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

共 13728 条记录，以下是4451-4460 订阅

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Visual Servoing and Obstacle Avoidance Method based Control Autonomous Robotic systems Servicing a Mechatronics Manufacturing Line 9

Visual Servoing and Obstacle Avoidance Method based Control ...

引用

9th IEEE International conference on Intelligent Data Acquisition and Advanced Computing systems - Technology and Applications (IDAACS)

作者： Ciubucciu, George Solea, Razvan Filipescu, Adrian Filipescu, Adriana Dunarea de Jos Univ Galati Fac Automat Control Comp Elect & Elect Engn Galati Romania

ISBN: (纸本)9781538606971

In this paper is intended to control the entire flexible line using two wheeled mobile robot and two robotic manipulators. The mechatronics manufacturing line has no possibility of automatic feed with pieces and no automatic pickup of the scrap pieces from the storage station. The robotic manipulators are used for charging the buffer of the flexible line as well as emptying the storage station. These tasks are achieved through several algorithms proposed in the present paper (accomplished in Matlab) using image processing with the help of two video cameras synchronized with the grippers of the two robotic manipulators. One of the robotic manipulator (Pioneer 5-DOF Arm), takes with the gripper the pieces stored, is mounted on a mobile robot (Pioneer P3-DX two driving wheels). The Pioneer P3-DX wheeled mobile robot transfer the pieces one by one to another wheeled mobile robot -PeopleBot used to transport the processed piece in the storeroom or at the first station of the flexible line. The second robotic manipulator (Cyton 1500 7-DOF Arm) is fixed and it is used to take the pieces from the PeopleBot mobile robot and to transfer them to the handling work station of the flexible line. PeopleBot mobile robot used to transport the pieces is controlled using new control techniques (trajectory tracking control) with the possibility of obstacle avoidance. Sliding-mode control method is used to control nonlinear processes (the mobile robots). The algorithms proposed in this paper were first tested in simulation and after were implemented in real time on existing mechatronic system, making it completely autonomous.

关键词： flexible manufacturing line wheeled mobile robot robotic manipulator sliding-mode controller image processing

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Proceedings of International conference on Cognition and Recognition: ICCR 2016

引用

2017年

作者： Devanur S Guru T Vasudev H K Chethan Y H Sharath Kumar

ISBN: (纸本)9789811051456

The book covers a comprehensive overview of the theory, methods, applications and tools of cognition and recognition. The book is a collection of best selected papers presented in the International conferenceon Cognition and Recognition 2016 (ICCR 2016) and helpful for scientists and researchers in the field of image processing, pattern recognition and computer vision for advance studies. Nowadays, researchers are working in interdisciplinary areas and the proceedings of ICCR 2016 plays a major role to accumulate those significant works at one place. The chapters included in the proceedings inculcates both theoretical as well as practical aspects of different areas like nature inspired algorithms, fuzzy systems, data mining, signal processing, image processing, text processing, wireless sensor networks, network security and cellular automata.

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High efficient optical remote sensing images acquisition for Nano-satellite: reconstruction algorithms 23

High efficient optical remote sensing images acquisition for...

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conference on image and Signal processing for Remote Sensing XXIII

作者： Liu, Yang Li, Feng Xin, Lei Fu, Jie Huang, Puming Qian Xuesen Lab Space Technol Beijing Peoples R China CAST Xian Inst Space Radio Technol Xian Shaanxi Peoples R China Lanzhou Jiaotong Univ Lanzhou Gansu Peoples R China

ISBN: (数字)9781510613195

ISBN: (纸本)9781510613195;9781510613188

Large amount of data is one of the most obvious features in satellite based remote sensing systems, which is also a burden for data processing and transmission. The theory of compressive sensing(CS) has been proposed for almost a decade, and massive experiments show that CS has favorable performance in data compression and recovery, so we apply CS theory to remote sensing images acquisition. In CS, the construction of classical sensing matrix for all sparse signals has to satisfy the Restricted Isometry Property (RIP) strictly, which limits applying CS in practical in image compression. While for remote sensing images, we know some inherent characteristics such as non-negative, smoothness and etc.. Therefore, the goal of this paper is to present a novel measurement matrix that breaks RIP. The new sensing matrix consists of two parts: the standard Nyquist sampling matrix for thumbnails and the conventional CS sampling matrix. Since most of sun-synchronous based satellites fly around the earth 90 minutes and the revisit cycle is also short, lots of previously captured remote sensing images of the same place are available in advance. This drives us to reconstruct remote sensing images through a deep learning approach with those measurements from the new framework. Therefore, we propose a novel deep convolutional neural network (CNN) architecture which takes in undersampsing measurements as input and outputs an intermediate reconstruction image. It is well known that the training procedure to the network costs long time, luckily, the training step can be done only once, which makes the approach attractive for a host of sparse recovery problems.

