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International Congress on image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Sen Lin Tianyang Xu Xinyong Yin School of Electronic and Information Engineering Liaoning Technical University Huludao China

ISBN: (纸本)9781509037117

The ROI (region of interest) extraction is the key step in palmprint or palm vein recognition, which is very important for the subsequent feature extraction and recognition. In this paper, the ROI extraction method for palmprint and palm vein recognition is mainly studied. Firstly, the preprocessing operation of palmprint and palm vein is carried out by using binary and morphological denoising technology, then the ROI regions are located and extracted based on the maximum inscribed circle and centroid methods. Finally, the algorithms are tested on PUT palm vein database and CASIA database, the experimental results show that the methods of this paper have a good effect, which are feasibility and validity. Furthermore, an online ROI extraction simulation system is designed in this article. The palmprint or palm vein image can be obtained in real-time by using the camera through the system, then the ROI can be extracted. The simulation system is intuitive and easy to operate, which provides a reliable experimental platform for the study of palmprint and palm vein recognition technology.

关键词： Veins Feature extraction Databases Data mining image recognition Real-time systems Fingers

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基于视觉几何的传送带测速方法研究

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仪器仪表学报 2016年第10期37卷 2307-2315页

作者：陈丹石国良福州大学电气工程与自动化学院福州350108

针对视觉测速系统误差较大且灵活性差的特点,提出了基于视觉几何的传送带上物体速度检测的方法,该方法无需摄像机标定。通过离线和在线图像处理相关算法提取图像参考点和工件特征点,结合视觉几何学算法,计算工件在相邻帧图像中的位移,... 详细信息

针对视觉测速系统误差较大且灵活性差的特点,提出了基于视觉几何的传送带上物体速度检测的方法,该方法无需摄像机标定。通过离线和在线图像处理相关算法提取图像参考点和工件特征点,结合视觉几何学算法,计算工件在相邻帧图像中的位移,同时记录采集、处理相邻帧图像所需时间;最后应用运动学公式计算物体的移动速度。应用无标定摄像机视觉系统检测工件的移动速度,不仅弥补了标定的摄像机系统灵活性差的问题,而且也减少了摄像机标定误差和镜头畸变带来的影响。实验结果证明所提出的方法灵活性强、精度高。

关键词：无标定摄像机传送带视觉几何图像处理速度检测

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SALIENCY ANALYSIS AND REGION-OF-INTEREST EXTRACTION FOR SATELLITE imageS BY BIOLOGICAL SPARSE MODELING

SALIENCY ANALYSIS AND REGION-OF-INTEREST EXTRACTION FOR SATE...

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IEEE International Conference on image processing

作者： Libao Zhang Xu Liang Jie Chen College of Information Science and Technology Beijing Normal University Beijing China

ISBN: (纸本)9781467399623

Traditional models for saliency analysis in satellite images cannot genuinely mimic the selection mechanism of human vision system. Furthermore, feature selection needs variant considering the complexity of data distribution of different satellite images thereby not being one-size-fits-all. Aiming at these problems, we propose a novel model based on sparse representation for saliency analysis with biological plausibility and preferably, our model only needs to decide the number of feature without considering feature complexity and massive parameters tuning in other feature learning algorithms. First, sparse filtering is adopted to learn a sparse dictionary for satellite images. Then, we use Incremental Coding Length (ICL) to measure the saliency contribution of every feature for the final saliency map. The region-of-interest (ROI) can be extracted based on saliency maps by thresholding segmentation. Experimental results show that our model achieves better performance compared with several traditional models for saliency analysis and ROIs extraction in satellite images.

关键词： Machine intelligence Geoscience and remote sensing Information processing Indexes Decision support systems Multimedia communication IEEE Multimedia

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GPGPU based concurrent classification using trained model of handwritten digits

GPGPU based concurrent classification using trained model of...

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International Conference on Open Source systems and Technologies (ICOSST)

作者： Syed Tahir Hussain Rizvi Gianpiero Cabodi Arslan Arif Muhammad Yaqoob Javed Muhammad Majid Gulzar Politecnico di Torino Torino Piemonte IT Politecnico Di Torino Turin Italy University of Science and Technology of China Hefei China University of Central Punjab Lahore Pakistan

ISBN: (纸本)9781509055876

In this paper, General Purpose Graphical processing Unit (GPGPU) based concurrent implementation of handwritten digit classifier is presented. Different styles of handwriting make it difficult to recognize a pattern but using neural network, it is not a difficult task to perform. Different softwares like torch and MATLAB provide the support of multiple training algorithms to train a network. By choosing an appropriate training algorithm for a specific application, speed of training can be increased. Furthermore, using computational power of GPUs, training and classification speed of neural network can be significantly improved. In this work, Modified National Institute of Standards and Technology (MNIST) database of handwritten digits is used to train the network. Accuracy and training time of digit classifier is evaluated for different algorithms and then concurrent training is performed by exploiting power of GPU. Trained parameters are imported and used for the concurrent classification with Compute Unified Device Architecture (CUDA) computing language which can be useful in numerous practical applications. Finally, the results of sequential and concurrent operations of training and classification are compared.

