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检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

共 1642 条记录，以下是1521-1530 订阅

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Melanoma Screening Using Deep Neural Networks

Melanoma Screening Using Deep Neural Networks

引用

international conference for Convergence of Technology (I2CT)

作者： Jayant Sachdev Shashank Shekhar S. Indu Delhi Technological University

Deep learning algorithms, in particular convolutional neural networks, have rapidly become a methodology of choice for analyzing images. This choice arises due to fact that Deep Learning reduces the feature engineering task which is very important for automatic analysis of various kinds of images including medical images. In this work we have implemented various Deep Convolutional Neural Networks architectures for the process of binary classification task efficient enough in predicting either the input RGB image of the skin lesion is melanoma or not. Diagnosing the skin lesion is the first step towards its treatment. In this work we have assimilated two approaches on the pretrained Convolutional Neural Networks on the imageNet dataset. Firstly, we use the transfer learning approach without any fine tuning and obtained features are fed for linear classification task. Secondly, we use the transfer learning approach with fine tuning. The adopted method requires no lesion segmentation or any other higher level image processing tasks. Finally, we have also developed a real time android application to perform the binary classification task. The result obtained using our android application were enthusiastic.

关键词： Melanoma Lesions Skin Tuning Feature extraction computer architecture Pipelines

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Breast Cancer Detection From Histopathological images Using Deep Learning

Breast Cancer Detection From Histopathological Images Using ...

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Recent Advances and Innovations in Engineering (ICRAIE)

作者： Naresh Khuriwal Nidhi Mishra Department of Computer Engineering Poornima University Jaipur Jaipur India

ISBN: (纸本)9781538645260;9781538645253

The cancer is the most dangerous diseases in the world, its mainly effective for women. So, our prime target must be curing the cancer through scientific investigation and the second main target should be early detection of cancer because the early detection of cancer can be helpful for remove the cancer completed. After reviewed 41 papers we found that several techniques are available for cancer detection. In this paper we proposed Deep Leaning algorithm convolutional neural network for diagnosed breast cancer using Mammograph MIAS database. The paper shows how we can use deep learning technology for diagnosis breast cancer using MIAS Dataset. Because deep learning techniques almost used for high task objective computer vision, image processing, Medical Diagnosis, Neural Language processing. But in this paper, we are applying deep learning technology on the MIAS Database and we have seen that is very beneficial for us for diagnosis breast cancer with accuracy 98%. This paper is divided in three parts first we have collect dataset and applied pre-processing algorithm for scaled and filter data then we have split dataset in training and testing purpose and generate some graph for visualization data. In last implement model on training dataset and achieved accuracy 98%. So, we have seen deep learning technology is a good way for diagnosis breast cancer with MIAS Dataset. This database provides200images and 12 features in the dataset. In this paper we have used 12 features for diagnosis breast cancer that we have got after pre-processing. But before train model we have applied some pre-processing algorithm like Watershed Segmentation, Colour based segmentation and Adaptive Mean Filters for scaled dataset then applied model and achieved accuracy. In this paper we also compare deep learning algorithm with other machine learning and seen our proposed system is proved best from others machine learning algorithm.

关键词： Breast cancer Convolutional neural networks Deep learning Machine learning algorithms conferences Classification algorithms

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rd-OPTIMIZED 3D PLANAR MODEL RECONSTRUCTION & ENCODING FOR VIDEO COMPRESSION

RD-OPTIMIZED 3D PLANAR MODEL RECONSTRUCTION & ENCODING FOR V...

引用

IEEE international conference on image processing

作者： Cheng Yang Gene Cheung Seishi Takamura National Institute of Informatics NTT

Conventional video coding approaches follow a hybrid motion prediction/residual transform coding paradigm, which limits the discovery of redundancy to individual pairs of video frames. On the other hand, computer vision techniques like structure-from-motion (SfM) have long exploited redundancy across a large group of frames to estimate a rigid 3D object structure. In this paper, leveraging on previous SfM techniques, we construct a rate-distortion (rd) optimized 3D planar model from a target spatial region in a frame group as a unified signal predictor for these frames. The prediction accuracy of the model is optimally traded off with the cost of coding such representation as side information (SI). Specifically, we approximate a roughly flat spatial region in the video as a 2D plane in 3D space, and project pixels from the frame group to a 2D grid on the plane with appropriate density. The boundary of the irregularly shaped pixel body on the plane is first coded using arithmetic edge coding (AEC), and then the body is encapsulated into a tight-fitting rectangular region, which is encoded as an intra-frame using HEVC. The pixels inside the rectangle but outside the pixel body—called don't care region (DCR)—are filled optimally by minimizing an l_1-norm of the transform coefficients using linear programming. Experimental results show that the rd-optimized planar model improves coding performance over native HEVC implementation.

