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检索条件"任意字段=15th Indian Conference on Computer Vision Graphics and Image Processing"

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An IDE-agnostic system to capture reading behaviour of C++ programs using eye-gaze tracker 5

An IDE-agnostic system to capture reading behaviour of C++ p...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2015

作者： Mondal, Sayani Das, Partha Pratim Rajendra Mishra School of Engineering Entrepreneurship Indian Institute of Technology Kharagpur721302 India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur721302 India

ISBN: (纸本)9781467385640

Over 70% of software development effort is spent in software maintenance comprising bug fixes and version updates. these activities involve fast comprehension of large codebases authored by multiple developers. Developers are mostly trained to write code while in practice they often need to read code. Hence effective and efficient systems are needed to assess the code reading ability of developers. © 2015 IEEE.

关键词： Eye tracking

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VidSeg-GAN: Generative Adversarial Network for Video Object Segmentation Tasks 11

VidSeg-GAN: Generative Adversarial Network for Video Object ...

引用

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

作者： Adak, Saptakatha Das, Sukhendu Indian Inst Technol Madras Chennai Tamil Nadu India

ISBN: (纸本)9781450366151

Video object segmentation aims to segment objects in a video sequence, given some user annotation which indicates the object of interest. Although Convolutional Neural Networks (CNNs) have been used in the recent past for the purpose of foreground segmentation in videos, adversarial training methods have not been used effectively to solve this problem, in spite of its extensive use for solving many other problems in computer vision. Earlier, flow features and motion trajectories have been extensively used to capture the temporal consistency between subsequent frames to segment moving objects in videos. However, we show that our proposed framework of processing the video frames independently using a deep generative adversarial network (GAN), is able to maintain the temporal coherency across frames without the use of any explicit trajectory based information, to provide superior results. Our main contribution lies in introducing a GAN based framework along with the incorporation of an Intersection-over-Union score based novel cost function for training the model, to solve the problem of foreground object segmentation in videos. the proposed method, when evaluated on popular real-world video segmentation datasets viz. DAVIS, SegTrack-v2 and YouTube-Objects, exhibits substantial performance gain over the recent state-of-the-art methods.

关键词： Generative adversarial networks

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Variational Distribution and Experience Replay for 3D Reconstruction in a Continual Learning Framework 24

Variational Distribution and Experience Replay for 3D Recons...

引用

15th indian conference on computer vision graphics and image processing

作者： Palit, Sanchar Biswas, Sandika Indian Inst Technol Elect Engn Mumbai Maharashtra India Monash Univ Melbourne Vic Australia

ISBN: (纸本)9798400710759

Single-image 3D reconstruction is a research challenge focused on predicting 3D object shapes from single-view images, requiring all training data for all objects to be available from the start. In dynamic environments, it's impractical to gather data for all objects at once;data becomes available in phases with restrictions on past data access. therefore, the model must reconstruct new objects while retaining the ability to reconstruct previous objects without accessing prior data. Additionally, existing 3D reconstruction methods in continual learning fail to reproduce previous shapes accurately, as they are not designed to manage changing shape information in dynamic scenes. To this end, we propose a continual learning-based 3D reconstruction method. Our goal is to design a model that can accurately reconstruct previously seen classes even after training on new ones, ensuring faithful reconstruction of both current and previous objects. To achieve this, we propose using variational distribution from the latent space, which represent abstract shapes and effectively retain shape information within a simplified code structure that requires minimal memory. Additionally, saliency maps preserve object attributes, capturing both local minor shape details and the overall shape structure. We employ experience replay to leverage these saliency maps effectively. Both methods ensure that the shape is faithfully reconstructed, preserving all minor details from the previous dataset. this is vital due to resource constraints in storing extensive training data. thorough experiments show competitive results compared to established methods, both quantitatively and qualitatively.

关键词： 3D reconstruction Continual Learning Variational Inference image saliency

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A Distribution-Independent Risk Estimator for image Denoising 16

A Distribution-Independent Risk Estimator for Image Denoisin...

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

作者： Subramanian, Babu Kishore Gupta, Ashutosh Seelamantula, Chandra Sekhar Sattva MedTech Bangalore 560025 Karnataka India Indian Space Res Org Ahmadabad 380015 Gujarat India Indian Inst Sci Bangalore 560012 Karnataka India

ISBN: (纸本)9781450347532

We address the problem of image denoising for an additive white noise model without placing any restrictions on the statistical distribution of noise. We assume knowledge of only the first-and second-order noise statistics. In the recent mean-square error (MSE) minimization approaches for image denoising, one considers a particular noise distribution and derives an expression for the unbiased risk estimate of the MSE. For additive white Gaussian noise, an unbiased estimate of the MSE is Stein's unbiased risk estimate (SURE), which relies on Stein's lemma. We derive an unbiased risk estimate without using Stein's lemma or its counterparts for additive white noise model irrespective of the noise distribution. We refer to the MSE estimate as the generic risk estimate (GenRE). We demonstrate the effectiveness of GenRE using shrinkage in the undecimated Haar wavelet transform domain as the denoising function. the estimated peak-signal-to-noise-ratio (PSNR) using GenRE is typically within 1% of the PSNR obtained when optimizing with the oracle MSE. the performance of the proposed method is on par with SURE for Gaussian noise distribution, and better than SURE-based methods for other noise distributions such as uniform and Laplacian distribution in terms of both PSNR and structural similarity (SSIM).

