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

共 2011 条记录，以下是371-380 订阅

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Dynamic Gesture Recognition with Pose-based CNN Features derived from videos using LSTM 11

Dynamic Gesture Recognition with Pose-based CNN Features der...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Roy, Kankana Sahay, Rajiv R. Indian Inst Technol Kharagpur Dept Comp Sci & Engn Computat Vis Lab Kharagpur W Bengal India Indian Inst Technol Kharagpur Dept Elect Engn Computat Vis Lab Kharagpur W Bengal India

ISBN: (纸本)9781450366151

In this work, we address the problem of dynamic gesture recognition using a pose based video descriptor. the proposed approach takes as input video frames and extracts pose-specific image regions which are further processed by a pre-trained Convolutional Neural Network (CNN) to derive a pose-based descriptor for each frame. A Long Short Term Memory (LSTM) network is trained from scratch for dynamic gesture classification by learning long-term spatiotemporal relations among features. We also demonstrate that only using video data (RGB frames and optical flow) one can design an effective model for recognizing dynamic gestures. We utilize ChaLearn multi-modal gesture challenge dataset [13] and Cambridge hand gesture dataset [18] for evaluation of the proposed algorithm achieving an accuracy of 91.27% and 96% respectively using only RGB data.

关键词： Long short term memory pose-based CNN feature video descriptor dynamic gesture classification

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Don't See Me, Just Filter Me: Towards secure cloud based filtering using Shamir's secret sharing and POB number system 16

Don't See Me, Just Filter Me: Towards secure cloud based fil...

引用

10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Singh, Priyanka Agarwal, Nishant Raman, Balasubramanian Indian Inst Technol Roorkee Uttar Pradesh India Natl Inst Technol Kurukshetra Kurukshetra Haryana India

ISBN: (纸本)9781450347532

Cloud computing paradigm is attracting individuals as well as organizations all round the globe due to its multiple facilities of powerful resources, storage hubs, computational power and cost effective solutions. However, distributed cloud data centers pose the threat of security breaches to the privacy of the data and pose high risks. A secured image data sharing scheme over cloud domain based on the Shamir's secret sharing and permutation ordered binary number system has been proposed here. It distributes the image information into multiple random shares that reveal no information and thus can be stored securely over the distributed cloud data centers. Different image operations could be applied in the encrypted domain over the cloud servers itself. this approach reduces the threat to security which is a major hindrance in utilizing the facilities of the cloud based architecture. Only the authentic owner possessing the secret keys could restore back the original image information from the random shares. Comparative results for both the plain domain as well as the encrypted domain have been presented to validate the efficiency of using the various image filtering operations like motion blur, unsharp masking, weiner, gaussian etc in the encrypted domain over cloud.

关键词： Cloud Computing Shamir's Secret Sharing Permutation Ordered Binary Number System Encrypted Domain

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Mosaicing Deep Underwater imagery 16

Mosaicing Deep Underwater Imagery

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Purohit, Kuldeep Vasu, Subeesh Rajagopalan, A. N. Jyothi, V. Bala Naga Raju, Ramesh IIT Madras Madras Tamil Nadu India Natl Inst Ocean Technol Madras Tamil Nadu India

ISBN: (纸本)9781450347532

Numerous sources of distortions render mosaicing of underwater (UW) images an immensely challenging effort. Methods that can process conventional photographs (terrestrial/aerial) fail to deliver the desired results on UW images. Taking the sources of underwater degradations into account is central to ensuring quality performance. the work described in this paper specifically deals with the problem of mosaicing deep UW images captured by Remotely Operated Vehicles (ROVs). these images are mainly degraded by haze, color changes, and non-uniform illumination. We propose a framework that restores these images in accordance with a suitably derived degradation model. Furthermore, our scheme harnesses the scene geometry information present in each image to aid in constructing a mosaic that is free from artifacts such as local blurring, ghosting, double contouring and visible seams. Several experiments on real underwater images sequences have been carried out to demonstrate the performance of our mosaicing pipeline along with comparisons.

关键词： Mosaic dehazing illumination correction scattering underwater

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image Captioning using Convolutional Neural Networks and Recurrent Neural Network 6

Image Captioning using Convolutional Neural Networks and Rec...

