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检索条件"任意字段=Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing"

共 557 条记录，以下是141-150 订阅

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Inclusion of a Visual Servoing Project into an Undergraduate Robotic Course 11

Inclusion of a Visual Servoing Project into an Undergraduate...

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11th IEEE International conference on Engineering Education (ICEED)

作者： Ma, Lili Alborati, Mohamed Song, Jiahui New York City Coll Technol Dept Comp Engn Technol Brooklyn NY 11201 USA Wentworth Inst Technol Dept Elect & Comp Engn Boston MA USA

ISBN: (纸本)9781728134604

this paper describes the inclusion of a visual servoing project into an undergraduate robotic course. the robotic platform is a vision-enhanced VEX robot, constructed by integrating the commercially-available VEX robot with the Raspberry PI single-board computer (plus its camera module). this project allows students to go through several key steps in a vision-based control task, including camera calibration, depth recovery, image processing, communication, and basic robot control. vision-based control, which was once a graduate-level subject, is now exposed to undergraduate students. this project will strengthen students' understanding of relevant topics, expose them to more complex robotic systems, provide them opportunities to program and design in multi-disciplinary areas, and prepare them with a more complete set of knowledge, skills, and hands-on experience to benefit other activities they do at school and beyond. Students' feedback confirms that this project is effective in enhancing their learning. the usage of commercially-available robotic kits & components allows this project to be easily produced at other institutions.

关键词： visual servoing image processing robotic education vision-enhanced VEX robot Raspberry PI

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Sparse Signal Recovery for Multiple Measurement Vectors with Temporally Correlated Entries: A Bayesian Perspective 18

Sparse Signal Recovery for Multiple Measurement Vectors with...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Shruti Sharma Santanu Chaudhury Jayadeva Jayadeva Snigdha Bhagat Indian Institute of Technology Delhi

ISBN: (纸本)9781450366151

Bayesian Sparse Signal Recovery (SSR) for Multiple Measurement Vectors, when elements of each row of solution matrix are correlated, is addressed in the paper. We propose a standard linear Gaussian observation model and a three-level hierarchical estimation framework, based on Gaussian Scale Mixture (GSM) model with some random and deterministic parameters, to model each row of the unknown solution matrix. this hierarchical model induces heavy-tailed marginal distribution over each row which encompasses several choices of distributions viz. Laplace distribution, Student's t distribution and Jeffery prior. Automatic Relevance Determination (ARD) phenomenon introduces sparsity in the model. It is interesting to see that Block Sparse Bayesian Learning framework is a special case of the proposed framework when induced marginal is Jeffrey prior. Experimental results for synthetic signals are provided to demonstrate its effectiveness. We also explore the possibility of using Multiple Measurement Vectors to model Dynamic Hand Posture Database which consists of sequence of temporally correlated hand posture sequence. It can be seen that by exploiting temporal correlation information present in the successive image samples, the proposed framework can reconstruct the data with less linear random measurements with high fidelity.

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Learning to Prevent Monocular SLAM Failure using Reinforcement Learning 18

Learning to Prevent Monocular SLAM Failure using Reinforceme...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Vignesh Prasad Karmesh Yadav Rohitashva Singh Saurabh Swapnil Daga Nahas Pareekutty K. Madhava Krishna Balaraman Ravindran Brojeshwar Bhowmick Embedded Systems & Robotics TCS Research & Innovation Kolkata India Robotics Institute Carnegie Mellon University Pittsburgh USA Dept. of Robotics Engineering John Hopkins University Baltimore USA Robotics Research Center KCIS IIIT Hyderabad India Dept. of Computer Science Indian Institute of Technology Madras India

ISBN: (纸本)9781450366151

Monocular SLAM refers to using a single camera to estimate robot ego motion while building a map of the environment. While Monocular SLAM is a well studied problem, automating Monocular SLAM by integrating it with trajectory planning frameworks is particularly challenging. this paper presents a novel formulation based on Reinforcement Learning (RL) that generates fail safe trajectories wherein the SLAM generated outputs do not deviate largely from their true values. Quintessentially, the RL framework successfully learns the otherwise complex relation between perceptual inputs and motor actions and uses this knowledge to generate trajectories that do not cause failure of SLAM. We show systematically in simulations how the quality of the SLAM dramatically improves when trajectories are computed using RL. Our method scales effectively across Monocular SLAM frameworks in both simulation and in real world experiments with a mobile robot.