关键词： Risk estimation undecimated wavelet transform image denoising wavelet shrinkage generic risk estimation (GenRE)

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User guided generation of corroded objects 16

User guided generation of corroded objects

引用

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

作者： Jain, Nisha Ranjan, Rohit Kalra, Prem Kumar, Subodh Indian Inst Technol Delhi New Delhi India

ISBN: (纸本)9781450347532

Rendering of corrosion often requires pain-staking modeling and texturing. On the other hand, there exist techniques for stochastic modeling of corrosion, which can automatically perform simulation and rendering under control of some user-specified parameters. Unfortunately, these parameters are non-intuitive and have a global impact. It is hard to determine the values of these parameters to obtain a desired look. For example, in real life corrosion gets influenced by both internal object-specific geometric factors, like sharp corners and curvatures, and external interventions like scratches, blemishes etc. Further, a graphics designer may want to selectively corrode areas to obtain a particular scene. We present a technique for user guided spread of corrosion. Our framework encapsulates both structural and aesthetic factors. Given the material properties and the surrounding environmental conditions of an object, we employ a physio-chemically based stochastic model to deduce the decay of different points on that object. Our system equips the user with a platform where the imperfections can be provided by either manual or systematic interference on a rendering of the three dimensional object. We demonstrate several user guided characteristic simulations encompassing varied influences including material, object characteristics and environment conditions. Our results are visually validated to understand the impact of imperfections with elapsed time.

关键词： Corrosion Simulation Rendering User guided Curvatures

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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...

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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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Exploring the Limits of VLMs: A Dataset for Evaluating Text-to-Video Generation 24

Exploring the Limits of VLMs: A Dataset for Evaluating Text-...

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15th indian conference on computer vision graphics and image processing

作者： Srivastava, Avnish Sista, Raviteja Chakrabarti, Partha P. Sheet, Debdoot Indian Inst Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9798400710759

vision language models (VLMs) integrate vision and text by learning from images and their textual descriptions for generating text from images and vice-versa. VLMs are used for various tasks such as image captioning, and visual question answering. As VLMs extend to video generation tasks, significant challenges arise. the generated videos often lack temporal consistency, and there exist issues with alignment between the generated video content and the input text. this work proposes a set of prompts for systematically evaluating VLMs on text-to-video generation focused on object coherence and temporal consistency. this dataset of prompt covers two categories: first, the complexity of prompts, and second, the number of objects and actions. the first category consists of four levels of prompt complexity: simple, mid-level, high-level, and unique and rare object prompts. the second category also consists of four levels: single object, single object in action, multiple objects, and multiple objects in action. thus, the dataset is a combination of 16 different prompt scenarios where each scenarios has 10 prompts resulting in a dataset of 160 prompts. this work explores and evaluates the outputs of three models from the family of diffusion-based VLMs for the task of text to video generation. the videos generated by the models were assessed by five participants on a 0-5 Likert scale. VLMs under study were able to generate temporally consistent videos and proper object in video in only 33.63% and 39.43% of the total evaluation scenario prompts. Most of these prompts belong to the categories of single object and simple prompts. the performance of models drops with increase in objects, actions and prompt complexity. the scenarios of actions, with rare prompts are where all models perform poorly. thus, reflecting on limitations of VLMs in generating videos. the proposed dataset can be extended to other video generation models to benchmark their performances on the basic aspects

关键词： Benchmark dataset Diffusion models Temporal consistency Video generation vision language models

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A Parallel Architecture for Successive Elimination Block Matching Algorithm

A Parallel Architecture for Successive Elimination Block Mat...

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6th indian conference on computer vision, graphics and image processing

作者： Srinivasarao, B. K. N. Chakrabarti, Indrajit Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India

ISBN: (纸本)9781424442195

this paper proposes a parallel architecture for a successive elimination algorithm (SEA), which is used in block matching motion estimation. SEA effectively eliminates the search points within the search window and thus decreases the number of matching evaluation instances that require very intensive computations compared to the standard full search algorithm (FSA). the proposed architecture for SEA decreases the time to calculate the motion vector by 57 percent compared to FSA. the performance while applying the SEA to several standard video clips has been shown to be same compared to the standard FSA. the proposed architecture uses 16 processing elements accompanied with use of intelligent data arrangement and memory configuration. A technique for reducing external memory accesses has also been developed. the proposed architecture for SEA provides an efficient solution for applications requiring real-time motion estimations. For, it serves to compute motion vectors in less amount of time while requiring almost same power and some increase in area compared to a similar architecture for implementing the full search algorithm. A register-transfer level implementation as well as simulation results on benchmark video clips are presented. Relevant design statistics on area and power for comparing between SEA and FSA implementations are also provided.

关键词： motion estimation parallel architectures successive elimination video processing processing element

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