关键词： image Compression image Acquisition image Restoration Compressive Sensing

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Grey-scale Skeletonization using Delaunay Triangulation 4

Grey-scale Skeletonization using Delaunay Triangulation

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IEEE International conference on Consumer Electronics - Taiwan (ICCE-TW)

作者： Sintunata, Vicky Aoki, Terumasa Tohoku Univ Grad Sch Informat Sci Sendai Miyagi Japan Tohoku Univ New Ind Hatchery Ctr Sendai Miyagi Japan

ISBN: (纸本)9781509040179

Conventional skeleton extraction methods require a closed boundary constraint to solve the problem. In natural images closed boundary constraint might not be easily satisfied due to the similarity of the object and its background, occlusion, etc. In this paper a novel approach based on the Delaunay triangulation to solve the skeleton extraction in natural images is proposed. The algorithm shows a promising result in extracting the skeleton in natural images.

关键词： Skeleton image edge detection image color analysis Clustering algorithms Transforms Gray-scale Standards

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Incremental Adaptive EEG Classification of Motor imagery-based BCI

Incremental Adaptive EEG Classification of Motor Imagery-bas...

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International Joint conference on Neural Networks (IJCNN)

作者： Hai-Jun Rong Changjun Li Rong-Jing Bao Badong Chen State Key Laboratory for Strength and Vibration of Mechanical Structures Shaanxi Key Laboratory of Environment and Control for Flight Vehicle School of Aerospace Engineering Xi’an Jiaotong University China Institute of Artificial Intelligence and Robotics School of the Electronic and Information Engineering Xi’an Jiaotong University China

Generally, electroencephalogram (EEG) signals recorded from the brain computer interface (BCI) systems are very noisy and non-stationary, which may affect the online performance of classifiers established from the prior session heavily. In order to address such problems, the classifiers should also be capable of adapting the change of EEG automatically during the processing of evaluation. In this paper, we propose an incremental adaptive EEG classification scheme. In this scheme, an extended sequential adaptive fuzzy inference system (ESAFIS) is used to evolve its structure dynamically and adapts the classifier automatically online to address the non-stationarity of the EEG signals. ESAFIS is an evolving system, wherein the fuzzy rules are evolved based on the modified influence of the rule. This paper presents the classification of 2-class motor imagery EEG based on ESAFIS with adaptive strategy. Simulations are conducted based on two datasets: one is the BCI Competition iv dataset 2b and the other one is recorded from our own BCI experiments. Compared to other methods such as ELM and LDA, the simulation results demonstrate that the proposed scheme produces better classification results.

关键词： Electroencephalography Feature extraction Training image segmentation Windows Adaptive systems Brain modeling

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Tukey's Biweight Constrain Function For A Surrogate image Enlargement Based On SISR Algorithm Using High Spectrum Estimation 8

Tukey's Biweight Constrain Function For A Surrogate Image En...

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8th International conference of Information and Communication Technology for Embedded systems (IC-ICTES)

作者： Thakulsukanant, Kornkamol Patanavijit, Vorapoj Assumption Univ Bangkok Thailand

ISBN: (纸本)9781509048090

Consistently, the classical image enlargement algorithm is a scientific analytical method for producing a better refined resolution image that is frequently required for advanced digital image processing (DIP) from a single lower resolution image that is frequently acquired from digital camera embedded system. Because of its less computation calculation, the Single-image Super-Resolution (SISR) that analytically applied for a single lower resolution image is one of the worldwide effective Super Resolution-Reconstruction (SRR) algorithms thus this paper proposes the image enlargement based on the SISR algorithm using high spectrum estimation and Tukey's Biweight constrain function. In general, the performance of this SISR algorithm is hinge on up to three parameters (b, h, k) however there are burdensome for determining these optimized values for these parameters (b, h, k). In order to solve this problem, the Tukey's Biweight constrain function, which is hinge on merely single parameter (T), contrary to three parameters like the classical constrain function, is engaged in the SISR algorithm. By examining on 14 benchmark images, which are profaned by considerable noise forms, in scientific analytical scrutinizing sector, the novel SISR algorithm illustrates that there is efficiently and effortlessly in parameter setting process but the performance of the novel SISR algorithm (with single parameters) is nearly equal to the original SISR (with three parameters). Due to greatly time reduction in the parameter setting process, this novel SISR algorithm is more advisable for real-time applications.

关键词： Tukey's Biweight SISR (Single-image Super-Resolution) SRR (Super Resolution-Reconstruction) Digital image Enlargement

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Joint autoregressive and hierarchical priors for learned image compression 18

Joint autoregressive and hierarchical priors for learned ima...

