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International Conference on Document Analysis and recognition

作者： Sauradip Nag Raghavendra Ramachandra Palaiahnakote Shivakumara Umapada Pal Tong Lu Mohan Kankanhalli Department of Computer Science & Engineering Kalyani Government Engineering College Kalyani India Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Norway Faculty of Computer System and Information Technology University of Malaya Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University China Department of computer science National University of Singapore Singapore

The primary challenge in tracing the participants in sports and marathon video or images is to detect and localize the jersey/Bib number that may present in different regions of their outfit captured in cluttered environment conditions. In this work, we proposed a new framework based on detecting the human body parts such that both Jersey Bib number and text is localized reliably. To achieve this, the proposed method first detects and localize the human in a given image using Single Shot Multibox Detector (SSD). In the next step, different human body parts namely, Torso, Left Thigh, Right Thigh, that generally contain a Bib number or text region is automatically extracted. These detected individual parts are processed individually to detect the Jersey Bib number/text using a deep CNN network based on the 2-channel architecture based on the novel adaptive weighting loss function. Finally, the detected text is cropped out and fed to a CNN-RNN based deep model abbreviated as CRNN for recognizing jersey/Bib/text. Extensive experiments are carried out on the four different datasets including both bench-marking dataset and a new dataset. The performance of the proposed method is compared with the state-of-the-art methods on all four datasets that indicates the improved performance of the proposed method on all four datasets.

关键词： Text recognition Sports Image recognition Hip Thigh Torso Biological system modeling

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Non-deterministic behavior of ranking-based metrics when evaluating embeddings

arXiv

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arXiv 2018年

作者： Nicolaou, Anguelos Dey, Sounak Christlein, Vincent Maier, Andreas Karatzas, Dimosthenis Computer Vision Center Edificio O Campus UAB Bellaterra08193 Spain Pattern Recognition Lab Friedrich-Alexander-Universitat Erlangen-Nurnberg

Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis of the ambiguity quantized distances introduce and provide bounds on the effect. We demonstrate that it can have a measurable effect in empirical data in state-of-the-art systems. We also approach the phenomenon from a computer security perspective and demonstrate how someone being evaluated by a third party can exploit this ambiguity and greatly outperform a random predictor without even access to the input data. We also suggest a simple solution making the performance metrics, which rely on ranking, totally deterministic and impervious to such exploits. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

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NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results

arXiv

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arXiv 2022年

作者： Guo, Yulan Wang, Longguang Wang, Yingqian Li, Juncheng Gu, Shuhang Timofte, Radu Chen, Liangyu Chu, Xiaojie Yu, Wenqing Jin, Kai Wei, Zeqiang Guo, Sha Yang, Angulia Zhou, Xiuzhuang Guo, Guodong Xiao, Huaxin Yan, Shen Liu, Yuxiang Cai, Hanxiao Dai, Bin Peng, Feiyue Cao, Pu Nie, Yang Yang, Lu Song, Qing Hu, Xiaotao Xu, Jun Xu, Mai Jing, Junpeng Deng, Xin Xing, Qunliang Qiao, Minglang Guan, Zhenyu Guo, Wenlong Peng, Chenxu Chen, Zan Chen, Junyang Li, Hao Chen, Junbin Li, Weijie Yang, Zhijing Li, Gen Li, Aijin Sun, Lei Zhang, Dafeng Liu, Shizhuo Zhang, Jiangtao Qu, Yanyun Yang, Hao-Hsiang Huang, Zhi-Kai Chen, Wei-Ting Chang, Hua-En Kuo, Sy-Yen Liang, Qiaohui Lin, Jianxin Wang, Yijun Yin, Lianying Zhang, Rongju Zhao, Wei Xiao, Peng Xu, Rongjian Zhang, Zhilu Zuo, Wangmeng Guo, Hansheng Gao, Guangwei Zeng, Tieyong Kim, Joohyeok Kim, HyeonA Park, Eunpil Sim, Jae-Young Pi, Huicheng Zhang, Shunli Zhai, Jucai Zeng, Pengcheng Liu, Yang Ma, Chihao Huang, Yulin Chen, Junying National University of Defense Technology China The Chinese University of Hong Kong Hong Kong The University of Sydney Australia University of Würzburg ETH Zürich Switzerland MEGVII Technology China Peking University China Bigo Technology Pte. Ltd Singapore Smart Healthcare Innovation Lab Beijing University of Posts and Telecommunications China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Head of Institute of Deep Learning Baidu Research College of Systems Engineering National University of Defense Technology China College of Liberal Arts and Sciences National University of Defense Technology China Pattern Recognition and Intelligent Vision Lab Beijing University of Posts and Telecommunications China College of Computer Science Nankai University Tianjin China School of Statistics and Data Science Nankai University Tianjin Singapore Beihang University China Zhejiang University of Technology China Guangdong University of Technology China Tencent OVBU SRC-B Xiamen University China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan College of Computer Science and Electronic Engineering Hunan University China Harbin Institude of Technology China The Chinese University of Hong Kong Hong Kong Nanjing University of Posts and Telecommunications China Department of Electrical Engineering Ulsan National Institute of Science and Technology Korea Republic of Graduate School of Artificial Intelligence Ulsan National Institute of Science and Technology Korea Republic of Beijing Jiaotong University China City University of Hong Kong Hong Kong South China University of Technology China

