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Secure image Restoration and Contrast Enhancement Using Wavelet Transform and Thresholding Technique

Secure Image Restoration and Contrast Enhancement Using Wave...

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International conference on VLSI, communications, and Signal processing (VCAS)

作者： Singh, Kumari Suniti Mishra, Yogesh Kumar Singh, Harsh Vikram KNIT Sultanpur Dept Elect Engn Sultanpur India

ISBN: (纸本)9789813297753;9789813297746

The paper represents a methodology which involves the use of Satellite images taken from Satellite Imaging Corporation and Google Earth. This technique proposes a satellite image enhancement system in two steps which consists of restoration and contrast enhancement. The work represents a methodology for enhancement of Satellite images using wavelet transform such as Discrete wavelet transform (DWT) and Stationary wavelet transform (SWT), thresholding and contrast enhancement using equalization based on absolute difference. The result of the evaluation indicates that the technique obtains good PSNR value of satellite images. The results also strengthen that the technique fares well in preserving the information content of the satellite images and also improve the visual quality of images.

关键词： Satellite images DWT SWT DWT with SWT Thresholding and equalization

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Medical image Fusion Based on Weighted Least Square Optimization and Deep Learning Algorithm

Medical Image Fusion Based on Weighted Least Square Optimiza...

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Japan-Egypt International conference on Electronics, communications and Computers (JEC-ECC)

作者： C. Ghandour Walid El-Shafai S. El-Rabaie Faculty of Electronic Engineering Menoufia University Menouf Egypt Security Engineering Lab Prince Sultan University Riyadh KSA

ISBN: (纸本)9781665482936

Recently, medical image processing has become a hot area of research, especially with the rapid development in technology and instrumentation, and that’s because of its effective role in the health sector. So that it becomes a very active research tool. This research introduces an image fusion algorithm that utilizes a deep learning model to produce only one medical fused image that includes all the traits from the medical source images. Firstly, the source images are separated into detailed content and base parts using the Gaussian and rolling guidance filters (RGF). Secondly, by the weighted averaging strategy, the base parts are fused. For the detail content, to quote traits of multi-layer which employ weighted average strategy to produce the fused detail content several candidates, the deep learning network is utilized. The max selection technique is employed to gain the last detailed content as soon as these candidates are gotten. Eventually, by uniting the fused detail and base layers, the fused image will be recreated. The experimental outcomes show that this algorithm can accomplish better results by comparing the other fusion methods in both thematic assessment and visual quality.

关键词： Deep learning visualization Instruments Filtering algorithms Information filters Task analysis image fusion

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Use of Depth Completion and SLAM Algorithm to Build Dense Maps of Large Scenes

Use of Depth Completion and SLAM Algorithm to Build Dense Ma...

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IEEE International conference on Signal and image processing (ICSIP)

作者： Yongxing Jia Mingcan Li Fenghui Xu Jing Yu College of Communications Engineering Army Engineering University of PLA Nanjing China

ISBN: (纸本)9781665446006

Limited by the range of sensor, constructing a dense outdoor map of large scene with a depth camera is difficult. Since the state-of-the-art performance on data fusion for pixel-level distance estimation, this paper proposes a mapping system which employs depth image generated by an unsupervised Lidar-Stereo network to achieve accurate reconstruction. Firstly, in the ORBSLAM2 stereo mode, the system estimates the pose information to finish camera localization. And in another thread LidarStereoNet is used to generate a depth image within visual angle to describe the pixel-level distance. Then, for correcting the completed depth image to truth ground, a nonlinear transformation between the generation and the rendering is performed to restore the accurate distance relationship. Finally, the RGB image, pose information and depth rendering image are entered into the dense mapping module to get a large-scale point cloud model. After running on KITTI dataset, the experiment results show that the system can construct a pretty comprehensive scene model after only one trip, and using the numerical truncation and filtering methods to extract usable areas can make the mapping results more accurate.

关键词： Point cloud compression Location awareness visualization Simultaneous localization and mapping Instruction sets Rendering (computer graphics) Cameras

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Deep LSTM Transfer Learning for Personalized ECG Anomaly Detection on Wearable Devices 5th

Deep LSTM Transfer Learning for Personalized ECG Anomaly Det...

