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10th International conference on Digital image processing (ICDIP)

作者： Zhang, Xingguo Chen, Guoyue Saruta, Kazuki Terata, Yuki Akita Prefectural Univ Fac Syst Sci & Technol 84-4 Aza Ebinokuchi Tsuchiya Yurihonjo City 0150055 Japan

ISBN: (数字)9781510622005

ISBN: (纸本)9781510622005

The qualitative and quantitative analysis of different types of histopathology images of cancerous tissue can not only help us in better understanding of tumor but also explore various options for cancer treatment. However, it is still a challenging task due to cellular heterogeneity. Deep learning approaches have been shown to produce encouraging results on image detection in various tasks. In this paper, we investigate issues involving Faster R-CNN for construction of end-to-end colorectal adenocarcinoma images analysis system. We experimented with different types of network for extract features, and analyzed the impact of time and accuracy. In addition, we optimize the various stages of the network training process. We have evaluated them on a large dataset of colorectal adenocarcinoma images, consisting of more than 20,000 annotated cells belonging to four different classes. Our results presenting competitive accuracy and acceptable running time. Prospectively, the proposed methods could offer benefit to pathology practice in terms of quantitative analysis of tissue constituents in whole-slide images. Code and dataset will be made publicly available.

关键词： Colon Cancer Cells Detection computer vision Deep network

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VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT images

arXiv

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arXiv 2020年

作者： Sekuboyina, Anjany Kumar Husseini, Malek E. Bayat, Amirhossein Löffler, Maximilian Liebl, Hans Li, Hongwei Tetteh, Giles Kukačka, Jan Payer, Christian Štern, Darko Urschler, Martin Chen, Maodong Cheng, Dalong Lessmann, Nikolas Hu, Yujin Wang, Tianfu Yang, Dong Xu, Daguang Ambellan, Felix Amiranashvili, Tamaz Ehlke, Moritz Lamecker, Hans Lehnert, Sebastian Lirio, Marilia de Olaguer, Nicolás Pérez Ramm, Heiko Sahu, Manish Tack, Alexander Zachow, Stefan Jiang, Tao Ma, Xinjun Angerman, Christoph Wang, Xin Brown, Kevin Kirszenberg, Alexandre Puybareau, Élodie Chen, Di Bai, Yiwei Rapazzo, Brandon H. Yeah, Timyoas Zhang, Amber Xu, Shangliang Hou, Feng He, Zhiqiang Zeng, Chan Xiangshang, Zheng Liming, Xu Netherton, Tucker J. Mumme, Raymond P. Court, Laurence E. Huang, Zixun He, Chenhang Wang, Li-Wen Ling, Sai Ho Huynh, Lê Duy Boutry, Nicolas Jakubicek, Roman Chmelik, Jiri Mulay, Supriti Sivaprakasam, Mohanasankar Paetzold, Johannes C. Shit, Suprosanna Ezhov, Ivan Wiestler, Benedikt Glocker, Ben Valentinitsch, Alexander Rempfler, Markus Menze, Björn H. Kirschke, Jan S. Department of Informatics Technical University of Munich Germany Munich School of BioEngineering Technical University of Munich Germany Department of Neuroradiology Klinikum Rechts der Isar Germany Department for Quantitative Biomedicine University of Zurich Switzerland Friedrich Miescher Institute for Biomedical Engineering Switzerland Institute of Biological and Medical Imaging Helmholtz Zentrum München Germany Department of Computing Imperial College London United Kingdom Institute of Computer Graphics and Vision Graz University of Technology Austria Gottfried Schatz Research Center: Biophysics Medical University of Graz Austria School of Computer Science The University of Auckland New Zealand Computer Vision Group iFLYTEK Research South China China Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen Netherlands Shenzhen Research Institute of Big Data China School of Biomedical Engineering Health Science Center Shenzhen University China NVIDIA Corporation United States Zuse Institute Berlin Germany 1000shapes GmbH Berlin Germany Damo Academy Alibaba Group China Department of Mathematics University of Innsbruck Austria Department of Electronic Engineering Fudan University China New York University United States France Deep Reasoning AI Inc United States Technical University of Munich Germany East China Normal University China Chinese Academy of Sciences China Lenovo Group China Ping An Technologies China College of Computer Science and Technology Zhejiang University China Real Doctor AI Research Centre Zhejiang University China The University of Texas MD Anderson Cancer Center United States Department of Electronic and Information Engineering The Hong Kong Polytechnic University China Department of Computing The Hong Kong Polytechnic University China The School of Biomedical Engineering University of Technology Sydney Australia Department of Biomedical Engineering Brno University of Technology Czech Republic India

Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International conference on Medical image Computing and computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://***/nqjyw/, https://***/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://***/anjany/verse. © 2020, CC BY-NC-ND.

