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

共 1497 条记录，以下是341-350 订阅

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Shrimps Classification Based on Multi-layer Feature Fusion 10

Shrimps Classification Based on Multi-layer Feature Fusion

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10th International conference on graphics and image processing (ICGIP)

作者： Zhang, Xiaoxue Wei, Zhiqiang Huang, Lei Ji, Xiaopeng Ocean Univ China Qingdao 266000 Shandong Peoples R China

ISBN: (数字)9781510628298

ISBN: (纸本)9781510628298

This paper aims to highlight vision related tasks centered on "shrimps". With the further study of computer vision of marine life, we show that "shrimps" has been largely neglected in comparison to other objects. In image classification, the degree of visual separation between different shrimp categories is highly uneven, the appearance of some categories in same genus is very similar, and it is more difficult to distinguish than others. Based on the classification model of traditional convolutional neural network, this paper presents a method of merging shallow and deep features extracting feature maps from different levels according to the characteristics of shrimp. In order to facilitate future shrimps-related research, we present our on-going effort in collecting a dataset in this paper, "ShrimpX", that covers not only shrimps and lobsters living in the sea, but also some freshwater shrimps. The "ShrimpX" dataset contains a variety of shrimp images crawled from image search engines. Experimental results on the "ShrimpX" dataset demonstrate that the proposed method can effectively improve the accuracy.

关键词： Convolutional Neural Network (CNN) image classification deep feature shallow feature multi-layer feature fusion

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Single image dehazing using image boundary constraint and nearest neighborhood optimization 18

Single image dehazing using image boundary constraint and ne...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Sidharth Gautam Tapan Kumar Gandhi B. K. panigrahi Indian Institute of Technology New Delhi India

ISBN: (纸本)9781450366151

Single image dehazing is an ill-posed problem that requires assumptions, priors and constraints to solve. In this paper, boundary constraint utilizing median filter has been proposed on the image radiance for the rough estimation of transmission-map in haze images. Furthermore, for the refinement of estimated transmission-map, the proposed method use a multi-dimensional feature space that uses nearest neighborhood optimization under the framework of non-local principle. Experimental results manifest that proposed method is effective and results in visually appealing dehaze images that can be useful for subsequent user or computer based application.

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Color image Denoising Based on Low-rank Tensor Train 10

Color Image Denoising Based on Low-rank Tensor Train

引用

10th International conference on graphics and image processing (ICGIP)

作者： Zhang, Yang Han, Zhi Tang, Yandong Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Beijing Peoples R China Chinese Acad Sci Inst Robot Beijing Peoples R China Chinese Acad Sci Inst Intelligent Mfg Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (数字)9781510628298

ISBN: (纸本)9781510628298

Tensor has been widely used in computer vision due to its ability to maintain spatial structure information. Owning to the well-balanced unfolding matrices, the recently proposed tensor train (TT) decomposition can make full use of information from tensors. Thereby, tensor train representation has a better performance in many fields compared to traditional methods of tensor decomposition. Inspired by the success of tensor train, in this paper, we firstly apply low-rank tensor train to recovering noisy color images. Meanwhile, we propose a novel algorithm for noise-contaminated images based on the block coordinate descent (BCD) method. The numerical experiments demonstrate that our algorithm can give a better result in the real color image both visually and numerically.

关键词： image denoising low rank tensor train tensor networks color image

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RECAL: Reuse of Established CNN Classifier Apropos Unsupervised Learning Paradigm 7th

RECAL: Reuse of Established CNN Classifier Apropos Unsupervi...

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

作者： Saha, Jayasree Mukherjee, Jayanta Indian Institute of Technology Kharagpur KharagpurWest Bengal721302 India

ISBN: (纸本)9789811586965

Recently, clustering with a deep network framework has attracted the attention of several researchers in the computer vision community. The Deep framework gains extensive attention due to its efficiency and scalability towards large-scale and high-dimensional data. In this paper, we transform supervised CNN classifier architecture into an unsupervised clustering model, called RECAL (https://***/abs/1906.06480). It jointly learns discriminative embedding subspace and cluster labels. RECAL is made up of feature extraction layers that are convolutional, followed by unsupervised classifier layers which is fully connected. A multinomial logistic regression function (softmax) stacked on top of classifier layers. We train this network using stochastic gradient descent (SGD) optimizer. However, the successful implementation of our model is revolved around the design of loss function. Our loss function uses the heuristics that true partitioning entails lower entropy, given that the class distribution is not heavily skewed. This is a trade-off between the situations of "skewed distribution" and "low-entropy". To handle this, we have proposed classification entropy and class entropy, which are the two components of our loss function. In this approach, the size of the mini-batch should be kept high. Experimental results indicate the consistent and competitive behavior of our model for clustering well-known digit, multi-viewed object, and face datasets. Moreover, we use this model to generate unsupervised patch segmentation for multi-spectral LISS-IV images. We observe that it is able to distinguish built-up areas, wetland, vegetation and water-body from the underlying scene. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Entropy

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Spatio-Temporal Analysis of indian Urban infrastructure growth using Deep Learning and 3-channel RGB satellite images 10

Spatio-Temporal Analysis of Indian Urban infrastructure grow...