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5th International conference on Cognitive Systems and Signal processing, ICCSIP 2020

作者： Deng, Xin Zhou, Jianqiao Xiao, Bin Xiang, Xiaohong Yuan, Yi Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunication Chongqing400065 China Chongqing Key Laboratory of Image Recognition Chongqing University of Posts and Telecommunication Chongqing400065 China

ISBN: (纸本)9789811623356

Cardiovascular diseases are the main cause of global non-communicable deaths, accounting for about one-third of the total deaths in the world. Wearable devices are used for early monitoring and early prevention of cardiovascular diseases. Aimed at the low configuration, low power consumption and personalized characteristics of the wearable ECG equipment, a transfer learned with deep LSTM model is proposed in this work. The model is computational efficient and serves as the patient-specific ECG anomaly detector that can be used on the wearable devices. Based on the MIT-BIH open dataset, the experiment results show that the transfer learning model produces superior weighted F2 scores compared to the general model and the naive model for detecting ECG anomalies, such as the premature ventricular contractions and atrial premature beats. The transfer learning model proposed here provides a sensitive, personalized ECG anomaly detection mechanism that has the added benefit of light, quick and robust learning process to be used on wearable devices, which can meet the requirement of ECG monitoring for patients by wearable devices. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Electrocardiograms

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In Which images Does This Corner Appears? A Novel Approach for Three-Dimensional Query of Historical Photographs Collections in Urban Heritage Research 2nd

In Which Images Does This Corner Appears? A Novel Approach f...

引用

2nd International conference on Research and Education in Urban History in the Age of Digital Libraries, UHDL 2019

作者： Suazo, Antonio Centro Nacional de Conservación y Restauración 683 Recoleta St Santiago Chile

ISBN: (纸本)9783030931858

In order to improve the cataloging and search of historical photographs available in public online archives, recent advances support for spatially orienting images in a three-dimensional environment to freely explore these collections, thus addressing navigation issues. However, common search tasks still depend on the traditional approach based on text descriptions and keywords. To overcome this, a novel approach is proposed for querying images based on recovered 3d spatial data. By projecting image pixels over the surface of reconstructed geometry from depicted objects, a resulting point cloud is stored and queried by a search volume, retrieving 3d points and so the associated images. The proposal and its potential are validated through a specific case study from the field of urban history, a dilapidated building located in Santiago of Chile, and its associated photographic collections. Finally, the results obtained are discussed, the types of queries that the system allows, and some limitations identified. The conclusion shows that a new set of possibilities emerges, which allows interrogating images with specific tools and functions derived from a 3d spatial database, improving the access and use of collections for historical research with three-dimensional interest. © 2021, Springer Nature Switzerland AG.

关键词： Query processing

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Generating Cooking Recipes from Cooking Videos Using Deep Learning Considering Previous Process with Video Encoding 2020

Generating Cooking Recipes from Cooking Videos Using Deep Le...

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3rd International conference on Applications of Intelligent Systems, APPIS 2020

作者： Fujii, Tatsuki Orihara, Ryohei Sei, Yuichi Tahara, Yasuyuki Ohsuga, Akihiko University of Electro-Communications Chofu Tokyo Japan

ISBN: (纸本)9781450376303

Research on generating natural language captions to visual data such as images and videos has produced considerable results with deep learning methods and attracted attention in recent years. In this research, we aim to generate recipe sentences from cooking videos acquired from YouTube. We treat the task as image captioning. There are two aspects to be considered in order to do so. We believe that the semantics of each process should be taken into account to improve the captioning ' s accuracy. Furthermore, data processing, that is obtaining images from each process using several visual processing methods such as object detection should be important. We propose a captioning model where a sentence vector is embedded to consider the consistency of the recipe. From differences between generated recipes and the reference recipe, we can calculate recipe scores. We use three metrics that are used in previous studies to evaluate the image captioning model. We compare the scores to with ones from baseline models. © 2020 ACM.

关键词： Object detection

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Analyzing image Classification via EEG 7th

Analyzing Image Classification via EEG

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7th National conference on Computer Vision, Pattern Recognition, image processing, and Graphics, NCVPRIPG 2019

作者： Mishra, Rahul Bhavsar, Arnav Multimedia Analytics Networks and Systems Lab School of Computing and Electrical Engineering IIT Mandi Mandi India

ISBN: (纸本)9789811586965

Electroencephalogram (EEG) has been a popular technique for brain-computer interface (BCI) and brain decoding studies. However, decoding perceptual information (e.g. images and sound) is only recently being considered. In this work, we make an attempt to experimentally analyze and address the task of classification of images that a person sees, based on the captured EEG during the visual task. For this work, we used a publicly available dataset and, as a part of our analysis, find some important concerns associated with that dataset, which also highlights challenges in EEG based image classification. After we process the data to address these concerns, we show that there may still be some discriminative traits in the EEG data for classifying a small number of classes. © 2020, Springer Nature Singapore Pte Ltd.

关键词： image classification

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A Deep Pyramid Attention Network for Single image Super-resolution

A Deep Pyramid Attention Network for Single Image Super-reso...

