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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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FWLBP: A scale invariant descriptor for texture classification

arXiv

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arXiv 2018年

作者： Roy, Swalpa Kumar Bhattacharya, Nilavra Chanda, Bhabatosh Chaudhuri, Bidyut B. Ghosh, Dipak Kumar Optical Character Recognition Laboratory Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India School of Information University of Texas AustinTX78712 United States Image Processing Laboratory Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata700108 India Department of Electronics and Communication Engineering National Institute of Technology Rourkela Rourkela769008 India

In this paper we propose a novel texture descriptor called Fractal Weighted Local Binary pattern (FWLBP). The fractal dimension (FD) measure is relatively invariant to scale-changes, and presents a good correlation with human viewpoint of surface roughness. We have utilized this property to construct a scale-invariant descriptor. Here, the input image is sampled using an augmented form of the local binary pattern (LBP) over three different radii, and then used an indexing operation to assign FD weights to the collected samples. The final histogram of the descriptor has its features calculated using LBP, and its weights computed from the FD image. The proposed descriptor is scale invariant, and is also robust in rotation or reflection, and partially tolerant to noise and illumination changes. In addition, the local fractal dimension is relatively insensitive to the bi-Lipschitz transformations, whereas its extension is adequate to precisely discriminate the fundamental of texture primitives. Experiment results carried out on standard texture databases show that the proposed descriptor achieved better classification rates compared to the state-of-the-art descriptors. Copyright © 2018, The Authors. All rights reserved.

关键词： Fractal dimension

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Comparative analysis of audio watermarking technique in MDCT domain with other references in spectral domain

Comparative analysis of audio watermarking technique in MDCT...

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9th International Multi-Conference on Systems, Signals and Devices, SSD 2012

作者： Bellaaj, Maha Ouni, Kais Research Unit of Signal Processing Image and Pattern Recognition National Engineering School of Tunis University Tunis Manar Tunisia

ISBN: (纸本)9781467315906

In this paper we propose a comparative review between the proposed digital audio watermarking technique and those achieved by Luigi Rosa and Rolf Brigola. The performed technique operates in the frequency domain. The time-frequency mapping is done using a Modified Discrete Cosine Transform (MDCT). The technique developed by Luigi Rosa operates in the frequency domain but using the Discrete Cosine Transform (DCT) as transformation and that proposed by Rolf Brigola uses the Fast Fourier Transform (FFT). We studied the robustness of each technique against different types of attack and we evaluated the inaudibility by using a statistical approach by calculating the SNR and an objective approach by calculating the ODG notes given by PEAQ. © 2012 IEEE.

关键词： Discrete cosine transforms

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Comparative analysis of audio watermarking technique in MDCT domain with other references in spectral domain

Comparative analysis of audio watermarking technique in MDCT...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Maha Bellaaj Kaïs Ouni Research Unit of Signal Processing Image and Pattern Recognition National Engineering School of Tunis University Tunis El Manar II Tunis Tunisia

ISBN: (纸本)9781467315906

关键词： Robustness Discrete cosine transforms Frequency domain analysis Watermarking Signal to noise ratio Niobium Psychoacoustic models

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People detection in complex scene using a cascade of Boosted classifiers based on Haar-like-features

People detection in complex scene using a cascade of Boosted...

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IEEE Intelligent Vehicles Symposium

作者： Siala, A. Khlifa, N. Bremond, F. Hamrouni, K. Research Unit in Signal Processing Image Processing and Pattern Recognition ENIT Tunisia PULSAR Team INRIA Sophia Antipolis Nice France

ISBN: (纸本)9781424435036

Pedestrian detection in a real scene is an interesting application for video surveillance systems. This paper presents our contribution to improve the work of Viola and Jones, originally designed to detect faces. This work uses a cascade of classifiers based on Adaboost using Haar features. It improves the learning step by including a decision tree presenting the different poses and possible occlusions. The method has been tested on real and complex sequences and has given a good detection despite occlusions and poses variation.

关键词： Adaptive boosting

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People detection in complex scene using a cascade of boosted classifiers based on Haar-like-features

People detection in complex scene using a cascade of boosted...