关键词： computerized tomography

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Semantic multinomial representation for scene images using CNN-based pseudo-concepts and concept neural network 6th

Semantic multinomial representation for scene images using C...

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Pradhan, Deepak Kumar Gupta, Shikha Thenkanidiyoor, Veena Aroor Dinesh, Dileep Department of Computer Science and Engineering National Institute of Technology Goa PondaGoa401403 India School of Computing and Electrical Engineering Indian Institute of Technology Mandi MandiH.P.175001 India

ISBN: (纸本)9789811300196

For challenging visual recognition tasks such as scene classification and object detection there is a need to bridge the semantic gap between low-level features and the semantic concept descriptors. This requires mapping a scene image onto a semantic representation. Semantic multinomial (SMN) representation is a semantic representation of an image that corresponds to a vector of posterior probabilities of concepts. In this work we propose to build a concept neural network (CoNN) to obtain the SMN representation for a scene image. An important issue in building a CoNN is that it requires the availability of ground truth concept labels. In this work we propose to use pseudo-concepts obtained from feature maps of higher level layers of convolutional neural network. The effectiveness of the proposed approaches are studied using standard datasets. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Semantics

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A method for detecting JPEG anti-forensics 6th

A method for detecting JPEG anti-forensics

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Bhardwaj, Dinesh Kumawat, Chothmal Pankajakshan, Vinod Department of Electronics and Communication Engineering Indian Institute of Technology Roorkee RoorkeeUttarakhand India

ISBN: (纸本)9789811300196

In this paper, a new approach is proposed for the detection of JPEG anti-forensic operations. It is based on the fact that when a JPEG anti-forensic operation is applied, the values of DCT coefficients are changed. This change decreases, especially in high frequency subbands, if we apply anti-forensic operation again. Hence, we propose to calculate a normalized difference between absolute values of DCT coefficients in 28 high frequency AC-subbands of the test image and its anti-forensically modified version. Based on this normalized feature, it is possible to differentiate between uncompressed and anti-forensically modified images. Experimental results show the effectiveness of the proposed method. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Digital forensics

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GMM based single depth image super-resolution 6th

GMM based single depth image super-resolution

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Balure, Chandra Shaker Kini, M. Ramesh Bhavsar, Arnav National Institute of Technology Karnataka Surathkal Mangalore India Indian Institute of Technology Mandi MandiH.P. India

ISBN: (纸本)9789811300196

Super-resolution (SR) is a technique to improve the resolution of an image from a sequence of input images or from a single image. As SR is an ill-posed inverse problem, it leads to many suboptimal solutions. Since modern depth cameras suffer from low-spatial resolution and are noisy, we present a Gaussian mixture model (GMM) based method for depth image super-resolution (SR). We train GMM from a set of high-resolution and low-resolution (HR-LR) synthetic training depth images to learn the relation between the HR and the LR patches in the form of covariance matrices. We use expectation-maximization (EM) algorithm to converge to an optimal solution. We show the promising results qualitatively and quantitatively in comparison to other depth image SR methods. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Covariance matrix

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Saliency map improvement using edge-aware filtering 6th

Saliency map improvement using edge-aware filtering

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Patel, Diptiben Raman, Shanmuganathan Electrical Engineering Indian Institute of Technology Gandhinagar GandhinagarGujarat382355 India

ISBN: (纸本)9789811300196

Content-aware applications in computational photography define the relative importance of objects or actions present in an image using a saliency map. Most saliency detection algorithms learn from the human visual system and try to find relatively important content as a salient region(s). This paper attempts to improve the saliency map defined by these algorithms using an iterative process. The saliency map of an image generated by an existing saliency detection algorithm is modified by filtering the image after segmenting into foreground and background. In order to enhance the saliency map values present in the salient region, the background is filtered using an edge-aware guided filter and the foreground is enhanced using a local Laplacian filter. The number of iterations required varies according to the image content. We show that the proposed framework enhances the saliency maps generated using the state-of-the-art saliency detection algorithms both qualitatively and quantitatively. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Iterative methods

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Detection of coal seam fires in summer seasons from landsat 8 OLI/TIRS in Dhanbad 6th

Detection of coal seam fires in summer seasons from landsat ...

