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A method for detecting JPEG anti-forensics 6th

A method for detecting JPEG anti-forensics

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Bhardwaj, Dinesh Kumawat, Chothmal Pankajakshan, Vinod Department of Electronics and Communication Engineering Indian Institute of Technology Roorkee RoorkeeUttarakhand India

ISBN: (纸本)9789811300196

In this paper, a new approach is proposed for the detection of JPEG anti-forensic operations. It is based on the fact that when a JPEG anti-forensic operation is applied, the values of DCT coefficients are changed. this change decreases, especially in high frequency subbands, if we apply anti-forensic operation again. Hence, we propose to calculate a normalized difference between absolute values of DCT coefficients in 28 high frequency AC-subbands of the test image and its anti-forensically modified version. Based on this normalized feature, it is possible to differentiate between uncompressed and anti-forensically modified images. Experimental results show the effectiveness of the proposed method. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Digital forensics

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Bangla Online Handwriting Recognition Using Recurrent Neural Network Architecture 16

Bangla Online Handwriting Recognition Using Recurrent Neural...

引用

10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Chakraborty, Bappaditya Mukherjee, Partha Sarathi Bhattacharya, Ujjwal Univ Calcutta AKCSIT Kolkata W Bengal India Indian Stat Inst CVPR Unit Kolkata W Bengal India

ISBN: (纸本)9781450347532

Recognition of unconstrained handwritten texts is always a difficult problem, particularly if the style of handwriting is a mixed cursive one. Among various indian scripts, only Bangla has this additional difficulty of tackling mixed cursiveness of its handwriting style in the pipeline of a method towards its automatic recognition. A few other common recognition difficulties of handwriting in an indian script include the large size of its alphabet and the extremely cursive nature of the shapes of its alphabetic characters. these are among the reasons of achieving only limited success in the study of unconstrained handwritten Bangla text recognition. Artificial Neural Network (ANN) models have often been used for solving difficult real-life pattern recognition problems. Recurrent Neural Network models (RNN) have been studied in the literature for modeling sequence data. In this study, we consider Long Short Term Memory (LSTM) network model, a useful member of this family. In fact, Bidirectional Long Short-Term Memory (BLSTM) neural networks is a special kind of RNN and have recently attracted special attention in solving sequence labelling problems. In this article, we present a BLSTM architecture based approach for unconstrained online handwritten Bangla text recognition.

关键词： RNN BLSTM-CTC Online Handwriting Recognition

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Automatic Detection of Malaria Infected RBCs from a Focus Stack of Bright Field Microscope Slide images 16

Automatic Detection of Malaria Infected RBCs from a Focus St...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Gopakumar, G. Swetha, M. Siva, Gorthi. Sai Subrahmanyam, G. R. K. S. Indian Inst Space Sci & Technol Dept ESS Trivandrum 695547 Kerala India Indian Inst Sci Dept Instrumentat & Appl Phys Bangalore 560012 Karnataka India Indian Inst Space Sci & Technol Dept Avion Trivandrum 695547 Kerala India

ISBN: (纸本)9781450347532

Malaria is a deadly infectious disease affecting red blood cells in humans due to the protozoan of type Plasmodium. In 2015, there is an estimated death toll of 438, 000 patients out of the total 214 million malaria cases reported worldwide. thus, building an accurate automatic system for detecting the malarial cases is beneficial and has huge medical value. this paper addresses the detection of Plasmodium Falciparum infected RBCs from Leishman's stained microscope slide images. Unlike the traditional way of examining a single focused image to detect the parasite, we make use of a focus stack of images collected using a bright field microscope. Rather than the conventional way of extracting the specific features we opt for using Convolutional Neural Network that can directly operate on images bypassing the need for hand-engineered features. We work with image patches at the suspected parasite location there by avoiding the need for cell segmentation. We experiment, report and compare the detection rate received when only a single focused image is used and when operated on the focus stack of images. Altogether the proposed novel approach results in highly accurate malaria detection.

关键词： Malaria Diagnosis Plasmodium Falciparum CNN

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Spectral Decomposition and Progressive Reconstruction of Scalar Volumes 16

Spectral Decomposition and Progressive Reconstruction of Sca...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Mukherjee, Uddipan Univ Calif Irvine Irvine CA 92697 USA

ISBN: (纸本)9781450347532

Modern 3D imaging technologies often generate large scale volume datasets that may be represented as 3-way tensors. these volume datasets are usually compressed for compact storage, and interactive visual analysis of the data warrants efficient decompression techniques at real time. Using well known tensor decomposition techniques like CP or Tucker decomposition the volume data can be represented by a few basis vectors, the number of such vectors, called the rank of the tensor, determining the visual quality. However, in such methods, the basis vectors used between successive ranks are completely different, thereby requiring a complete recomputation of basis vectors whenever the visual quality needs to be altered. In this work, a new progressive decomposition technique is introduced for scalar volumes wherein new basis vectors are added to the already existing lower rank basis vectors. Large scale datasets are usually divided into bricks of smaller size and each such brick is represented in a compressed form. the bases used for the different bricks are data dependent and are completely different from one another. the decomposition method introduced here uses the same basis vectors for all the bricks at all hierarchical levels of detail. the basis vectors are data independent thereby minimizing storage and allowing fast data reconstruction.