关键词： Training Graphics processing units Neurons image processing MATLAB Biological neural networks

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image processing algorithms FOR HISTOPATHOLOGICAL imageS

IMAGE PROCESSING ALGORITHMS FOR HISTOPATHOLOGICAL IMAGES

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作者： Oguzhan Oguz Bilkent University

学位级别：硕士

Conventionally, a pathologist examines cancer cell morphologies under mi- croscope. This process takes a lot of time and is subject to human mistakes. Computer aided diagnosis (CAD) systems and modules aim to help pathologists in their work to decrease the time consumption and the human mistakes. This thesis proposes a CAD module and algorithms which assist the pathologist in seg- mentation, detection and the classification problems in histopatholgic images. A multi-resolution super-pixel based segmentation algorithm is developed to mea- sure the cell size, count the number of cells and track the motion of cells in Mesenchymal Stem Cell (MSC) images. The proposed algorithm is compared with Simple Linear Iterative Clustering (SLIC) algorithm. It is experimentally observed that in the segmentation stage, the cell detection rate is increased by 7% and the false alarm is decreased by 5%. In addition to this, two novel decision rules for merging similar neighboring super-pixels are proposed. One dimensional version of the Scale Invariant Feature Transform (SIFT) based merging algorithm is developed and applied to the histograms of the neighboring super-pixels to de- termine the similar regions. It is also shown that the merging process can be made with the use of wavelets. Moreover, it is shown that region covariance and codifference matrices can be used in detection of cancer stem cells (CSC) and a CAD module for the CSC detection in liver cancer tissue images are devel- oped. The system locates CSCs in CD13 stained liver tissue images. The method has an online learning approach which improves the accuracy of detection. It is experimentally shown that, applying the proposed approach with the user guid- ance, increases the overall detection quality and accuracy up to 25% compared to using region descriptors alone. Also, the proposed module is compared with the similar plug-ins of imageJ and Fiji. It is shown that, when the similar features are used, the implemented modul

关键词： Super-Pixel 1-D SIFT Wavelet Transform Region Covariance Matrix Region Co-difference Matrix Cancer Stem Cells CD13 Stain H&E Stain

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Unified smoke and flame detection for intelligent surveillance system

Unified smoke and flame detection for intelligent surveillan...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Alexander Filonenko Danilo Cáceres Hernández Ajmal Shahbaz Kang-Hyun Jo Graduate School of Electrical Engineering University of Ulsan Ulsan Republic of Korea

ISBN: (纸本)9781509008742

This paper explains the way of unification of flame and smoke detection algorithms by merging the common steps into a single processing flow. Scenario, discussed in the current manuscript, considers using fixed surveillance cameras that allows using background subtraction to detect changes in a scene. Due to imperfection of background subtraction, foreground pixels, belonging to the same real object, are often separated. These pixels are united by morphological operations. All pixels are then labeled by connected components labeling algorithm, and tiny objects are removed since noticeable smoke and flames are to be detected. All the previous steps are processed only once, and then separate smoke and flame parts are started which use the same input image obtained after removing tiny objects. Smoke detection includes color probability, boundary roughness, edge density, and area variability filtering processes. Flame detection uses color probability, boundary roughness, and area variability filtering. Preliminary results show that applying unification to smoke and flame detection algorithms makes processing time similar to a single smoke detection algorithm if smoke and flame are processed in parallel. If the whole algorithm is implemented on a single thread, processing time is still lower comparing to running smoke and fire detection without unification. The result of unified processing part can also be used as input for multiple tasks of intelligent surveillance systems.

关键词： image color analysis Probability density function Cameras Fires Surveillance image edge detection Training

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消除阶梯效应与增强细节的变分Retinex红外图像增强算法

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光学学报 2016年第9期36卷 122-131页

作者：温海滨毕笃彦马时平何林远空军工程大学航空航天工程学院陕西西安710038

提出一种消除阶梯效应与增强细节的变分Retinex红外图像增强新算法。将高斯曲率正则项应用到变分Retinex模型的构建中,采用一阶微分对模型添加细节增强约束项,实现细节信息的自适应增强。结合邻域差分,引入曲率滤波方法对变分模型求取... 详细信息

提出一种消除阶梯效应与增强细节的变分Retinex红外图像增强新算法。将高斯曲率正则项应用到变分Retinex模型的构建中,采用一阶微分对模型添加细节增强约束项,实现细节信息的自适应增强。结合邻域差分,引入曲率滤波方法对变分模型求取最优解。实验结果表明,与采用其他变分Retinex方法相比,采用新算法处理后的图像在定量评价指标上优于对比算法。新算法能消除阶梯效应,增强图像的细节和视觉效果。

关键词：成像系统图像处理变分Retinex 高斯曲率全变分阶梯效应细节增强

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Single image haze removal algorithm using pixel-based airlight constraints

Single image haze removal algorithm using pixel-based airlig...