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IEEE Symposium on Intelligent Vehicle

作者： M. Siala N. Khlifa F. Bremond K. Hamrouni Research Unit in Signal Processing Image Processing and Pattern Recognition(ENIT Tunisia) PULSAR Team (INRIA Sophia Antipolis Nice) Research Unit in Signal Processing Image Processing and Pattern Recognition(ENIT Tunisia) Tunisia

ISBN: (纸本)9781424435036

关键词： Layout Face detection Object detection Support vector machines Motion detection Support vector machine classification Humans Testing Robustness Detectors

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A framework for nuclear image enhancement based on the Anscomb transform and the bayesian thresholding

A framework for nuclear image enhancement based on the Ansco...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Imen Mbazaa Najla Gribaa Nawres Khlifa Kamel Hamrouni Research Unit of Signal Processing Image Processing and Pattern Recognition National Engineering School of Sfax (ENIS) Tunisia

Scintigraphic imagery is an important data source since it provides morphological and functional clinical informations. However, scintigraphic images present very bad quality because of several degradation factors. In fact, the recorded data are embedded in noise modelled as the realisation of Poisson process. The aim of this paper is to provide a framework able to enhance nuclear images quality by Poisson intensity estimation. This framework consists, in a first step, of performing variance-stabilizing step for the Poisson process thanks to the Anscombe transformation. So the obtained data can be considered as contaminated by a white Gaussian noise. In a second step, it uses a Bayesian technique inspired of Pizurica approach, known in the literature for exhibiting good results as for white Gaussian noise. In fact, the complex wavelet packets were exploited regarding to Pizurica algorithm.

关键词： image enhancement Bayesian methods Gaussian noise Noise reduction Wavelet packets Wavelet transforms image processing image quality Pixel Cameras

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Invariant Primitives for Handwritten Arabic Script: A Contrastive Study of Four Feature Sets

Invariant Primitives for Handwritten Arabic Script: A Contra...

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International Conference on Document Analysis and recognition

作者： Sofiene Haboubi Samia Maddouri Noureddine Ellouze Hailkal El-Abed Image Processing and Pattern Recognition Unit National Engineering School of Tunis (ENIT) Tunis Tunisia Department of Signal Processing of Mobile Information Systems Institute for Communications Technology Brunswick Germany

ISBN: (纸本)9781424445004

The choice of relevant features is very decisive in handwriting recognition rate. Our aim is to present some useful structural and statistical features and see their degree of variability. In this paper, we start with a description of the variability of the Arabic handwriting and the way how to reduce it. Four kinds of feature sets used by our handwriting systems are then presented evaluated and discussed. The comparison is carried on a database of images from IFN/ENIT databases. The neural network multilayer perceptrons is our method of classification. A contrastive study of these primitives is done according to recognition their time and memory consuming and their variability degree.

关键词： Handwriting recognition image segmentation image analysis pattern recognition image recognition image databases Spatial databases Skeleton Data mining Text analysis

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Scintigraphic images restoration using jointly Fourier and Wavelet domains

Scintigraphic images restoration using jointly Fourier and W...

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3rd International Conference on Information and Communication Technologies, vol.2

作者： Nejla Gribaa Nawres Khlifa Kamel Hamrouni Search unit of Signal processing Image processing and Pattern Recognition National Engineering School of Tunis (ENIT) Tunisia

The paper focus on the problem of scintigraphic images restoration. These images are often disturbed by the bad detection equipment quality. The aim is to improve regions of interest perception, to help useful information extraction and so to allow good understanding of the pathological phenomenon. The restoration is an ill-posed problem. So, inverting the distortion model in presence of additive noise is often numerically unstable. We propose, then, a new framework based on the Fourier and the Wavelet domain, in order to benefit from the advantages of each one. The Fourier regularized deconvolution exploits the Fourier representation efficiency of the colored noise. Whereas the wavelet packets denoising exploits the wavelet representation efficiency and the good localization of inherent noise in this domain. We noticed the performance of the proposed method in terms of edges preservation, contrast and uniformity in the images.

关键词： Nuclear medicine image restoration Regularisation Wavelet packets transform Fourier transform

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Fractal Volume Rendering

Fractal Volume Rendering

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xiaoliang Li Hongxing Qin Jie Yang Yuemin Zhu Institute of Image Processing & Pattern Recognition Shanghai Jiaotong University Shanghai China Research unit 5515 & Inserm unit 630 CREATIS/CNRS Villeurbanne France

Efficient visualization of large volumetric data is a challenge for image processing community. In this paper, we present a novel volume rendering algorithm based on the concept of fractal. It consists of dividing the volumetric data set into sub-blocks, calculating the 3D fractal coefficients of each sub-block, projecting them to 2D image plane, and generating sub-images through 2D inverse fractal transform. The final rendered image is then obtained by simply summing the sub-images. Compared to the conventional ray casting technique, the proposed fractal volume rendering (FVR) method presents the advantage of reducing time complexity as well as memory complexity while maintaining good rendering quality. Moreover, the progressive refinement is supported owing to the iterative convergent process of sub-image generation

关键词： Fractals Rendering (computer graphics) Data visualization image generation Casting image processing image converters Computed tomography Magnetic resonance imaging Monte Carlo methods

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