关键词： Monocular SLAM

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Salient Object Detection based on Bayesian Surprise of Restricted Boltzmann Machine 18

Salient Object Detection based on Bayesian Surprise of Restr...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Rahul Roy Susmita Ghosh Ashish Ghosh Machine Intelligence Unit Indian Statistical Institute Kolkata West Bengal Department of Computer Science and Technology Jadavpur University Kolkata West Bengal

ISBN: (纸本)9781450366151

this article presents an algorithm for salient object detection by leveraging the Bayesian surprise of the Restricted Boltzmann Machine (RBM). Here an RBM is trained on patches sampled randomly from the input image. Due to this random sampling, the RBM is likely to get more exposed to background patches than that of the object. thus, the trained RBM will minimize the free energy of its hidden states with respect to the background patches as opposed to the object. this, according to the free energy principle, implies minimizing Bayesian surprise which is a measure for saliency based on Kullback Leibler divergence between the input and reconstructed patch distribution. Hence, when the trained RBM is exposed to patches from the object region, it would have high divergence and in turn a high Bayesian surprise. thus such pixels with high Bayesian surprise could be considered as salient pixels. For each pixel, a neighborhood (with the same size of training patch) is considered and is fed to the trained RBM to obtain the reconstructed patch. thereafter, the Kullback Leibler divergence between the input and reconstructed neighborhood of each pixel is computed to measure the Bayesian surprise and is stored in the corresponding position in a matrix to form the saliency map. Experiments are carried out on three datasets namely MSRA-10K, ECSSD and DUTS. the results obtained depict promising performance by the proposed approach.

关键词： Bayesian Surprise

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Augmented reality book as a narrative medium 13

Augmented reality book as a narrative medium

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13th International conference on Interfaces and Human computer Interaction 2019, IHCI 2019, 12th International conference on Game and Entertainment Technologies 2019, GET 2019 and 13th International conference on computer graphics, Visualization, computer vision and image processing 2019, CGVCVIP 2019

作者： Argasiński, Jan K. Woynarowski, Jakub Jagiellonian University in Krakow Faculty of Physics Astronomy and Applied Computer Science ul. Lojasiewicza 11 Krakow30-348 Poland in Krakow plac Jana Matejki 13 Krakow31-157 Poland

ISBN: (纸本)9789898533913

the article takes a critical aim at one of the most popular uses of Augmented Reality technology - that is enriching literary works. the authors look at popular publications by analyzing the methods of communication used in them. Next, an art project called "Stilleben" is presented. It is an attempt to create a work that is essentially coherent with the medium. the inspirations, motivations and implementation of the "Stilleben" are presented. © Copyright 2019 IADIS Press. All rights reserved.

关键词： Augmented reality

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Zero-shot Learning using Graph Regularized Latent Discriminative Cross-domain Triplets 2018

Zero-shot Learning using Graph Regularized Latent Discrimina...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Omkar Gune Meet Vora Biplab Banerjee Subhasis Chaudhuri Indian Institute of Technology Bombay India Indian Institute of Technology Roorkee India

ISBN: (纸本)9781450366151

Zero-shot learning (ZSL) for visual recognition aims at identifying the previously unseen class samples given a trained model on the labeled visual samples of seen classes and additional class-level semantic side information for all classes. Often ZSL is tackled by learning an embedding function from the visual to semantic space or vice-versa. However, learning this mapping often results in loss of discriminative property of learned embedding space, thus severely compromising the recognition performance on the test samples. In order to ensure improved discrimination in the embedding space, we introduce a ZSL framework by leveraging the intuitive idea of cross-domain triplets based metric learning for learning such a space. Additionally, we introduce a novel graph Laplacian based regularizer which aligns the graph structures of the visual and semantic spaces in the learned embedding space. Simultaneously optimizing both the criteria results in a compact, discriminative, and meaningful embedding space, which is experimentally found to be superior to most of its existing counterparts on both the standard ZSL (AwA and CUB) and the challenging generalized ZSL (AwA1, AwA2, CUB) settings.

