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Digital Restoration of Cultural Heritage With Data-Driven Computing: A Survey
Arkaprabha Basu;Sandip Paul;Sreeya Ghosh;Swagatam Das;Bhabatosh Chanda;Chakravarthy Bhagvati;Václav Snasel
Digitized methodologies in the recent era contribute to various fields of automation that used to hold different interests and meanings of human life. Buildings with historical significance, cultural values, and beliefs are becoming an interdisciplinary field of interest, engaging more computer scientists nowadays. Such structures need more attention towards reconstructing their values using a fla...Show More
An Unsupervised Approach towards Varying Human Skin Tone Using Generative Adversarial Networks
2020 25th International Conference on Pattern Recognition (ICPR)
Year: 2021 | Conference Paper |
With the increasing popularity of augmented and virtual reality, retailers are now focusing more towards customer satisfaction to increase the amount of sales. Although augmented reality is not a new concept but it has gained much needed attention over the past few years. Our present work is targeted towards this direction which may be used to enhance user experience in various virtual and augment...Show More
DARecNet-BS: Unsupervised Dual-Attention Reconstruction Network for Hyperspectral Band Selection
Swalpa Kumar Roy;Sayantan Das;Tiecheng Song;Bhabatosh Chanda
Due to the existence of noise and spectral redundancies in hyperspectral images (HSIs), the band selection (BS) is highly required and can be achieved through the attention mechanism. However, existing BS methods fail to consider global interaction between the spectral information and spatial information in a nonlinear fashion. In this letter, we propose an end-to-end unsupervised dual-attention r...Show More
Incorporating Human Body Shape Guidance for Cloth Warping in Model to Person Virtual Try-on Problems
2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
The world of retail has witnessed a lot of change in the last few decades and with a size of 2.4 trillion, the fashion industry is way ahead of others in this aspect. With the blessings of technology like virtual try-on (vton), now even online shoppers can virtually try a product before buying. However, the current image-based virtual try-on methods still have a long way to go when it comes to pro...Show More
NTIRE 2019 Image Dehazing Challenge Report
Codruta O. Ancuti;Cosmin Ancuti;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Tiantong Guo;Xuelu Li;Venkateswararao Cherukuri;Vishal Monga;Hao Jiang;Siyuan Yang;Yan Liu;Xiaochao Qu;Pengfei Wan;Dongwon Park;Se Young Chun;Ming Hong;Jinying Huang;Yizi Chen;Shuxin Chen;Bomin Wang;Pablo Navarrete Michelini;Hanwen Liu;Dan Zhu;Jing Liu;Sanchayan Santra;Ranjan Mondal;Bhabatosh Chanda;Peter Morales;Tzofi Klinghoffer;Le Manh Quan;Yong-Guk Kim;Xiao Liang;Runde Li;Jinshan Pan;Jinhui Tang;Kuldeep Purohit;Maitreya Suin;A.N. Rajagopalan;Raimondo Schettini;Simone Bianco;Flavio Piccoli;C. Cusano;Luigi Celona;Sunhee Hwang;Yu Seung Ma;Hyeran Byun;Subrahmanyam Murala;Akshay Dudhane;Harsh Aulakh;Tianxiang Zheng;Tao Zhang;Weining Qin;Runnan Zhou;Shanhu Wang;Jean-Philippe Tarel;Chuansheng Wang;Jiawei Wu
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2019 | Conference Paper |
Cited by: Papers (42)
This paper reviews the second NTIRE challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The training data consists from 55 hazy images (with dense haze generated in an indoor or outdoor environment) and their corresponding ground truth (haze-free) images of the same scene. The dense haze has been produced using a professional haze/f...Show More
NTIRE 2019 Image Dehazing Challenge Report
Codruta O. Ancuti;Cosmin Ancuti;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Tiantong Guo;Xuelu Li;Venkateswararao Cherukuri;Vishal Monga;Hao Jiang;Siyuan Yang;Yan Liu;Xiaochao Qu;Pengfei Wan;Dongwon Park;Se Young Chun;Ming Hong;Jinying Huang;Yizi Chen;Shuxin Chen;Bomin Wang;Pablo Navarrete Michelini;Hanwen Liu;Dan Zhu;Jing Liu;Sanchayan Santra;Ranjan Mondal;Bhabatosh Chanda;Peter Morales;Tzofi Klinghoffer;Le Manh Quan;Yong-Guk Kim;Xiao Liang;Runde Li;Jinshan Pan;Jinhui Tang;Kuldeep Purohit;Maitreya Suin;A.N. Rajagopalan;Raimondo Schettini;Simone Bianco;Flavio Piccoli;C. Cusano;Luigi Celona;Sunhee Hwang;Yu Seung Ma;Hyeran Byun;Subrahmanyam Murala;Akshay Dudhane;Harsh Aulakh;Tianxiang Zheng;Tao Zhang;Weining Qin;Runnan Zhou;Shanhu Wang;Jean-Philippe Tarel;Chuansheng Wang;Jiawei Wu
