for the ASC task, within an acceptable search cost compared to other GAs. After searching for 14 GPU days using a single NVIDIA Tesla K80 GPU on the development dataset of DCASE2020 Task lA, the proposed GA has optimized a CNN architecture that achieved a test accuracy of 68.5 % on the same dataset. the same network has been evaluated on the development dataset of DCASE2018 Task 1A and achieved a classification accuracy of 73.7% without repeating the search process, which demonstrates the generalization capability of the generated CNNs.

关键词： image analysis Neural networks Graphics processing units Computer architecture Acoustics Classification algorithms Convolutional neural networks

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Research on Influence of Content Diversity on Full-Reference image Quality Assessment 16th

Research on Influence of Content Diversity on Full-Reference...

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16th International Forum on Digital TV and Wireless Multimedia Communication, IFTC 2019

作者： Zhang, Huiqing Li, Shuo Xia, Zhifang Chen, Weiling Tang, Lijuan Faculty of Information Technology Beijing University of Technology Beijing100124 China Engineering Research Center of Digital Community Ministry of Education Beijing100124 China The State Information Center of China Beijing100045 China Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350000 China Jiangsu Vocational College of Business Jiangsu226000 China

ISBN: (纸本)9789811533402

With the development of image quality assessment (IQA), the full-reference (FR) IQA algorithms are becoming more and more mature. this paper analyzes the performance of five FR natural scene (NS) IQA algorithms and three FR screen content (SC) IQA algorithms, and makes analysis on the eight IQA algorithms in terms of prediction performance and design principle. Experiments show, (1) the performance of perceptual similarity based IQA method proposed by Gu et al. is better;(2) the multi-scale technology plays an important role in improving the performance of algorithms. Finally, summarized and expounded the development direction of image quality evaluation. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Quality control

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A Deep Learning-Based Approach for Defect Detection and Removing on Archival Photos 18

A Deep Learning-Based Approach for Defect Detection and Remo...

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18th image processing: algorithms and systems Conference, IPAS 2020

作者： Sizyakin, R. Voronin, V. Gapon, N. Zelensky, A. Pižurica, A. Department Telecommunications and Information Processing IPITELIN-imec Ghent University Ghent Belgium Moscow State University of Technology "STANKIN" Moscow Russia Lab. "Mathematical methods of image processing and intelligent computer vision systems" Don State Technical University Rostov-on-Don Russia

Many archival photos are unique, existed only in a single copy. Some of them are damaged due to improper archiving (e.g. affected by direct sunlight, humidity, insects, etc.) or have physical damage resulting in the appearance of cracks, scratches on photographs, non-necessary signs, spots, dust, and so on. this paper proposed a system for detection and removing image defects based on machine learning. the method for detecting damage to an image consists of two main steps: the first step is to use morphological filtering as a pre-processing, the second step is to use the machine learning method, which is necessary to classify pixels that have received a massive response in the preprocessing phase. the second part of the proposed method is based on the use of the adversarial convolutional neural network for the reconstruction of damages detected at the previous stage. the effectiveness of the proposed method in comparison with traditional methods of defects detection and removal was confirmed experimentally. © 2020, Society for Imaging Science and Technology

关键词： Defects

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Resource Efficient Dynamic Voltage and Frequency Scaling on Xilinx FPGAs 16th

Resource Efficient Dynamic Voltage and Frequency Scaling on ...

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16th International symposium on Applied Reconfigurable Computing

作者： Akgun, Gokhan Kalms, Lester Goehringer, Diana Tech Univ Dresden Adapt Dynam Syst Dresden Germany Tech Univ Dresden Ctr Tactile Internet Human Loop CeTi Dresden Germany

ISBN: (纸本)9783030445331;9783030445348

As FPGA devices become increasingly ubiquitous, the need for energy-conscious implementations for battery-powered devices arises. these new energy constraints have to be met in addition to the well-known area, latency and throughput requirements. Furthermore, the power dissipation of such systems is usually considered as a hardware problem. However, it can be solved effectively through hardware and software implementations of power-saving techniques. One generic energy-saving technique that does not require retroactive alteration of an HW/SW-design is dynamic voltage and frequency scaling (DVFS) which adjusts the power consumption and performance of an embedded device at run-time based on its workload and operating conditions. this work investigates the power monitoring and scaling capabilities of Xilinx Zynq-7000 SoCs and UltraScale+ MPSoCs. A real-time operating system (RTOS) manages the resources of an application, the voltage/frequency scaling and the power monitoring with its preemptive scheduling policies. Furthermore, the frequency is scaled without using additional hardware resources on the programmable logic from the processing system. the methodology can easily be used for changing the processor frequency at run-time. As a case study, we apply our technique to find energy-optimal voltage and frequency pairs for an image processing application designed using the open-source high-level synthesis library HiFlipVX. the proposed frequency scaling architecture requires up to 20% less flip-flops and look-up tables as compared to the same design with clocking wizard on the programmable logic.

