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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"

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proceedings of the 2020 4th International conference on vision, image and Signal processing, ICVISP 2020

Proceedings of the 2020 4th International Conference on Visi...

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4th International conference on vision, image and Signal processing, ICVISP 2020

ISBN: (纸本)9781450389532

The proceedings contain 60 papers. The topics discussed include: generative adversarial and self-attention based fine-grained cross-media retrieval;region-attentioned network with location scoring dynamic-threshold NMS for object detection in remote sensing images;a dynamic feature weighting method for mangrove pests image classification with heavy-tailed distributions;semi-supervised learning combining 2DCNNs and video compression for action recognition;computer vision based indoor navigation for shopping complexes;CroP: color constancy benchmark dataset generator;virtual indian sign language interpreter;fast algorithm for three-dimensional cylindrical shape data reduction;and accuracy analysis of ToF and structured light with different baseline lengths in the mobile smart terminals.

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TexFusionNet: An Ensemble of Deep CNN Feature for Texture Classification 3rd

TexFusionNet: An Ensemble of Deep CNN Feature for Texture Cl...

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3rd International conference on computer vision and image processing (CVIP)

作者： Roy, Swalpa Kumar Dubey, Shiv Ram Chanda, Bhabatosh Chaudhuri, Bidyut B. Ghosh, Dipak Kumar Jalpaiguri Govt Engn Coll Jalpaiguri 735102 India Indian Inst Informat Technol Sri City 517646 Andhra Pradesh India Indian Stat Inst Kolkata 700108 India Adamas Univ Kolkata 700126 India

ISBN: (纸本)9789813292918;9789813292901

The texture classification from images is one of the important problems in pattern recognition. Several hand-designed methods have been proposed in last few decades for this problem. Nowadays, it is observed that the convolutional neural networks (CNN) perform extremely well for the classification taskmainly over object and scene images. This improved performance of CNN is caused by the availability of huge amount of images in object and scene databases such as imageNet. Still, the focus of CNN in texture classification is limited due to non-availability of large texture image data sets. Thus, the trained CNN over imagenet database is used for texture classification by fine-tuning the last few layers of the network. In this paper, a fused CNN (TexFusionNet) is proposed for texture classification by fusing the last representation layer of widely adapted AlexNet and VGG16. On the top of the fused layer, a fully connected layer is used to generate the class score. The categorical cross-entropy loss is used to generate the error during training, which is used to train the added layer after the fusion layer. The results are computed over several well-known Brodatz, CUReT, and KTH-TIPS texture data sets and compared with the state-of-the-art texture classification methods. The experimental results confirm outstanding performance of the proposed TexFusionNet architecture for texture classification.

关键词： Convolutional neural network (CNN) Deep learning Texture classification Fusion

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Optimal Number of Seed Point Selection Algorithm of Unknown Dataset 3rd

Optimal Number of Seed Point Selection Algorithm of Unknown ...

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3rd International conference on computer vision and image processing (CVIP)

作者： Chowdhury, Kuntal Chaudhuri, Debasis Pal, Arup Kumar Indian Sch Mines Dept Comp Sci & Engn Indian Inst Technol IIT ISM Dhanbad 826004 Jharkhand India DRDO Integrat Ctr Panagarh W Bengal India

ISBN: (纸本)9789813292918;9789813292901

In the present world, clustering is considered to be the most important data mining tool which is applied to huge data to help the futuristic decision-making processes. It is an unsupervised classification technique by which the data points are grouped to form the homogeneous entity. Cluster analysis is used to find out the clusters from a unlabeled data. The position of the seed points primarily affects the performances of most partitional clustering techniques. The correct number of clusters in a dataset plays an important role to judge the quality of the partitional clustering technique. Selection of initial seed of K-means clustering is a critical problem for the formation of the optimal number of the cluster with the benefit of fast stability. In this paper, we have described the optimal number of seed points selection algorithm of an unknown data based on two important internal cluster validity indices, namely, Dunn Index and Silhouette Index. Here, Shannon's entropy with the threshold value of distance has been used to calculate the position of the seed point. The algorithm is applied to different datasets and the results are comparatively better than other methods. Moreover, the comparisons have been done with other algorithms in terms of different parameters to distinguish the novelty of our proposed method.

关键词： Clustering Cluster validity indices Data mining Seed point K-means Shannons entropy

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Target Tracking Based Upon Dominant Orientation Template and Kalman Filter 3rd

Target Tracking Based Upon Dominant Orientation Template and...

