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DELP-DAR system for license plate detection and recognition

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arXiv 2019年

作者： Selmi, Zied Halima, Mohamed Ben Pal, Umapada Alimi, M. Adel REGIM-Lab: Research Groups in Intelligent Machines University of Sfax ENIS BP 1173 Sfax3038 Tunisia Computer vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road olkata700108 India

Automatic License Plate detection and recognition (ALPR) is a quite popular and active research topic in the field of computer vision, image processing and intelligent transport systems. ALPR is used to make detection and recognition processes more robust and efficient in highly complicated environments and backgrounds. Several research investigations are still necessary due to some constraints such as: completeness of numbering systems of countries, different colors, various languages, multiple sizes and varied fonts. For this, we present in this paper an automatic framework for License Plate (LP) detection and recognition from complex scenes. Our framework is based on mask region convolutional neural networks used for LP detection, segmentation and recognition. Although some studies have focused on LP detection, LP recognition, LP segmentation or just two of them, our study uses the maskr-cnn in the three stages. The evaluation of our framework is enhanced by four datasets for different countries and consequently with various languages. In fact, it tested on four datasets including images captured from multiple scenes under numerous conditions such as varied orientation, poor quality images, blurred images and complex environmental backgrounds. Extensive experiments show the robustness and efficiency of our suggested framework in all datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： License plates (automobile)

Chebyshev-Harmonic-Fourier-Moments and Deep CNNs for Detecting Forged Handwriting

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Chebyshev-Harmonic-Fourier-Moments and Deep CNNs for Detecti...

International Conference on pattern recognition

作者： Lokesh Nandanwar Palaiahnakote Shivakumara Sayani Kundu Umapada Pal Tong Lu Daniel Lopresti Faculty of Computer Science 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 Computer Science & Engineering Lehigh University Bethlehem PA USA

Recently developed sophisticated image processing techniques and tools have made easier the creation of high-quality forgeries of handwritten documents including financial and property records. To detect such forgeries of handwritten documents, this paper presents a new method by exploring the combination of Chebyshev-Harmonic-Fourier-Moments (CHFM) and deep Convolutional Neural Networks (D-CNNs). Unlike existing methods work based on abrupt changes due to distortion created by forgery operation, the proposed method works based on inconsistencies and irregular changes created by forgery operations. Inspired by the special properties of CHFM, such as its reconstruction ability by removing redundant information, the proposed method explores CHFM to obtain reconstructed images for the color components of the Original, Forged Noisy and Blurred classes. Motivated by the strong discriminative power of deep CNNs, for the reconstructed images of respective color components, the proposed method used deep CNNs for forged handwriting detection. Experimental results on our dataset and benchmark datasets (namely, ACPR 2019, ICPR 2018 FCD and IMEI datasets) show that the proposed method outperforms existing methods in terms of classification rate.

关键词： Image color analysis Chebyshev approximation Tools Benchmark testing Distortion Forgery pattern recognition

Motion Analysis of Endovascular Stent-Grafts by MDL Based Registration

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Motion Analysis of Endovascular Stent-Grafts by MDL Based Re...

International Conference on computer vision (ICCV)

作者： Georg Langs Nikos Paragios Rene Donner Pascal Desgranges Alain Rahmouni Hicham Kobeiter GALEN Group Laboratoire de Mathématiques Appliquées aux Systàmes Ecole Centrale de Paris France Institute for Computer Graphics and Vision Graz University of Technology Austria Pattern Recognition and Image Processing Group University of Technology Vienna Austria University of Paris XII Paris France

The endovascular repair of a traumatic rupture of the thoracic aorta - that would otherwise lead to the death of the patient - is performed by delivering a stent-graft into the vessel at the rupture location. The age range of the affected patients is large and the stent-graft will stay in the body for the remaining life. The technique is relatively new, and no experience with regard to long-term effects, and durability exists. To predict long-term complications, such as ruptures or destructive interactions with surrounding tissue during the life of the patient, it is important to understand the - rather intense and constant - movement of the stent- graft during the cardiac cycle. A computed tomography with heart gating (gated CT) acquires sequences that show the region of the stent-graft at different time points. We analyze the motion of stent-grafts with a model based approach. Stent-grafts are represented as sparse sets of axis points extracted from the gated CT, and motion patterns are captured by a minimum description length based group-wise registration of the stent-graft at different time points. No parameterization or a priori definition of the topology is necessary, and highly variable elasticity properties in the data volume can by accounted for by the sparse statistical model, that captures correlations and motion components of the stent-graft. We report results for deformation models and registration accuracy for 5 patients.