In this paper, we summarize the 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new solutions and results. This challenge has 1 track aiming at the stereo image super-resolution problem under a standard bicubic degradation. In total, 238 participants were successfully registered, and 21 teams competed in the final testing phase. Among those participants, 20 teams successfully submitted results with PSNR (RGB) scores better than the baseline. This challenge establishes a new benchmark for stereo image SR. Copyright © 2022, The Authors. All rights reserved.

关键词： Stereo image processing

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MsEDNet: Multi-Scale Deep Saliency Learning for Moving Object Detection

MsEDNet: Multi-Scale Deep Saliency Learning for Moving Objec...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Prashant W. Patil Subrahmanyam Murala Abhinav Dhall Sachin Chaudhary Indian Institute of Technology Delhi New Delhi Delhi IN Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar INDIA Learning Afffect and Semantic Image AnalysIs (LASII) Group Indian Institute of Technology Ropar INDIA

Moving object detection (foreground and background) is an important problem in computer vision. Most of the works in this problem are based on background subtraction. However, these approaches are not able to handle scenarios with infrequent motion of object, illumination changes, shadow, camouflage etc. To overcome these, here a two stage robust and compact method for moving object detection (MOD) is proposed. In first stage, to generate the saliency map, background image is estimated using a temporal histogram technique with the help of several input frames. In the second stage, multiscale encoder-decoder network is used to learn multiscale semantic feature of estimated saliency for foreground extraction. The encoder is used to extract multi-scale features from multi-scale saliency map. The decoder part is designed to learn the mapping of low resolution multi-scale features into high resolution output frame. To observe the efficacy of proposed MsEDNet, experiments are conducted on two benchmark datasets (change detection (CDnet-2014) and Wallflower) for MOD. The precision, recall and F-measure are used as performance parameter for comparison with the existing state-of-the-art methods. Experimental results show a significant improvement in detection accuracy and decrement in execution time as compared to the state-of-the-art methods for MOD.

关键词： Feature extraction Estimation Decoding Object detection Histograms Lighting Semantics

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A deep learning architecture for limited-angle computed tomography reconstruction

A deep learning architecture for limited-angle computed tomo...

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Workshops on Image processing for the medicine, 2017

作者： Hammernik, Kerstin Würfl, Tobias Pock, Thomas Maier, Andreas Institute of Computer Graphics and Vision Graz University of Technology Austria Pattern Recognition Lab Friedrich-Alexander-University Germany Digital Safety and Security Department AIT Austrian Institute of Technology Austria

ISBN: (纸本)9783662543443

Limited-angle computed tomography suffers from missing data in the projection domain, which results in intensity inhomogeneities and streaking artifacts in the image domain. We address both challenges by a two-step deep learning architecture: First, we learn compensation weights that account for the missing data in the projection domain and correct for intensity changes. Second, we formulate an image restoration problem as a variational network to eliminate coherent streaking artifacts. We perform our experiments on realistic data and we achieve superior results for destreaking compared to state-of-the-art non-linear filtering methods in literature. We show that our approach eliminates the need for manual tuning and enables joint optimization of both correction schemes. © Springer-Verlag GmbH Deutschland 2017.

