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

共 1469 条记录，以下是251-260 订阅

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Intelligent video editing: Incorporating modern talking face generation algorithms in a video editor 21

Intelligent video editing: Incorporating modern talking face...

引用

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

作者： Gupta, Anchit Khan, Faizan Farooq Mukhopadhyay, Rudrabha Namboodiri, Vinay P. Jawahar, C.V. IIIT Telangana Hyderabad India University of Bath United Kingdom

ISBN: (纸本)9781450391276

This paper proposes a video editor based on OpenShot with several state-of-the-art facial video editing algorithms as added functionalities. Our editor provides an easy-to-use interface to apply modern lip-syncing algorithms interactively. Apart from lip-syncing, the editor also uses audio and facial re-enactment to generate expressive talking faces. The manual control improves the overall experience of video editing without missing out on the benefits of modern synthetic video generation algorithms. This control enables us to lip-sync complex dubbed movie scenes, interviews, television shows, and other visual content. Furthermore, our editor provides features that automatically translate lectures from spoken content, lip-sync of the professor, and background content like slides. While doing so, we also tackle the critical aspect of synchronizing background content with the translated speech. We qualitatively evaluate the usefulness of the proposed editor by conducting human evaluations. Our evaluations show a clear improvement in the efficiency of using human editors and an improved video generation quality. We attach demo videos with the supplementary material clearly explaining the tool and also showcasing multiple results. © 2021 ACM.

关键词： Video signal processing

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proceedings - 11th indian conference on computer vision, graphics and image processing, ICVGIP 2018

Proceedings - 11th Indian Conference on Computer Vision, Gra...

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11th indian conference on computer vision, graphics and image processing, ICVGIP 2018

ISBN: (纸本)9781450366151

The proceedings contain 80 papers. The topics discussed include: PredGAN - a deep multi-scale video prediction framework for detecting anomalies in videos;multiple kernel fisher discriminant metric learning for person re-identification;vision-based steering angle prediction by the fusion of depth and intensity deep features;zero-shot learning using graph regularized latent discriminative cross-domain triplets;perfectly secure Shamir’s secret sharing scheme for privacy preserving image processing over cloud;moving average recurrent neural network model for video-based person re-identification;activity recognition in egocentric videos using bag of key action units;and learning end-to-end autonomous driving using guided auxiliary supervision.

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Detection of Down Syndrome Using Deep Facial Recognition 3rd

Detection of Down Syndrome Using Deep Facial Recognition

引用

3rd International conference on computer vision and image processing (CVIP)

作者： Mittal, Ankush Gaur, Hardik Mishra, Manish Graph Era Deemed Univ Dehra Dun Uttarakhand India

ISBN: (纸本)9789813290884;9789813290877

Down syndrome is a genetic disorder that affects 1 in every 1000 babies born worldwide. The cases of Down syndrome have increased in the past decade. It has been observed that humans with Down syndrome generally tend to have distinct facial features. This paper proposes a model to identify people suffering from Down syndrome based on their facial features. Deep representation from different parts of the face is extracted and combined with the aid of Deep Convolutional Neural Networks. The combined representations are then classified using a Random Forest-based pipeline. The model was tested on a dataset of over 800 individuals suffering from Down syndrome and was able to achieve a recognition rate of 98.47%.

关键词： KNN CNN HOG Machine learning computer vision Random forest Healthcare Down syndrome

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Robust Detection of Defective Parts Using Pattern Matching for Online Machine vision System 3rd

Robust Detection of Defective Parts Using Pattern Matching f...

引用

3rd International conference on computer vision and image processing (CVIP)

作者： Singh, Namita Jaju, Abhishek Sharma, Sanjeev Bhabha Atom Res Ctr Div Remote Handling & Robot Mumbai 400085 Maharashtra India

ISBN: (纸本)9789813290884;9789813290877

Reliable detection of defective parts is an essential step for ensuring high-quality assurance standards. This requirement is of primary importance for online vision-based automated pellet stacking system. During nuclear fuel pin manufacturing, the image of a single row (consecutively placed components with no gap) is processed and analyzed to extract meaningful edges. Generally, these edges follow a regular pattern;however, the presence of surface cracks and chips can alter this pattern. In this paper, we formalize the detection of defective parts as a pattern matching problem. Three different patterns are proposed and evaluated for sensitivity, specificity, and accuracy. An experiment performed with the proposed pattern matching techniques show that multi-pattern matching is the most effective method for identifying defective parts.