Year: 2019 | Conference Paper |
Learning 2D Morphological Network for Old Document Image Binarization
2019 International Conference on Document Analysis and Recognition (ICDAR)
Year: 2019 | Conference Paper |
Cited by: Papers (7)
Document image binarization, especially old handwritten documents, is a very important yet challenging task. There are various bottlenecks for binarizing historical documents due to different types of degradation present imultaneously such as back impression, ink bleed through, faded colours, and wear and tear of the writing media. We consider these degradation as various types of noise in the doc...Show More
Historical documents are priceless, irreplaceable and an integral part of national and world history. Handwritten documents can be a letter, notes, journal, memoir etc. or any formally written manuscript implicating a historical event or cultural heritage. These types of documents are often fragile and degraded. They have limited timespan because of materials used, and need to be digitized, restor...Show More
Crowd behavior analysis is a significant task in the context of surveillance and crowd management. For a moving crowd, analyzing the motion pattern is very important. In this work, we present a simple scheme to categorize such crowds as structured, semi-structured and unstructured ones. The categorization is achieved based on the regularity of the motion pattern of the collection of objects (human...Show More
Novel Features for Diagnosis of Parkinson’s Disease From off-Line Archimedean Spiral Images
2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)
Year: 2019 | Conference Paper |
Cited by: Papers (10)
Parkinson's Disease (PD) is difficult to diagnose and is commonly a diagnosis of exclusion. A common early symptom of PD is handwriting and/or drawing difficulty. Most of the early systems rely on on-line handwritten / hand-drawn data which need specialized equipments to capture. Such costly systems may not be available where infrastructural facilities are limited. So we intend to devise a low cos...Show More
Multiple Pyramids Based Image Inpainting Using Local Patch Statistics and Steering Kernel Feature
Mrinmoy Ghorai;Soumitra Samanta;Sekhar Mandal;Bhabatosh Chanda
In this paper, we propose a novel multiple pyramids based image inpainting method using local patch statistics and geometric feature-based sparse representation to maintain texture consistency and structure coherence. First, we approximate each patch in the target region (region to be inpainted) by statistically dominant local candidate patches to preserve local consistency. Then each approximated...Show More
NTIRE 2018 Challenge on Image Dehazing: Methods and Results
Cosmin Ancuti;Codruta O. Ancuti;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Vishal M. Patel;He Zhang;Vishwanath A. Sindagi;Ruhao Zhao;Xiaoping Ma;Yong Qin;Limin Jia;Klaus Friedel;Sehwan Ki;Hyeonjun Sim;Jae-Seok Choi;Sooye Kim;Soomin Seo;Saehun Kim;Munchurl Kim;Ranjan Mondal;Sanchayan Santra;Bhabatosh Chanda;Jinlin Liu;Kangfu Mei;Juncheng Li;Luyao;Faming Fang;Aiwen Jiang;Xiaochao Qu;Ting Liu;Pengfei Wang;Biao Sun;Jiangfan Deng;Yuhang Zhao;Ming Hong;Jingying Huang;Yizhi Chen;Erin Chen;Xiaoli Yu;Tingting Wu;Anil Genc;Deniz Engin;Hazim Kemal Ekenel;Wenzhe Liu;Tong Tong;Gen Li;Qinquan Gao;Zhan Li;Daofa Tang;Yuling Chen;Ziying Huo;Aitor Alvarez-Gila;Adrian Galdran;Alessandro Bria;Javier Vazquez-Corral;Marcelo Bertalmo;H. Seckin Demir;Omer Faruk Adil;Huynh Xuan Phung;Xin Jin;Jiale Chen;Chaowei Shan;Zhibo Chen
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (87)
This paper reviews the first challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The challenge had 2 tracks. Track 1 employed the indoor images (using I-HAZE dataset), while Track 2 outdoor images (using O-HAZE dataset). The hazy images have been captured in presence of real haze, generated by professional haze machines. I-HAZE data...Show More
NTIRE 2018 Challenge on Image Dehazing: Methods and Results
