BookDOI
Computer Vision, Graphics, and Image Processing
Snehasis Mukherjee,Suvadip Mukherjee,Dipti Prasad Mukherjee,Jayanthi Sivaswamy,Suyash Awate,Srirangaraj Setlur,Anoop M. Namboodiri,Santanu Chaudhury +7 more
TLDR
A novel intelligent multiple watermarking techniques are proposed that has reduced the amount of data to be embedded and consequently improved perceptual quality of the watermarked image.Abstract:
Most of the past document image watermarking schemes focus on providing same level of integrity and copyright protection for information present in the source document image. However, in a document image the information contents possess various levels of sensitivity. Each level of sensitivity needs different type of protection and this demands multiple watermarking techniques. In this paper, a novel intelligent multiple watermarking techniques are proposed. The sensitivity of the information content of a block is based on the homogeneity and relative energy contribution parameters. Appropriate watermarking scheme is applied based on sensitivity classification of the block. Experiments are conducted exhaustively on documents. Experimental results reveal the accurate identification of the sensitivity of information content in the block. The results reveal that multiple watermarking schemes has reduced the amount of data to be embedded and consequently improved perceptual quality of the watermarked image.read more
Citations
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Journal ArticleDOI
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions
TL;DR: In this article, a review of the recent developments in medical image analysis with deep learning can be found and a critical review of related major aspects is provided. But the authors do not assume prior knowledge of deep learning and make a significant contribution in explaining the core deep learning concepts to the non-experts in the Medical Community.
Journal ArticleDOI
A Survey of Simple Geometric Primitives Detection Methods for Captured 3D Data
TL;DR: This survey reviews the algorithms which extract simple geometric primitives from raw dense 3D data and proposes an application‐oriented characterization, designed to help select an appropriate method based on one's application needs and compare recent approaches.
Journal ArticleDOI
Modified cuckoo search algorithm in microscopic image segmentation of hippocampus
Shouvik Chakraborty,Sankhadeep Chatterjee,Nilanjan Dey,Amira S. Ashour,Ahmed S. Ashour,Fuqian Shi,Kalyani Mali +6 more
TL;DR: A novel method for cell segmentation and identification has been proposed that incorporated marking cells in cuckoo search (CS) algorithm and experimental results established that the Kapur's entropy segmentation method based on the modified CS required the least computational time.
Journal ArticleDOI
A Morphological Hessian Based Approach for Retinal Blood Vessels Segmentation and Denoising Using Region Based Otsu Thresholding.
TL;DR: A less computational unsupervised automated technique with promising results for detection of retinal vasculature by using morphological hessian based approach and region based Otsu thresholding is proposed.
Journal ArticleDOI
A benchmark image database of isolated Bangla handwritten compound characters
TL;DR: A benchmark image database of isolated handwritten Bangla compound characters, used in the standard Bangla literature, is presented, which may facilitate research on handwritten character recognition, especially related to Bangla form document processing systems.
References
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Journal ArticleDOI
Eigenfaces for recognition
Matthew Turk,Alex Pentland +1 more
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Proceedings ArticleDOI
FaceNet: A unified embedding for face recognition and clustering
TL;DR: A system that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure offace similarity, and achieves state-of-the-art face recognition performance using only 128-bytes perface.
Proceedings ArticleDOI
FaceNet: A Unified Embedding for Face Recognition and Clustering
TL;DR: FaceNet as discussed by the authors uses a deep convolutional network trained to directly optimize the embedding itself, rather than an intermediate bottleneck layer as in previous deep learning approaches, and achieves state-of-the-art face recognition performance using only 128 bytes per face.
Journal ArticleDOI
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
Bjoern H. Menze,Andras Jakab,Stefan Bauer,Jayashree Kalpathy-Cramer,Keyvan Farahani,Justin Kirby,Yuliya Burren,N Porz,Johannes Slotboom,Roland Wiest,Levente Lanczi,Elizabeth R. Gerstner,Marc-André Weber,Tal Arbel,Brian B. Avants,Nicholas Ayache,Patricia Buendia,D. Louis Collins,Nicolas Cordier,Jason J. Corso,Antonio Criminisi,Tilak Das,Hervé Delingette,Çağatay Demiralp,Christopher R. Durst,Michel Dojat,Senan Doyle,Joana Festa,Florence Forbes,Ezequiel Geremia,Ben Glocker,Polina Golland,Xiaotao Guo,Andac Hamamci,Khan M. Iftekharuddin,Raj Jena,Nigel M. John,Ender Konukoglu,Danial Lashkari,José Mariz,Raphael Meier,Sérgio Pereira,Doina Precup,Stephen J. Price,Tammy Riklin Raviv,Syed M. S. Reza,Michael Ryan,Duygu Sarikaya,Lawrence H. Schwartz,Hoo-Chang Shin,Jamie Shotton,Carlos A. Silva,Nuno Sousa,Nagesh K. Subbanna,Gábor Székely,Thomas J. Taylor,Owen M. Thomas,Nicholas J. Tustison,Gozde Unal,Flor Vasseur,Max Wintermark,Dong Hye Ye,Liang Zhao,Binsheng Zhao,Darko Zikic,Marcel Prastawa,Mauricio Reyes,Koen Van Leemput +67 more
TL;DR: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) as mentioned in this paper was organized in conjunction with the MICCAI 2012 and 2013 conferences, and twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low and high grade glioma patients.
Proceedings ArticleDOI
Deep Learning Face Representation from Predicting 10,000 Classes
Yi Sun,Xiaogang Wang,Xiaoou Tang +2 more
TL;DR: It is argued that DeepID can be effectively learned through challenging multi-class face identification tasks, whilst they can be generalized to other tasks (such as verification) and new identities unseen in the training set.