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检索条件"任意字段=6th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2008"

共 102 条记录，以下是71-80 订阅

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Saliency map improvement using edge-aware filtering 6th

Saliency map improvement using edge-aware filtering

引用

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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Mapping Archaeological Remains of 14th Century Fort of Jahanpanah Using Geospatial Analysis 12th

Mapping Archaeological Remains of 14th Century Fort of Jahan...

引用

12th indian conference on computer vision, graphics, and image processing, icvgip 2021

作者： Pal, Gaurav Kumar Rajani, M.B. National Institute of Advanced Studies IISc Campus Bangalore India

ISBN: (纸本)9789811941351

Among the seven historical cities of Delhi, Jahanpanah is the one that apparently has the least number of archaeological remains. this could well be a notion arising from a lack of proper identification and mapping of archaeological remains connected to this settlement. the area that was part of the erstwhile Jahanpanah is known to have lain between Qila Rai Pithora and Sirifort, but the exact boundaries have been blurred by the urban fabric. However, there are several neglected ruins and mounds across different parts of South Delhi whose context is unknown. this study highlights this gap in the case of Jahanpanah and using geospatial analysis of old maps, and satellite images, offers new discoveries regarding the exact location of the fort wall and its remains contextualising them in the current urban setting. the study has potentially found the buried remains of the eastern wall (along with a large bastion) adjacent to Lal Bahadur Shashtri Marg and the western wall in Qutub Institutional Area. the southern wall runs along Press Enclave Road, where subtle remains could be detected in the images of the 1960s. the study has also mapped the monuments within the fort area. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mapping

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Trajectory based integrated features for action classification from depth data 6th

Trajectory based integrated features for action classificati...

引用

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

作者： Shukla, Parul Arora, Noopur Biswas, Kanad K. Indian Institute of Technology Delhi India Works Application Singapore Singapore Bennett University Greater NoidaUttar Pradesh India

ISBN: (纸本)9789811300196

We present an approach for Human Action Recognition based on amalgamation of features from depth maps and body-joint data. this Integrated feature set consists of depth features based on gradient orientation and motion energy, in addition to features from 3D- skeleton data capturing its statistical details. Feature selection is carried out to extract a relevant set of features for action recognition. the resultant set of features are evaluated using SVM classifier. We validate our proposed method on various benchmark datasets for Action Recognition such as MSR-Daily Activity and UT-Kinect dataset. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Metals

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computer vision, Pattern Recognition, image processing, and graphics 1st ed. 2018

引用

丛书名： Communications in computer and Information Science

2018年

作者： Renu Rameshan Chetan Arora Sumantra Dutta Roy

ISBN: (数字)9789811300202

ISBN: (纸本)9789811300196

this book constitutes the refereed proceedings of the 6th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2017, held in Mandi, India, in December 2017. the 48 revised full papers presented in this volume were carefully reviewed and selected from 147 submissions. the papers are organized in topical sections on video processing; image and signal processing; segmentation, retrieval, captioning; pattern recognition applications.

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Wireless Networks and Computational Intelligence 1

引用

丛书名： Communications in computer and Information Science

1000年

作者： K. R. Venugopal L. M. Patnaik

ISBN: (数字)9783642316869

ISBN: (纸本)9783642316852

this book constitutes the refereed proceedings of the 6th International conference on Information processing, ICIP 2012, held in Bangalore, India, in August 2012. the 75 revised full papers presented were carefully reviewed and selected from 380 submissions. the papers are organized in topical sections on wireless networks; image processing; pattern recognition and classification; computer architecture and distributed computing; software engineering, information technology and optimization techniques; data mining techniques; computer networks and network security.

关键词： computer Communication Networks computer Imaging, vision, Pattern Recognition and graphics Information Storage and Retrieval Systems and Data Security Artificial Intelligence Information Systems Applications (incl. Internet)

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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 ...

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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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Writer identification using a deep neural network 14

Writer identification using a deep neural network

引用

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

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

引用

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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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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Dataset augmentation with synthetic images improves semantic segmentation 6th

Dataset augmentation with synthetic images improves semantic...

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

作者： Goyal, Manik Rajpura, Param Bojinov, Hristo Hegde, Ravi Varanasi Varanasi221005 India Indian Institute of Technology Gandhinagar Gandhinagar382355 India Innit Inc. Redwood City94063 United States

ISBN: (纸本)9789811300196

Although Deep Convolutional Neural Networks trained with strong pixel-level annotations have significantly pushed the performance in semantic segmentation, annotation efforts required for the creation of training data remains a roadblock for further improvements. We show that augmentation of the weakly annotated training dataset with synthetic images minimizes both the annotation efforts and also the cost of capturing images with sufficient variety. Evaluation on the PASCAL 2012 validation dataset shows an increase in mean IOU from 52.80% to 55.47% by adding just 100 synthetic images per object class. Our approach is thus a promising solution to the problems of annotation and dataset collection. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Semantic Segmentation

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