Cosmin Ancuti;Codruta O. Ancuti;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Vishal M. Patel;He Zhang;Vishwanath A. Sindagi;Ruhao Zhao;Xiaoping Ma;Yong Qin;Limin Jia;Klaus Friedel;Sehwan Ki;Hyeonjun Sim;Jae-Seok Choi;Sooye Kim;Soomin Seo;Saehun Kim;Munchurl Kim;Ranjan Mondal;Sanchayan Santra;Bhabatosh Chanda;Jinlin Liu;Kangfu Mei;Juncheng Li;Luyao;Faming Fang;Aiwen Jiang;Xiaochao Qu;Ting Liu;Pengfei Wang;Biao Sun;Jiangfan Deng;Yuhang Zhao;Ming Hong;Jingying Huang;Yizhi Chen;Erin Chen;Xiaoli Yu;Tingting Wu;Anil Genc;Deniz Engin;Hazim Kemal Ekenel;Wenzhe Liu;Tong Tong;Gen Li;Qinquan Gao;Zhan Li;Daofa Tang;Yuling Chen;Ziying Huo;Aitor Alvarez-Gila;Adrian Galdran;Alessandro Bria;Javier Vazquez-Corral;Marcelo Bertalmo;H. Seckin Demir;Omer Faruk Adil;Huynh Xuan Phung;Xin Jin;Jiale Chen;Chaowei Shan;Zhibo Chen
Year: 2018 | Conference Paper |
Image Dehazing by Joint Estimation of Transmittance and Airlight Using Bi-Directional Consistency Loss Minimized FCN
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (19)
Very few of the existing image dehazing methods have laid stress on the accurate restoration of color from hazy images, although it is crucial for proper removal of haze. In this paper, we are proposing a Fully Convolutional Neural Network (FCN) based image dehazing method. We have designed a network that jointly estimates scene transmittance and airlight. The network is trained using a custom des...Show More
Learning a Patch Quality Comparator for Single Image Dehazing
In bad weather conditions such as fog and haze, the particles present in the atmosphere scatter incident light in different directions. As a result, the image taken under these conditions suffers from reduced visibility and lack of contrast, and as a result, it appears colorless. An image dehazing method tries to recover a haze-free portrayal of the given hazy image. In this paper, we propose a me...Show More
A Group-Based Image Inpainting Using Patch Refinement in MRF Framework
This paper presents a Markov random field (MRF)-based image inpainting algorithm using patch selection from groups of similar patches and optimal patch assignment through joint patch refinement. In patch selection, a novel group formation strategy based on subspace clustering is introduced to search the candidate patches in relevant source region only. This improves patch searching in terms of bot...Show More
Violent/Non-Violent Video Classification based on Deep Neural Network
Sounak Mondal;Soumyajit Pal;Sanjoy Kumar Saha;Bhabatosh Chanda
2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)
Year: 2017 | Conference Paper |
Cited by: Papers (3)
In the context of video surveillance, detecting violence is an important task. This work presents a deep neural network based novel video classification to label violence/non-violence classes. First of all, frame-level descriptors are constructed based on optical flow and variants of Weber Local Descriptor. A novel scheme is presented to summarize the frame-level descriptors in to the video-level ...Show More
A Complete Dual-Cross Pattern for Unconstrained Texture Classification
Swalpa K. Roy;Bhabatosh Chanda;Bidyut B. Chaudhari;Dipak K. Ghosh;Shiv Ram Dubey
2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)
Year: 2017 | Conference Paper |
Cited by: Papers (8)
In order to perform unconstrained texture classification, this paper presents a novel and computationally efficient texture descriptor called Complete Dual-Cross Pattern (CDCP), which is robust to gray-scale changes and surface rotation. To extract CDCP, at first a gray scale normalization scheme is used to reduce the illumination effect and, then CDCP feature is computed from holistic and compone...Show More
A novel palmprint recognition system using patch based filter response
2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)
Year: 2017 | Conference Paper |
Cited by: Papers (2)
A novel palmprint recognition system is proposed in this paper. The proposed system consists of pre-processing, feature extraction and classification components. During preprocessing a region-of-interest for palm texture is extracted from a given hand image and for this thresholding and mathematical morphological based operators are employed. The features extracted from the detected palmprint text...Show More
Restricted posed fish category recognition based on contour curvature
2016 1st India International Conference on Information Processing (IICIP)
Year: 2016 | Conference Paper |
Cited by: Papers (1)
A method of fish category recognition is proposed. Our novelty is in feature extraction technique, which is based on determining the curvature of the contour of fish image. The category recognition of test fish is done by a suitable linear multi-class SVM classifier. To apply our algorithm, we have developed a fish image database containing six different categories of fish. The overall accuracy of...Show More