关键词： Three-dimensional displays Encoding Video coding Solid modeling Two dimensional displays image reconstruction Computational modeling

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Sub-pixel X-marker detection by Hough transform

Sub-pixel X-marker detection by Hough transform

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Iranian conference of Biomedical Engineering (ICBME)

作者： Ali Yazdani Hojjat Aalizadeh Farshid Karimi Saeed Solouki Hamid Soltanian-Zadeh School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Control and Intelligent Processing Center of Excellence(CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781538679531;9781538679524

High precision center detection of X-markers is required in many applications such as navigation surgery systems and camera calibration. Hough transform is a preferable tool for extracting intersecting lines in an image, which leads to center detection. In this paper, we detect X-marker centers by the sub-pixel precision, using Hough transform. Switching to Hough space helps us to apply processes like thresholding, filtering and weighted averaging on coordinates. The algorithm involves two parameters `Hough Size' and `Filter Size' required to be adjusted for best performance of the algorithm. A dataset of 900 images is used and best performance is achieved by values of 180 and 23 for the above parameters, respectively. Using this setting, 90.8% of the centers are detected successfully by the sub-pixel precision. The average distance between detected centers and reference centers is 0.51 pixels. This suggests that the proposed algorithm has the potential to be utilized for sub-pixel marker detection.

关键词： image edge detection Transforms Feature extraction Cameras Biomedical engineering Calibration Filtering algorithms

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Non-Homogeneous Haze image Formation Model Based Single image Dehazing

Non-Homogeneous Haze Image Formation Model Based Single Imag...

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international conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Banala Revanth Manoj Kumar Sanjay K. Dwivedi DCS BBA University Lucknow UP India

image dehazing is an image processing technique that involves removing the haze effect from a given image. Restoration of hazy images is currently an established computer vision issue due to its applications in the real world, like automated vehicles, surveillance, etc. This paper proposes a single image dehazing model by estimating atmospheric light and transmission map simultaneously using two networks. Atmospheric light and transmission map are estimated by a non-homogeneous light estimation network and a novel refined transmission map estimation network. The non-homogeneous light estimation network has two subnetworks: a white-balanced network and a deep convolutional neural network called Global Atmospheric Light Estimation Network. The white-balanced error network for correction of white-balanced error in a hazy image and a Global Atmospheric Light Estimation Network for estimation of global atmospheric light in a corrected white-balanced error hazy image. A refined transmission map estimation network is used to create a refined transmission map of the hazy image. The design of a refined transmission map estimation network is based on U-Net with skip connections, which estimates and refines the transmission map of the input haze image. The non-homogeneous haze image formation model takes both estimated global atmospheric light and a refined transmission map as input to restore the hazy-free image. The global atmospheric light estimation network and refined transmission map estimation network are trained using mean square error (MSE) and hybrid loss functions, respectively. In terms of structural similarity index Matric (SSIM) and peak signal-to-noise ratio (PSNR), the proposed model is equal to the cutting-edge approaches on the NR-Haze, I-Haze, and O-Haze datasets.

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A Review of Intrinsic image Decomposition

A Review of Intrinsic Image Decomposition

引用

image processing and Media Computing (ICIPMC), international conference on

作者： Siyuan Liu Xiaoyue Jiang Letian Liu Zhaoqiang Xia Sihang Dang Xiaoyi Feng School of Electronics and Information Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798350386660

ISBN: (纸本)9798350386677

Intrinsic image decomposition (IID) aims to extract intrinsic components from natural images, namely shading and reflectance, which is widely applied in computer vision and image processing tasks for the better understanding of the objects and scenes. However, IID is a severely ill-posed problem, extra constraints should be applied to address the decomposition problem. In this paper, we reviewed recent papers about intrinsic image decomposition. The extra cues from lighting, shape, color and textures are used to constrain the ill-posed problem. These papers can be mainly classified into two categories: physics-based methods and learning-based methods. Physics-based methods mainly introduced extra constraints into an optimization framework to solve the IID problem. While learning-based methods mainly depend on an encoder-decoder network with the explicit constraint from the labeled intrinsic images. Additionally, extra priors are introduced into the network by the form of network structure or specific feature. In order to make the IID problem more practical, researchers may explore how to achieve fast and accurate image decomposition with lower computing resources through optimization algorithms and model design.