关键词： Multi-pattern matching Aho-Corasick algorithm Finite state automata Fuel pellet Stack Defects

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HDRVideo-GAN: Deep generative HDR video reconstruction 21

HDRVideo-GAN: Deep generative HDR video reconstruction

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

作者： Anand, Mrinal Harilal, Nidhin Kumar, Chandan Raman, Shanmuganathan Indian Institute of Technology Gujarat Gandhinagar India University of Colorado BoulderCO United States

ISBN: (纸本)9781450391276

High dynamic range (HDR) videos provide a more visually realistic experience than the standard low dynamic range (LDR) videos. Despite having significant progress in HDR imaging, it is still a challenging task to capture high-quality HDR video with a conventional off-the-shelf camera. Existing approaches rely entirely on using dense optical flow between the neighboring LDR sequences to reconstruct an HDR frame. However, they lead to inconsistencies in color and exposure over time when applied to alternating exposures with noisy frames. In this paper, we propose an end-to-end GAN-based framework for HDR video reconstruction from LDR sequences with alternating exposures. We first extract clean LDR frames from noisy LDR video with alternating exposures with a denoising network trained in a self-supervised setting. Using optical flow, we then align the neighboring alternating-exposure frames to a reference frame and then reconstruct high-quality HDR frames in a complete adversarial setting. To further improve the robustness and quality of generated frames, we incorporate temporal stability-based regularization term along with content and style-based losses in the cost function during the training procedure. Experimental results demonstrate that our framework achieves state-of-the-art performance and generates superior quality HDR frames of a video over the existing methods. © 2021 ACM.

关键词： Optical flows

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Few-shot classification without forgetting of event-camera data 21

Few-shot classification without forgetting of event-camera d...

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

作者： Goyal, Anik Biswas, Soma Indian Institute of Science Karnataka Bangalore India

ISBN: (纸本)9781450391276

Event-based cameras can capture changes in brightness in the form of asynchronous events, unlike traditional cameras, which has sparked tremendous interest due to their wide range of applications. In this work, we address for the first time in literature, the task of few-shot classification of event data without forgetting the base classes on which it has been initially trained. This not only relaxes the constraint of data availability from all possible classes before the initial model is trained, but also the constraint of capturing large amounts of training data for each of the classes we want to classify. The proposed framework has three main stages: First, we train the base classifier by augmenting the original event data using a data mixing technique, so that the feature extractor can better generalize to unseen classes. We also utilize an adaptive semantic similarity between the classifier weights. This guarantees that the margin between similar classes is greater than that between dissimilar classes which in turn reduces confusion between similar classes. Second, weight imprinting is employed to learn the initial classifier weights for the new classes with few examples. Finally, we finetune the entire framework using a class-imbalance aware loss in an end-to-end manner. This is accomplished by converting the event data via a series of differentiable operations, which are then fed into our network. Extensive experiments on few-shot versions of two standard event-camera datasets justify the effectiveness of the proposed framework. We believe that this study will serve as a solid foundation for future work in this critical field. © 2021 ACM.

关键词： Cameras

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Caption-Based Region Extraction in images 3rd

Caption-Based Region Extraction in Images

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3rd International conference on computer vision and image processing (CVIP)

作者： Agrawal, Palash Yadav, Rahul Yadav, Vikas De, Kanjar Roy, Partha Pratim Indian Inst Technol Roorkee Roorkee Uttar Pradesh India

