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Interpreting Intrinsic image Decomposition using Concept Activations 13

Interpreting Intrinsic Image Decomposition using Concept Act...

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13th indian conference on computer vision, graphics, and image processing, ICVGIP 2022

作者： Gupta, Avani Saini, Saurabh Narayanan, P.J. CVIT KCIS IIIT-Hyderabad India

ISBN: (纸本)9781450398237

Evaluation of ill-posed problems like Intrinsic image Decomposition (IID) is challenging. IID involves decomposing an image into its constituent illumination-invariant Reflectance (R) and albedo-invariant Shading (S) components. Contemporary IID methods use Deep Learning models and require large datasets for training. the evaluation of IID is carried out on either synthetic Ground Truth images or sparsely annotated natural images. A scene can be split into reflectance and shading in multiple, valid ways. Comparison with one specific decomposition in the ground-truth images used by current IID evaluation metrics like LMSE, MSE, DSSIM, WHDR, SAW AP%, etc., is inadequate. Measuring R-S disentanglement is a better way to evaluate the quality of IID. Inspired by ML interpretability methods, we propose Concept Sensitivity Metrics (CSM) that directly measure disentanglement using sensitivity to relevant concepts. Activation vectors for albedo invariance and illumination invariance concepts are used for the IID problem. We evaluate and interpret three recent IID methods on our synthetic benchmark of controlled albedo and illumination invariance sets. We also compare our disentanglement score with existing IID evaluation metrics on both natural and synthetic scenes and report our observations. Our code and data are publicly available for reproducibility 1. © 2022 ACM.

关键词： Large dataset

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Hyper vision Net: Kidney Tumor Segmentation Using Coordinate Convolutional Layer and Attention Unit 7th

Hyper Vision Net: Kidney Tumor Segmentation Using Coordinate...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Sabarinathan, D. Parisa Beham, M. Mansoor Roomi, S. M. Md. Couger Inc Tokyo Japan Sethu Institute of Technology VirudhunagarTamilnadu India Thiagarajar College of Engineering MaduraiTamil Nadu India

ISBN: (纸本)9789811586965

Challenges in accurate tumor detection paves the way to haste the improvement of solid kidney tumor semantic segmentation methodologies. Accurate segmentation of kidney tumor in computer tomography (CT) images is a challenging task due to the non-uniform motion, similar appearance and various shape. Inspired by this fact, in this manuscript, we present a novel kidney tumor segmentation method using deep learning network termed as hyper vision Net model. All the existing U-net models are using a modified version of U-net to segment the kidney tumor region. In the proposed architecture, we introduced supervision layers in the decoder part, and it refines even minimal regions in the output. A dataset consists of real arterial phase abdominal CT scans of 300 patients, including 45964 images has been provided from KiTs19 for training and validation of the proposed model. Compared with the state-of-the-art segmentation methods, the results demonstrate the superiority of our approach on training dice value score of 0.9552 and 0.9633 in tumor region and kidney region, respectively. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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image Analysis 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Arnt-Børre Salberg Jon Yngve Hardeberg Robert Jenssen

ISBN: (数字)9783642022302

ISBN: (纸本)9783642022296

this volume contains the papers presented at the Scandinavian conference on image Analysis, SCIA 2009, which was held at the Radisson SAS Scandinavian Hotel, Oslo, Norway, June 15–18. SCIA 2009 was the 16th in the biennial series of conferences, which has been organized in turn by the Scandinavian countries Sweden, Finland, D- mark and Norway since 1980. the event itself has always attracted participants and author contributions from outside the Scandinavian countries, making it an international conference. theconferenceincludedafulldayoftutorialsand?vekeynotetalksprovided by world-renowned experts. the program covered high-quality scienti?c cont- butions within image analysis, human and action analysis, pattern and object recognition,colorimagingandquality,medicalandbiomedicalapplications,face andheadanalysis,computer vision,andmultispectralcoloranalysis. the papers werecarefully selected based on at least two reviews. Among 154 submissions 79 wereaccepted,leadingtoanacceptancerateof51%. SinceSCIAwasarrangedas a single-track event, 30 papers were presented in the oral sessions and 49 papers were presented in the poster sessions. A separate session on multispectral color science was organized in cooperation with the 11th Symposium of Multispectral Color Science (MCS 2009). Since 2009 was proclaimed the “International Year of Astronomy” by the United Nations General Assembly, the conference also contained a session on the topic “imageand PatternAnalysis in Astronomyand Astrophysics. ” SCIA has a reputation of having a friendly environment, in addition to hi- quality scienti?c contributions. We focused on maintaining this reputation, by designing a technical and social program that we hope the participants found interesting and inspiring for new research ideas and network extensions. We thank the authors for submitting their valuable work to SCIA.

