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Color image Super Resolution in Real Noise 18

Color Image Super Resolution in Real Noise

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

作者： Srimanta Mandal Kuldeep Purohit A. N. Rajagopalan DA-IICT Gandhinagar Gujarat IIT Madras Chennai Tamil Nadu

ISBN: (纸本)9781450366151

In practice, images can contain different amounts of noise for different color channels, which is not acknowledged by existing super-resolution approaches. In this paper, we propose to super-resolve noisy color images by considering the color channels jointly. Noise statistics are blindly estimated from the input low-resolution image and are used to assign different weights to different color channels in the data cost. Implicit low-rank structure of visual data is enforced via nuclear norm minimization in association with adaptive weights, which is added as a regularization term to the cost. Additionally, multi-scale details of the image are added to the model through another regularization term that involves projection onto PCA basis, which is constructed using similar patches extracted across different scales of the input image. the results demonstrate the super-resolving capability of the approach in real scenarios.

关键词： Noise

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Low Resource Degraded Quality Document image Binarization – Domain Adaptation is the Way 22

Low Resource Degraded Quality Document Image Binarization –...

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Proceedings of the thirteenth indian conference on computer vision, graphics and image processing

作者： Ahana Kundu Ujjwal Bhattacharya Computer Vision and Pattern Recognition Indian Statistical Institute IN Computer Vision and Pattern Recognition ISI Kolkata IN

ISBN: (纸本)9781450398220

Usually, image binarization plays a crucial role in automatic analysis of degraded documents from their captured images. However, this binarization task is often difficult due to a number of reasons including the high similarity between noisy background and faded foreground pixels. the study presented here is particularly focused on binarization of images of low-resource degraded quality documents based on a set of recently collected image samples of several rare, ancient and severely degraded quality printed documents of Bangla, the 2nd and 5th most popular script of India and the world respectively. this new collection of degraded document image samples will henceforth be referred as ’ISIDDI2’ and it consists of 139 images of Bangla old document pages. Samples of ’ISIDDI’, another existing database of degraded Bangla document image samples, have also been used in the present study. A novel deep architecture based on attention UNET++ with dilated convolution operation is proposed for this binarization task. the model is optimized using human vision perceptible distance reciprocal distortion (DRD) loss. Since the binarization ground truth of samples of both ’ISIDDI2’ and ’ISIDDI’ are not available, the proposed network has been trained using samples of DIBCO and H-DIBCO datasets and an unsupervised domain adaptation (DA) module is employed for adaptation of the proposed architecture to the degradation patterns of ’ISIDDI2’ or ’ISIDDI’ samples. the proposed binarization strategy includes certain post-processing operation based on a modified k-neighbourhood based approach for recovery of broken characters. Results of our extensive experimentation show that the proposed binarization strategy has improved the binarization output of state-of-the-art methods on both ISIDDI2 and ISIDDI datasets. Also, its performance on well-known DIBCO samples is satisfactory.

关键词： UNET++ degraded document image domain adaptation image binarization attention

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LEDNet: Deep Network for Single image Haze Removal 2018

LEDNet: Deep Network for Single Image Haze Removal

引用

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

作者： Akshay Dudhane Subrahmanyam Murala Abhinav Dhall Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar India Learning Affect and Semantic Image Analysis Group Indian Institute of Technology Ropar India

ISBN: (纸本)9781450366151

Haze during the bad weather, degrades the visibility of the scene drastically. Degradation of scene visibility varies with respect to the transmission coefficient/map (Tc) of the scene. Estimation of accurate Tc is key step to reconstruct the haze free scene. Previously, local as well as global priors were proposed to estimate the Tc. We, on the other hand, propose integration of local and global approaches to learn both point level and object level Tc. the proposed local encoder decoder network (LEDNet) estimates the scene transmission map in two stages. During first stage, network estimates the point level Tc using parallel convolutional filters and spatial invariance filtering. the second stage comprises of a two level encoder-decoder architecture which anticipates the object level Tc. We also propose, local air-light estimation (LAE) algorithm, which is able to obtain the air-light component of the outdoor scene. Combination of LEDNet and LAE improves the accuracy of haze model to recover the scene radiance. Structural similarity index, mean square error and peak signal to noise ratio are used to evaluate the performance of the proposed approach for single image haze removal. Experiments on benchmark datasets show that LEDNet outperforms the existing state-of-the-art methods for single image haze removal.

关键词： Haze removal transmission map CNN

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Detecting Counterfeit indian Currency A Comparative Analysis of Machine Learning Algorithms and image processing

Detecting Counterfeit Indian Currency A Comparative Analysis...