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3rd International conference on computer vision and image processing (CVIP)

作者： Kumar, Nikhil Dhakrey, Puran Kandpal, Neeta Def Res & Dev Org Instruments Res & Dev Estab Dehra Dun 248008 Uttarakhand India

ISBN: (纸本)9789813292918;9789813292901

Due to the proliferation of surveillance systems in defense aswell as civilian sectors, target tracking field has always remained adorable to researchers of the computer vision community. Templatematching based approaches are a conventional way to solve target tracking problems. Ironically such approaches are computationally complex due to a requirement of repetitive arithmetic operations. A number of attempts have been made to reduce this computational overhead;Dominant Orientation Template (DOT) based template matching has given a new dimension to solve problems of such a kind using logical operations only. It has been observed that the performance of template matching based approaches degrades drastically in the presence of occlusion. DOT-based template matching is also not an exception to this. Proposed approach introduces a novel target tracking framework having severe occlusion handling capacity by integrating DOT-based template matching and Kalman Filtering. In the present approach, a small target search window is formulated around the Kalman estimated location of the considered frame;rather than exploring the whole frame. DOT-based template matching works inside this window only. A parameter known as Kalman error is defined here which is considered as the measure of occlusion. Initially, DOT calculated location is assumed as measured value but in case of occlusion when this Kalman error becomes sufficiently large the Kalman estimated location is regarded as the measurement. A customized dataset having occluded targets in FLIR as well as visible video sequences is generated to provide a robust testbed for the proposed approach.

关键词： Dominant Orientation Template (DOT) Kalman Filter Target tracking Template matching Occlusion Gradient orientation

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Detecting Face Morphing Attacks with Collaborative Representation of Steerable Features 3rd

Detecting Face Morphing Attacks with Collaborative Represent...

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3rd International conference on computer vision and image processing (CVIP)

作者： Ramachandra, Raghavendra Venkatesh, Sushma Raja, Kiran Busch, Christoph Norwegian Univ Sci & Technol NTNU Gjovik Norway

ISBN: (纸本)9789813290884;9789813290877

Passports have used face characteristics to verify and establish the identity of an individual. Face images provide high accuracy in verification and also present the opportunity of verifying the identity visually against the passport face image if the need arises. Morphed image-based identity attacks are recently shown to exploit the vulnerability of passport issuance and verification systems, where two different identities are morphed into one image to match against both images. The challenge is further increased when the properties in the digital domain are lost after the process of print and scan. This work addresses such a problem of detecting the morphing of face images such that the attacks are detected even after the print and scan process. As the first contribution of this work, we extend an existing database with 693 bonafide and 1202 morphed face images with the newly added of 579 bonafide and 1315 morphed images. We further propose a new approach based on extracting textural features across scale-space and classifying them using collaborative representation. With a set of extensive experiments and benchmarking against the traditional (non-deep-learning methods) and deep-learning methods, we illustrate the applicability of the proposed approach in detecting the morphing attacks. With an obtained Bonafide Presentation Classification Error (BPCER) of 13.12% at Attack Presentation Classification Error Rate (APCER) of 10%, the use of the proposed method can be envisioned for detecting morph attacks even after print and scan process.

关键词： Biometrics Face morphing Spoofing attacks

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Activity Recognition for Indoor Fall Detection in 360-Degree Videos Using Deep Learning Techniques 3rd

Activity Recognition for Indoor Fall Detection in 360-Degree...

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3rd International conference on computer vision and image processing (CVIP)

作者： Dhiraj Manekar, Raunak Saurav, Sumeet Maiti, Somsukla Singh, Sanjay Chaudhury, Santanu Neeraj Kumar, Ravi Chaudhary, Kamal Cent Elect Engn Res Inst CSIR Pilani Rajasthan India Samsung Res India New Delhi India

ISBN: (纸本)9789813292918;9789813292901

Human activity recognition (HAR) targets the methodologies to recognize the different actions from a sequence of observations. vision-based activity recognition is among the most popular unobtrusive technique for activity recognition. Caring for the elderly who are living alone from a remote location is one of the biggest challenges of modern human society and is an area of active research. The usage of smart homes with an increasing number of cameras in our daily environment provides the platform to use that technology for activity recognition also. The omnidirectional cameras can be utilized for fall detection activity which minimizes the requirement of multiple cameras for fall detection in an indoor living scenario. Consequently, two vision-based solutions have been proposed: one using convolutional neural networks in 3D-mode and another using a hybrid approach by combining convolutional neural networks and long short-term memory networks using 360-degree videos for human fall detection. An omnidirectional video dataset has been generated by recording a set of activities performed by different people as no such 360-degree video dataset is available in the public domain for human activity recognition. Both, the models provide fall detection accuracy of more than 90% for omnidirectional videos and can be used for developing a fall detection system for indoor health care.

