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A Neural Network Based Low-Light image Denoising Method 3

A Neural Network Based Low-Light Image Denoising Method

引用

3rd IEEE International conference on Computer and communications (ICCC)

作者： Zhang, Dan Zhao, Lei Xu, Duanqing Lu, Dongming Zhejiang Univ Dept Comp Sci & Technol Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781509063529

Low-light image denoising has been a hotspot for its great importance in solving vision applications, and various solutions have been proposed. In recent years, neural network has shown great potential in single image denoising. However, lacking ground truth data, most existing neural network based methods only train on images synthesized by Gaussian noise or Poisson noise or mixed Poisson-Gaussian noise rather than realistic low-light images. In this paper, we introduce a novel convolutional neural network based model for low-light image denoising. In particular, we also present a method to collecting realistic low-light images and our neural network model is trained on the obtained dataset. Evaluating experiment results by the subjective visual observation as well as the objective quality measures shows that the proposed neural network based method is comparable to state-of-the-art.

关键词： component denoising low-light image denoisng neural network image processing

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A single Tier Homogeneous visual Sensor Network for Real Time face recognition 8

A single Tier Homogeneous Visual Sensor Network for Real Tim...

引用

8th IEEE International conference on Computational Intelligence and Computing Research (IEEE ICCIC)

作者： Sriharsha, K. V. Alphonse, P. J. A. Natl Inst Technol Dept Comp Applicat Tiruchirappalli Tamil Nadu India

ISBN: (纸本)9781509066216

In centralized based IP surveillance architectures, the information pertaining to the event captured in each camera is sent to the master data base that is located in central control server. Later for crime or theft detection in surveillance region, the recorded data is accessed and analyzed. As on today,with ongoing acts of vandalism, it is highly essential for surveillance to be reactive rather than proactive. The objective is fulfilled by incorporating on board processing features such as face detection and recognition with in the sensor node(s).Soon person enters the vicinity of camera,features are captured, processed and then recognized with in the *** order to demonstrate this on board processing capability,we came up with a single Tier homogeneous sensor network design featuring the real time face recognition without using database(s) and also optimal transmission in the network using image dimensionality reduction *** order to assess the similarity in face recognition statistical measures like mean square error and correlation coefficient are taken for consideration and the system is tested and verified with a confidence level of 98% in a resource constrained environment.

关键词： visual sensor node on board processing image resizing similarity prediction

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Medical images Contrast Enhancement using Quad Weighted Histogram Equalization with Adaptive Gama Correction and Homomorphic Filtering 7

Medical Images Contrast Enhancement using Quad Weighted Hist...

引用

7th International conference on Advances in Computing and communications (ICACC)

作者： Agarwal, Monika Mahajan, Rashima GD Goenka Univ Dept SOE Gurugram 122102 India

image contrast enhancement plays a vital role in various applications of digital image processing field like face recognition, satellite imaging and medical imaging This paper proposes an efficient algorithm to cater the limitation of over enhancement with maximum entropy preservation. In the proposed algorithm, input image histogram is segmented first based on its valley positions and then weighted distribution is applied to all segmented sub histograms followed by the histogram equalization, gamma correction and homomorphic filtering. Results reveal that the proposed technique outer performs other conventional histogram equalization techniques both in terms of visual quality along with maximum entropy preservation and contrast enhancement. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： Medical image Contrast Enhancement Histogram Equalization Adaptive Gama Correction Homomorphic Filtering Weighted Distribution Probability Density function

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Architecture for Fast Object Detection Supporting CPU-GPU Hybrid and Distributed Computing

Architecture for Fast Object Detection Supporting CPU-GPU Hy...