引用

International conference on Computing and Networking Technology (ICCNT)

作者： M R V Vyshnavi N.L. Varshitha Konda V. S. Harshith Kumar Tripty Singh Mansi Sharma Suman Chatterjee Department of Computer Science Engineering Amrita School of Computing Bengaluru India Graphics Era (Deemed to be university) Dehradun India Department of Mechanical Engineering Kongju National University Cheonan India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the degree of secured currency is one of the key areas that makes its contribution in solving the problem of economic insecurity, highlights the most important. Even as it can be quite strained to actually identify which precise time frame this fake process was prepared in, complex fakes render the use of validation techniques quite challenging. this paper has aimed at displaying and evaluating the overall outcome of the different strategies of image, and the acquired classifiers in the identification of the fake FI currency. the following are the subsequent of the other operation that was applied in order to achieve the objectives of the intended work; image capture this is whereby the images to be used are taken by the appropriate camera or scanner. Others used include the Naïve Bayes, Support Vector Classifier and Gradient Descent and Artificial Neural Network in this paper. As in the conclusion, with the help of the mathematical formulation, it can be confirmed that in fact, within an experiment, Neural Networks do have a higher level of optimization, much more than an average image processing if it is provoked to identify fake notes. In other words if used and implemented with the best of the machine learning approach and best of the image processing method then solitaire for detection of the counterfeit currency can be made more dependable than all the combined methods stated above. the objective of this study is to build and compare the performance of different machine learning algorithms and image processing methodologies for the identification of fake indian currency notes.

关键词： Economics Training Machine learning algorithms Machine learning Artificial neural networks Vectors Real-time systems Bayes methods Reliability Currencies

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SpoofNET: Resolving Facial Makeup based Spoofs 2018

SpoofNET: Resolving Facial Makeup based Spoofs

引用

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

作者： Avishek Bhattacharjee Samik Banerjee Sukhendu Das Indian Institute of Technology Madras Chennai Tamil Nadu India

ISBN: (纸本)9781450366151

Face Recognition (FR) under adversarial conditions has been a big challenge for researchers in the computer vision and Machine Learning communities in the recent past. Most of state-of-the-art face recognition systems have been designed to overcome degradations in a face due to variations in pose, illumination, contrast, resolution, along with blur. However, interestingly none have addressed the fascinating issue of makeup as a spoof attack, which drastically changes the appearance of a face, making it difficult for even humans to detect and identify the impostor. In this paper, we propose a novel multi-component deep convolutional neural network (CNN) based architecture which performs the complex task of makeup removal from a disguised face, to reveal the original mugshot image of the impostor (i.e. without makeup). the proposed network also performs the hard tasks of FR on a disguised face in addition to recognition of identity and generation of the face of the spoofed target, by minimizing a novel multi-component objective function. Comparison of performance with a few recent state-of-the-art methods of FR over three benchmark datasets reveals the superiority of our proposed method for both synthesis as well as recognition (FR) tasks.

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A Fast Method for Very Low Bit-Rate Coding of Hyperspectral images 2018

A Fast Method for Very Low Bit-Rate Coding of Hyperspectral ...

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

作者： Saurabh Kumar Subhasis Chaudhuri Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9781450366151

In this work, we propose a computationally efficient compressive sensing based approach for very low bit rate lossy coding of hyperspectral (HS) image data by exploiting the redundancy inherent in this imaging modality. We divide the HS datacube into subsets of adjacent bands, each of which is encoded into a coded snapshot using a random code matrix. these coded snapshot images are encoded using the wavelet-based SPIHT compression technique. the decompression from the coded snapshots at the receiver is done using the orthogonal matching pursuit with the help of an overcomplete dictionary learned on a general purpose training dataset. We provide ample experimental results and performance comparisons to substantiate the usefulness of the proposed method. In the proposed technique the encoder is free from any decoder and it offers a significant saving in computation and yet yields a much higher compression quality.

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Perfectly Secure Shamir's Secret Sharing Scheme for Privacy Preserving image processing Over Cloud 2018

Perfectly Secure Shamir's Secret Sharing Scheme for Privacy ...