关键词： Motion analysis Computed tomography Biomedical imaging Heart Buildings computer graphics pattern recognition Image processing Hospitals Data mining

Reflash Dropout in Image Super-Resolution

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arXiv 2021年

作者： Kong, Xiangtao Liu, Xina Gu, Jinjin Qiao, Yu Dong, Chao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab. Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China The University of Sydney Australia Shanghai AI Laboratory Shanghai China

Dropout is designed to relieve the overfitting problem in high-level vision tasks but is rarely applied in low-level vision tasks, like image super-resolution (SR). As a classic regression problem, SR exhibits a different behaviour as high-level tasks and is sensitive to the dropout operation. However, in this paper, we show that appropriate usage of dropout benefits SR networks and improves the generalization ability. Specifically, dropout is better embedded at the end of the network and is significantly helpful for the multi-degradation settings. This discovery breaks our common sense and inspires us to explore its working mechanism. We further use two analysis tools – one is from a recent network interpretation work, and the other is specially designed for this task. The analysis results provide side proofs to our experimental findings and show us a new perspective to understand SR networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical resolving power

diffGrad: An optimization method for convolutional neural networks

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arXiv 2019年

作者： Dubey, Shiv Ram Chakraborty, Soumendu Roy, Swalpa Kumar Mukherjee, Snehasis Singh, Satish Kumar Chaudhuri, Bidyut Baran The Computer Vision Group Indian Institute of Information Technology Sri City Andhra Pradesh Chittoor517646 India The Indian Institute of Information Technology Uttar Pradesh Lucknow India The Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India Techno India University Sector V Salt Lake City Kolkata700091 India The Computer Vision and Biometrics Laboratory Indian Institute of Information Technology Allahabad211015 India

Stochastic Gradient Decent (SGD) is one of the core techniques behind the success of deep neural networks. The gradient provides information on the direction in which a function has the steepest rate of change. The main problem with basic SGD is to change by equal sized steps for all parameters, irrespective of gradient behavior. Hence, an efficient way of deep network optimization is to make adaptive step sizes for each parameter. Recently, several attempts have been made to improve gradient descent methods such as AdaGrad, AdaDelta, RMSProp and Adam. These methods rely on the square roots of exponential moving averages of squared past gradients. Thus, these methods do not take advantage of local change in gradients. In this paper, a novel optimizer is proposed based on the difference between the present and the immediate past gradient (i.e., diffGrad). In the proposed diffGrad optimization technique, the step size is adjusted for each parameter in such a way that it should have a larger step size for faster gradient changing parameters and a lower step size for lower gradient changing parameters. The convergence analysis is done using the regret bound approach of online learning framework. Rigorous analysis is made in this paper over three synthetic complex non-convex functions. The image categorization experiments are also conducted over the CIFAR10 and CIFAR100 datasets to observe the performance of diffGrad with respect to the state-of-the-art optimizers such as SGDM, AdaGrad, AdaDelta, RMSProp, AMSGrad, and Adam. The residual unit (ResNet) based Convolutional Neural Networks (CNN) architecture is used in the experiments. The experiments show that diffGrad outperforms other optimizers. Also, we show that diffGrad performs uniformly well for training CNN using different activation functions. The source code is made publicly available at https://***/shivram1987/diffGrad. Copyright © 2019, The Authors. All rights reserved.

关键词： Optimization

New Sharpness Features for Image Type Classification Based on Textual Information

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New Sharpness Features for Image Type Classification Based o...

IAPR International Workshop on Document Analysis Systems, DAS

作者： K. S. Raghunandan Palaiahnakote Shivakumara G. Hemantha Kumar Umapada Pal Tong Lu Department of Studies in Computer Science University of Mysore Karnataka India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Narional Key Lab for Novel Software Technology Nanjing University Nanjing China

Achieving good recognition results from a single method for text lines in video/natural scene images captured by high resolution cameras or low resolution mobile cameras, and images in web pages, is often hard. In this paper, we propose new sharpness based features of textual portion of each input text line image using HSI color space for the classification of an input image into one of the four classes (video, scene, mobile or born digital). This helps in choosing an appropriate method based on the class type of the input text for its improved recognition rate. For a given input text line image, the proposed method obtains H, S and I images. Then Canny edge images are obtained for H, S and I spaces, which results in text candidates. We perform sliding window operation over the text candidate image of each text line of each color space to estimate new sharpness by calculating stroke width and gradient information. The sharpness values of the text lines of the three color spaces are then fed to k-means clustering with maximum, minimum and average guesses, which results in three respective clusters. The mean of each cluster for respective color spaces outputs a feature vector having nine feature values for image classification with the help of an SVM classifier. Experimental results on standard datasets, namely, ICDAR 2013, ICDAR 2015 video, ICDAR 2015 natural scene data, ICDAR 2013 born digital data and the images captured by a mobile camera (our own data) show that the proposed classification method helps in improving recognition results.