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6th International conference for Convergence in Technology (I2CT)

作者： Calvin, Rachel Suresh, Shravya Xavier Inst Engn Dept Informat Technol Mumbai Maharashtra India Bangalore Inst Technol Dept Comp Sci Engn Bangalore Karnataka India

ISBN: (纸本)9781728188768

image Caption is a concept of gathering the right description of the given image on the internet use computer vision and natural language processing. the following is achieved using the Deep learning techniques called as convolution neural network and recurrent neural network. the dataset used for implementation is called as the Flickr8_k Dataset. the model uses the combination of convolution neural network which helps in extraction whereas the recurrent neural network helps in generation of the right text

关键词： Convolution Neural network (CNN) Recurrent Neural Network (RNN) Natural Language processing computer vision ResNet Long Short-Term Memory (LSTM)

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A Differential Excitation Based Rotational Invariance for Convolutional Neural Networks 16

A Differential Excitation Based Rotational Invariance for Co...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Kandi, Haribabu Mishra, Deepak Subrahmanyam, G. R. K. S. Indian Inst Space Sci & Technol Dept Avion Trivandrum 695547 Kerala India

ISBN: (纸本)9781450347532

Deep Learning (DL) methods extract complex set of features using architectures containing hierarchical set of layers. the features so learned have high discriminative power and thus represents the input to the network in the most efficient manner. Convolutional Neural Networks (CNN) are one of the deep learning architectures, extracts structural features with little invariance to smaller translational, scaling and other forms of distortions. In this paper, the learning capabilities of CNN's are explored towards providing improvement in rotational invariance to its architecture. We propose a new CNN architecture with an additional layer formed by differential excitation against distance for the improvement of rotational invariance and is called as RICNN. Moreover, we show that the proposed method is giving superior performance towards invariance to rotations against the original CNN architecture (training samples with different orientations are not considered) without disturbing the invariance to smaller translational, scaling and other forms of distortions. Different profiles like training time, testing time and accuracies are evaluated at different percentages of training data for comparing the performance of the proposed configuration with original configuration.

关键词： Differential Excitation Rotational Invariance Deep Learning Convolutional Neural Networks Back Propagation

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Prototypes based Discriminative Appearance Model for Object Tracking 16

Prototypes based Discriminative Appearance Model for Object ...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Mondal, Ajoy Ghosh, Ashish Ghosh, Susmita Indian Stat Inst Machine Intelligence Unit Kolkata India Jadavpur Univ Dept CSE Kolkata India

ISBN: (纸本)9781450347532

Occlusion is one of the major challenges for object tracking in real life scenario. Various techniques in particle filter framework have been developed to solve this problem. this framework depends on two issues: motion model and observation (likelihood) model. Due to the lack of effective observation model and efficient motion model, problem of occlusion still remains unsolvable in the tracking task. In this article, an effective observation model is proposed based on confidence (classification) score provided by the developing online prototypes based discriminative appearance model. this appearance model is constructed with the prior knowledge of two classes (object and background) and tries to discriminate between three classes such as object, background and occluded part of the object. the considered composite motion model can handle both the object motion as well as scale change of the object. the proposed update mechanism is able to adapt the appearance changes during tracking. We show a realization of the proposed method and demonstrate its performance (both quantitatively and qualitatively) with respect to state-of-the-art techniques on several challenging sequences. Analysis of the results concludes that the proposed technique can track (fully or partially) occluded objects as well as objects in various complex environments in a better way as compared to the existing ones.

关键词： Occluded object prototypes likelihood

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Complementary Tracker's Fusion for Robust Visual Tracking 16

Complementary Tracker's Fusion for Robust Visual Tracking

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Kakanuru, Sumithra Rapuru, Madan Kumar Mishra, Deepak Gorthi, Sai Subrahmanyam Indian Inst Space Sci & Technol Dept Avion Thiruvananthapuram Kerala India

ISBN: (纸本)9781450347532

though visual object tracking algorithms are capable of handling various challenging scenarios individually, still none of them is robust enough to handle all the challenges simultaneously. this paper aims at proposing a novel robust tracking algorithm by elegantly fusing the frame level detection strategy of Tracking Learning & Detection (TLD) with systematic model update strategy of Kernelized Correlation Filter tracker (KCF). the motivation behind the selection of trackers is their complementary nature in handling tracking challenges. the proposed algorithm efficiently combines the two tracking algorithms based on conservative correspondence measure with strategic model updates, which takes advantages of both and outperforms them on their short-ends by the virtue of other. the proposed fusion approach is quite general and any complimentary tracker (not just KCF) can be fused with TLD to leverage the best performance. Extensive evaluation of the proposed method based on different metrics is carried out on the datasets ALOV300++, Online Tracking Benchmark (OTB) and Visual Object Tracking (VOT2015) and demonstrated its superiority in terms of robustness and success rate by comparing with state-of-theart trackers.

