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Layered depth inference from flow in underwater imagery 14

Layered depth inference from flow in underwater imagery

引用

9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Seemakurthy, Karthik Rajagopalan, A.N. Indian Institute of Technology Madras Chennai600036 India

ISBN: (纸本)1595930361

Motion blur is a common phenomenon in the presence of camera shake or object motion. In this paper, we deal with the challenging situation of underwater imaging. Specifically, we assume a static camera looking vertically down-wards at a scene but through a flowing water surface. the source of motion blur is due to the dynamic medium between the scene and the camera. Under reasonable assumptions, we establish that the motion blur induced by commonly observed fluid flows can serve as a valuable cue for inferring the underlying depth layers of the scene. We validate our approach with synthetic and real examples. Copyright 2014 ACM.

关键词： Flow of fluids

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MSDNet: A novel multi-stage progressive image dehazing network 21

MSDNet: A novel multi-stage progressive image dehazing netwo...

引用

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

作者： Manu, Chippy M. Sreeni, K.G. Computer Vision Lab Dept. Electronics and Communication College of Engineering Kerala Trivandrum India

ISBN: (纸本)9781450391276

this paper presents a novel algorithm to dehaze a given hazy input image using a Multi-Stage progressive image Dehazing Network (MSDNet) architecture. the proposed multi-stage strategy framework splits the challenge of image recovery into sub-tasks for a degraded image to be gradually restored. the network is trained with images from two benchmark datasets - RESIDE and NTIRE 2021. Experimental evaluations have been conducted with several hazy images from different datasets. the performance evaluation of MSDNet is done using various metrics such as Peak-Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), Feature Similarity Metric (FSIM), Visibility Index (VI), Realness Index (RI), Fog Aware Density Evaluator (FADE), Naturalness image Quality Evaluator (NIQE) and Blind/Referenceless image Spatial Quality Evaluator (BRISQUE). An average PSNR of 27.687, 36.25, 34.98, 20.52, and 20.97 have been obtained with images from ITS, SOTS indoor, SOTS outdoor, Dense-Haze, and NH-Haze datasets, respectively. the experimental results reveals the enhanced performance of MSDNet compared to other state-of-the-art techniques. © 2021 ACM.

关键词： image reconstruction

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Towards interpretable facial emotion recognition 21

Towards interpretable facial emotion recognition

引用

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

作者： Malik, Sarthak Kumar, Puneet Raman, Balasubramanian Indian Institute of Technology Roorkee India

ISBN: (纸本)9781450391276

In this paper, an interpretable deep-learning-based system has been proposed for facial emotion recognition. A novel approach to interpret the proposed system's results, Divide & Conquer based Shapley additive explanations (DnCShap), has also been developed. the proposed approach computes 'Shapley values' that denote the contribution of each image feature towards a particular prediction. the Divide and Conquer algorithm has been incorporated for computing the Shapley values in linear time instead of the exponential time taken by the existing interpretability approaches. the experiments performed on four facial emotion recognition datasets, i.e., FER-13, FERG, JAFFE, and CK+, resulted in the emotion classification accuracy of 62.62%, 99.68%, 91.97%, and 99.67%, respectively. the results show that DnCShap has consistently interpreted the highly relevant facial features for the emotion classification for various datasets. © 2021 ACM.

关键词： Emotion Recognition

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Fuzzy graph modeling for text segmentation from land map images 12

Fuzzy graph modeling for text segmentation from land map ima...

引用

8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Biswas, Samit Das, Amit Kumar Department of Computer Science and Engineering Bengal Institute of Technology Kolkata India Department of Computer Science and Technology Bengal Engineering and Science University Shibpur Howrah India

ISBN: (纸本)9781450316606

Map image text segmentation has always been one of the difficult tasks because of its variety. the texts in a map may have the myriad background consists of various intensity values, different orientations, overlapping objects, intersected lines etc. Common problems for text extraction from map images are the lack of prior knowledge of text features such as color, font, size and orientation as well as the location of the probable text regions. Extracted texts can be used as an input to OCR for recognition. this paper presents an approach for text segmentation from map images using fuzzy graph analysis. Fuzzy graph is constructed from the map image. Fuzzy similarity value between two nodes within text region will be higher than other non-text regions. Seed points are selected through the fuzzy graph analysis. these seed points lie within texts in a map image. F*seed growing algorithm is used here for text localization. the originality of this work lies in the fuzzy graph construction from map image and selection of seed points. the proposed text segmentation approach is tested on a collected dataset of paper map images (containing texts in indian languages;like Bangla, Hindi etc.) and the results are encouraging. © 2012 ACM.

关键词： Fuzzy sets

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Robust tracking of objects through turbulence 14

Robust tracking of objects through turbulence

引用

9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Apuroop, Adithya Deshmukh, Ajinkya S. Medasani, Swarup S. Image Understanding Group Uurmi System Pvt. Ltd. Hyderabad India

ISBN: (纸本)1595930361

Long range surveillance videos are often distorted by random perturbations of the optical pathways caused by atmospheric turbulence. While humans can easily perceive and separate the moving objects from the turbulent motion, it is still a challenge for vision systems. In this paper, we present a near real-time approach to detecting moving objects in the presence of turbulence. As a first step, our method learns the dynamics of the latent turbulence and extracts an approximate foreground. Statistical analysis of the foreground object properties is used to eliminate noise due to turbulence preserving only the true moving objects. Our approach also results in a stable background with minimal turbulence. We tested our method on a wide range of scenarios corrupted by various levels of turbulence. Compared with state of the art, our results are quite promising. Copyright 2014 ACM.

