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visualizing Salient Network Activations in Convolutional Neural Networks for Medical image Modality Classification 2nd

Visualizing Salient Network Activations in Convolutional Neu...

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2nd International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2018

作者： Rajaraman, Sivaramakrishnan Antani, Sameer Lister Hill National Center for Biomedical Communications National Library of Medicine BethesdaMD20894 United States

ISBN: (纸本)9789811391835

Convolutional neural network (CNN) has become the architecture of choice for visual recognition tasks. However, these models are perceived as black boxes since there is a lack of understanding of their learned behavior from the underlying task of interest. This lack of transparency is a drawback since poorly understood model behavior could adversely impact subsequent decision-making. Researchers use novel machine learning (ML) tools to classify the medical imaging modalities. However, it is poorly understood how these algorithms discriminate the modalities and if there are implicit opportunities for improving visual information access applications in computational biomedicine. In this study, we visualize the learned weights and salient network activations in a CNN based Deep Learning (DL) model to determine the image characteristics that lend themselves for improved classification with a goal of developing informed clinical question-answering systems. To support our analysis we cross-validate model performance to reduce bias and generalization errors and perform statistical analyses to assess performance differences. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Decision making

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3D Imaging of Sparse Wireless Signal Reconstructions via Machine Learning

3D Imaging of Sparse Wireless Signal Reconstructions via Mac...

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IEEE International conference on communications (ICC)

作者： Scott Fowler Gabriel G. Baravdish George Baravdish Communications and Transport Systems Linköping University Norrköping Sweden Media and Information Technology Linköping University Norrköping Sweden Physics Electronics and Mathematics Linköping University Norrköping Sweden

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Wireless devices have been used to investigate the environment and to understand our physical world. In this work, we undertake the challenging problem of identifying location of obstacles and objects by WiFi signals. Gathering wireless sensory data to form an image is difficult since wireless signals are susceptible to multipath. Moreover, reconstructing an image of unknown objects based on the measurements of sparse signals is an ill-posed problem. To tackle these problems, we first present a linear model using received signal strength indicator (RSSI) measurements. We define the sparse beamforming problem as an ℓ 0 -norm optimization problem, then use the iterative reweighted ℓ 1 heuristic algorithm to obtain an optimal solution as a multipath. Finally, the multipath fading is removed by using Machine Learning. More specifically, we use Support Vector Regression (SVR) to identify a clear image of the unknown object. Our results show that the proposed method can reconstruct signals as a 3D image with a satisfactory visual appearance, i.e. the generated data mesh is well defined and smooth compared to previous work.

关键词： Wireless communication Wireless sensor networks image reconstruction Signal processing algorithms Three-dimensional displays Object recognition Kernel

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A Corrosion Detection Algorithm Via The Random Forest Model 17

A Corrosion Detection Algorithm Via The Random Forest Model

引用

17th International conference on Optical communications and Networks (ICOCN)

作者： Liu Tingting Kang Kai Zhang Fen Ni Jialiang Wang Tianyun Dept China Satellite Maritime Tracking & Control Jiang Yin 214431 Peoples R China

ISBN: (数字)9781510627710

ISBN: (纸本)9781510627710

Corrosion is a serious issue causing damage in steel facilities. Timely inspection and repair is essential to avoid unprecedented structural failures. Employing non-destructive methods of manual inspection for large number of antennas to detect corrosion and related damages is time consuming and expensive. In addition to this, safety of inspector to climb structures possibly weakened by corrosion. In such a situation non-contact approach of automated visual inspection for corrosion and related damage detection through image processing of aerial based images is a viable option. For robust corrosion segmentation and detection, we investigate color classification based on random forest. A random forest is a statistical framework with a very high generalization accuracy and quick training times. We evaluate random forest based corrosion detection and compare it to Bayesian network, Multilayer Perceptron, SVM, Naive Bayes and RBF network. Results on a database of real images with manually annotated pixel-level ground truth show that with the IHLS colour space, the random forest approach outperforms other approaches.

关键词： corrosion detection image segmentation random forest antenna corrosion

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Comparative Analysis of Neural Models for Abstractive Text Summarization 2nd

Comparative Analysis of Neural Models for Abstractive Text S...

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2nd International conference on Machine Learning, image processing, Network Security and Data Sciences, MIND 2020

作者： Kumari, Heena Sarkar, Sunita Rajput, Vikrant Roy, Arindam Assam University Silchar India National Institute of Technology Silchar India

ISBN: (纸本)9789811563171

Abstractive text summarization is the task of generating the summary of text documents like humans do. It’s completely laborious and time taking process to summarize the lengthy documents manually. Abstractive text summarization takes a document as input and produces a summary by combining a piece of information from different source sentences and paraphrase them while maintaining the overall meaning of the document. Here, the abstractive text summarization task is done using various sequence-to-sequence (seq2seq) models and their performance has been analyzed on the MSMO dataset. Seq2seq with attention, pointer generator network (PGN) and pointer generator with coverage models are used to generate the summary. To improve accuracy, hyper-parameters were tuned and successfully obtained good results. ROUGE and BLEU scores are used to evaluate the performance of these models. Seq2seq with attention, PGN, and PGN with coverage models achieved ROUGE-1 scores 35.49, 38.19, 37.68 respectively. These neural abstractive text summarization models have also performed effectively in terms of the BLEU score and achieved BLEU-1 scores 40.60, 44.50, 45.20 respectively. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Text processing

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Recent Advances in End-to-End Learned image and Video Compression

Recent Advances in End-to-End Learned Image and Video Compre...