关键词： Activity recognition Omnidirectional video 3D convolutional networks Long short-term memory networks Fall activity Daily activity

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Retinal-Layer Segmentation Using Dilated Convolutions 3rd

Retinal-Layer Segmentation Using Dilated Convolutions

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3rd International conference on computer vision and image processing (CVIP)

作者： Reddy, T. Guru Pradeep Ashritha, Kandiraju Sai Prajwala, T. M. Girish, G. N. Kothari, Abhishek R. Koolagudi, Shashidhar G. Rajan, Jeny Natl Inst Technol Karnataka Dept Comp Sci & Engn Surathkal India Pink City Eye & Retina Ctr Jaipur Rajasthan India

ISBN: (纸本)9789813290884;9789813290877

Visualization and analysis of Spectral Domain Optical Coherence Tomography (SD-OCT) cross-sectional scans has gained a lot of importance in the diagnosis of several retinal abnormalities. Quantitative analytic techniques like retinal thickness and volumetric analysis are performed on cross-sectional images of the retina for early diagnosis and prognosis of retinal diseases. However, segmentation of retinal layers from OCT images is a complicated task on account of certain factors like speckle noise, low image contrast and low signal-to-noise ratio amongst many others. Owing to the importance of retinal layer segmentation in diagnosing ophthalmic diseases, manual segmentation techniques have been proposed and adopted in clinical practice. Nonetheless, manual segmentations suffer from erroneous boundary detection issues. This paper thus proposes a fully automated semantic segmentation technique that uses an encoder-decoder architecture to accurately segment the prominent retinal layers.

关键词： Retinal layer segmentation Optical coherence tomography Dilated convolutions Deep learning Retina

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Comparison Between LGBP and DCLBP for Non-frontal Emotion Recognition 3rd

Comparison Between LGBP and DCLBP for Non-frontal Emotion Re...

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3rd International conference on computer vision and image processing (CVIP)

作者： Dosi, Hardik Keshri, Rahul Srivastav, Pravin Agrawal, Anupam Indian Inst Informat Technol Interact Technol & Multimedia Res Lab Allahabad Uttar Pradesh India

ISBN: (纸本)9789813292918;9789813292901

Emotion recognition has captured the attention of many researchers these days. However most of the researches have happened in emotion recognition from frontal faces. In real-world conditions, we may not always be able to capture the frontal faces. Hence, emotion recognition from non-frontal faces is the new research area. The Local Binary Patterns (LBP) is an important feature extraction technique. In our effort to find better variant of LBP for non-frontal emotion detection, we have used two possible variants namely Local Gabor Binary Pattern (LGBP) and Diagonal Crisscross Local Binary Pattern (DCLBP). The LGBP is further implemented with and without Angle Classification;which leads to total three methods of feature extraction. These three methods are used to classify the images based on facial emotion expressions. An image is divided into number of blocks. Feature vectors are created by concatenating histograms computed from each sub-block. Multi-class SVMs are used to classify angles and expressions. A comparative analysis of the three methods for non-frontal emotion recognition has been carried out and is presented in this paper. By analyzing different variants of a LBP, we can understand the importance of feature representation in non-frontal emotion recognition. Our experimental studies show that the LGBP with angle classification outperforms other two variants.

关键词： Emotion recognition Non-frontal LBP LGBP DCLBP

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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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Video Key Frame Detection Using Block Sparse Coding 3rd

Video Key Frame Detection Using Block Sparse Coding

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3rd International conference on computer vision and image processing (CVIP)

作者： GogiReddy, Hema Sundara Srinivasula Reddy Sinha, Neelam Samsung R&D Inst India Bangalore Bangalore 560037 Karnataka India IIIT Bangalore Elect Bangalore 560100 Karnataka India

ISBN: (纸本)9789813290884;9789813290877

In the advent of video data explosion, to understand the concept of the video, knowledge of representative data selection and summarization has become essential. In this regard, application of video key frame detection is becoming increasingly critical. Key frame selection of videos is the process of selecting one or more informative frames that depict the essence of the video. In state of the art, researchers have experimented with shot importance measure [1], epitome-based methods [2], and sparse coding techniques [3] to find informative frames of video. We propose block sparse coding formulation, which exploits the temporal correlation of video frames within the sparse coding framework for key frames selection. We solved the block sparse coding formulation using the Alternating Direction Method of Multipliers (ADMM) optimization. We show the comparison of results obtained with the proposed method, state-of-the-art algorithm [3] and ground truth on TRECVID 2002 [4] dataset. Comparison results show 8x run time and 6% F-score improvement compared to state of the art.

关键词： Key frame detection Video summarization Block sparse coding

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