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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341-350 订阅

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indian Plant Recognition in the Wild 7th

Indian Plant Recognition in the Wild

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

Unsupervised Domain Adaptation for Remote Sensing Images Usi...

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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...

引用

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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3rd Workshop on computer vision Applications, WCVA 2018, held in conjunction with the 11th indian conference on computer vision, graphics and image processing, ICVGIP 2018

3rd Workshop on Computer Vision Applications, WCVA 2018, hel...

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3rd Workshop on computer vision Applications, WCVA 2018, held in conjunction with the 11th indian conference on computer vision, graphics and image processing, ICVGIP 2018

ISBN: (纸本)9789811513862

The proceedings contain 10 papers. The special focus in this conference is on computer vision Applications. The topics include: AECNN: Autoencoder with Convolutional Neural Network for Hyperspectral image Classification;optic Disc Segmentation in Fundus images Using Anatomical Atlases with Nonrigid Registration;bird Species Classification Using Transfer Learning with Multistage Training;a Deep Learning Paradigm for Automated Face Attendance;Robust Detection of Iris Region Using an Adapted SSD Framework;dynamic image Networks for Human Fall Detection in 360-degree Videos;image Segmentation and Geometric Feature Based Approach for Fast Video Summarization of Surveillance Videos;supervised Hashing for Retrieval of Multimodal Biometric Data.

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Detector-SegMentor Network for Skin Lesion Localization and Segmentation 7th

Detector-SegMentor Network for Skin Lesion Localization and ...

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

作者： Saini, S. Gupta, D. Tiwari, A.K. Department of Electrical Engineering Indian Institute of Technology Jodhpur Jodhpur India

ISBN: (纸本)9789811586965

Melanoma is a life-threatening form of skin cancer when left undiagnosed at the early stages. Although there are more cases of non-melanoma cancer than melanoma cancer, melanoma cancer is more deadly. Early detection of melanoma is crucial for the timely diagnosis of melanoma cancer and prohibit its spread to distant body parts. Segmentation of skin lesion is a crucial step in the classification of melanoma cancer from the cancerous lesions in dermoscopic images. Manual segmentation of dermoscopic skin images is very time consuming and error-prone resulting in an urgent need for an intelligent and accurate algorithm. In this study, we propose a simple yet novel network-in-network convolution neural network (CNN) based approach for segmentation of the skin lesion. A Faster Region-based CNN (Faster RCNN) is used for preprocessing to predict bounding boxes of the lesions in the whole image which are subsequently cropped and fed into the segmentation network to obtain the lesion mask. The segmentation network is a combination of the UNet and Hourglass networks. We trained and evaluated our models on ISIC 2018 dataset and also cross-validated on PH2 and ISBI 2017 datasets. Our proposed method surpassed the state-of-the-art with Dice Similarity Coefficient of 0.915 and Accuracy 0.959 on ISIC 2018 dataset and Dice Similarity Coefficient of 0.947 and Accuracy 0.971 on ISBI 2017 dataset. © 2020, Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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Artificially Intelligent Game Framework Based on Facial Expression Recognition 7th

Artificially Intelligent Game Framework Based on Facial Expr...

引用

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

作者： Patidar, Itisha Modh, Karan Sanjay Chattopadhyay, Chiranjoy Indian Institute of Technology Jodhpur Jodhpur India

ISBN: (纸本)9789811586965

During gameplay, a player experiences emotional turmoil. In most of the cases, these emotions directly reflect the outcome of the game. Adapting game features based on players’ emotions necessitates a way to detect the current emotional state. Researchers in the area of "video game user research" has studied biometric data as a way to address the diverse characteristics of players, their individual preferences, gameplay expertise, and experiences. Identification of the player’s current state is fundamental for designing a game, which interacts with the player adaptively. In this paper, we present an artificially intelligent game framework with smart features based on automatic facial expression recognition and adaptive game features based on the gamer’s emotion. The gamer’s emotions are recognized at run-time during gameplay using Deep Convolutional Neural Networks (CNN), and the game is adapted accordingly to the emotional condition. Once identified, these features directly modify critical parameters of the underlying game engine to make the game more exciting and challenging. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Automated Detection of Mine Water Bodies Using Landsat 8 OLI/TIRS in Jharia 7th

Automated Detection of Mine Water Bodies Using Landsat 8 OLI...

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

作者： Mukherjee, Jit Mukherjee, Jayanta Chakravarty, Debashish Advance Technology Development Centre Kharagpur India Department of Computer Science and Engineering Kharagpur India Department of Mining Engineering Kharagpur India Indian Institute of Technology Kharagpur KharagpurWest Bengal India

ISBN: (纸本)9789811586965

Mine water bodies create enormous water pollution due to heavy use of water in different stages of mining. Detection and monitoring of such water bodies are necessary for environmental benefits. In the past, mine water bodies are classified along with non mine water bodies through manual intervention. The motivation of this work is to automate the process of detection of mine water bodies from Landsat 8 OLI/TIRS images. First, automated water extraction index (AWEI) is used to detect water bodies in adaptive manner. Most mine water bodies can be found near mining regions. Further, mine water bodies are detected through connected component and bounding box analysis using this cue. Coal Mine Index (CMI) can differentiate coal mine regions from other land classes. The proposed method uses the feature space of CMI to detect mine water bodies in an automated fashion with average precision, and recall of respectively. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Coal mines

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DocDescribor: Digits + Alphabets + Math Symbols - A Complete OCR for Handwritten Documents 7th

DocDescribor: Digits + Alphabets + Math Symbols - A Complete...

引用

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

作者： Aggarwal, Ridhi Jain, Hiteshi Harit, Gaurav Tiwari, Anil Kumar Department of Computer Science and Engineering Indian Institute of Technology Jodhpur JodhpurRajasthan India Department of Electrical Engineering Indian Institute of Technology Jodhpur JodhpurRajasthan India

ISBN: (纸本)9789811586965

This paper presents an Optical Character Recognition (OCR) system for documents with English text and mathematical expressions. Neural network architectures using CNN layers and/or dense layers achieve high level accuracy in character recognition task. However, these models require large amount of data to train the network, with balanced number of samples for each class. Recognition of mathematical symbols poses challenges of the imbalance and paucity of training data available. To address this issue, we pose the character recognition problem as a Distance Metric Learning problem. We propose a Siamese-CNN Network that learns discriminative features to identify if the two images in a pair contain similar or dissimilar characters. The network is then used to recognize different characters by character matching where test images are compared to sample images of any target class which may or may not be included during training. Thus our model can scale to new symbols easily. The proposed approach is invariant to author’s handwriting. Our model has been tested over images extracted from a dataset of scanned answer scripts collected by us. It is seen that our approach achieves comparable performance to other architectures using convolutional layers or dense layers while using lesser training data. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Optical character recognition

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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...

引用

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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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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