关键词： Dynamic voltage and frequency scaling image processing application HiFlipVX Frequency scaling without MMCM

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Efficient algorithms for Sum Rate Maximization in Fronthaul-Constrained C-RANs 16

Efficient Algorithms for Sum Rate Maximization in Fronthaul-...

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16th International symposium on Wireless Communication systems (ISWCS)

作者： Nguyen, Kien-Giang Vu, Quang-Doanh Tran, Le-Nam Juntti, Markku Univ Oulu Ctr Wireless Commun POB 4500 FI-90014 Oulu Finland Univ Coll Dublin Sch Elect & Elect Engn Dublin Ireland

ISBN: (纸本)9781728125275

We consider downlink transmission of a fronthaulconstrained cloud radio access network. Our aim is to maximize the system sum data rate via jointly designing beamforming and user association. the problem is basically a mixed integer non-convex programs for which a global solution requires a prohibitively high computational effort. the focus is thus on efficient solutions capable of achieving the near optimal performance with low complexity. To this end, we transform the design problem into continuous programs by two approaches: penalty and sparse approximation methods. the resulting continuous nonconvex problems are then solved by the successive convex approximation framework. Numerical results indicate that the proposed methods are near-optimal, and outperform existing suboptimal methods in terms of achieved performances and computational complexity.

关键词： Array signal processing Convergence Approximation algorithms Wireless communication Optimization Downlink Interference

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DB M-Net: An Efficient Segmentation Network for Esophagus and Esophageal Tumor in Computed Tomography images 2020

DB M-Net: An Efficient Segmentation Network for Esophagus an...

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2nd symposium on Signal processing systems (SSPS)

作者： Zhou, Donghao Huang, Guoheng Ling, Wing-Kuen Ni, Haomin Cheng, Lianglun Zhou, Jian Guangdong Univ Technol Guangzhou Peoples R China Sun Yat Sen Univ Canc Ctr Guangzhou Peoples R China

ISBN: (纸本)9781450388627

Esophageal cancer is one of the diseases afflicting human beings. Automatic segmentation of esophagus and esophageal tumor from computed tomography (CT) images is a challenging problem, which can assist in the diagnosis of esophageal cancer. In this paper, DB M-Net is proposed for the segmentation of esophagus and esophageal tumor from CT images, which combines M-Net modified from U-Net with an approximate function for binarization called differentiable binarization (DB). We construct the multi-scale input layers and the multi-level output layers in the network to facilitate features fusion, and DB is performed to enhance the robustness. Fewer parameters are applied in our DB M-Net but the network achieves a better performance. the experiments are based on the dataset of 2,219 slices from 16 CT scans, which show our DB M-Net outperforms other existing algorithms.

关键词： Deep learning image segmentation Differentiable binarization Esophageal cancer Computed tomography

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Using Algorithm Selection for Adaptive Vehicle Perception aboard UAV 16

Using Algorithm Selection for Adaptive Vehicle Perception ab...

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16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Hellert, Christian Koch, Simon Stuetz, Peter Univ Bundeswehr Munich Inst Flight Syst Neubiberg Germany

ISBN: (纸本)9781728109909

Surveillance sensors aboard UAV are affected by environmental influences, e.g. atmospheric or topographic factors. this paper proposes a method for the automatic adaption of airborne sensor applications such as street surveillance to changing environmental conditions, preventing overall performance degradation with minimum human intervention. the basic principle of the concept relies on the selection of the most appropriate data processing algorithm available on board. To facilitate the determination of the most effective algorithm, performance models are used to predict the expected suitability of each algorithm for the given environmental conditions. Modeling the relation between the environmental state and the performance of the algorithms is achieved by two approaches leveraging expert knowledge and machine learning methods. An evaluation was carried out in simulation as well as in real flight experiments showing that the proposed method is able to improve overall vehicle perception performance.

关键词： Data models Prediction algorithms Adaptation models image sensors Sensor systems Data processing

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