关键词： Target tracking

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3D face recognition using kinect 14

3D face recognition using kinect

引用

9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Ajmera, Rahul Nigam, Aditya Gupta, Phalguni Department of Computer Science and Engineering Indian Institute of Technology Kanpur India School of Computing and Electrical Engineering Indian Institute of Technology Mandi India

ISBN: (纸本)1595930361

2D Face recognition systems bound to fail on images with varying pose angles and occlusions. Many pose invariant methods are proposed in recent years but they are still not able to achieve very good accuracies. So in order to achieve a better accuracy we need to extend algorithms over 3D faces. Due to the high cost involved in acquisition of 3D faces we developed our approach for low-cost and low-quality Microsoft Kinect Sensor and propose an algorithm to produce better results than existing 2D Face recognition techniques even after compromising on the quality of the images from the sensor. Our proposed algorithm is based on modified SURF descriptors on RGB images combined with various enhancements on automatically generated training images using Depth and Color images. We compare our results obtained with State Of the Art Techniques obtained on publicly available RGB-D Face databases. Our System obtained recognition rate of 98.07% on 30° CurtinFace Database, 89.28% on EURECOM Database, 98.00% on 15° Internal Database and 81.00% on 30° Internal Database.

关键词： Face recognition

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A Novel Channel Attention Module for Integrity and Importance of Feature Map 15

A Novel Channel Attention Module for Integrity and Importanc...

引用

15th International conference on graphics and image processing, ICGIP 2023

作者： Yu, Jiahao Hu, Zhenyang Sun, Gaofei Xing, Xiaoshuang Li, Feng Changshu Institute of Technology Changshu215500 China

ISBN: (纸本)9781510675018

To enhance the performance of Convolutional Neural Networks(CNNs), channel attention mechanism is widely employed in CNNs recently. Most existing channel attention mechanisms assign weights to feature maps to capture the information, reflecting the relative importance of feature maps and thus improving the overall network performance. However, they bring higher latency for CNNs. Furthermore, many studies indicate that CNNs generate redundant feature maps during the convolution process, and they are similar and unimportant relatively. Empirically, we have observed that assigning weights between 0 and 1 to important feature maps can lead to distortion of their importance. In order to overcome the balance between performance and complexity while retaining information about important feature maps and learn the weights of unimportant feature maps effectively, we propose a novel Efficient Weight Learning Channel Attention(EWLCA) mechanism. the attention module effectively learns the weights of unimportant feature maps, which prevents distortion without adding high latency. therefore, the proposed EWLCA has stronger representational ability. In addition, we demonstrate the effectiveness of our method through experiments on CIFAR100, imageNet and VOC datasets, including comparison with other attention modules. On imageNet classification, our EWLCA achieves a Top-1 accuracy of 81.44% in ResNet34, which is 1.28% higher than the original ResNet34. © 2024 SPIE. All rights reserved.

关键词： computer vision

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Automated tree generation using grammar & particle system 21

Automated tree generation using grammar & particle system

引用

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

作者： Jain, Aryamaan Sunkara, Jyoti Shah, Ishaan Sharma, Avinash Rajan, K.S. IIIT Hyderabad India

ISBN: (纸本)9781450391276

Trees are an integral part of many outdoor scenes and are rendered in a wide variety of computer applications like computer games, movies, simulations, architectural models, AR and VR. this has led to increasing demand for realistic, intuitive, lightweight and easy to produce computer-generated trees. the current approaches at 3D tree generation using a library of trees lack variations in structure and are repetitive. this paper presents an extended grammar-based automated solution for 3D tree generation that can model a wide range of species, both Western and indian. For the foliage, we adopt a particle system approach that models the leaf, its size, orientation and changes. the proposed solution additionally allows control for individual trees, thus modelling the tree growth variations, changes in foliage across seasons, and leaf structure. this enables the generation of virtual forests with different tree compositions. In addition, a Blender add-on has been developed for use and will be released. © 2021 ACM.

关键词： computer games

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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...

引用

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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Tweening boundary curves of non-simple immersions of a disk 12

Tweening boundary curves of non-simple immersions of a disk

引用

8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Mukherjee, Uddipan Gopi, M. Department of Computer Science University of California Irvine Irvine CA 92697-3435 United States

ISBN: (纸本)9781450316606

Tweening, also known as shape morphing, is an important concept in keyframe animation wherein an initial shape is transformed smoothly into a final shape. the huge body of existing literature in the areas of shape transformations and tweening in 2D is mostly restricted to transformations between simple non-self-intersecting polygonal shapes. In this paper we introduce a robust tweening algorithm which is capable of creating smooth transformations between non-simple polygonal shapes that are immersions of a disk. All the intermediate shapes generated by our algorithm are guaranteed to be disk immersions. We achieve this by calculating high quality triangulations, from given arbitrary triangulations, of the initial and final non-simple polygons and establishing a homeomorphism between the disks of the two immersions. If the triangulations of the source and target shapes are incompatible, we change the geometry as well as the triangulation smoothly during the morphing process. © 2012 ACM.

关键词： Triangulation

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