关键词： Zero-shot learning cross-domain alignment

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image Segmentation and Geometric Feature Based Approach for Fast Video Summarization of Surveillance Videos 1

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3rd Workshop on computer vision Applications, WCVA 2018, held in conjunction with the 11th indian conference on computer vision, graphics and image processing, ICVGIP 2018

作者： Rohan, Raju Dhanakshirur ara Patel, Zeba Yadavannavar, Smita C. Sujata, C. Mudengudi, Uma KLE Technological University Hubli India

ISBN: (数字)9789811513879

ISBN: (纸本)9789811513862

In this paper, we propose a geometric feature and frame segmentation based approach for video summarization. Video summarization aims to generate a summarized video with all the salient activities of the input video. We propose to retain the salient frames towards generation of video summary. We detect saliency in foreground and background of the image separately. We propose to model the image as MRF (Markov Random Field) and use MAP (Maximum a-posteriori) as final solution to segment the image into foreground and background. the salient frame is defined by the variation in feature descriptors using the geometric features. We propose to combine the probabilities of foreground and background segments being salient using DSCR (Dempster Shafer Combination Rule). We consider the summarized video as a combination of salient frames for a user defined time. We demonstrate the results using several videos in BL-7F dataset and compare the same with state of art techniques using retention ratio and condensation ratio as quality parameters. © Springer Nature Singapore Pte Ltd, 2019.

关键词： image segmentation

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An Efficient Character Segmentation Algorithm for Connected Handwritten Documents 4th

An Efficient Character Segmentation Algorithm for Connected ...

引用

4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

作者： Rajput, Vishal Jayanthi, N. Indu, S. Department of Electronics and Communication Engineering PDPM-Indian Institute of Information Technology Design and Manufacturing Jabalpur JabalpurMP482005 India Department of Electronics and Communication Engineering Delhi Technological University Delhi India

ISBN: (纸本)9789811393600

this paper proposes an efficient method of character segmentation for handwritten text. the main challenge in character segmentation of hand-written text is the varied size of each letter in different documents, connected alphabets in a word in cursive writing and the presence of ligatures within an open character. Hence, this paper proposes an adaptive vertical pixel count algorithm to solve the problem of over-segmentation due to the presence of open characters such as ‘w’, ‘v’ and ‘m’. Proposed algorithm works effectively against both the handwritten and standard text. the proposed method is evaluated on IAM and self-created data set. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Optical character recognition

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MultiDIAS: A Hierarchical Multi-layered Document image Annotation System 4th

MultiDIAS: A Hierarchical Multi-layered Document Image Annot...

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

作者： Poddar, Arnab Mukherjee, Rohan Mukhopadhyay, Jayanta Biswas, Prabir Kumar Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (纸本)9789811393600

Content of the document images are often shows hierarchical multi-layered tree structure. Further, the algorithms for document image applications like line detection, paragraph detection, word recognition, layout analysis etc. require pixel level annotation. In this paper, a Multi-layered Document image Annotation System (MultiDIAS) has been introduced. the proposed system simultaneously provide a platform for hierarchical and pixel level annotation of document. MultiDIAS label the document image in four hierarchical layers (layout type, entity type, line type, word type) assigned by the user. the output generated are four ground-truth images and an XML file representing the metadata information. the MultiDIAS is tested on a complex handwritten manuscript written by renowned film director Satyajit Ray for the movie ‘Goopi Gyne Bagha Byne’. this annotated data generated using MultiDIAS can further be used in a wide range of applications of document image understanding and analysis. © Springer Nature Singapore Pte Ltd. 2019.

关键词： image annotation

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An Efficient FCN based Neural Network for image Semantic Segmentation 11

An Efficient FCN based Neural Network for Image Semantic Seg...

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11th International conference on Digital image processing (ICDIP)

作者： Yang, Ruixin Mu, Chengpo Yang, Yu Li, Xuejian Beijing Inst Technol Beijing 100081 Peoples R China

ISBN: (数字)9781510630765

ISBN: (纸本)9781510630765

image segmentation has always been a key research issue in the field of computer vision. image segmentation networks that use deep learning methods require a large number of finely labeled samples, which is difficult to obtain. In this paper, we combine the focal loss function with the fully convolutional networks to improve network performance. And we collected and built a dataset contents 1500 samples with complex background. We trained the improved network with the dataset to achieve 81.55% in mean average precision and 76.13% in mean intersection over union.

关键词： image Segmentation FCN Focal Loss Convolutional Neural Network

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