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检索条件"任意字段=7th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2010"

共 47 条记录，以下是1-10 订阅

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U-RME: Underwater Refined Motion Estimation in Hazy, Cluttered and Dynamic Environments 7th

U-RME: Underwater Refined Motion Estimation in Hazy, Clutter...

引用

7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Gupta, Shilpi Mukherjee, Prerana Chaudhury, Santanu Lall, Brejesh Indian Institute of Technology Delhi India Indian Institute of Information Technology Sri CityAndhra Pradesh India Indian Institute of Technology Jodhpur India

ISBN: (纸本)9789811586965

Optical Flow is a popular method of computer vision for motion estimation. In this paper, we present a refined optical flow estimation method. Central to our approach is exploiting contour information as most of the motion lies on the edges. Further, we have formulated it as sparse to dense motion estimation. Proposed method has been evaluated on challenging real life image sequences of KITTI and Fish4Knowledge database. Results demonstrate that method performs well in case of low contrast, highly cluttered background, dynamic background, occlusion and illumination change. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Optical flows

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Putting Jewellery and Accessories on a 3D Face Model Generated from 2D image 7th

Putting Jewellery and Accessories on a 3D Face Model Generat...

引用

7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Naik, Roshan Singh, Parth Kalra, Prem Indian Institute of Technology Delhi New Delhi India

ISBN: (纸本)9789811586965

Most of the work on adding accessories onto faces has been done in 2D. these methods use deep learning models and directly process the 2D images. In this paper, we perform this process in 3D space. this is done by converting 2D images into 3D face models and then placing the 3D model of the accessory on this face model. the automation has been done by using feature points and Procrustes analysis. We have also performed similar automation in Blender using the python API. the accessories include hats, glasses and indian jewellery in various colours containing various diamonds and gemstones. © 2020, Springer Nature Singapore Pte Ltd.

关键词： 3D modeling

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A Robust Pose Transformational GAN for Pose Guided Person image Synthesis 7th

A Robust Pose Transformational GAN for Pose Guided Person Im...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Karmakar, Arnab Mishra, Deepak Indian Institute of Space Science and Technology ThiruvananthapuramKerala695547 India

ISBN: (纸本)9789811586965

Generating photorealistic images of human subjects in any unseen pose have crucial applications in generating a complete appearance model of the subject. However, from a computer vision perspective, this task becomes significantly challenging due to the inability of modelling the data distribution conditioned on pose. Existing works use a complicated pose transformation model with various additional features such as foreground segmentation, human body parsing etc. to achieve robustness that leads to computational overhead. In this work, we propose a simple yet effective pose transformation GAN by utilizing the Residual Learning method without any additional feature learning to generate a given human image in any arbitrary pose. Using effective data augmentation techniques and cleverly tuning the model, we achieve robustness in terms of illumination, occlusion, distortion and scale. We present a detailed study, both qualitative and quantitative, to demonstrate the superiority of our model over the existing methods on two large datasets. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Large dataset

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Pose Estimation of UAVs Using Stereovision 7th

Pose Estimation of UAVs Using Stereovision

引用

7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Sahu, Samvram Simha, Harsha Mishra, Deepak Department of Avionics Indian Institute of Space Science and Technology Thiruvananthapuram India

ISBN: (纸本)9789811586965

Pose of an UAV has been traditionally estimated by On Board computers (OBCs) using Inertial Measurement Unit (IMU) sensor data as input. In this paper, the development of an architecture for estimating the pose of an UAV using popular computer vision based methods has bis proposed. Which consists of three sub modules, namely, image Segmentation Block (IS), Perspective Transform (PT) and Pose Determination (PD) respectively. IS block uses segmentation to detect salient points from the image where as the PT block transforms image coordinates to world coordinates, finally the PD block uses camera parameters and object dimensions to provide Attitude and Translation matrix of the UAV. the proposed approach can adjust with change in environmental parameters. the system was characterized by observing the error in the estimated yaw and estimated depth. Analysis was made on the nature and variation of error with various experimental parameters. An in depth analysis of the paper was carried out. We were finally able to devise an algorithm for estimating the pose of a body without establishing any communication with the body. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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LR-HyClassify: A Low Rank Based Framework for Classification of Degraded Hyperspectral images 7th

LR-HyClassify: A Low Rank Based Framework for Classification...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Sarkar, Sourish Sahay, Rajiv R. Department of Electrical Engineering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9789811586965

We propose a novel framework for the classification of hyperspectral data corrupted by severe degradation. We propose an optimization framework for extracting discriminative features from noisy hyperspectral data which are then passed onto a simple classifier which exploits both the spatial and spectral correlations in the data. Instead of directly extracting the features from the noisy input data, we learn a basis matrix from the underlying clean data using a combination of non-negative matrix factorization and nuclear-norm minimization. the input degraded data are then projected onto the basis vectors to obtain features. We use structural incoherence to maximize the discriminative ability of the features, which, to the best of our knowledge, is being used for the first time in hyperspectral image processing literature. We show the deterioration in the performance of classification algorithms with the progressive addition of noise while demonstrating the robustness of our algorithm. We evaluate the performance of our algorithm on three well known datasets, namely, Botswana, Salinas and indian Pines, obtaining state-of-the-art results that validate the efficacy of our algorithm. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Non-negative matrix factorization

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indian Plant Recognition in the Wild 7th

Indian Plant Recognition in the Wild

引用

7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Muthireddy, Vamsidhar Jawahar, C.V. IIIT Hyderabad Hyderabad India