ISBN: (纸本)9789813292918;9789813292901

image captioning and object detection are some of the most growing and popular research areas in the field of computer vision. Almost every upcoming technology uses vision in some way, and with various people researching in the field of object detection, many vision problems which seemed intractable seem close to solved now. But there has been less research in identifying regions associating actions with objects. Dense image Captioning [8] is one such application, which localizes all the important regions in an image along with their description. Something very similar to normal image captioning, but repeated for every salient region in the image. In this paper, we address the aforementioned problem of detecting regions explaining the query caption. We use edge boxes for efficient object proposals, which we further filter down using a scoremeasure. The object proposals are then captioned using a pretrained Inception [19] model. The captions of each of these regions are checked for similarity with the query caption using the skip-thought vectors [9]. This proposed framework produces interesting and efficient results. We provide a quantitative measure of our experiment by taking the intersection over union (IoU) with the ground truth on the visual genome [10] dataset. By combining the above techniques in an orderly manner, we have been able to achieve encouraging results.

关键词： image captioning Region proposal network Skip thought vectors Long short-term memory Inception networks

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DeepAttent: Saliency Prediction with Deep Multi-scale Residual Network 3rd

DeepAttent: Saliency Prediction with Deep Multi-scale Residu...

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3rd International conference on computer vision and image processing (CVIP)

作者： Dwivedi, Kshitij Singh, Nitin Shanmugham, Sabari R. Kumar, Manoj Singapore Univ Technol & Design Singapore Singapore Samsung Res Inst Bengaluru India DataRobot Singapore Singapore

ISBN: (纸本)9789813292918;9789813292901

Predicting where humans look in a given scene is a well-known problem with multiple applications in consumer cameras, human-computer interaction, robotics, and gaming. With large-scale image datasets available for human fixation, it is now possible to train deep neural networks for generating a fixationmap. Human fixations are a function of both local visual features and global context. We incorporate this in a deep neural network by using global and local features of an image to predict human fixations. We sample multi-scale features of the deep residual network and introduce a new method for incorporating these multi-scale features for the end-to-end training of our network. Our model DeepAttent obtains competitive results on SALICON and iSUN datasets and outperforms state-of-the-art methods on various metrics.

关键词： Saliency Neural networks Multi-scale

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Hand Down, Face Up: Innovative Mobile Attendance System Using Face Recognition Deep Learning 3rd

Hand Down, Face Up: Innovative Mobile Attendance System Usin...

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3rd International conference on computer vision and image processing (CVIP)

作者： Agrawal, Aditi Garg, Mahak Prakash, Surya Joshi, Piyush Srivastava, Akhilesh M. Indian Inst Technol Indore Discipline Comp Sci & Engn Indore 453552 India

ISBN: (纸本)9789813292918;9789813292901

computer vision is considered as the science and technology of the machines that see. When paired with deep learning, it has limitless applications in various fields. Among various applications, face recognition is one of the most useful real-life problem-solving applications. We propose a technique that uses image enhancement and facial recognition technique to develop an innovative and timesaving class attendance system. The idea is to train a Convolutional Neural Network (CNN) using the enhanced images of the students in a certain course and then using that learned model, to recognize multiple students present in a lecture. We propose the use of deep learning model that is provided by OpenFace to train and recognize the images. This proposed solution can be easily installed in any organization, if the images of all persons to be marked this way are available with the administration. The proposed system marks attendance of students 100% accurately when captured images have faces in right pose and are not occluded.

关键词： Automated attendance management Mobile biometrics Face recognition image enhancement OpenFace Convolutional neural network (CNN)

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Linear Regression Correlation Filter: An Application to Face Recognition 3rd

Linear Regression Correlation Filter: An Application to Face...

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3rd International conference on computer vision and image processing (CVIP)

作者： Ghosh, Tiash Banerjee, Pradipta K. Future Inst Engn & Management Dept Elect Engn Kolkata India

ISBN: (纸本)9789813290884;9789813290877

This paper proposes a novel method of designing a correlation filter for frequency domain pattern recognition. The proposed correlation filter is designed with linear regression technique and termed as linear regression correlation filter. The design methodology of linear regression correlation filter is completely different from standard correlation filter design techniques. The proposed linear regression correlation filter is estimated or predicted from a linear subspace of weak classifiers. The proposed filter is evaluated on standard benchmark database and promising results are reported.

关键词： Linear regression Correlation filter Face recognition

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