关键词： Image reconstruction

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Weighted-Gradient Features for Handwritten Line Segmentation

Weighted-Gradient Features for Handwritten Line Segmentation

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International Conference on pattern recognition

作者： Vijeta Khare Palaiahnakote Shivakumara B.J. Navya G.C. Swetha D. S. Guru Umapada Pal Tong Lu Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Studies in Computer Science University of Mysore Karnataka India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China

Text line segmentation from handwritten documents is challenging when a document image contains severe touching. In this paper, we propose a new idea based on Weighted-Gradient Features (WGF) for segmenting text lines. The proposed method finds the number of zero crossing points for every row of Canny edge image of the input one, which is considered as the weights of respective rows. The weights are then multiplied with gradient values of respective rows of the image to widen the gap between pixels in the middle portion of text and the other portions. Next, k-means clustering is performed on WGF to classify middle and other pixels of text. The method performs morphological operation to obtain word components as patches for the result of clustering. The patches in both the clusters are matched to find common patch areas, which helps in reducing touching effect. Then the proposed method checks linearity and non-linearity iteratively based on patch direction to segment text lines. The method is tested on our own and standard datasets, namely, Alaei, ICDAR 2013 robust competition on handwriting context and ICDAR 2015-HTR, to evaluate the performance. Further, the method is compared with the state of art methods to show its effectiveness and usefulness.

关键词： Image segmentation Image edge detection Linearity Writing Morphological operations Handwriting recognition Image restoration

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Adaptive Multi-Gradient Kernels for Handwritting Based Gender Identification

Adaptive Multi-Gradient Kernels for Handwritting Based Gende...

引用

International Workshop on Frontiers in Handwriting recognition

作者： B. J Navya Palaiahnakote Shivakumara G.C Shwetha Sangheeta Roy D. S. Guru Umapada Pal Tong Lu Department of Studies in Computer Science University of Mysore Karnataka India Faculty of Computer System and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China

Handwriting based Gender identification is challenging due to unconstrained handwriting and individual differences in writing. To solve this problem, we propose a new adaptive multi-gradient of Sobel kernels for extracting Adaptive Multi-Gradient Features (AMGF). For extracted text lines, the proposed method finds dominant pixels based on directional symmetry of text pixels given by AMGF. We perform histogram operation for adaptive multi-gradient values extracted corresponding to dominant pixels. The gradient values that give the highest peak in respective histograms is chosen as features. This results in feature vector having four AMGF values. The same vector are generated for successive text lines in each image to study either consistency, which is expected for females or inconsistency, which is expected for males in writing styles. The correlation is estimated based on feature vectors of the first and the successive text lines until converging or diverging criteria is met. If convergence happens, the input document is considered as female else is considered as male. The method is tested on our own dataset, which includes large variations and standard datasets, namely, QUWI, IAM-1+IAM-2 and KHATT, to demonstrate the effectiveness of the proposed method. Experimental results show that the proposed method outperforms the existing methods.

关键词： Feature extraction Kernel Image edge detection Writing Histograms Junctions Laplace equations

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Multi-Gradient Directional Features for Gender Identification

Multi-Gradient Directional Features for Gender Identificatio...

引用

International Conference on pattern recognition

作者： B.J. Navya G. C. Swetha Palaiahnakote Shivakumara Sangheeta Roy D. S. Guru Umapada Pal Tong Lu Department of Studies in Computer Science University of Mysore Karnataka India Faculty of Computer System and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China

Gender identification based on handwriting analysis has received a special attention to researchers in the field of document image analysis as it is useful for several real-time applications like forensic, population counting, etc. In this paper, we explore Multi-Gradient Directional (MGD) features, which provide direction of dominant pixels obtained by Canny edge image, and gradient direction symmetry. The proposed method further performs histogram operation for gradient angle information of dominant pixels of respective multi-gradient directional images to select angles, which contribute to the highest peak. This results in feature vectors. The process of feature vector formation continues for the segmented first, second, and third text lines in each image by male or female. Next, correlation is estimated for the vector of the first line with successive lines until converging or diverging criteria is met. If the convergence happens, a document is considered as by female, else is considered as by male. The method is tested on our own dataset, which includes images of different scripts, writers, papers, pens, and ages, and the standard database QUWI which includes Arabic and English texts, to demonstrate the efficiency of the proposed method. Comparative studies with the state of the art methods show that the proposed method is effective and useful.