关键词： image processing and computer vision Pattern Recognition computer graphics

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Compressive Sensing Based Privacy for Fall Detection 7th

Compressive Sensing Based Privacy for Fall Detection

引用

7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Gupta, Ronak Anand, Prashant Chaudhury, Santanu Lall, Brejesh Singh, Sanjay Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi India Indian Institute of Technology Jodhpur Jheepasani India Cognitive Computing Group CSIR-CEERI Pilani India

ISBN: (纸本)9789811586965

Fall detection holds immense importance in the field of healthcare, where timely detection allows for instant medical assistance. In this context, we propose a 3D ConvNet architecture which consists of 3D Inception modules for fall detection. the proposed architecture is a custom version of Inflated 3D (I3D) architecture, that takes compressed measurements of video sequence as spatio-temporal input, obtained from compressive sensing framework, rather than video sequence as input, as in the case of I3D convolutional neural network. this is adopted since privacy raises a huge concern for patients being monitored through these RGB cameras. the proposed framework for fall detection is flexible enough with respect to a wide variety of measurement matrices. Ten action classes randomly selected from Kinetics-400 with no fall examples, are employed to train our 3D ConvNet post compressive sensing with different types of sensing matrices on the original video clips. Our results show that 3D ConvNet performance remains unchanged with different sensing matrices. Also, the performance obtained with Kinetics pre-trained 3D ConvNet on compressively sensed fall videos from benchmark datasets is better than the state-of-the-art techniques. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Compressed sensing

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MMAG: Mutually Motivated Attention Gates for Simultaneous Extraction of Contextual and Spatial Information from a Monocular image 14

MMAG: Mutually Motivated Attention Gates for Simultaneous Ex...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Singh, Aditya Mohan, Saloni Kushwaha, Vandana Nandi, G.C. Center of Intelligent Robotics Indian Institute of Information Technology Allahabad Uttar Pradesh Prayagraj India

ISBN: (纸本)9798400716256

In order to effectively interact with its environment, an agent must possess the ability to comprehend the'what' and the'where.' Two vision-based approaches, namely semantic segmentation and depth estimation, can be employed to provide this information. this paper introduces a unified model that combines these two tasks and utilizes a shared latent space. the chosen approach for the model involves an encoder-decoder architecture accompanied by a novel attention gate mechanism called MMAG, which has been proven to be highly effective. Furthermore, a common skip connection method, in conjunction with the proposed attention gate, is included to emphasize crucial features for both prediction purposes. By leveraging the complementary information within the shared representations, the model is capable of generating more accurate predictions compared to other joint-prediction models, all while utilizing fewer network parameters. To enhance performance, dilated layers are incorporated, allowing for focused attention through different receptive fields. the model's performance has been evaluated on the NYU Depth v2 and Camvid datasets, demonstrating improved results on both tasks when compared to other state-ofthe-art models. With its reduced parameter count, this model is particularly suitable for low-cost robots and has undergone successful testing for inference on the LoCoBot robot. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Semantic Segmentation

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Writer identification using a deep neural network 14

Writer identification using a deep neural network

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Chu, Jun Srihari, Sargur Department of Computer Science and Engineering University at Buffalo State University of New York BuffaloNY14269 United States

ISBN: (纸本)1595930361

Most work on automatic writer identification relies on hand-writing features defined by humans[6, 4]. these features correspond to basic units such as letters and words of text. Instead of relying on human-defined features, we consider here the determination of writing similarity using automatically determined word-level features learnt by a deep neural network. We generalize the problem of writer identification to the definition of a content-irrelevant handwriting similarity. Our method first takes whether two words were written by the same person as a discriminative label for word-level feature training. then, based on word-level features, we define writing similarity between passages. this similarity not only shows the distinction between writing styles of different people, but also the development of style of the same person. Performance with several hidden layers in the neural network are evaluated. the method is applied to determine how a person's writing style changes with time considering a children's writing dataset. the children's handwriting data are annually collected. they were written by children of 2nd, 3rd or 4th grade. Results are given with a whole passage (50 words) of writing over one-year change. As a comparison, similar experiments on a small amount of data using conventional generative model are also given. Copyright 2014 ACM.

关键词： Deep neural networks

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Optical flow based background subtraction method for lung nodule segmentation 4th

Optical flow based background subtraction method for lung no...