引用

IEEE International conference on Consumer Electronics (ICCE)

作者： Bae, Yuseok Park, Jongyoul ETRI SW Res Lab Visual Intelligence SW Res Sect Daejeon South Korea

This paper describes architecture for fast object detection that integrates uniform local binary patterns (ULBP) with convolutional neural networks (CNN). The proposed architecture also supports CPU-GPU hybrid and dis... 详细信息

ISBN: (纸本)9781509055449

关键词： Object detection Feature extraction Computer architecture Distributed computing Graphics processing units Computational modeling Support vector machines

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8th International conference on image processing and communications (IP&C)

作者： Luszczkiewicz-Piatek, Maria Univ Lodz Dept Appl Comp Sci Banacha 22 PL-90238 Lodz Poland

ISBN: (纸本)9783319472744;9783319472737

As color is a useful characteristic of our surrounding world, it gives clue for the recognition, indexing and retrieval of the images presenting the visual similarity. Thus, this paper focuses on the proper choice of the similarity measure applied to compare features evaluated in process the modeling of lossy coded color image information, based on the mixture approximation of chromaticity histogram. The analyzed similarity measure are those based on Kullback - Leibler Diverence, as Goldberger approximation and V ariational approximation. Signaturebased distance function as Hausdorff Distance, Perceptually Modified Hausdorff Distance and EarthMover ' sDistance were also investigated. Retrieval results were obtained for RGB, I1I2I3, Y UV, CIE XY Z, CIE L* a* b*, HSx, LSLM and TSL color spaces.

关键词： Color image retrieval Mixture modeling Similarity measure Color space Lossy compression

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Design and Implementation of Automatic Color Matching APP Based on Color Quantization 10

Design and Implementation of Automatic Color Matching APP Ba...

引用

EEE International conference on Internet of Things (iThings) and IEEE Green Computing and communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)

作者： Qi, Xuan Zhang, Wen Xu, Qian Zhongnan Univ Econ & Law China Korea Inst New Media Wuhan Hubei Peoples R China Zhongnan Univ Econ & Law Sch Journalism & Culture Commun Wuhan Hubei Peoples R China

ISBN: (纸本)9781538630662

Computer aided pattern design system has been widely used in the field of carpet, weaving and printing. To obtain the most representative color information from the pattern is one of the key technologies to be solved in the design of the pattern and the quantity of materials. At present, most domestic and foreign pattern design system has color information is incomplete, inconsistent systems, human-computer interaction and inaccurate center colors, calculating the defect rate is relatively slow;and cannot be directly applied to the design of industrial materials. To obtain accurate color information due to the need to consider the minimum distortion, algorithm complexity and human visual characteristics of many problems from the image, there is a lack in the image processing of color layers and some key details of the algorithm is very satisfactory. In this paper, based on the analysis and research of the color information acquisition method, the common quantization algorithm and the image data, a new color quantization algorithm based on visual features is proposed. Different from the previous algorithm, the algorithm integrates the advantages of the traditional algorithm, and combines the color and spatial information of the image. On this basis, the design of an automatic color matching based on color APP, through the analysis of the user's personal color management practices, analysis of user needs, and the user will be reflected in the general requirements of specific examples. At the same time, the system will use handler to deal with the problem of the user often switch interface, the use of MVC to solve the problem of data. In order to solve the problem that users often change the interface of the software, we use Handler and MVR. The placement of advertising images, the use of GG View to control. In view of the logic of the jump, the design of each module can be controlled by the logic control method, and put them into the Lc_Activity.

关键词： digital image processing color quantization color information acquisition APP system design

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PaletteNet: image Recolorization with Given Color Palette 30

PaletteNet: Image Recolorization with Given Color Palette

引用

30th IEEE/CVF conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Cho, Junho Yun, Sangdoo Lee, Kyoungmu Choi, Jin Young Seoul Natl Univ Dept Elect & Comp Eng ASRI Seoul South Korea

ISBN: (纸本)9781538607336

image recolorization enhances the visual perception of an image for design and artistic purposes. In this work, we present a deep neural network, referred to as PaletteNet, which recolors an image according to a given target color palette that is useful to express the color concept of an image. PaletteNet takes two inputs: a source image to be recolored and a target palette. PaletteNet is then designed to change the color concept of a source image so that the palette of the output image is close to the target palette. To train PaletteNet, the proposed multi-task loss is composed of Euclidean loss and adversarial loss. The experimental results show that the proposed method outperforms the existing recolorization methods. Human experts with a commercial software take on average 18 minutes to recolor an image, while PaletteNet automatically recolors plausible results in less than a second.

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

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Automatically Diagnosing HER2 Amplification Status for Breast Cancer Patients Using Large FISH images 25

Automatically Diagnosing HER2 Amplification Status for Breas...

