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检索条件"任意字段=17th Image Processing: Algorithms and Systems Conference, IPAS 2019"

共 241 条记录，以下是151-160 订阅

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Preliminary study on an improved weight updating for fuzzy c-means with applications to image segmentation 17

Preliminary study on an improved weight updating for fuzzy c...

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Joint 17th World Congress of International-Fuzzy-systems-Association / 9th International conference on Soft Computing and Intelligent systems (IFSA-SCIS)

作者： Girish, Deeptha Singh, Vineeta Ralescu, Anca Univ Cincinnati EECS Dept Cincinnati OH 45220 USA

ISBN: (纸本)9781509049172

Fuzzy c-means is a popular clustering algorithm which allows a single data point to belong to more than one class at any given point. It has been used for a variety of applications especially when the applications are subjective and ambiguous. image segmentation is one such application in which the decision of a certain pixel belonging to a particular cluster is very fuzzy. the weight associated with every data point is very important as it controls the decision of assigning the data point to a particular cluster. In this study, two novel methods of updating weights that take into account the goodness of clustering and spatial relationships are proposed in order to improve the results of clustering. Fuzzy c-means with our proposed method of updating weights is applied to different kinds of images to perform image segmentation.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Object Tracking With A Robotic Manipulator Mounted On Ground Vehicle Using image Based Visual Servoing 17

Object Tracking With A Robotic Manipulator Mounted On Ground...

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17th International conference on Control, Automation and systems (ICCAS)

作者： Sami, Muhammad Ahsan Masood, Muhammad Umar Waqar, Muhammad Mujtaba, Muhammad Shah, Abrahim Ali Shujah, Adnan Rashid, Nasir Tiwana, Mohsin I. Iqbal, Javaid Natl Univ Sci & Technol Dept Mechatron Engn H12 Islamabad Pakistan

ISBN: (纸本)9788993215144

this paper presents an application of a novel approach for detecting and tracking an object with a 2 DOF robotic manipulator which can be equipped with an array of electrically controlled actuators. the said approach utilizes the image Based Visual Servoing (IBVS) technique. the developed system is able to determine the object pose in real time from features in the image. Object is detected using shaped based approach algorithms of image processing. the position and orientation of the world coordinates of the object being tracked are calculated from the coordinates of the object in image plane using camera's intrinsic and extrinsic parameters. Experimental results demonstrate the effectiveness of this proposed approach.

关键词： Robotic Manipulator image Jacobian Inverse Kinematics PID Canny Filter thresholding Object Detection Object Tracking

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Deep Convolutional Networks for Human Sketches by means of the Evolutionary Deep Learning 17

Deep Convolutional Networks for Human Sketches by means of t...

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Joint 17th World Congress of International-Fuzzy-systems-Association / 9th International conference on Soft Computing and Intelligent systems (IFSA-SCIS)

作者： Fujino, Saya Mori, Naoki Matsumoto, Keinosuke Osaka Prefecture Univ Coll Engn Naka Ku 1-1 Gakuencho Sakai Osaka 5998531 Japan Osaka Prefecture Univ Grad Sch Engn Naka Ku 1-1 Gakuencho Sakai Osaka 5998531 Japan

ISBN: (纸本)9781509049172

Recognition of human sketches is one of the most interesting and difficult issues in image recognition. Recently, deep convolutional neural networks (DCNNs) have been successfully applied to various image recognition tasks. though the DCNN is a very powerful method, the high computational effort required to tune its hyperparameters represents a critical problem. In this paper, we propose a novel method called evolutionary deep learning (evoDL) that uses a genetic algorithm in order to obtain effective deep learning networks. the generalization ability of the network structure obtained using the proposed method is confirmed by a computer experiment.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Generalizing an Interval-Valued image Magnification Algorithm Using Homogeneity Measures and Interval Fusion Functions 17

Generalizing an Interval-Valued Image Magnification Algorith...

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Joint 17th World Congress of International-Fuzzy-systems-Association / 9th International conference on Soft Computing and Intelligent systems (IFSA-SCIS)

作者： Paternain, Daniel Jurio, Aranzazu Pagola, Miguel Barrenechea, Edurne Bustince, Humberto Univ Publ Navarra Dept Automat & Comp Pamplona 31006 Spain Univ Publ Navarra Inst Smart Cities Pamplona 31006 Spain

ISBN: (纸本)9781509049172

In this work we study and generalize an image magnification algorithm based on the use of interval-valued fuzzy sets. the first proposed generalization incorporates an homogeneity measure that allows to model the length of the intervals generated by the algorithm. the second one makes use of several homogeneity measures and, by means of a fusion function, it combines the intervals generated by each individual homogeneity measure. the results show that our generalization outperforms the original algorithm when an appropriate homogeneity measure is used. Moreover, experiments have demonstrated that the second generalization, based on interval fusion functions, avoids low quality results due to bad homogeneity measures.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Convolutional Neural Networks: Estimating Relations in the Ising Model on Overfitting

Convolutional Neural Networks: Estimating Relations in the I...