引用

IEEE visual communications and image processing (VCIP)

作者： Wen-Hsiao Peng Hsueh-Ming Hang National Chiao Tung University Taiwan

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

The DCT-based transform coding technique was adopted by the international standards (ISO JPEG, ITU H.261/264/265, ISO MPEG-2/4/H, and many others) for nearly 30 years. Although researchers are still trying to improve its efficiency by fine-tuning its components and parameters, the basic structure has not changed in the past two *** deep learning technology recently developed may provide a new direction for constructing a high-compression image/video coding system. Recent results, particularly from the Challenge on Learned image Compression (CLIC) at CVPR, indicate that this new type of schemes (often trained end-to-end) may have good potential for further improving compression *** the first part of this tutorial, we shall (1) summarize briefly the progress of this topic in the past 3 or so years, including an overview of CLIC results and JPEG AI Call-for-Evidence Challenge on Learning-based image Coding (issued in early 2020). Because Deep Neural Network (DNN)-based image compression is a new area, several techniques and structures have been tested. The recently published autoencoder-based schemes can achieve similar PSNR to BPG (Better Portable Graphics, H.265 still image standard) and has superior subject quality (e.g., MSSSIM), especially at the very low bit rates. In the second part, we shall (2) address the detailed design concepts of image compression algorithms using the autoencoder structure. In the third part, we shall switch gears to (3) explore the emerging area of DNN-based video compression. Recent publications in this area have indicated that end-to-end trained video compression can achieve comparable or superior rate-distortion performance to HEVC/H.265. The CLIC at CVPR 2020 also created for the first time a new track dedicated to P-frame coding.

关键词： image coding Transform coding Standards Video compression Streaming media MPEG standards High efficiency video coding

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Fical: Focal inter-class angular loss for image classification 34

Fical: Focal inter-class angular loss for image classificati...

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34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： Wei, Xinran Chang, Dongliang Xie, Jiyang Zheng, Yixiao Gong, Chen Zhang, Chuang Ma, Zhanyu Beijing University of Posts and Telecommunications Pattern Recognition and Intelligent Systems Lab Beijing China Key Lab of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education PCA Lab China Jiangsu Key Lab of Image and Video Understanding for Social Security China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781728137230

Convolutional Neural Networks (CNNs) have been successfully applied in various image analysis tasks and gradually become one of the most powerful machine learning approaches. In order to improve the capability of the model generalization and performance in image classification, a new trend is to learn more discriminative features via CNNs. The main contribution of this paper is to increase the angles between the categories to extract discriminative features and enlarge the inter-class variance. To this end, we propose a loss function named focal inter-class angular loss (FICAL) which introduces the confusion rate-weighted cosine distance as the similarity measurement between categories. This measurement is dynamically evaluated during each iteration to adapt the model. Compared with other loss functions, experimental results demonstrate that the proposed FICAL achieved best performance among the referred loss functions on two image classificaton datasets. © 2019 IEEE.

关键词： image classification

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Novel Quality Metric for image Super Resolution Algorithms - Super Resolution Entropy Metric (SREM) 2nd

Novel Quality Metric for Image Super Resolution Algorithms -...

引用

2nd International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2018

作者： Greeshma, M.S. Bindu, V.R. School of Computer Sciences Mahatma Gandhi University KottayamKerala India

ISBN: (纸本)9789811391804

Even with the topical developments of numerous image Super Resolution (SR) algorithms, how to quantify the visual quality scores of a super resolved image is still an open research problem. Majority of SR images are evaluated by full-reference metric with the support of a reference image. There are some circumstances when a reference image is unavailable or is with degraded quality. We propose a super resolution benchmark Super Resolution Entropy Metric (SREM) which can be used to evaluate the effectiveness of pixel reconstruction and quality of the image in the absence of reference image automatically. SREM measures the experimental quality of an SR image based on the perceptions of acutance and spatial discontinuity features in the gradient domain and wavelet domain. Experimental scores illustrate that the SREM metric is competent for assessing the visual quality of super-resolved images. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Quality control

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A Mixed Appearance-based and Coding Distortion-based CNN Fusion Approach for In-loop Filtering in Video Coding

A Mixed Appearance-based and Coding Distortion-based CNN Fus...