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4th International conference on computer vision and image processing, CVIP 2019

作者： Suji, R. Jenkin Bhadouria, Sarita Singh Dhar, Joydip Godfrey, W. Wilfred Atal Bihari Vajpayee Indian Institute of Information Technology and Management GwaliorMadhya Pradesh India Rajiv Gandhi Proudyogika Vishwavidhyalaya Bhopal India

ISBN: (纸本)9789811540141

image based Lung cancer diagnosis offers an effective and non-intrusive method for identification and diagnosis of lung cancer in a patient. this is done by segmentation of lung nodules from radiological images obtained from patients. this paper proposes a framework to segment nodule lesions in images. the novelty of the proposed framework is that we use optical flow information along with the morphological operations on images. the proposed framework applies the Farneback method of optical flow extraction algorithms for the nodule segmentation task. this work validates and demonstrates that optical flow information is useful for nodule segmentation and can be used in tandom with other features. © Springer Nature Singapore Pte Ltd 2020.

关键词： Optical flows

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Linking theory and Practice of Digital Libraries 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Gianmaria Silvello Giorgio Maria Di Nunzio Nicola Ferro Oscar Corcho Paolo Manghi Koraljka Golub Antonella Poggi

ISBN: (数字)9783031168024

ISBN: (纸本)9783031168017

this book constitutes the proceedings of the 26th International conference on theory and Practice of Digital Libraries, TPDL 2022, which took place in Padua, Italy, in September 2022.;the 18 full papers, 27 short papers and 15 accelerating innovation papers included in these proceedings were carefully reviewed and selected from 107 submissions. they focus on digital libraries and associated technical, practical, and social issues.

关键词： computer Applications Coding and Information theory Software Engineering/Programming and Operating Systems Natural Language processing (NLP) computer Imaging, vision, Pattern Recognition and graphics Signal, image and Speech processing

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Advancing Fingerprint Recognition Quality Assessment: Introducing the FRBQ Metric for Enhanced Fingerprint Recognition 14

Advancing Fingerprint Recognition Quality Assessment: Introd...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Jaiswal, Prateek Koner, Arka Namboodiri, Anoop M. CVIT IIIT Hyderabad India India

ISBN: (纸本)9798400716256

In the field of biometric security, the quality assessment of fingerprint images is paramount for boosting the accuracy of fingerprint recognition systems. these systems are fundamental for the secure and efficient authentication and identification of individuals. Our research presents FRBQ (Fingerprint Recognition-Based Quality), an innovative quality metric designed to navigate the limitations of the NFIQ2 model. FRBQ exploits deep learning algorithms in a weakly supervised setting and utilizes matching scores from DeepPrint, a Fixed-Length Fingerprint Representation Model. Each score is paired with labels indicating the robustness of fingerprint image matches. However, in a fully referenced setting, these labels can be subjective, lacking a clear definition of what "image quality" inherently means. this weakly labeled approach strives to capture diverse perspectives on image quality, potentially making it a more encompassing metric. In comparison to NFIQ2, our research showcases the superior performance of the FRBQ model. It not only correlates better with recognition scores but also effectively evaluates challenging images that NFIQ2 struggles with. Validated by the esteemed FVC 2004 dataset, FRBQ proves its efficacy in fingerprint image quality assessment. this study underscores the transformative potential of AI in biometrics, emphasizing its capability to capture details that traditional methods might overlook. Our work stresses the critical role of precise quality assessment in the evolution of fingerprint recognition systems. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： image quality

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Super Resolution Land Cover Mapping Using Deep Multi Scale Residual Dense Network 7th

Super Resolution Land Cover Mapping Using Deep Multi Scale R...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Synthiya Vinothini, D. Sathya Bama, B. Selva, Nirmal Kumar, Naveen Thiagarajar College of Engineering MaduraiTamilnadu625015 India

ISBN: (纸本)9789811586965

Super Resolution Mapping (SRM) is a land cover mapping method that generates land surface cover maps at fine spatial resolution from a coarse spatial resolution Remote Sensing (RS) image. Recently deep networks have shown impressive performance for image super resolution and image segmentation. Inspired by this performance a deep Multi-Scale Residual Dense Network (MSRDN) is proposed for SRM application of satellite data which extracts hierarchical features that can efficiently map sub-pixels to an accurate class. A MSRDN network is trained with coarse resolution images and its corresponding fine resolution class cover patches to learn a super resolution mapping of land cover. the accuracy of the Conventional SRM techniques is restricted by the performance of soft classification methods. Hence this work utilizes the full power of deep learning to generate a fine resolution land cover map directly from a coarse resolution image neglecting the intermediate soft classification result. the results of the experiments show that MSRDN can become a best alternative to the conventional SRM techniques, to generate a precise land cover information directly from a coarse data. © 2020, Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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