关键词： Learning systems Accuracy image color analysis Shape Computational modeling image decomposition Real-time systems

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Adaptive Clustering SOFC image Segmentation Based on Particle Swarm Optimization

Adaptive Clustering SOFC Image Segmentation Based on Particl...

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2019 3rd international conference on Machine vision and Information Technology (CMVIT 2019)

作者： Xuefei Yang Xiaowei Fu Xi Li College of Computer Science and Technology Wuhan University of Science and TechnologyHubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System State Key Laboratory of Materials Processing and Die & Mould Technology Huazhong University of Science and Technology College of Automation Huazhong University of Science and Technology

Microstructural parameters are important for analyzing the chemistry and performance of solid oxide fuel cells（SOFCs）. Aiming at the YSZ/Ni anode optical microscopy（OM） image of SOFC, in this paper, particle swarm intelligent optimization algorithm is used to improve the fuzzy C-means clustering algorithm for image segmentation. Particle swarm optimization is used to adaptively search the initial clustering center, helping to avoid local optimization and preserve more image detail. The experimental results show that the proposed method can improve the segmentation accuracy of images. At the same time, it can accurately segment the SOFC three-phase and provide effective image segmentation results for the microstructure parameters.

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Deep Dual-Branch Network: Design and Optimization of High-Accuracy Sky Segmentation

Deep Dual-Branch Network: Design and Optimization of High-Ac...

引用

computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), IEEE international conference on

作者： Xiangrong Fu Yang Shu Liya Tian Binzi Wang Wanyu Yang Youdong Ding Shanghai Film Academy Shanghai University Shanghai China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

This paper presents an innovative network based on deep learning for sky segmentation, a key task in image processing. Sky segmentation has various applications in computer vision, but traditional methods struggle to meet accuracy and efficiency requirements due to the diverse and complex nature of the sky in different scenes. To address this challenge, we propose a semantic-detail dual-branch sky segmentation network that draws inspiration from the visual characteristics and segmentation needs of the sky. The network aims to enhance the accuracy and detail sensitivity of the sky area through the parallel design of the semantic branch and the detail branch, along with the introduction of a feature fusion module. We train the network using the AED20K subset of datasets, which enables fine segmentation of sky regions. Through extensive experiments on multiple datasets, we demonstrate that our network achieves significant performance improvements in sky segmentation tasks, delivering excellent accuracy and outstanding sensitivity to detail.

关键词： Deep learning image segmentation Visualization computer vision Sensitivity Semantics Process control

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Scanned to Digital Face images Matching With Siamese Network

Scanned to Digital Face Images Matching With Siamese Network

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Recent Advances and Innovations in Engineering (ICRAIE)

作者： J. C. Joshi Anubhav Gupta Kamlesh Tiwari Karunesh. K. Gupta Department of Electrical and Electronics Engineering Birla Institute of Technology and Science (BITS) Pilani India Department of Computer Science and Information Systems Birla Institute of Technology and Science (BITS) Pilani India

ISBN: (纸本)9781538645260;9781538645253

Often in law enforcement and forensic application it is needed to match scanned facial image with a digital face image. This is because in many scenario, non-digital face images are obtained from the crime scene, news articles etc. that are needed to be identified. Non-digital face images are first scanned and then enhanced to match against the database. Challenges arrives because of poor quality of non-digital image, artifacts introduced in scanning process and high saturation etc. therefore matching becomes difficult. The methods used in literature involve specialized hand crafted pre-processing. In our paper, we propose an automated way of matching by using Siamese networks. The proposed method have been able to achieve an EER of 2.346% that is better than the current state-of-the-art.

关键词： Face Feature extraction Digital images Training Databases Neurons Gray-scale

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2018 3rd international conference on Communication,image and Signal processing（CCISP 2018）

作者： E Bustillos D Pour Yousefian Barfeh Department of Computer Science Adamson University

Shape similarity plays important role in human perception. Humans always look for similarities between the shape of what they see and the shape of what they saw via comparison. Shape similarity has many applications in different fields such as cognitive science, medicine and computer science. This is a study in which a system is designed and implemented to measure similarities of two circular drawing shapes. The system follows different steps and techniques related to digital image processing such as;binarization, morphological operations and image segmentation. The novelty of the system comes from image alignment in which some transformation is performed to eliminate size dependency in similarity measurement. The transformation works on changing the origin point followed by stretching. In this study 30 pairs of circular shapes are considered. The results shows the system has 91.38% accuracy in average while maximum accuracy among 30 pairs of samples is 99.98% and minimum accuracy is 69.15%.

关键词： frame Drawing Similarity Measurement Using Object Alignment Object

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