引用

IEEE visual communications and image processing (VCIP)

作者： Jian Yue Yanbo Gao Shuai Li Menghu Jia School of Information & Comunication Engineering University of Electronoc Science & Technology of China Chengdu China School of Software Shandong University Jinan China School of Control Science and Engineering Shandong University Jinan China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

With the success of the convolutional neural networks (CNNs) in image denoising and other computer vision tasks, CNNs have been investigated for in-loop filtering in video coding. Many existing methods directly use CNNs as powerful tools for filtering without much analysis on its effect. Considering the in-loop filters process the reconstructed video frames produced from a fixed line of video coding operations, the coding distortion in the reconstructed frames may share similar properties that can be learned by CNNs in addition to being a noisy image. Therefore, in this paper, we first categorize the CNN based filtering into two types of processes: appearance-based CNN filtering and coding distortion-based CNN filtering, and develop a two-stream CNN fusion framework accordingly. In the appearance-based CNN filtering, a CNN processes the reconstructed frame as a distorted image and extracts the global appearance information to restore the original image. In order to extract the global information, a CNN with pooling is used first to increase the receptive field and up-sampling is added in the late stage to produce pixel-level frame information. On the contrary, in the coding distortion-based filtering, a CNN processes the reconstructed frame as blocks with certain types of distortions by focusing on the local information to learn the coding distortion resulted by the fixed video coding pipeline. Finally, the appearance-based filtering stream and the coding distortion-based filtering stream are fused together to combine the two aspects of CNN filtering, and also the global and local information. To further reduce the complexity, the similar initial and last convolutional layers are shared over two streams to generate a mixed CNN. Experiments demonstrate that the proposed method achieves better performance than the existing CNN-based filtering methods, with 11.26% BD-rate saving under the All Intra configuration.

关键词： Distortion Feature extraction image reconstruction Encoding Convolutional codes image restoration Deep learning

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On 2D-3D image Feature Detections for image-To-Geometry Registration in Virtual Dental Model

On 2D-3D Image Feature Detections for Image-To-Geometry Regi...

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IEEE visual communications and image processing (VCIP)

作者： Hui Tang Tai-Chiu Hsung Walter Y.H. Lam Leo Y. Y. Cheng Edmond H.N. Pow School of Information Engineering Guangdong University of Technology Guangzhou People's Republic of China Faculty of Dentistry The University of Hong Kong Hong Kong SAR People's Republic of China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

3D digital smile design (DSD) gains great interest in dentistry because it enables esthetic design of teeth and gum. However, the color texture of teeth and gum is often lost/distorted in the digitization process. Recently, the image-to-geometry registration shade mapping (IGRSM) method was proposed for registering color texture from 2D photography to 3D mesh model. It allows better control of illumination and color calibration for automatic teeth shade matching. In this paper, we investigate automated techniques to find the correspondences between 3D tooth model and color intraoral photographs for accurately perform the IGRSM. We propose to use the tooth cusp tips as the correspondence points for the IGR because they can be reliably detected both in 2D photography and 3D surface scan. A modified gradient descent method with directional priority (GDDP) and region growing are developed to find the 3D correspondence points. For the 2D image, the tooth tips contour lines are extracted based on luminosity and chromaticity, the contour peaks are then detected as the correspondence points. From the experimental results, the proposed method shows excellent accuracy in detecting the correspondence points between 2D photography and 3D tooth model. The average registration error is less than 15 pixels for 4752×3168 size intraoral image.

关键词： Teeth Three-dimensional displays Two dimensional displays image color analysis Dentistry Surface morphology Solid modeling

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A Fast Feature Extraction Algorithm for image and Video processing

A Fast Feature Extraction Algorithm for Image and Video Proc...

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International Joint conference on Neural Networks (IJCNN)

作者： Abdulhussain, Sadiq H. Ramli, Abd Rahman Mahmmod, Basheera M. Saripan, M. Iqbal Al-Haddad, S. A. R. Baker, Thar Flayyih, Wameedh N. Jassim, Wissam A. Univ Baghdad Dept Comp Engn Baghdad Iraq Univ Putra Malaysia Comp & Commun Engn Seri Kembangan Selangor Malaysia Liverpool John Moores Univ Dept Comp Sci Liverpool Merseyside England Univ Dublin Trinity Coll Dublin Sch Engn ADAPT Ctr Dublin 2 Ireland

ISBN: (纸本)9781728119854

Medical images and videos are utilized to discover, diagnose and treat diseases. Managing, storing, and retrieving stored images effectively are considered important topics. The rapid growth of multimedia data, including medical images and videos, has caused a swift rise in data transmission volume and repository size. Multimedia data contains useful information;however, it consumes an enormous storage space. Therefore, high processing time for that sheer volume of data will be required. image and video applications demand for reduction in computational cost (processing time) when extracting features. This paper introduces a novel method to compute transform coefficients (features) from images or video frames. These features are used to represent the local visual content of images and video frames. We compared the proposed method with the traditional approach of feature extraction using a standard image technique. Furthermore, the proposed method is employed for shot boundary detection (SBD) applications to detect transitions in video frames. The standard TRECVID 2005, 2006, and 2007 video datasets are used to evaluate the performance of the SBD applications. The achieved results show that the proposed algorithm significantly reduces the computational cost in comparison to the traditional method.

关键词： Block processing Feature Extraction Orthogonal Polynomials Shot Boundary Detection Temporal Video Segmentation Abrupt Transition Detection

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