引用

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

作者： Debanjan Sadhya Akshay Agarwal Balasubramanian Raman Indian Institute of Technology Roorkee Roorkee India Manipal Institute of Technology Manipal India

ISBN: (纸本)9781450366151

Cloud computing is a highly prospective paradigm in which computational resources from third parties are used for processing outsourced data. Nonetheless, the distributed architecture of this concept poses many security and privacy threats for the data owners. Shamir's secret sharing is an effective technique for distributing and processing secret images over the encrypted domain. However, it has got some critical limitations primarily due to the presence of correlated information between the image pixels. Our study addresses this problem by proposing a perfectly secure Shamir's secret sharing scheme for images. Our work builds upon the formal notion of perfect secrecy for encoding the Shamir's shares in a particular manner such that they (i.e. encoded shares) do not reveal any additional information about the original image. Importantly, we have provided both theoretical and empirical validation of our proposed approach. We have also performed several image filtering operations on the stored shares and found the resulting PSNR and NCC values to be similar in the plain and encrypted domains. Hence our work provides a privacy-preserving and secure framework for working with images over a cloud-based architecture.

关键词： Perfect Secrecy Encrypted Domain Cloud Computing Shamir's Secret Sharing

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Cluster Loss for Person Re-Identification 18

Cluster Loss for Person Re-Identification

引用

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

作者： Doney Alex Zishan Sami Sumandeep Banerjee Subrat Panda Capillary Technologies Indian Institute of Technology Kharagpur

ISBN: (纸本)9781450366151

Person re-identification (ReID) is an important problem in computer vision, especially for video surveillance applications. the problem focuses on identifying people across different cameras or across different frames of same camera. the main challenge lies in identifying similarity of the same person against large appearance and structure variations, while differentiating between individuals. Recently, deep learning networks with triplet loss has become a common framework for person ReID. However, triplet loss focuses on obtaining correct orders on the training set. We demonstrate that it performs inferior in a clustering task. In this paper, we design a cluster loss, which can lead to the model output with a larger inter-class variation and a smaller intra-class variation compared to the triplet loss. As a result, our model has a better generalisation ability and can achieve a higher accuracy on the test set especially for a clustering task. We also introduce a batch hard training mechanism for improving the results and faster convergence of training.

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SegFast: A Faster SqueezeNet based Semantic image Segmentation Technique using Depth-wise Separable Convolutions 18

SegFast: A Faster SqueezeNet based Semantic Image Segmentati...

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

作者： Anisha Pal Shourya Jaiswal Swarnendu Ghosh Nibaran Das Mita Nasipuri Dept. of Information and Communication Technology Manipal Institute of Technology Manipal Karnataka Dept. of Electronics and Communication Engineering Manipal Institute of Technology Manipal Karnataka Dept. of Computer Science and Engineering Jadavpur University Kolkata West Bengal

ISBN: (纸本)9781450366151

Recent trends in image segmentation algorithms have shown various large scale networks with impressive performance for natural scene images. However most of the networks come with costly overheads such has large memory requirements or dependence on huge number of parallel processing units. In most cases costly graphics processing units or GPUs are used to boost computational capability. However for creating products in the real world we need to consider speed, performance as well as cost of deployment. We propose a novel "spark" module which is a combination of the "fire" module of SqueezeNet and depth-wise separable convolutions. Along with this modified SqueezeNet as an encoder we also propose the use of depth-wise separable transposed convolution for a decoder. the resultant encoder-decoder network has approximately 49 times lesser number of parameters than SegNet and almost 223 times lesser number of parameters than fully convolutional networks(FCN). Even in a CPU the network completes a forward pass for a single sample in approximately 0.39 seconds which is almost 5.1 times faster as compared to SegNet and almost 8.7 times faster compared to FCN.

关键词： Deep Learning

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VidSeg-GAN: Generative Adversarial Network for Video Object Segmentation Tasks 2018

VidSeg-GAN: Generative Adversarial Network for Video Object ...

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

作者： Saptakatha Adak Sukhendu Das Indian Institute of Technology Madras Chennai India

ISBN: (纸本)9781450366151

Video object segmentation aims to segment objects in a video sequence, given some user annotation which indicates the object of interest. Although Convolutional Neural Networks (CNNs) have been used in the recent past for the purpose of foreground segmentation in videos, adversarial training methods have not been used effectively to solve this problem, in spite of its extensive use for solving many other problems in computer vision. Earlier, flow features and motion trajectories have been extensively used to capture the temporal consistency between subsequent frames to segment moving objects in videos. However, we show that our proposed framework of processing the video frames independently using a deep generative adversarial network (GAN), is able to maintain the temporal coherency across frames without the use of any explicit trajectory based information, to provide superior results. Our main contribution lies in introducing a GAN based framework along with the incorporation of an Intersection-over-Union score based novel cost function for training the model, to solve the problem of foreground object segmentation in videos. the proposed method, when evaluated on popular real-world video segmentation datasets viz. DAVIS, SegTrack-v2 and YouTube-Objects, exhibits substantial performance gain over the recent state-of-the-art methods.

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