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arXiv 2024年

作者： Bilik, Simon Janakova, Ilona Ligocki, Adam Ficek, Dominik Horak, Karel Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland

Many application from the bee colony health state monitoring could be efficiently solved using a computer vision techniques. One of such challenges is an efficient way for counting the number of incoming and outcoming bees, which could be used to further analyse many trends, such as the bee colony health state, blooming periods, or for investigating the effects of agricultural spraying. In this paper, we compare three methods for the automated bee counting over two own datasets. The best performing method is based on the ResNet-50 convolutional neural network classifier, which achieved accuracy of 87% over the BUT1 dataset and the accuracy of 93% over the BUT2 dataset. © 2024, CC BY.

关键词： Object detection

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Text-independent writer identification by feature fusion

Text-independent writer identification by feature fusion

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International Conference on Intelligent Control and Information Processing (ICICIP)

作者： Yongjie Hu Wenming Yang Youbin Chen Department of Electronic Engineering of Tsinghua University Zhejiang University Research interests include digital image processing and computer vision Department of Electronic Engineering of Tsinghua University Research interests include digital image processing and pattern recognition

ISBN: (纸本)9781479936519

This paper explores text-independent writer identification by combining Bag of Features (BoF), contour-hinge and SIFT scales feature. The BoF method adopted differs from the common BoF approach for writer identification in that it extracts SIFT descriptors and uses Locality-constrained Linear Coding to get feature vector of each document. The Locality-constrained Linear Coding (LLC) tries to reconstruct each feature through locality constraint and has much more discriminative power than the common used Vector Quantization (VQ). Contour-hinge feature can capture orientation and curvature of the ink trace. Modification is made to the original contour-hinge to improve the identification rate. Besides, we also use SIFT scale information and integrate these three kinds of features together. Experiments are conducted the challenging ICDAR2013 writer identification contest dataset and dataset for "ICFHR2012 Writer Identification Contest, Challenge 1: Latin Documents". The experiment results show that the proposed BoF approach outperforms the common ones that adopt VQ, and after the integration, our method achieves the best result on the entire ICDAR2013 and ICFHR2012 dataset under soft evaluation.

关键词： Feature extraction Vectors Encoding Image reconstruction Text analysis Educational institutions

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Non-Uniform Illumination Attack for Fooling Convolutional Neural Networks

arXiv

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arXiv 2024年

作者： Jain, Akshay Dubey, Shiv Ram Singh, Satish Kumar Santosh, K.C. Chaudhuri, Bidyut Baran The Computer Vision and Biometrics Lab Department of Information Technology Indian Institute of Information Technology Allahabad Uttar Pradesh Prayagraj211015 India The AI Research Lab Department of Computer Science University of South Dakota VermillionSD57069 United States The Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India

Convolutional Neural Networks (CNNs) have made remarkable strides;however, they remain susceptible to vulnerabilities, particularly in the face of minor image perturbations that humans can easily recognize. This weakness, often termed as ‘attacks,’ underscores the limited robustness of CNNs and the need for research into fortifying their resistance against such manipulations. This study introduces a novel Non-Uniform Illumination (NUI) attack technique, where images are subtly altered using varying NUI masks. Extensive experiments are conducted on widely-accepted datasets including CIFAR10, TinyImageNet, and CalTech256, focusing on image classification with 12 different NUI attack models. The resilience of VGG, ResNet, MobilenetV3-small and InceptionV3 models against NUI attacks are evaluated. Our results show a substantial decline in the CNN models’ classification accuracy when subjected to NUI attacks, indicating their vulnerability under non-uniform illumination. To mitigate this, a defense strategy is proposed, including NUI-attacked images, generated through the new NUI transformation, into the training set. The results demonstrate a significant enhancement in CNN model performance when confronted with perturbed images affected by NUI attacks. This strategy seeks to bolster CNN models’ resilience against NUI attacks. 1 Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A colour image encryption based on DNA coding and chaotic sequences

A colour image encryption based on DNA coding and chaotic se...