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10th International conference on graphics and image processing (ICGIP)

作者： Awasthi, Naman Pandharkar, Rohit Naik, Nikhil Welingkar Inst Management REDX WeSch Lab Mumbai Maharashtra India MIT Media Lab Boston MA USA

ISBN: (数字)9781510628298

ISBN: (纸本)9781510628298

Land cover detection and classification has been an important component of Geographic Information Systems. They are used in policy planning, socio-economic analysis, cartography and Government scheme planning and evaluation. Our study uses high-resolution time-series satellite images of indian cities between years 20002017 and measures the changes in area occupied by infrastructure such as buildings and hutments during that period. To detect buildings and hutments we train a U-Net model(1) for image segmentation task and highlight the boundaries for man-made constructions i.e. buildings and hutments for each block in our New Delhi data collection. We have also provided sample contrast against the development information available on BHUVAN portal, made publicly available by indian Space Research Organization (ISRO) study. Using the time-series data of building and hutment growth, we can enable urban planners and policy makers to identify necessity of supplementary resources like government hospitals, roads, gardens, etc.

关键词： Deep Learning Spatial Analysis Time Series analysis image Segmentation U-Net Building Extraction Delhi

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Hierarchical Traffic Sign Recognition for Autonomous Driving 9th

Hierarchical Traffic Sign Recognition for Autonomous Driving

引用

9th International conference on Pattern Recognition Applications and Methods (ICPRAM)

作者： Sengar, Vartika Rameshan, Renu M. Ponkumar, Senthil Indian Inst Technol Sch Comp & Engn Mandi Himachal Prades India Continental Automot Components Pvt Ltd Bengaluru Karnataka India

ISBN: (纸本)9789897583971

Traffic Sign Recognition is very crucial for self-driving cars and Advanced Driver Assistance Systems. As the vehicle moves within a region or across regions, it encounters a variety of signs which needs to be recognized with very high accuracy. It is generally observed that traffic signs have large intra-class variability and small inter-class variability. This makes visual distinguishability between distinct classes extremely irregular. In this paper we propose a hierarchical classifier in which the number of coarse classes is automatically determined. This gives the advantage of dedicated classifiers trained for classes which are more difficult to distinguish. This is an application oriented work which involves systematic and intelligent combination of machine learning and computer vision based algorithms with required modifications for designing fully automated hierarchical classification framework for traffic sign recognition. The proposed solution is a real-time scalable machine learning based approach which can efficiently take care of wide intra-class variations without extracting desired handcrafted features beforehand. It eliminates the need for manually observing and grouping relevant features, thereby reducing human time and efforts. The classifier performance accuracy is surpassing the accuracy achieved by humans on publicly available GTSRB traffic sign dataset with lesser parameters than the existing solutions.

关键词： Hierarchical Classification Spectral Clustering Convolutional Neural Networks Machine Learning Data processing image processing Pattern Recognition

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Compressive Sensing Based Privacy for Fall Detection 7th

Compressive Sensing Based Privacy for Fall Detection

引用

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

作者： Gupta, Ronak Anand, Prashant Chaudhury, Santanu Lall, Brejesh Singh, Sanjay Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi India Indian Institute of Technology Jodhpur Jheepasani India Cognitive Computing Group CSIR-CEERI Pilani India

ISBN: (纸本)9789811586965

Fall detection holds immense importance in the field of healthcare, where timely detection allows for instant medical assistance. In this context, we propose a 3D ConvNet architecture which consists of 3D Inception modules for fall detection. The proposed architecture is a custom version of Inflated 3D (I3D) architecture, that takes compressed measurements of video sequence as spatio-temporal input, obtained from compressive sensing framework, rather than video sequence as input, as in the case of I3D convolutional neural network. This is adopted since privacy raises a huge concern for patients being monitored through these RGB cameras. The proposed framework for fall detection is flexible enough with respect to a wide variety of measurement matrices. Ten action classes randomly selected from Kinetics-400 with no fall examples, are employed to train our 3D ConvNet post compressive sensing with different types of sensing matrices on the original video clips. Our results show that 3D ConvNet performance remains unchanged with different sensing matrices. Also, the performance obtained with Kinetics pre-trained 3D ConvNet on compressively sensed fall videos from benchmark datasets is better than the state-of-the-art techniques. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Compressed sensing

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360x180 Stereoscopic Panoramas from Static Light Field Capturing 10

360x180 Stereoscopic Panoramas from Static Light Field Captu...