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IEEE International conference on Cognitive Informatics

作者： Andrei Gavrilov Alex Jordache Maya Vasdani Jack Deng Bachelor of Applied Science University of British Columbia Vancouver BC Canada Bachelor of Commerce University of British Columbia Vancouver BC Canada Bachelor of Arts University of British Columbia Vancouver BC Canada

ISBN: (纸本)9781538633618

A CNN (Convolutional Neural Network) is one of actively researched and broadly applied deep machine learning methods. A CNN is composed of a feed-forward neural network that takes in images as inputs, and outputs a probability value associated to a class that best describes the image. As well, it is constructed of multiple layers, which include convolutional, max-pooling and fully connected layers. However, the training set has a large influence on the accuracy of a network, and hence it is paramount to create a network architecture that prevents overfitting (when a trained model cannot differentiate newly input data from its test data) and underfitting (the inability of a model to find relationships among inputs). this paper addresses the above deficiencies by comparing the statistics of CNN image recognition algorithms to the Ising model. the Ising model consists of magnetic dipole moments that can be in one of two states: +1 or -1. Using a two-dimensional square-lattice array once a training set of such data is complete, we determine the impact that network parameters, specifically learning rate and regularization rate, have on the adaptability of convolutional neural networks for image classification. Our results not only contribute to a better theoretical understanding of a CNN, but also provide concrete guidance on preventing model overfitting and underfitting when a CNN is applied for image recognition.

关键词： Handheld computers Data models Cognitive systems Convolutional neural networks image processing Cognitive informatics Convolution

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Principal Component Analysis Approach in Selecting Type-1 and Type-2 Fuzzy Membership Functions for High-dimensional Data 17

Principal Component Analysis Approach in Selecting Type-1 an...

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Joint 17th World Congress of International-Fuzzy-systems-Association / 9th International conference on Soft Computing and Intelligent systems (IFSA-SCIS)

作者： Raj, Desh Gupta, Aditya Tanna, Kenil Garg, Bhuvnesh Rhee, Frank Chung-Hoon Indian Inst Technol Guwahati Dept Comp Sci & Engn Gauhati 781039 India Indian Inst Technol Guwahati Dept Math Gauhati 781039 India Hanyang Univ Dept Elect & Commun Engn Ansan 15588 South Korea

ISBN: (纸本)9781509049172

With increased interest in learning from data, algorithms that manipulate datasets containing hundreds of features have become popular in various fields such as medicine, image processing, geolocation, biochemistry, and computational linguistics. Since a number of these applications exploit the power of fuzzy sets in representing uncertainties, it may be considered essential to describe a method for selecting the most suitable fuzzy membership function to represent a high-dimensional dataset. In this paper, we propose such a method, which is based on dimensionality reduction using the Principal Component Analysis (PCA) technique, followed by the Wilcoxon Minimal Bin Size algorithm, which has earlier been evaluated on multidimensional datasets up to 8 dimensions. We further demonstrate our proposed method using two real datasets consisting of 281 and 500 features, respectively.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Real-time Smart Stereo Camera based on FPGA-SoC 17

Real-time Smart Stereo Camera based on FPGA-SoC

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IEEE-RAS 17th International conference on Humanoid Robotics (Humanoids)

作者： Michalik, Soenke Michalik, Soeren Naghmouchi, Jamin Berekovic, Mladen TU Braunschweig D-38106 Braunschweig Germany

ISBN: (纸本)9781538646786

Stereo image processing is one of the most demanding tasks in the field of 3D computer vision and robot vision requiring high-performance computing capabilities within embedded systems. Real-time constraints for autonomous vehicles such as humanoid robots, lead to hardware acceleration approaches for high resolution stereo imaging in human-like vision systems, where commonly FPGA device are employed to handle very high sensor data rates. this work presents a real-time smart stereo camera system implementation resembling the full stereo processing pipeline in a single FPGA device. We introduce the novel memory optimized stereo processing algorithm "Sparse Retina Census Correlation" (SRCC) that embodies a combination of two well established window based stereo matching approaches. We have leveraged a Sum of Absolute Difference (SAD) of Sobel-filtered images and a Sum of Hamming Distance (SHD) using a modified Retina based Census Transform for increased robustness to lighting variations and for high accuracy. A color rectification module has been implemented to cope with the high frame rate of the stereo pipelining calculating image transformations and rectified pixel coordinates in real-time using parameters for camera intrinsic, image rotation, image distortion and image projection. In addition multiple post-processing algorithms like texture filtering, uniqueness filtering, speckle removal and disparity to depth conversion have been implemented to further enhance the output results. the presented smart camera solution has demonstrated real-time stereo processing of 1280x720 pixel depth images with 256 disparities on a Zynq XC7Z030 FPGA device at 60fps. Due to the universal USB3.0 UVC interface and the onboard depth calculation it is a replacement for RGBD 3D-Sensors with improved image quality and outdoor performance. the camera can easily be used in conjunction with ROS-enabled robots and in automotive or industrial applications.