关键词： Mobile communication Text recognition Image edge detection Image resolution Image color analysis Digital images Optical character recognition software

Generalist Segmentation Algorithm for Photoreceptors Analysis in Adaptive Optics Imaging

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arXiv 2024年

作者： Kulyabin, Mikhail Sindel, Aline Pedersen, Hilde Pedersen, Hilde R. Gilson, Stuart Baraas, Rigmor Maier, Andreas Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany National Centre for Optics Vision and Eye Care Faculty of Health and Social Sciences University of South-Eastern Norway Kongsberg Norway

Analyzing the cone photoreceptor pattern in images obtained from the living human retina using quantitative methods can be crucial for the early detection and management of various eye conditions. Confocal adaptive optics scanning light ophthalmoscope (AOSLO) imaging enables visualization of the cones from reflections of waveguiding cone photoreceptors. While there have been significant improvements in automated algorithms for segmenting cones in confocal AOSLO images, the process of labeling data remains labor-intensive and manual. This paper introduces a method based on deep learning (DL) for detecting and segmenting cones in AOSLO images. The models were trained on a semi-automatically labeled dataset of 20 AOSLO batches of images of 18 participants for 0◦, 1◦, and 2◦ from the foveal center. F1 scores were 0.968, 0.958, and 0.954 for 0◦, 1◦, and 2◦, respectively, which is better than previously reported DL approaches. Our method minimizes the need for labeled data by only necessitating a fraction of labeled cones, which is especially beneficial in the field of ophthalmology, where labeled data can often be limited. © 2024, CC BY.

关键词： Adaptive optics

Temporal correction of detected R-peaks in ECG signals: A crucial step to improve QRS detection algorithms

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Temporal correction of detected R-peaks in ECG signals: A cr...

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Stefan Gradl Heike Leutheuser Mohamed Elgendi Nadine Lang Bjoern M. Eskofier Digital Sports Group Pattern Recognition Lab Friedrich-Alexander University Erlangen-Niirnberg (FAU) Germany Electrical and Computer Engineering in Medicine Group University of British Columbia Vancouver British Columbia Canada Fraunhofer Institute for Integrated Circuits IIS Erlangen Germany

In the last decade the interest for heart rate variability analysis has increased tremendously. Related algorithms depend on accurate temporal localization of the heartbeat, e.g. the R-peak in electrocardiogram signals, especially in the presence of arrhythmia. This localization can be delivered by numerous solutions found in the literature which all lack an exact specification of their temporal precision. We implemented three different state-of-the-art algorithms and evaluated the precision of their R-peak localization. We suggest a method to estimate the overall R-peak temporal inaccuracy-dubbed beat slackness-of QRS detectors with respect to normal and abnormal beats. We also propose a simple algorithm that can complement existing detectors to reduce this slackness. Furthermore we define improvements to one of the three detectors allowing it to be used in real-time on mobile devices or embedded hardware. Across the entire MIT-BIH Arrhythmia Database, the average slackness of all the tested algorithms was 9ms for normal beats and 13ms for abnormal beats. Using our complementing algorithm this could be reduced to 4ms for normal beats and to 7ms for abnormal beats. The presented methods can be used to significantly improve the precision of R-peak detection and provide an additional measurement for QRS detector performance.

关键词： Detectors Electrocardiography Databases Knowledge based systems Heart rate variability Algorithm design and analysis Automata

Automated Segmentation and Analysis of Cone Photoreceptors in Multimodal Adaptive Optics Imaging

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arXiv 2024年

作者： Shrestha, Prajol Kulyabin, Mikhail Sindel, Aline Pedersen, Hilde R. Gilson, Stuart Baraas, Rigmor Maier, Andreas Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany National Centre for Optics Vision and Eye Care Faculty of Health and Social Sciences University of South-Eastern Norway Kongsberg Norway

Accurate detection and segmentation of cone cells in the retina are essential for diagnosing and managing retinal diseases. In this study, we used advanced imaging techniques, including confocal and non-confocal split detector images from adaptive optics scanning light ophthalmoscopy (AOSLO), to analyze photoreceptors for improved accuracy. Precise segmentation is crucial for understanding each cone cell’s shape, area, and distribution. It helps to estimate the surrounding areas occupied by rods, which allows the calculation of the density of cone photoreceptors in the area of interest. In turn, density is critical for evaluating overall retinal health and functionality. We explored two U-Net-based segmentation models: StarDist for confocal and Cellpose for calculated modalities. Analyzing cone cells in images from two modalities and achieving consistent results demonstrates the study’s reliability and potential for clinical application. © 2024, CC BY.

关键词： Adaptive optics