引用

6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Mukherjee, Jit Mukherjee, Jayanta Chakravarty, Debashish Advance Technology Development Centre Indian Institute of Technology Kharagpur KharagpurWest Bengal India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur KharagpurWest Bengal India Department of Mining Engineering Indian Institute of Technology Kharagpur KharagpurWest Bengal India

ISBN: (纸本)9789811300196

Surface and sub-surface coal seam fires are detected by estimating Land Surface Temperature (LST). The LST of an area depends on several factors such as, seasonal variation, nature of soil, urban settlements, etc. Temperatures of several areas of Dhanbad region of Eastern India are affected by the presence of surface and sub-surface coal seam fires. Coal seam fire detection has several challenges. Specially in summer season, thermal anomalies provide false classifications of such fires. It has been observed that during summer season, water bodies have high temperatures, and thus affecting the performance of detection of fires. This paper proposes a novel method to detect surface and subsurface fires in summer from satellite data by removing the high temperature water bodies. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Landsat

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Hand Tracking based on Compressed Sensing and Multiple Feature Descriptors 10

Hand Tracking based on Compressed Sensing and Multiple Featu...

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10th International conference on Digital image processing (ICDIP)

作者： Zheng, Yi Zheng, Ping Shandong Technol & Business Univ Sch Informat & Elect Engn Yantai 264005 Peoples R China Anhui Univ Sci & Technol Sch Comp Sci & Engn Huainan 232001 Peoples R China

ISBN: (数字)9781510622005

ISBN: (纸本)9781510622005

computer vision based interaction between bare hands and virtual objects is an urgent problem to be solved in augmented reality and teleoperation. Bare hand tracking is one of the key issues. An effective hand tracking method based on compressed sensing and multiple feature descriptors is studied in depth. Firstly, a rectangular tracking window containing the hand is determined manually in the initial frame. Using the compressed sensing theory, key Haar feature values and HOG (abbreviation of histogram of oriented gradients) feature values of the initial tracking window are calculated respectively. Thus the classifier is initialized. For the subsequent frames, those positive samples and negative ones around the moving hand are captured, their feature values are calculated, and the classifier is updated. The candidate region corresponding to the maximum of the classifier is taken as the target region of the moving hand in each frame. In the process, Haar feature values and HOG feature values of the candidate region samples are calculated respectively. Simulation experiments and real experiments are carried out by using the proposed tracking method. Experimental results demonstrate that the proposed method can track the moving hand effectively. The proposed hand tracking method can be used in the fields of human computer interaction, augmented reality and teleoperation.

关键词： human computer interaction hand tracking compressed sensing Haar feature descriptor HOG feature descriptor

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Exploring memory and time efficient neural networks for image captioning 6th

Exploring memory and time efficient neural networks for imag...

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Parameswaran, Sandeep Narayan Visualization and Perception Lab Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai600036 India

ISBN: (纸本)9789811300196

Automatically describing the contents of an image is one of the fundamental problems in artificial intelligence. Recent research has primarily focussed on improving the quality of the generated descriptions. It is possible to construct multiple architectures that achieve equivalent performance for the same task. Among these, the smaller architecture is desirable as they require less communication across servers during distributed training and less bandwidth to export a new model from one place to another through a network. Generally, a deep learning architecture for image captioning consists of a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) clubbed together within an encoder-decoder framework. We propose to combine a significantly smaller CNN architecture termed SqueezeNet and a memory and computation efficient LightRNN within a visual attention framework. Experimental evaluation of the proposed architecture on Flickr8k, Flickr30k and MS-COCO datasets reveal superior result when compared to the state of the art. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Recurrent neural networks

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Visual odometry based omni-directional hyperlapse 6th

Visual odometry based omni-directional hyperlapse

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Rani, Prachi Jangid, Arpit Namboodiri, Vinay P. Venkatesh, K.S. Department of Elecrical Engineering Indian Institute of Technology Kanpur Kanpur India Computer Science and Engineering Department Indian Institute of Technology Kanpur Kanpur India

ISBN: (纸本)9789811300196

The prohibitive amounts of time required to review the large amounts of data captured by surveillance and other cameras has brought into question the very utility of large scale video logging. Yet, one recognizes that such logging and analysis are indispensable to security applications. The only way out of this paradox is to devise expedited browsing, by the creation of hyperlapse. We address the hyperlapse problem for the very challenging category of intensive egomotion which makes the hyperlapse highly jerky. We propose an economical approach for trajectory estimation based on Visual Odometry and implement cost functions to penalize pose and path deviations. Also, this is implemented on data taken by omni-directional camera, so that the viewer can opt to observe any direction while browsing. This requires many innovations, including handling the massive radial distortions and implementing scene stabilization that need to be operated upon the least distorted region of the omni view. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Cost functions

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