引用

25th Signal processing and communications Applications conference (SIU)

作者： Razavi, Salar Hatipoglu, Gokhan Yalcin, Hulya Istanbul Tech Univ Elect & Telecommun Engn Dept Visual Intelligence Lab Istanbul Turkey Virasoft Yazilim Istanbul Turkey

ISBN: (纸本)9781509064946

Fluorescence in situ hybridization (FISH) is a technique that prepares acceptable results for molecular imaging biomarkers to precisely and dependably detect and diagnose disorders which are sign of cancers. Since contemporary manual FISH signal analysis is low-effective and inconsistent, it is an attractive research area to develop automated FISH image scanning systems and computer-aided diagnosis (CAD) schemes. The gene expression of epidermal growth factor receptors 2 (HER2) is highly related to results of patients with probable breast cancer. Although FISH technology outperforms other methods, yet it has so many drawbacks. Traditional approaches on FISH analysis are performed manually by clinician. This lets the results are highly dependent to human eye. Also FISH test colors constitutes of dark blue and black regions, it is reasonable that human eye will fail to distinguish between colors. Therefore, the success of computer vision algorithms compared to human eye in analyzing gene expression rate in FISH images will be discussed in this study. Another objective of this study is to expand a CAD program to evaluate HER2 status using acquired images that have MIRAX format. Different large FISH images were chosen for this study from pathology laboratory from Acibadem Maslak hospital. The proposed CAD scheme first applies pre-processing median and gaussian filters. An adaptive thresholding method followed by a watershed segmentation algorithm is employed to segment cells of interest areas. Furthermore, analyzable cells are found and non detectable cells because of cell overlapping or image staining are discarded. The scheme then splits the detected analyzable region of interest into two red and green color spaces which is also followed by application of a scanning algorithm to detect the CEP17 green and HER2/neu red FISH signals separately. Finally, the proposed method calculates the ratio between independent green and red FISH signals of all analyzable cells identi

关键词： Breast Cancer Medical diagnostic imaging Fluorescent in situ hybridization (FISH) image HER2 testing

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Fine-Grained visual Classification Based on image Foreground and Sub-category Similarity 2nd

引用

2nd CCF Chinese conference on Computer Vision (CCCV)

作者： Jiang, Xianjin Lin, Xin Ji, Yi Yang, Jianyu Liu, Chunping Soochow Univ Sch Comp Sci & Technol Suzhou Jiangsu Peoples R China

ISBN: (纸本)9789811073052;9789811073045

We propose a fine-grained visual classification algorithm based on image foreground and sub-category similarity. In the processing of feature extracting, our model calculates the gradient of image pixels in a classification network to obtain the foreground of the image. Then input the foreground image and the original image into the bilinear convolution network to obtain the feature of the image. At the classification stage, we propose an improved SD-SVM algorithm, which takes the advantages of the similarities among sub-categories and the differences among the similarities of sub-category. Experimental results manifest that our algorithm can achieve 85.12% accuracy on the CUB-2011 dataset and 85.21% accuracy on the FGVC-aircrafts dataset even with only the category labels, which outperforms state-of-the-art fine-grained categorization methods.

关键词： image classification

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Video Question Answering Using a Forget Memory Network 2nd

Video Question Answering Using a Forget Memory Network

引用

2nd CCF Chinese conference on Computer Vision (CCCV)

作者： Ge, Yuanyuan Xu, Youjiang Han, Yahong Tianjin Univ Sch Comp Sci & Technol Tianjin 300350 Peoples R China

ISBN: (纸本)9789811072994;9789811072987

visual question answering combines the fields of computer vision and natural language processing. It has received much attention in recent years. image question answering (image QA) targets to automatically answer questions about visual content of an image. Different from image QA, video question answering (Video QA) needs to explore a sequence of images to answer the question. It is difficult to focus on the local region features which are related to the question from a sequence of images. In this paper, we propose a forget memory network (FMN) for Video QA to solve this problem. When the forget memory network embeds the video frame features, it can select the local region features that are related to the question and forget the irrelevant features to the question. Then we use the embedded video and question features to predict the answer from multiple-choice answers. Our proposed approaches achieve good performance on the MovieQA [21] and TACoS [28] dataset.

关键词： image QA Video QA Forget memory network

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