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International Conference on Emerging Trends and Applications in computer Science (ICETACS)

作者： Sukalyan Som Atanu Kotal Ayantika Chatterjee Soumista Dey Sarbani Palit Department of Computer Science Barrackpore Rastraguru Surendranath College Kolkata West Bengal India Department of Computer Science and Engineering Techno India College of Technology Kolkata West Bengal India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata West Bengal India

In this communication, a Chaos Based Symmetric Key Encryption of RGB Color Images with DNA Coding and a Chaos based Pseudorandom Binary Number Generator (PRBNG) has been proposed. In the proposed algorithm, the plain image is first scrambled using generalized Arnold Cat Map to achieve confusion. The scrambled image pixel are converted to DNA codes and again reconverted to integers where the choice of DNA coding rule is made pseudorandom based on the binary sequences generated by chaos based pseudorandom binary number generator. The integers thus obtained are diffused by performing exclusive OR operation with the integer sequences generated by 1D Logistic map producing the cipher image. The experimental results depicts that the proposed algorithm can successfully encrypt and decrypt RGB color images with secret keys. The simulation analysis also exhibit that the proposed method is loss-less, secure and efficient measured in terms of statistical tests(like histogram analysis, correlation coefficient analysis, measures of central tendency and dispersion), key sensitivity test, key space analysis, information entropy test, encryption quality by MSE, PSNR, NPCR and UACI.

关键词： DNA Ciphers Encryption Chaotic communication Logistics

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Optimal sub-shape models by minimum description length

Optimal sub-shape models by minimum description length

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Conference on computer vision and pattern recognition (CVPR)

作者： G. Langs P. Peloschek H. Bischof Institute for Computer Graphics and Vision Graz University of Technology Graz Austria Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria Department of Clinical Radiology Vienna Medical University Vienna Austria

Active shape models are powerful and widely used tool to interpret complex image data. By building models of shape variation they enable search algorithms to use a priori knowledge in an efficient and gainful way. However, due to the linearity of PCA, non-linearities like rotations or independently moving sub-parts in the data can deteriorate the resulting model considerably. Although non-linear extensions of active shape models have been proposed and application specific solutions have been used, they still need a certain amount of user interaction during model building. In this paper the task of building/choosing optimal models is tackled in a more generic information theoretic fashion. In particular, we propose an algorithm based on the minimum description length principle to find an optimal subdivision of the data into sub-parts, each adequate for linear modeling. This results in an overall more compact model configuration. Which in turn leads to a better model in terms of modes of variations. The proposed method is evaluated on synthetic data, medical images and hand contours.

关键词： Active shape model Biomedical imaging Principal component analysis computer vision Linearity Polynomials pattern recognition computer graphics Radiology Image processing

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Automatic image-to-model framework for patient-specific electromechanical modeling of the heart

Automatic image-to-model framework for patient-specific elec...

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IEEE International Symposium on Biomedical Imaging

作者： Dominik Neumann Tommaso Mansi Sasa Grbic Ingmar Voigt Bogdan Georgescu Elham Kayvanpour Ali Amr Farbod Sedaghat-Hamedani Jan Haas Hugo Katus Benjamin Meder Joachim Hornegger Ali Kamen Dorin Comaniciu Imaging and Computer Vision Siemens Corporate Technology Princeton NJ Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Department of Internal Medicine III University Hospital Heidelberg Germany

A key requirement for recent advances in computational modeling to be clinically applicable is the ability to fit models to patient data. Various personalization techniques have been proposed for isolated sub-components of complex models of heart physiology. However, no work has been presented that focuses on personalizing full electromechanical (EM) models in a streamlined, consistent and automatic fashion, which has been evaluated on a large population. We present an integrated system for full EM personalization from routinely acquired clinical data. The importance of mechanical parameters is analyzed in a comprehensive sensitivity study, revealing that myocyte contraction and Young's modulus are the main determinants of model output variation, what lead to the proposed personalization strategy. On a large, physiologically diverse set of 15 patients, we demonstrate the effectiveness of our framework by comparing measured and calculated parameters, yielding left ventricular ejection fraction and stroke volume errors of 6.6% and 9.2 mL, respectively.

关键词： Computational modeling Biological system modeling Heart Solid modeling Data models Electrocardiography Biomechanics

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ICDAR2019 robust reading challenge on multi-lingual scene text detection and recognition-RRC-MLT-2019 15

ICDAR2019 robust reading challenge on multi-lingual scene te...