引用

10th International conference on graphics and image processing (ICGIP)

作者： Wang, Kai Wu, Chunhong Ren, Shaojie Xiao, Feng Liu, Yunluo Univ Sci & Technol Beijing Beijing Peoples R China Beijing FengYun Vis Technol Co Ltd Beijing Peoples R China

ISBN: (数字)9781510628298

ISBN: (纸本)9781510628298

With the increasing demand in visual experience, visual effects with good immersion and stereoscopic sense have become one of the research hotpots in computer vision and computer graphics. This paper presents a stereoscopic panoramic image generation method based on static light field capture. The work can be divided into two parts: the acquisition of the static light field and the stereoscopic panoramic image generation. The light field data is recorded by two mutually perpendicular cameras rotated around a fixed axis. The two cameras capture images with every 9 rotating degrees and a total of 40x2 images were obtained after spin a circle. Both two cameras are calibrated and the recorded images are re-projected to a spherical surface. The optical flow information was calculated from the overlapping part of the spherical projection using a block matching method based on pyramid model. As a result, the light filed information which contains texture and disparity information is acquired based on the spherical expression. On the basis of the above work, the stereoscopic panoramic images under the new viewpoints were then resampled densely based on a pixel level from the obtained light field. A Poisson blending algorithm is used afterward to fuse the image strips from different perspectives and eliminate the brightness difference between those different strips. The high quality stereoscopic panoramic images with an 8k resolution were achieved in the end. Compared with the commonly used panoramic image generating method, our method has the advantages of low cost, simple operation, and high automation. More importantly, it can generate stereoscopic panoramic images containing parallax information with the vertical 180 degrees of view.

关键词： static light field spherical projection optical flow Poisson fusion stereoscopic panoramic image

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A Chaos Based Robust and Secure image Hashing Framework 18

A Chaos Based Robust and Secure Image Hashing Framework

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Satendra Pal Singh Gaurav Bhatnagar Indian Institute of Technology Jodhpur India

ISBN: (纸本)9781450366151

In this paper, a robust image hashing framework is presented using discrete cosine transformation and singular value decomposition. Firstly, the input image is normalized using geometric moment and normalized coefficients are divided into non-overlapping blocks. The selected blocks based on a peace-wise non-linear chaotic map are transformed using discrete cosine transom followed by singular value decomposition. Then a feature matrix is constructed in reliance on Hessian matrix and the final hash values are obtained. The proposed hashing system is resilient to different content-preserving image distortions such as geometric and filtering operations. The simulated results demonstrate the efficiency proposed framework in terms of security and robustness.

关键词： Discrete cosine transformation

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Exploring Temporal Differences in 3D Convolutional Neural Networks 7th

Exploring Temporal Differences in 3D Convolutional Neural Ne...

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

作者： Kanojia, Gagan Kumawat, Sudhakar Raman, Shanmuganathan Indian Institute of Technology Gandhinagar Gandhinagar India

ISBN: (纸本)9789811586965

Traditional 3D convolutions are computationally expensive, memory intensive, and due to large number of parameters, they often tend to overfit. On the other hand, 2D CNNs are less computationally expensive and less memory intensive than 3D CNNs and have shown remarkable results in applications like image classification and object recognition. However, in previous works, it has been observed that they are inferior to 3D CNNs when applied on a spatio-temporal input. In this work, we propose a convolutional block which extracts the spatial information by performing a 2D convolution and extracts the temporal information by exploiting temporal differences, i.e., the change in the spatial information at different time instances, using simple operations of shift, subtract and add without utilizing any trainable parameters. The proposed convolutional block has same number of parameters as of a 2D convolution kernel of size, and has n times lesser parameters than an 3D convolution kernel. We show that the 3D CNNs perform better when the 3D convolution kernels are replaced by the proposed convolutional blocks. We evaluate the proposed convolutional block on UCF101 and ModelNet datasets. All the codes and pretrained models are publicly available at https://***/GaganKanojia/SSA-ResNet. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Object recognition

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