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

共 249 条记录，以下是11-20 订阅

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Gradient management and algebraic reconstruction for single image super resolution 17

Gradient management and algebraic reconstruction for single ...

引用

17th image processing: algorithms and systems conference, ipas 2019

作者： Delfin, Leandro Morera Elias, Raul Pinto de Jesús Ochoa Domínguez, Humberto Center of Investigation and Technological Development CENIDET Morelos Mexico Universidad Autónoma de Ciudad Juárez UACJ Chihuahua Mexico

In this paper, a single image multi-scale super-resolution technique is proposed. the concept under study is the learning procedure between steps of amplification in order to predict the next high scale of resolution. the method integrates two different approaches for the prediction of a high resolution multi-scale scheme, a pure interpolation and a gradient regularization. In the first step a pure interpolation is carried out. It is used a prediction scheme with algebraic reconstruction through different scales to produce the high resolution output. In the last step, the residual blur is reduced by a gradient auto-regularization method. the gradients are adapted by using a weight in a neighbour. Precision of method can be controlled by the parameters of an algebraic reconstruction technique (ART). the proposed model avoids the fast decrease of the output resolution as the amplification factor increases. the proposed system was tested with a dictionary. Results show that the output image quality is improved despite of the increment of the scale factor. © 2019, Society for Imaging Science and Technology.

关键词： Interpolation

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Microscope image matching in scope of multi-resolution observation system 17

Microscope image matching in scope of multi-resolution obser...

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17th image processing: algorithms and systems conference, ipas 2019

作者： Putranto, Evan H.E. Usuki, Shin Miura, Kenjiro T. Graduate School of Engineering Shizuoka University Japan Research Institute of Electronics Shizuoka University Japan

Multi-resolution image processing are part of this concept that has a purpose to extracting the detail information of the multi-scale input image. However, in general, to process a multi-scale image there are issue that need to be solved, for an instance color difference, image matching failure, and lack of data which can be observe in a same time. In the same way, observation are work where object of interest are being analyze and being perceive. Observing a multi-resolution image with a lot of object interest would be a daunting task to do. there have been many attempts to match and stitch two or more images into one but still has a chance to get failure. In this article, the author propose a multi-resolution image observation system which are focus on three improvement field successfulness rating of image matching, utilize a classification algorithm to help perceiving object interest and user-friendly interface viewer. On pre-processing, this system take a four type magnification level of image;10 ×, 20 ×, 50 ×, 150 ×. By investigating image matching parameter to stitching group of images, handling false positive of feature point occurred, and then finally arrange and show group of synthesized images inside web application, we believe user can observe microscopic image in a cushioned way. © 2019, Society for Imaging Science and Technology.

关键词： image matching

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Multi-frame super-resolution utilizing spatially adaptive regularization for ToF camera 17

Multi-frame super-resolution utilizing spatially adaptive re...

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17th image processing: algorithms and systems conference, ipas 2019

作者： Lee, Haegeun Kim, Jonghyun Han, Jaeduk Kang, Moon G.I. School of Electrical and Electronic Engineering Yonsei University 50 Yonsei-ro Seodaemun-gu Seoul03722 Korea Republic of

Recently, 3D time-of-flight cameras have been developed. the development enables utilization of depth images in various fields. However, acquired depth images are corrupted by noise during the image acquisition process and have relatively lower resolution than RGB images due to the limitation of ToF cameras. In this paper, a multi-frame super-resolution reconstruction algorithm is proposed for ToF depth images to overcome such limits. the purpose of the multi-frame super-resolution reconstruction is to reconstruct a high-resolution image from observed multiple low-resolution images through the sequential process of subpixel estimation and restoration. A conventional regularized super-resolution reconstruction algorithm which takes Tikhonov regularization has a major drawback of over-smoothing around edges. To overcome the disadvantage, the spatially adaptive regularization is suggested for preservation of edges. Experimental results show that the image reconstructed by the proposed super-resolution reconstruction algorithm contains significantly higher resolution with less amount of noise and sharper edges than the observed data. © 2019, Society for Imaging Science and Technology.

关键词： image acquisition

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Optimal Performance Prediction of ADAS algorithms on Embedded Parallel Architectures 17

Optimal Performance Prediction of ADAS Algorithms on Embedde...