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15th IAPR International Conference on Document Analysis and recognition, ICDAR 2019

作者： Nayef, Nibal Liu, Cheng-Lin Ogier, Jean-Marc Patel, Yash Busta, Michal Chowdhury, Pinaki Nath Karatzas, Dimosthenis Khlif, Wafa Matas, Jiri Pal, Umapada Burie, Jean-Christophe L3i Laboratory University of la Rochelle France Computer Vision Center Universitat Autonoma de Barcelona Spain CVPR Unit Indian Statistical Institute India Robotics Institute Carnegie Mellon Universiry Pittsburgh United States Center for Machine Perception Department of Cybernetics Czech Technical University Prague Czech Republic National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9781728128610

With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense. With the goal to systematically benchmark and push the state-of-the-art forward, the proposed competition builds on top of the RRC-MLT-2017 with an additional end-to-end task, an additional language in the real images dataset, a large scale multi-lingual synthetic dataset to assist the training, and a baseline End-to-End recognition method. The real dataset consists of 20,000 images containing text from 10 languages. The challenge has 4 tasks covering various aspects of multi-lingual scene text: (a) text detection, (b) cropped word script classification, (c) joint text detection and script classification and (d) end-to-end detection and recognition. In total, the competition received 60 submissions from the research and industrial communities. This paper presents the dataset, the tasks and the findings of the presented RRC-MLT-2019 challenge. © 2019 IEEE.

关键词： Competition

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computer vision Approaches for Automated Bee Counting Application

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IFAC-PapersOnLine 2024年第9期58卷 43-48页

作者： Simon Bilik Ilona Janakova Adam Ligocki Dominik Ficek Karel Horak Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic and Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic

关键词： Signal Processing Biomedical systems bee counting bee traffic monitoring computer vision deep classification object detection

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Automatic Handwritten Indian Scripts Identification

Automatic Handwritten Indian Scripts Identification

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International Workshop on Frontiers in Handwriting recognition

作者： Rajmohan Pardeshi B.B. Chaudhuri Mallikarjun Hangarge K.C. Santosh Department of Computer Science Karnatak Arts Science & Commerce College Bidar INDIA Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata INDIA US National Library of Medicine (NLM) National Institutes of Health (NIH) Bethesda MD USA

Since OCR engines are usually script-dependent, automatic text recognition in multi-script document requires a pre-processor module that identifies the scripts. Based on this motivation, in this paper, we present a word level handwritten Indian script identification technique. To handle this, words are first segmented by morphological dilation and performed connected component labelling. We then employ the Radon transform, discrete wavelet transform, statistical filters and discrete cosine transform to extract the directional multi-resolution spatial features. We tested the features by using linear discriminant analysis, support vector machine and K-nearest neighbour classifiers over 11 different major Indian scripts (including Roman) in bi-script and tri-script scenario. In our tests, we have achieved maximum accuracies of 98% and 96% for bi-script and tri-scipt respectively.

关键词： Discrete cosine transforms Discrete wavelet transforms Support vector machines Accuracy Feature extraction Kernel

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HIVE-Net: Centerline-Aware HIerarchical View-Ensemble Convolutional Network for Mitochondria Segmentation in EM Images

arXiv

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arXiv 2021年

作者： Yuan, Zhimin Ma, Xiaofen Yi, Jiajin Luo, Zhengrong Peng, Jialin College of Computer Science and Technology Huaqiao University Xiamen361021 China Department of Medical Imaging Guangdong Second Provincial General Hospital Guangzhou510317 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen361021 China

Background and objective: With the advancement of electron microscopy (EM) imaging technology, neuroscientists can investigate the function of various intracellular organelles, e.g, mitochondria, at nano-scale. Semantic segmentation of electron microscopy (EM) is an essential step to efficiently obtain reliable morphological statistics. Despite the great success achieved using deep convolutional neural networks (CNNs), they still produce coarse segmentations with lots of discontinuities and false positives for mitochondria segmentation. Methods: In this study, we introduce a centerline-aware multitask network by utilizing centerline as an intrinsic shape cue of mitochondria to regularize the segmentation. Since the application of 3D CNNs on large medical volumes is usually hindered by their substantial computational cost and storage overhead, we introduce a novel hierarchical view-ensemble convolution (HVEC), a simple alternative of 3D convolution to learn 3D spatial contexts using more efficient 2D convolutions. The HVEC enables both decomposing and sharing multi-view information, leading to increased learning capacity. Results: Extensive validation results on two challenging benchmarks show that, the proposed method performs favorably against the state-of-the-art methods in accuracy and visual quality but with a greatly reduced model size. Moreover, the proposed model also shows significantly improved generalization ability, especially when training with quite limited amount of training data. Detailed sensitivity analysis and ablation study have also been conducted, which show the robustness of the proposed model and effectiveness of the proposed modules. Conclusions: The experiments highlighted that the proposed architecture enables both simplicity and efficiency leading to increased capacity of learning spatial contexts. Moreover, incorporating shape cues such as centerline information is a promising approach to improve the performance of mitochondria segmentati

关键词： Convolution

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