关键词： Feature extraction Image edge detection Writing Junctions Histograms Face Support vector machines

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A New RGB Based Fusion for Forged IMEI Number Detection in Mobile Images

A New RGB Based Fusion for Forged IMEI Number Detection in M...

引用

International Workshop on Frontiers in Handwriting recognition

作者： Palaiahnakote Shivakumara V. Basavaraja Harsha S. Gowda D. S. Guru Umapada Pal Tong Lu Faculty of Computer System and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Studies in Computer Science University of Mysore Mysore Karnataka India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China

As technology advances to make living comfortable for people, at the same time, different crimes also increase. One such sensitive crime is creating fake International Mobile Equipment Identity (IMEI) for smart mobile devices. In this paper, we present a new fusion based method using R, G and B color components for detecting forged IMEI numbers. To the best of our knowledge, this is the first work for forged IMEI number detection in mobile images. The proposed method first finds variances for R, G and B images of a forged input image to study local changes. The variances are used to derive weights for respective color components. The same weights are convolved with respective pixel values of R, G and B components, which results in the fused image. For the fused image, the proposed method extracts features based on sparsity, the number of connected components, and the average intensity values for edge components in respective R, G and B components, which gives six features. The proposed method finds absolute difference between fused and input images, which gives feature vector containing six difference values. The proposed method constructs templates based on samples chosen randomly. Feature vectors are compared with the templates for detecting forged IMEI numbers. Experiments are conducted on our own dataset and standard datasets to evaluate the proposed method. Furthermore, comparative studies with the related existing methods show that the proposed method outperforms the existing methods.

关键词： Feature extraction Image color analysis Image edge detection Forgery Software Printers Image segmentation

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How many annotators do we need? - A study on the influence of inter-observer variability on the reliability of automatic mitotic figure assessment

arXiv

引用

arXiv 2020年

作者： Wilm, Frauke Bertram, Christof A. Marzahl, Christian Bartel, Alexander Donovan, Taryn A. Assenmacher, Charles-Antoine Becker, Kathrin Bennett, Mark Corner, Sarah Cossic, Brieuc Denk, Daniela Dettwiler, Martina Gonzalez, Beatriz Garcia Gurtner, Corinne Heier, Annabelle Lehmbecker, Annika Merz, Sophie Plog, Stephanie Schmidt, Anja Sebastian, Franziska Smedley, Rebecca C. Tecilla, Marco Thaiwong, Tuddow Breininger, Katharina Kiupel, Matti Maier, Andreas Klopfleisch, Robert Aubreville, Marc Pattern Recognition Lab Computer Sciences Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Institute of Veterinary Pathology Freie Universität Berlin Germany Institute for Veterinary Epidemiology and Biostatistics Freie Universität Berlin Department of Anatomic Pathology Animal Medical Center New York United States Department of Pathobiology University of Pennsylvania Philadelphia United States Department of Pathology University of Veterinary Medicine Hannover Germany Synlab's VPG Histology Bristol United Kingdom Veterinary Diagnostic Laboratory Michigan State University Lansing United States Idorsia Pharmaceuticals Ltd Switzerland International Zoo Veterinary Group Keighley United Kingdom Institute of Animal Pathology Vetsuisse Faculty University of Bern Switzerland IDEXX Vet Med Labor GmbH Kornwestheim Germany F. Hoffmann-La Roche Ltd Basel Switzerland Technische Hochschule Ingolstadt Ingoldstadt Germany

Density of mitotic figures in histologic sections is a prognostically relevant characteristic for many tumours. Due to high inter-pathologist variability, deep learning-based algorithms are a promising solution to improve tumour prognostication. Pathologists are the gold standard for database development, however, labelling errors may hamper development of accurate algorithms. In the present work we evaluated the benefit of multi-expert consensus (n = 3, 5, 7, 9, 11) on algorithmic performance. While training with individual databases resulted in highly variable F1 scores, performance was notably increased and more consistent when using the consensus of three annotators. Adding more annotators only resulted in minor improvements. We conclude that databases by few pathologists and high label accuracy may be the best compromise between high algorithmic performance and time investment. Copyright © 2020, The Authors. All rights reserved.

关键词： Tumors

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