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2015 IEEE 17th International conference on High Performance Computing and Communications (HPCC)

作者： Saussard, Romain Bouzid, Boubker Vasiliu, Marius Reynaud, Roger Renault SAS Guyancourt France Univ Paris 11 Inst Elect Fondamentale Orsay France

ISBN: (纸本)9781479989379

ADAS (Advanced Driver Assistance systems) algorithms increasingly use heavy image processing operations. To embed this type of algorithms, semiconductor companies offer many heterogeneous architectures. these SoCs (System on Chip) are composed of different processing units, with different capabilities, and often with massively parallel computing unit. Due to the complexity of these SoCs, predicting if a given algorithm can be executed in real time on a given architecture is not trivial. In fact it is not a simple task for automotive industry actors to choose the most suited heterogeneous SoC for a given application. Moreover, embedding complex algorithms on these systems remains a difficult task due to heterogeneity, it is not easy to decide how to allocate parts of a given algorithm on the different computing units of a given SoC. In order to help automotive industry in embedding algorithms on heterogeneous architectures, we propose a novel approach to predict performances of image processing algorithms applicable on different types of computing units. Our methodology is able to predict a more or less wide interval of execution time with a degree of confidence using only high level description of algorithms, and a few characteristics of computing units.

关键词： Heterogeneous Architectures Embedded systems Performance Prediction image processing

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Enhanced guided image filter using trilateral kernel for disparity error correction 17

Enhanced guided image filter using trilateral kernel for dis...

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17th image processing: algorithms and systems conference, ipas 2019

作者： Chang, Yong-Jun Ho, Yo-Sung 123 Cheomdangwagi-ro Buk-gu Gwangju 61005 Korea Republic of

Stereo matching methods estimate the depth information from stereoscopic images using the characteristics of binocular disparity. We try to find corresponding points from the left and right viewpoint images to obtain the disparity map. Once we get the disparity value between the two correspondences, we can calculate the depth information of the object. If there is no texture in the image, the stereo matching operation may not find the accurate disparity value. Especially, it is quite difficult to estimate correct disparity values in occluded regions. In this paper, we propose a new method to detect disparity errors in the disparity map and correct those errors using the enhanced guided image filter (GIF). An error correction using the GIF causes disparity-smoothing near the occluded region because of a coefficient-smoothing process in the GIF. thus, our method employs a trilateral kernel for the coefficient smoothing process to alleviate the problem. © 2019, Society for Imaging Science and Technology.

关键词： Error correction

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Construction of facial emotion database through subjective experiments and its application to deep learning-based facial image processing 17

Construction of facial emotion database through subjective e...

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17th image processing: algorithms and systems conference, ipas 2019

作者： Takanashi, Tomoyuki Hirai, Keita Horiuchi, Takahiko Department of Imaging Sciences Chiba University 1-33 Yayoi-cho Inage-ku Chiba-shi Chiba263-8522 Japan

As the development of interactive robots and machines, studies to understand and reproduce facial emotions by computers have become important research areas. For achieving this goal, several deep learning-based facial image analysis and synthesis techniques recently have been proposed. However, there are difficulties in the construction of facial image dataset having accurate emotion tags (annotations, metadata), because such emotion tags significantly depend on human perception and cognition. In this study, we constructed facial image dataset having accurate emotion tags through subjective experiments. First, based on image retrieval using the emotion terms, we collected more than 1,600,000 facial images from SNS. Next, based on a face detection image processing, we obtained approximately 380,000 facial region images as "big data." then, through subjective experiments, we manually checked the facial expression and the corresponding emotion tags of the facial regions. Finally, we achieved approximately 5,500 facial images having accurate emotion tags as "good data." For validating our facial image dataset in deep learning-based facial image analysis and synthesis, we applied our dataset to CNN-based facial emotion recognition and GAN-based facial emotion reconstruction. through these experiments, we confirmed the feasibility of our facial image dataset in deep learning-based emotion recognition and reconstruction. © 2019, Society for Imaging Science and Technology.