关键词： Field programmable gate arrays Cameras Robot sensing systems Correlation Transforms Real-time systems

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Automatic Brightness Adjustment System by Fuzzy Inference System for Object Recognition 17

Automatic Brightness Adjustment System by Fuzzy Inference Sy...

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Joint 17th World Congress of International-Fuzzy-systems-Association / 9th International conference on Soft Computing and Intelligent systems (IFSA-SCIS)

作者： Kim, Eun Kyeong Lee, Hansoo Kim, Sungshin Cho, Hyunhak Pusan Natl Univ Dept Elect & Comp Engn Busan South Korea Pusan Natl Univ Dept Interdisciplinary Cooperat Course Robot Busan South Korea

ISBN: (纸本)9781509049172

A camera has been widely used in practical fields with a diversity of purposes recently. there is a variety purpose of photography: images for memory, medical images for diagnosis, images for object recognition, surveillance images, and so on. In case of images for object recognition, the clarity of images is necessary to analyze the images which are obtained using vision sensors. However, a brightness of the image highly depends on the intensity of illumination in the certain environment. therefore, we propose a method to solve the problems mentioned above by adjusting brightness automatically by utilizing CIE L*a*b* color space and fuzzy inference system. At first, the proposed method adjusts the brightness of a given image by considering both RGB component and L component of CIE L*a*b* color space. Secondly, the proposed method applies the fuzzy inference system to determine adjustment coefficients of each pixel for adjusting brightness of the image. through the processes as mentioned above, we can obtain the result which is adjusted its brightness. To verify the proposed method, we compare the result image with two different images, a reference image, and an adjusted image by using offset. It is confirmed that the proposed method can adjust a given image efficiently and automatically.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Which Players will Leave their Community? Predicting Guild Abandonments in World of Warcraft Game Data 17

Which Players will Leave Their Community? Predicting Guild A...

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Joint 17th World Congress of International-Fuzzy-systems-Association / 9th International conference on Soft Computing and Intelligent systems (IFSA-SCIS)

作者： Posfai, Gergely Magyar, Gabor Koczy, Laszlo T. Budapest Univ Technol & Econ Dept Telecommun & Media Informat Budapest Hungary

ISBN: (纸本)9781509049172

World of Warcraft (WoW) is one of the most popular massively multiplayer online role-playing games (MMORPGs) having more than 10 million subscribers over the world. In order to engage and retain users understanding and predicting their behavior can be very useful for game developers. An important component of WoW are so-called guilds, which are social communities whose members can act together efficiently to accomplish more difficult goals and also provide a social atmosphere in which the game might be more entertaining. In this paper, we build predictive models to forecast which of the players will leave their guild in the close future. Our best model uses fuzzy c-means clustering to capture groups of similar guilds, that serve as the basis of an ensemble model, which computes predictions for each cluster separately and combines individual predictions into one final prediction using the memberships of the fuzzy clusters. Empirical analysis on WoW game data shows that our methods convincingly outperform the only existing method in the literature. To ensure transparency and reproducibility we publish the source codes of the research and also provide a Docker image, which makes it possible for anyone who has Docker installed to reproduce all of our results with a single command.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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image Analysis using Convolutional Neural Networks for Modeling 2D Fracture Propagation 17

Image Analysis using Convolutional Neural Networks for Model...

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17th IEEE International conference on Data Mining (ICDMW)

作者： Miller, Robyn L. Moore, Bryan Viswanathan, Hari Srinivasan, Gowri Los Alamos Natl Lab Los Alamos NM 87545 USA

ISBN: (纸本)9781538638002

the primary failure mechanism in brittle materials such as ceramics, granite and some metal alloys is through the presence of defects which result in crack formation and propagation under the application of load. We are interested in studying this process of crack propagation, interaction and coalescence, which degrades the strength of the specimen. Traditionally, engineering applications that study these materials employ finite element mesh-based methods that require hundreds of hours of processing time on multi-core high performance clusters. We have developed a graph-based reduced order model that captures key geometric and topological features of the dynamic fracture propagation network. We report here the early stages of our study in which deep neural networks will be applied to dynamic directed weighted graphs capturing various metrics of crack-pair interaction strength with the aim of predicting crack lengths, dynamic crack growth/coalescence properties, distributions of these properties over the entire material through time, failure paths and time to failure. Our graph-based representations allow us to consider detailed topology in conjunction with metric geometry to gain insights into the dominant mechanisms that drive the physics in these systems.

关键词： Physics Feature extraction Data models Integrated circuits Data mining Geometry Heuristic algorithms

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