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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331-340 订阅

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Eye-tracking based Relevance Feedback for Iterative Face image Retrieval 10

Eye-tracking based Relevance Feedback for Iterative Face Ima...

引用

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

作者： Sun, Mengli Wang, Jiajun Chi, Zheru Soochow Univ Suzhou Sch Elect & Informat Engn Suzhou Peoples R China Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China

ISBN: (数字)9781510628298

ISBN: (纸本)9781510628298

The performance of computer-vision based face image retrieval system declines significantly when large illumination, pose, and facial expression variations are presented. To tackle such a problem, we propose a closed loop face image retrieval system with implicit eye-tracking based feedback. It combines the state-of-the-art computer vision method Face++ with the powerful cognitive ability of human. In this system, the Face++ provides initial retrieving results corresponding to a target sample face image whose top ranked 36 images are then displayed on the screen for collecting eye-tracking data of the users. Upon mining the user's cognition results from the eye-tracking data with a deep neural network and feeding them back to the system, the system begins its new round retrieving process. Experimental results from 10 volunteers in a face database containing 1,500 images of 50 celebrities show that the performance of our system becomes better and better over iterations and finally our system achieve an average precision of higher than 0.918 and an average recall rate of higher than 0.897 upon convergence.

关键词： face image retrieval computer vision eye-tracking relevance feedback

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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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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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Multi-class Breast Tumor Region Detection for Gigapixel Pathology images Using Deep Neural Network with Rescale Approach 10

Multi-class Breast Tumor Region Detection for Gigapixel Path...

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

作者： Zheng, Xianfei Sun, Lingling Wang, Yaqi Ding, Longqian Duan, Yang Hangzhou Dianzi Univ Key Lab RF Circuits & Syst Minist Educ Hangzhou 310018 Zhejiang Peoples R China Hangzhou Dianzi Univ Zhejiang Prov Lab Integrated Circuits Design Hangzhou 310018 Zhejiang Peoples R China

ISBN: (数字)9781510628298

ISBN: (纸本)9781510628298

Breast cancer has become a worldwide disease in recent years. However, despite its growing prominence, the number of pathologists equipped to handle these cases is insufficient. computer-aided diagnosis (CAD) system contributes to reduce costs and improve efficiency of this process. A framework based on convolutional neural networks (CNNs) which could be used to automatically detect the multi-class cancer areas on gigapixel pathology slide images was proposed. Moreover, combining the slide image characters, rescale and careful data augmentation methods were used to train the patch-based model with a small dataset. To validate the developed framework, we conducted experiments with Breast Cancer Histology Challenge (BACH) dataset and obtained International conference on image Analysis and Recognition (ICIAR) score of 0.582, outperforming the second-place finisher in BACH2018, for the 4-class tissue segmentation task.

关键词： Artificial intelligence image segmentation medical image deep learning computer vision

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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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image Smoothing Which is Invariant Under General Coordinate Transformation 10

Image Smoothing Which is Invariant Under General Coordinate ...

引用

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

作者： Toriu, Takashi Osaka City Univ Centor Res & Dev Higher Educ Osaka Japan

ISBN: (数字)9781510628298

ISBN: (纸本)9781510628298

This paper proposes a method for image smoothing which is invariant under general coordinate transformation. This method is based on a position dependent metric tensor which transforms appropriately against the general coordinate transformation. Using this metric tensor, a method for invariant image smoothing against the general coordinate transformation is constructed. Effectiveness of the proposed method is confirmed by computer experiments.

关键词： image smoothing general coordinate transformation invariance metric tensor image processing

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Artificially Intelligent Game Framework Based on Facial Expression Recognition 7th

Artificially Intelligent Game Framework Based on Facial Expr...

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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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Coin Classification in a Complex Environment 10

Coin Classification in a Complex Environment

引用

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

作者： Chang, Huanhuan Wei, Zhiqiang Huang, Lei Nie, Jie Zhang, Wenfeng Wang, Lu Ocean Univ China Qingdao 266000 Shandong Peoples R China

ISBN: (数字)9781510628298

ISBN: (纸本)9781510628298

Coin classification automatically plays important roles in many applications, e.g., vending systems. Glossy reflection is one of the key factor that affect the performance of vision-based coin classification, especially in a complex environment. In this paper, we propose a novel method for robust coin classification. Contrary to the previous method, we get the glossy area first. Edge features and texture features are used in glossy area detection. Then the deep learning features are extracted based on non-glossy area instead of the whole coin image. Finally, the coin classification results are got from the VGG nets scheme. Comprehensive experiments show that our method is robust under various complex environments. The comparison experiments demonstrate that our method can outperform the state-of-the-art method. Our method achieves 95.80% accuracy.

关键词： coin classification deep learning mirror reflection computer vision glossy area

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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...

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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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