关键词： Visual tracking trackers fusion correspondence measure

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Limitations with Measuring Performance of Techniques for Abnormality Localization in Surveillance Video and How to Overcome them? 16

Limitations with Measuring Performance of Techniques for Abn...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Sharma, Manoj Kumar Sarcar, Sayan Sheet, Debdoot Biswas, Prabir Kumar Indian Inst Technol Kharagpur Kharagpur W Bengal India Kochi Univ Technol Kami Kochi Japan

ISBN: (纸本)9781450347532

Now a days video surveillance is becoming more popular due to global security concerns and with the increasing need for effective monitoring of public places. the key goal of video surveillance is to detect suspicious or abnormal behavior. Various efforts have been made to detect an abnormality in the video. Further to these advancements, there is a need for better techniques for evaluation of abnormality localization in video surveillance. Existing technique mainly uses forty percent overlap rule with ground-truth data, and does not considers the extra predicted region into the computation. Existing metrics have been found to be inaccurate when more than one region is present within the frame which may or may not be correctly localized or marked as abnormal. this work attempts to bridge these limitations in existing metrics. In this paper, we investigate three existing metrics and discuss their benefits and limitations for evaluating localization of abnormality in video. We further extend the existing work by introducing penalty functions and substantiate the validity of proposed metrics with a sufficient number of instances. the presented metric are validated on data (35 different situations) for which the overlap has been computed analytically.

关键词： Video surveillance Abnormality localization Evaluation technique

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Perfectly Secure Shamir's Secret Sharing Scheme for Privacy Preserving image processing Over Cloud 11

Perfectly Secure Shamir's Secret Sharing Scheme for Privacy ...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Sadhya, Debanjan Agarwal, Akshay Raman, Balasubramanian Indian Inst Technol Roorkee Roorkee Uttar Pradesh India Manipal Inst Technol Manipal India

ISBN: (纸本)9781450366151

Cloud computing is a highly prospective paradigm in which computational resources from third parties are used for processing outsourced data. Nonetheless, the distributed architecture of this concept poses many security and privacy threats for the data owners. Shamir's secret sharing is an effective technique for distributing and processing secret images over the encrypted domain. However, it has got some critical limitations primarily due to the presence of correlated information between the image pixels. Our study addresses this problem by proposing a perfectly secure Shamir's secret sharing scheme for images. Our work builds upon the formal notion of perfect secrecy for encoding the Shamir's shares in a particular manner such that they (i.e. encoded shares) do not reveal any additional information about the original image. Importantly, we have provided both theoretical and empirical validation of our proposed approach. We have also performed several image filtering operations on the stored shares and found the resulting PSNR and NCC values to be similar in the plain and encrypted domains. Hence our work provides a privacy-preserving and secure framework for working with images over a cloud-based architecture.

关键词： Cloud Computing Shamir's Secret Sharing Perfect Secrecy Encrypted Domain

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MPMF: Multi-part Multi-feature Based Object Tracking 16

MPMF: Multi-part Multi-feature Based Object Tracking

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Bhargava, Neha Chaudhuri, Subhasis Indian Inst Technol Bombay Maharashtra India

ISBN: (纸本)9781450347532

the objective of tracking is to determine the states of an object in video frames while maintaining appearance and motion consistency. In this paper, we propose a novel multi-part multi-feature (MPMF) based object tracking which falls in the category of part-based trackers. We represent a target by a set of fixed parts (not semantic as body parts such as limbs, face) and each part is represented by a set of features. the multi-part representation of the object aids in partial occlusion handling and the multi-feature based object description increases robustness of the target representation. Instead of considering all the features of the parts, we measure tracker's confidence for a candidate by utilizing only the strong features of the candidate. this ensures that weak features do not interfere in the decision making. We also present an automatic method for selecting this subset of appropriate features for each part. To increase the tracker's speed and to reduce the number of erroneous candidates, we do not search in the whole frame. We keep the size of search area adaptive that depends on the tracker's confidence for the predicted location of the object. Additionally, it is easy to integrate more parts and features to the proposed tracker. the results on various challenging videos from VOT dataset are encouraging. MPMF outperforms state-of-the-art trackers on some of the standard challenging videos.

关键词： tracking part based algorithm feature selection

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