关键词： Brick

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Deep Neural Networks for Segmentation of Basal Ganglia Sub-Structures in Brain MR images 16

Deep Neural Networks for Segmentation of Basal Ganglia Sub-S...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Sethi, Akshay Sinha, Akshat Agarwal, Ayush Arora, Chetan Gupta, Anubha Indraprastha Inst Informat Technol Delhi Delhi India

ISBN: (纸本)9781450347532

Automated segmentation of brain structure in magnetic resonance imaging (MRI) scans is an important first step in diagnosis of many neurological diseases. In this paper, we focus on segmentation of the constituent sub-structures of basal ganglia (BG) region of the brain that are responsible for controlling movement and routine learning. Low contrast voxels and undefined boundaries across sub-regions of BG pose a challenge for automated segmentation. We pose the segmentation as a voxel classification problem and propose a Deep Neural Network (DNN) based classifier for BG segmentation. the DNN is able to learn distinct regional features for voxel-wise classification of BG area into four sub-regions, namely, Caudate, Putamen, Pallidum, and Accumbens. We use a public dataset with a collection of 83 T-1 weighted uniform dimension structural MRI scans of healthy and diseased (Bipolar with and without Psychosis, Schizophrenia) subjects. In order to build a robust classifier, the proposed classifier has been trained on a mixed collection of healthy and diseased MRs. We report an accuracy of above 94% (as calculated using the dice coefficient) for all the four classes of healthy and diseased dataset.

关键词： Basal Ganglia MR segmentation Deep Neural Network

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LEDNet: Deep Network for Single image Haze Removal 11

LEDNet: Deep Network for Single Image Haze Removal

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Dudhane, Akshay Murala, Subrahmanyam Dhall, Abhinav Indian Inst Technol Ropar Comp Vis & Pattern Recognit Lab Rupnagar India Indian Inst Technol Ropar Learning Affect & Semant Image Anal Grp Rupnagar 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 (T-c) of the scene. Estimation of accurate T-c is key step to reconstruct the haze free scene. Previously, local as well as global priors were proposed to estimate the T-c. We, on the other hand, propose integration of local and global approaches to learn both point level and object level T-c. the proposed local encoder decoder network (LEDNet) estimates the scene transmission map in two stages. During first stage, network estimates the point level T-c using parallel convolutional filters and spatial invariance filtering. the second stage comprises of a two level encoder-decoder architecture which anticipates the object level T-c. 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 CNN transmission map

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On-Demand Augmentation of Contour Trees 11

On-Demand Augmentation of Contour Trees

引用

11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Sharma, Mohit Natarajan, Vijay Indian Inst Sci Bangalore Karnataka India

ISBN: (纸本)9781450366151

the contour tree represents the topology of level sets of a scalar function. Nodes of the tree correspond to critical level sets and arcs of the tree represent a collection of topologically equivalent level sets connecting two critical level sets. the augmented contour tree contains degree-2 nodes on the arcs that represent regular level sets. the degree-2 nodes correspond to regular points of the scalar function and other critical points that do not affect the number of level set components. the augmented contour tree is significantly larger in size and requires more effort to compute when compared to the contour tree. Applications of the contour tree to data exploration and visualization require the augmented contour tree. Current approaches propose algorithms to compute the contour tree and the augmented contour tree from scratch. Precomputing and storing the large augmented contour tree will not be necessary if the contour tree can be augmented on-demand. this paper poses the problem of computing the augmented contour tree given a contour tree as input. Computational experiments demonstrate that the on-demand augmentation can be computed fast while resulting in good memory savings.

关键词： Data visualization

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SegFast : A Faster SqueezeNet based Semantic image Segmentation Technique using Depth-wise Separable Convolutions 11

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

引用

11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Pal, Anisha Jaiswal, Shourya Ghosh, Swarnendu Das, Nibaran Nasipuri, Mita Manipal Inst Technol Dept Informat & Commun Technol Manipal Karnataka India Manipal Inst Technol Dept Elect & Commun Engn Manipal Karnataka India Jadavpur Univ Dept Comp Sci & Engn Kolkata W Bengal India

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 image Segmentation Squeeze Net Depthwise separable convolution

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CRF based method for Curb Detection using semantic cues and stereo depth 16

CRF based method for Curb Detection using semantic cues and ...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Sodhi, Danish Upadhyay, Sarthak Bhatt, Dhaivat Krishna, K. Madhava Swarup, Shanti IIIT Hyderabad KCIS RRC Hyderabad Andhra Pradesh India Uurmi Syst Hyderabad Andhra Pradesh India

ISBN: (纸本)9781450347532

Curb detection is a critical component of driver assistance and autonomous driving systems. In this paper, we present a discriminative approach to the problem of curb detection under diverse road conditions. We define curbs as the intersection of drivable and non-drivable area which are classified using dense Conditional random fields(CRF). In our method, we fuse output of a neural network used for pixelwise semantic segmentation with depth and color information from stereo cameras. CRF fuses the output of a deep model and height information available in stereo data and provides improved segmentation. Further we introduce temporal smoothness using a weighted average of Segnet output and output from a probabilistic voxel grid as our unary potential. Finally, we show improvements over the current state of the art neural networks. Our proposed method shows accurate results over large range of variations in curb curvature and appearance, without the need of retraining the model for the specific dataset.

关键词： Stereovision Conditional Random Field Deep learning Curbs

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computer vision — ECCV 2000 1

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丛书名： Lecture Notes in computer Science

1000年

作者： David Vernon

ISBN: (数字)9783540450535

ISBN: (纸本)9783540676867

the two-volume set LNCS 1842/1843 constitutes the refereed proceedings of the 6th European conference on computer vision, ECCV 2000, held in Dublin, Ireland in June/July 2000. the 116 revised full papers presented were carefully selected from a total of 266 submissions. the two volumes offer topical sections on recognitions and modelling; stereoscopic vision; texture and shading; shape; structure from motion; image features; active, real-time, and robot vision; segmentation and grouping; vision systems engineering and evaluation; calibration; medical image understanding; and visual motion.

关键词： image processing and computer vision Pattern Recognition computer graphics Artificial Intelligence

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