关键词： Emotion Recognition

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image processing: algorithms and systems XVII

Image Processing: Algorithms and Systems XVII

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17th image processing: algorithms and systems conference, ipas 2019

the proceedings contain 269 papers. the topics discussed include: high-speed multiview 3D structured light imaging technique;integration of advanced stereo obstacle detection with perspectively correct surround views;recreating Van Gogh's original colors on museum displays;modeling long range features from serial section imagery of continuous fiber reinforced composites;the quality of stereo disparity in the polar regions of a stereo panorama;do different radiologists perceive medical images the same way? some insights from representational similarity analysis;the intersection of artificial intelligence and augmented reality;self-calibrated surface acquisition for integrated positioning verification in medical applications;gradient management and algebraic reconstruction for single image super resolution;segmentation-based detection of local defects on printed pages;automated optical inspection for abnormal-shaped packages;towards combining domain knowledge and deep learning for computational imaging;a 360-degrees holographic true 3D display unit using a Fresnel phase plate;and visual analytic process to familiarize the average person with ways to apply machine learning.

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Uncertainty quantification for semi-supervised multi-class classification in image processing and ego-motion analysis of body-worn videos 17

Uncertainty quantification for semi-supervised multi-class c...

引用

17th image processing: algorithms and systems conference, ipas 2019

作者： Qiao, Yiling Shi, Chang Wang, Chenjian Li, Hao Haberland, Matt Luo, Xiyang Stuart, Andrew M. Bertozzi, Andrea L. University of Chinese Academy of Science Beijing China Renmin University of China Beijing China University of California Los Angeles Los AngelesCA United States California Polytechnic State University San Luis ObispoCA United States California Institute of Technology PasadenaCA United States

Semi-supervised learning uses underlying relationships in data with a scarcity of ground-truth labels. In this paper, we introduce an uncertainty quantification (UQ) method for graph-based semi-supervised multi-class classification problems. We not only predict the class label for each data point, but also provide a confidence score for the prediction. We adopt a Bayesian approach and propose a graphical multi-class probit model together with an effective Gibbs sampling procedure. Furthermore, we propose a confidence measure for each data point that correlates with the classification performance. We use the empirical properties of the proposed confidence measure to guide the design of a human-in-the-loop system. the uncertainty quantification algorithm and the human-in-the-loop system are successfully applied to classification problems in image processing and ego-motion analysis of body-worn videos. © 2019, Society for Imaging Science and Technology.

关键词： Motion analysis

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the quaternion-based anisotropic gradient for the color images 17

The quaternion-based anisotropic gradient for the color imag...

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17th image processing: algorithms and systems conference, ipas 2019

作者： Voronin, V. Zelensky, A. Agaian, S. Don State Technical University Lab. «Mathematical Methods of Image Processing and Computer Vision Intelligent Systems» Gagarina 1 Rostov on Don Russia Moscow State University of Technology"STANKIN" Moscow Russia CUNY College of Staten Island Dept. of Computer Science New York United States

image gradient, as a preprocessing step is an essential tool in image processing in many research areas such as edge detection, segmentation, smoothing, inpainting, etc. In the present paper, we develop a new gradient by integrated the quaternion framework with LPA-ICI (local polynomial approximation - the intersection of confidence intervals) based on anisotropic gradient concepts for the color image processing applications. Computer simulations on the Berkeley Segmentation Dataset show that the new quaternion anisotropic gradient exhibits fewer color artifacts compared to state-of-the-art techniques including well-knowledge gradient operators. © 2019, Society for Imaging Science and Technology.

关键词： Polynomial approximation

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P-AFLC: a parallel scalable fuzzy clustering algorithm

P-AFLC: a parallel scalable fuzzy clustering algorithm

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17th International conference on Pattern Recognition (ICPR)

作者： Petrosino, A Verde, M Complesso Univ CNR Sect Naples Italian Natl Res CouncilICAR I-80126 Naples Italy

ISBN: (纸本)0769521282

Clustering is the unsupervised classification of data items into homogeneous groups called clusters. Clustering algorithms are computationally intensive, particularly when they are used to analyze large amounts of data and this is the case in many pattern recognition, image analysis applications. A possible approach to reduce the processing time is based on the implementation of clustering algorithms on scalable parallel computers. this paper describes the design and implementation of P-AFLC, a parallel version of the Adaptive Fuzzy Leader Clustering system based upon the competitive learning model for determining optimal classes in large data sets. the system architecture, its implementation, and experimental performance results are reported, together with theoretical performance evaluation.

关键词： Parallel processing systems

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