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International Conference on Automation and Computing (ICAC)

作者： Zhenwei Gao Yongqiang Bai Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing China

Fast single image dehazing has been a challenging problem in many fields, such as computer vision and real-time applications. The existing image dehazing algorithms cannot achieve a trade-off between the dehazing performance and the computational complexity. The proposed approach first applies the mean filter twice to estimate airlight, which include pixel-based dark channel and bright channel constraints. And then the relationship between channel values of the restored image and atmospheric light is qualitatively analyzed to give the optimum estimate of atmospheric light. Using the airlight and atmospheric light, we can easily restore the scene radiance via the atmospheric scattering model. Compared with others, the main advantage of the proposed approach is its high speed and significant visibility improvement even in the sky and white areas. This speed allows the enhanced haze image to be applied in real-time processing applications. A comparative study and quantitative evaluation are proposed with a few other state of the art algorithms which demonstrates that similar or better quality results are obtained.

关键词： Atmospheric modeling image restoration Real-time systems Channel estimation Scattering Filtering Computational modeling

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Sparse regularization methods in ultrafast ultrasound imaging

Sparse regularization methods in ultrafast ultrasound imagin...

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European Signal processing Conference (EUSIPCO)

作者： Adrien Besson Rafael E. Carrillo Miaomiao Zhang Denis Friboulet Olivier Bernard Yves Wiaux Jean-Philippe Thiran Institute of Sensors Signals and Systems Heriot-Watt University Edinburgh UK Signal Processing Laboratory (LTS5) Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland CREATIS University of Lyon1 Villeurbanne France Department of Radiology University of Lausanne (UNIL) Lausanne Switzerland

Ultrafast ultrasound (US) imaging based on plane wave (PW) insonification is a widely used modality nowadays. Two main types of approaches have been proposed for image reconstruction either based on classical delay-and-sum (DAS) or on Fourier reconstruction. Using a single PW, these methods lead to a lower image quality than DAS with multi-focused beams. In this paper we review recent beamforming approaches based on sparse regularization methods. The imaging problem, either spatial-based (DAS) or Fourier-based, is formulated as a linear inverse problem and convex optimization algorithms coupled with sparsity priors are used to solve the ill-posed problem. We describe two applications of the framework namely the sparse inversion of the beamforming problem and the compressed beamforming in which the framework is combined with compressed sensing. Based on numerical simulations and experimental studies, we show the advantage of the proposed methods in terms of image quality compared to classical methods.

关键词： Array signal processing Mathematical model Imaging image quality image reconstruction Interpolation Inverse problems

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An Efficient FPGA Implementation of Optimized Anisotropic Diffusion Filtering of images

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INTERNATIONAL JOURNAL OF RECONFIGURABLE COMPUTING 2016年第1期2016卷 1-17页

作者： Pal, Chandrajit Kotal, Avik Samanta, Asit Chakrabarti, Amlan Ghosh, Ranjan Univ Calcutta AK Choudhury Sch Informat Technol JD-2Sect 3 Kolkata 700098 India Univ Calcutta Dept Appl Opt & Photon JD-2Sect 3 Kolkata 700098 India Univ Calcutta Inst Radio Phys & Elect JD-2Sect 3 Kolkata 700098 India

Digital image processing is an exciting area of research with a variety of applications including medical, surveillance security systems, defence, and space applications. Noise removal as a preprocessing step helps to improve the performance of the signal processing algorithms, thereby enhancing image quality. Anisotropic diffusion filtering proposed by Perona and Malik can be used as an edge-preserving smoother, removing high-frequency components of images without blurring their edges. In this paper, we present the FPGA implementation of an edge-preserving anisotropic diffusion filter for digital images. The designed architecture completely replaced the convolution operation and implemented the same using simple arithmetic subtraction of the neighboring intensities within a kernel, preceded by multiple operations in parallel within the kernel. To improve the image reconstruction quality, the diffusion coefficient parameter, responsible for controlling the filtering process, has been properly analyzed. Its signal behavior has been studied by subsequently scaling and differentiating the signal. The hardware implementation of the proposed design shows better performance in terms of reconstruction quality and accelerated performance with respect to its software implementation. It also reduces computation, power consumption, and resource utilization with respect to other related works.

关键词： diffusion filter image processing Resource utilization Field programmable gate arrays image reconstruction Anisotropic magnetoresistance images software implementation Filtration edge protection BLURRING Seeds

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