Day/night unconstrained image dehazing
2016 23rd International Conference on Pattern Recognition (ICPR)
Year: 2016 | Conference Paper |
Cited by: Papers (19)
Images taken under fog or haze have their visibility reduced due to the existence of aerosols in the atmosphere. Image dehazing methods try to recover haze-free versions of these images by removing the effect of haze. Methods proposed till now are exclusively for daytime scene images or for night-time scene. The method we propose here can dehaze an image independent of whether it was captured duri...Show More
Patch sparsity based image inpainting using local patch statistics and steering kernel descriptor
2016 23rd International Conference on Pattern Recognition (ICPR)
Year: 2016 | Conference Paper |
Cited by: Papers (4)
This paper presents a sparse representation based image inpainting method using local patch analysis and geometric structure based feature extraction. In local patch analysis, we approximate the target region by weighted average of some local patches which are frequently occurred within a neighborhood. Local patch statistics is applied to find the most relevant neighbors for each target patch. Fur...Show More
Single image dehazing with varying atmospheric light intensity
2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)
Year: 2015 | Conference Paper |
Cited by: Papers (7)
Images taken in bad weather conditions like haze and fog suffer from loss of contrast and color shift. The object radiance is attenuated in the atmosphere and the atmospheric light is added to the scene radiance creating a veil like semi-transparent layer called airlight. The methods proposed till now assumes that the atmospheric light is constant throughout the image domain, which may not be true...Show More
Biometric recognition system for challenging faces
2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)
Year: 2015 | Conference Paper |
Cited by: Papers (7)
A novel method for face recognition system using challenging profile and frontal faces is proposed in this paper. The proposed face recognition system consists of pre-processing, feature extraction and classification components. In this work, for pre-processing, the face region is extracted using facial landmark points, obtained by the tree structured part model. During feature extraction, SIFT de...Show More
Detection and identification of logic gates from document images using mathematical morphology
2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)
Year: 2015 | Conference Paper |
Cited by: Papers (7)
Decomposition and representation of digital logic circuit drawings to a suitable vector form has widespread applications related to data compression, storage, analysis, and editing. In this paper, we propose an efficient method for segmenting and identifying logic gate symbols from the image of a circuit drawing. The segmentation procedure is based on morphological operations. After segmentation, ...Show More
Binarization of degraded handwritten documents based on morphological contrast intensification
2015 Third International Conference on Image Information Processing (ICIIP)
Year: 2015 | Conference Paper |
Cited by: Papers (8)
Degraded handwritten document images pose several challenges such as faint characters, bleeding-through and large background ink stains for binarization. Traditional binarization techniques fail to handle all these degradations and related problems efficiently. In this paper we present a hybrid binarization technique based on morphological contrast intensification to set up a global threshold for ...Show More
Topological simplification of electrical circuits by super-component analysis
2015 13th International Conference on Document Analysis and Recognition (ICDAR)
Year: 2015 | Conference Paper |
Cited by: Papers (2)
An electrical or an electronic circuit often contains special combinations of circuit symbols in the form of sub-circuits. Identification and analysis of these sub-circuits can substantially simplify the underlying topology of a circuit. This paper explains our maiden attempt towards topological simplification of a circuit by identifying two important classes of sub-circuits—one formed by symbols ...Show More
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