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Proceedings of the 32nd International conference on Neural Information processing systems

作者： David Minnen Johannes Ballé George Toderici Google Research

Recent models for learned image compression are based on autoencoders that learn approximately invertible mappings from pixels to a quantized latent representation. The transforms are combined with an entropy model, which is a prior on the latent representation that can be used with standard arithmetic coding algorithms to generate a compressed bitstream. Recently, hierarchical entropy models were introduced as a way to exploit more structure in the latents than previous fully factorized priors, improving compression performance while maintaining end-to-end optimization. Inspired by the success of autoregressive priors in probabilistic generative models, we examine autoregressive, hierarchical, and combined priors as alternatives, weighing their costs and benefits in the context of image compression. While it is well known that autoregressive models can incur a significant computational penalty, we find that in terms of compression performance, autoregressive and hierarchical priors are complementary and can be combined to exploit the probabilistic structure in the latents better than all previous learned models. The combined model yields state-of-the-art rate–distortion performance and generates smaller files than existing methods: 15.8% rate reductions over the baseline hierarchical model and 59.8%, 35%, and 8.4% savings over JPEG, JPEG2000, and BPG, respectively. To the best of our knowledge, our model is the first learning-based method to outperform the top standard image codec (BPG) on both the PSNR and MS-SSIM distortion metrics.

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Real-Time Lane Detection Using Spatio-Temporal Incremental Clustering 20

Real-Time Lane Detection Using Spatio-Temporal Incremental C...

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20th IEEE International conference on Intelligent Transportation systems (ITSC)

作者： Gupta, Any Choudhary, Ayesha Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi India

ISBN: (纸本)9781538615263

In this paper, we propose a novel method for lane detection in real-time based on unsupervised learning using images from a camera mounted on the dashboard of a vehicle. Lane detection is a crucial element of an intelligent vehicle safety system. Lane departure warning systems rely on accurate detection of lanes and are important for a driver assistant system. In our system, we extract the features of interest and detect the lane markings or foreground regions in each frame. We develop a spatio-temporal incremental clustering algorithm coupled with curve fitting for detecting the lanes on-the-fly. The spatio-temporal incremental clustering is performed over the detected foreground region for finding the lanes accurately. Each cluster represents a lane across space and time. The lanes are then identified by fitting curves on these clusters. Our system is capable of accurately detecting straight and curved lanes, noncontinuous and continuous lanes and is independent of number of lanes in the frame and the system is fast because we have not used any database for processing the images. Experimental results show that our algorithm can accurately perform lane detection in challenging scenarios.

关键词： Roads Clustering algorithms Cameras Real-time systems Feature extraction image color analysis Unsupervised learning

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A Complete Processor for SIFT Feature Matching in Video Sequences 9

A Complete Processor for SIFT Feature Matching in Video Sequ...

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9th IEEE International conference on Intelligent Data Acquisition and Advanced Computing systems - Technology and Applications (IDAACS)

作者： Vourvoulakis, John Kalomiros, John Lygouras, John Democritus Univ Thrace Dept Elect & Comp Engn GR-67100 Xanthi Greece Technol & Educ Inst Cent Macedonia Dept Informat Engn Serres 62124 Greece

ISBN: (纸本)9781538606971

Efficient detection and reliable matching of image features constitute a fundamental task in computer vision. When real-time operation is required, the solution to this problem becomes a real challenge, because of increased processing requirements. Scale Invariant Feature Transform (SIFT) is considered as a stable and robust algorithm for the extraction of invariant features, however special hardware is required for high frame-rate applications, in order to overcome its computational complexity in real-time. In this paper, a complete processor for SIFT feature matching in video sequences is presented. It performs SIFT feature extraction and matching, as well as rejection of false correspondences using the random sample consensus (RANSAC) algorithm. The processor was evaluated using metrics like response, repeatability and recall. It was found that the proposed fixed-point hardware implementation has a comparable performance with software implementations of SIFT. The processor architecture can process more than 36 fps with resolution 640x480, when it is implemented in Cyclone iv FPGA technology.

关键词： SIFT FPGA feature matching image processing

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Azimuthally invariant Mueller-matrix mapping of biological optically anisotropic network 40

Azimuthally invariant Mueller-matrix mapping of biological o...

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conference on Applications of Digital image processing XL

作者： Ushenko, Yu. O. Vanchuliak, O. Bodnar, G. B. Ushenko, V. O. Grytsyuk, M. Pavlyukovich, N. Pavlyukovich, O. V. Antonyuk, O. Chernivtsi Natl Univ 2 Kotsyubinsky Str UA-58012 Chernovtsy Ukraine Bukovinian State Med Univ UA-58000 Chernovtsy Ukraine

ISBN: (数字)9781510612501

ISBN: (纸本)9781510612501;9781510612495

A new technique of Mueller-matrix mapping of polycrystalline structure of histological sections of biological tissues is suggested. The algorithms of reconstruction of distribution of parameters of linear and circular dichroism of histological sections liver tissue of mice with different degrees of severity of diabetes are found. The interconnections between such distributions and parameters of linear and circular dichroism of liver of mice tissue histological sections are defined. The comparative investigations of coordinate distributions of parameters of amplitude anisotropy formed by Liver tissue with varying severity of diabetes (10 days and 24 days) are performed. The values and ranges of change of the statistical (moments of the 1st - 4th order) parameters of coordinate distributions of the value of linear and circular dichroism are defined. The objective criteria of cause of the degree of severity of the diabetes differentiation are determined.

关键词： polarization Fourier optics and signal processing imaging systems medical and biological imaging

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