ISBN: (纸本)9789811586965

Conservation efforts to protect biodiversity rely on an accurate identification process. In the case of plant identification, traditional methods used are manual, time-consuming and require a degree of expertise to operate. As a result, there is an increasing interest today for an automated plant identification system. Such a system can help in aiding plant-related education, promoting ecotourism, creating a digital heritage for plant species among many others. We propose a solution using modern convolutional neural network architectures which achieves state-of-the-art performance for plant classification in the wild. An exhaustive set of experiments are performed to classify 112 species of plants from the challenging Indic-Leaf dataset. the best performing model gives Top-1 precision of 90.08 and Top-5 precision of 96.90. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Biodiversity

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Single Storage Semi-Global Matching for Real Time Depth processing 7th

Single Storage Semi-Global Matching for Real Time Depth Proc...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Sawant, Prathmesh Temburu, Yashwant Datar, Mandar Ahmed, Imran Shriniwas, Vinayak Patkar, Sachin Department of Electrical Engineering Indian Institute of Technology Bombay Mumbai India Defence Research and Development Organization New Delhi India

ISBN: (纸本)9789811586965

Depth-map is the key computation in computer vision and robotics. One of the most popular approach is via computation of disparity-map of images obtained from Stereo Camera. Semi Global Matching (SGM) method is a popular choice for good accuracy with reasonable computation time. To use such compute-intensive algorithms for real-time applications such as for autonomous aerial vehicles, blind Aid, etc. acceleration using GPU, FPGA is necessary. In this paper, we show the design and implementation of a stereo-vision system, which is based on FPGA-implementation of More Global Matching (MGM)[7]. MGM is a variant of SGM. We use 4 paths but store a single cumulative cost value for a corresponding pixel. Our stereo-vision prototype uses Zedboard containing an ARM-based Zynq-SoC[10], ZED-stereo-camera/ELP stereo-camera/Intel RealSense D435i, and VGA for visualization. the power consumption attributed to the custom FPGA-based acceleration of disparity map computation required for depth-map is just 0.72 watt. the update rate of the disparity map is realistic 10.5 fps. © 2020, Springer Nature Singapore Pte Ltd.

关键词： System-on-chip

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A Performance Evaluation of Loss Functions for Deep Face Recognition 7th

A Performance Evaluation of Loss Functions for Deep Face Rec...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Srivastava, Yash Murali, Vaishnav Dubey, Shiv Ram Computer Vision Group Indian Institute of Information Technology Chittor Sri CityA.P. India

ISBN: (纸本)9789811586965

Face recognition is one of the most widely publicized feature in the devices today and hence represents an important problem that should be studied with the utmost priority. As per the recent trends, the Convolutional Neural Network (CNN) based approaches are highly successful in many tasks of computer vision including face recognition. the loss function is used on the top of CNN to judge the goodness of any network. In this paper, we present a performance comparison of different loss functions such as Cross-Entropy, Angular Softmax, Additive-Margin Softmax, ArcFace and Marginal Loss for face recognition. the experiments are conducted with two CNN architectures namely, ResNet and MobileNet. Two widely used face datasets namely, CASIA-Webface and MS-Celeb-1M are used for the training and benchmark Labeled Faces in the Wild (LFW) face dataset is used for the testing. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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Unsupervised Domain Adaptation for Remote Sensing images Using Metric Learning and Correlation Alignment 7th

Unsupervised Domain Adaptation for Remote Sensing Images Usi...

引用

7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Mahapatra, Aniruddha Banerjee, Biplab Indian Institute of Technology Roorkee India Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9789811586965

We address the problem of domain adaptation (DA) in the context of remote sensing (RS) image classification in this paper. By definition, the problem of unsupervised DA aims at classifying samples from a target domain which is strictly devoid of any label information while assuming that enough training data are available from a related yet non-identical (in terms of data distributions) source domain. A number of existing approaches in this regard are focused towards matching the underlying distributions of the data from both the domains in a shared latent space without explicitly considering: i) the discriminativeness of the embedding space, ii) the usefulness of a manifold distance is pulling the domains towards each other over the standard Euclidean measures. However, we argue the importance of both the aspects in learning the latent space, particularly for fine-grained classes. Our model jointly optimizes both the terms in an end-to-end fashion and the learned latent space is found to properly align the classes with high precision. Experimental results obtained on a hyper-spectral and a multi-spectral dataset confirm the superior performance of the approach over a number of techniques from the literature. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Remote sensing

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PoshakNet: Framework for Matching Dresses from Real-Life Photos Using GAN and Siamese Network 7th

PoshakNet: Framework for Matching Dresses from Real-Life Pho...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Khaund, Abhigyan thapar, Daksh Nigam, Aditya Indian Institute of Technology Mandi India

ISBN: (纸本)9789811586965

Online garment shopping has gained many customers in recent years. Describing a dress using keywords does not always yield the proper results, which in turn leads to dissatisfaction of customers. A visual search based system will be enormously beneficent to the industry. Hence, we propose a framework that can retrieve similar clothes that can be found in an image. the first task is to extract the garment from the input image (street photo). there are various challenges for that, including pose, illumination, and background clutter. We use a Generative Adversarial Network for the task of retrieving the garment that the person in the image was wearing. It has been shown that GAN can retrieve the garment very efficiently despite the challenges of street photos. Finally, a siamese based matching system takes the retrieved cloth image and matches it with the clothes in the dataset, giving us the top k matches. We take a pre-trained inception-ResNet v1 module as a siamese network (trained using triplet loss for face detection) and fine-tune it on the shopping dataset using center loss. the dataset has been collected inhouse. For training the GAN, we use the LookBook dataset [14], which is publically available. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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