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Japan-Egypt International conference on Electronics, communications and Computers (JEC-ECC)

作者： Garas Gendy Hazem Mohammed Nabil Sabor Guanghui He Shanghai Jiao Tong University Shanghai China Faculty of Engineering Assiut University Assiut Egypt

ISBN: (纸本)9781665482936

The pyramid attention (PA) network is a new structure developed for digital image processing. This network was designed to extract long-range features at different locations and scales. Recently, PA architecture has been introduced in single image super-Resolution (SISR) to improve the model's ability to benefit from data's self-similarity. However, the effects of location and number of PA on extracting the self-similarity are not explored. In this paper, a Deep Pyramid Attention Network (DPANet) is proposed for SISR based on exploring the PA block. This is performed by studying the effect of varying the number of PA blocks and their locations on the model performance. Moreover, the effect of the residual scale on the PA's performance is studied. Evaluated based on five benchmark datasets, we concluded that using five PA blocks without down-scale residual interchanging with Resblocks in the network achieves significantly better results compared to the state-of-the-art methods. In addition, our model achieves superior visual quality and accuracy.

关键词： visualization Correlation Computational modeling Simulation Digital images Superresolution Computer architecture

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Efficient Binary Multi-view Subspace Learning for Instance-Level image Retrieval 27th

Efficient Binary Multi-view Subspace Learning for Instance-L...

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27th International conference on Neural Information processing

作者： Wu, Zhijian Li, Jun Xu, Jianhua Nanjing Normal Univ Sch Comp Sci & Technol Nanjing 210023 Jiangsu Peoples R China

ISBN: (纸本)9783030638191;9783030638207

The existing hashing methods mainly handle either the feature based nearest-neighbour search or the category-level image retrieval, whereas a few efforts are devoted to instance retrieval problem. Besides, although multi-view hashing methods are capable of exploring the complementarity among multiple heterogeneous visual features, they heavily rely on massive labeled training data, and somewhat affects the real-world applications. In this paper, we propose a binary multi-view fusion framework for directly recovering a latent Hamming subspace from the multi-view features. More specifically, the multi-view subspace reconstruction and the binary quantization are integrated in a unified framework so as to minimize the discrepancy between the original multi-view high-dimensional Euclidean space and the resulting compact Hamming subspace. In addition, our method is amenable to efficient iterative optimization for learning a compact similarity-preserving binary code. The resulting binary codes demonstrate significant advantage in retrieval precision and computational efficiency at the cost of limited memory footprint. More importantly, our method is essentially an unsupervised learning scheme without any labeled data involved, and thus can be used in the cases when the supervised information is unavailable or insufficient. Experiments on public benchmark and large-scale datasets reveal that our method achieves competitive retrieval performance comparable to the state-of-the-art and has excellent scalability in large-scale scenario.

关键词： Instance retrieval Multi-view fusion Multi-view subspace reconstruction Binary quantization

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Three-Dimensional Structure Measurement and Optimization Method of Indoor Scene Based on Single image

Three-Dimensional Structure Measurement and Optimization Met...

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7th International conference on communications, Signal processing, and Systems (CSPS)

作者： Wang, Ronghe Zhang, Xinhai Zhang, Bo Bi, Jianning Guo, Xiaolei China Elect Technol Grp Corp China Acad Elect & Informat Technol Natl Engn Lab Publ Secur Risk Percept & Control B 11 Shuangyuan Rd Beijing 100041 Peoples R China

ISBN: (纸本)9789811365089;9789811365072

In the paper, we detect 2D surfaces and recognize 3D structures in images, and generate occluded part of three-dimensional structure. We detect objects vanish points and geometric segmentation lines, and generate the vector and normal plane. We measure the objects boundaries which have been detected. In the process, we have found the best linear segmentation positions, generated buildings, and other 3D models in room. We propose some effective assumptions to recognize the object model from images by geometric reasoning. At the same time, we put forward structure prediction technology combined with the volume reasoning by parameter representation of spatial objects. We detect scene model relationships of each other by combining image-rich appearance with geometric features. We proposed image geometric modeling grammar framework according to previous discriminative classifier. It is used to represent the physical structure of the visual component. The framework has broken the traditional probability texture context grammar tree model. We proposed spatial context theory and model generation rules. Finally, on the public existing data sets, we proved that we only use structure prediction and linear segmentation of scene to recover 3D structure by a lot of experiments, comparing to restoration algorithm using whole image structure, ours method can produce more credible scene entity models and space constraint relationships.

关键词： Models detection Geometric reasoning Energy minimization method Models generation Scene grammar model

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