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Academic literature on the topic 'Histogram and partition based filter'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Histogram and partition based filter"

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Yang, Si, Lihua Zheng, Wanlin Gao, Bingbing Wang, Xia Hao, Jiaqi Mi, and Minjuan Wang. "An Efficient Processing Approach for Colored Point Cloud-Based High-Throughput Seedling Phenotyping." Remote Sensing 12, no. 10 (May 12, 2020): 1540. http://dx.doi.org/10.3390/rs12101540.

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Plant height and leaf area are important morphological properties of leafy vegetable seedlings, and they can be particularly useful for plant growth and health research. The traditional measurement scheme is time-consuming and not suitable for continuously monitoring plant growth and health. Individual vegetable seedling quick segmentation is the prerequisite for high-throughput seedling phenotype data extraction at individual seedling level. This paper proposes an efficient learning- and model-free 3D point cloud data processing pipeline to measure the plant height and leaf area of every single seedling in a plug tray. The 3D point clouds are obtained by a low-cost red–green–blue (RGB)-Depth (RGB-D) camera. Firstly, noise reduction is performed on the original point clouds through the processing of useable-area filter, depth cut-off filter, and neighbor count filter. Secondly, the surface feature histograms-based approach is used to automatically remove the complicated natural background. Then, the Voxel Cloud Connectivity Segmentation (VCCS) and Locally Convex Connected Patches (LCCP) algorithms are employed for individual vegetable seedling partition. Finally, the height and projected leaf area of respective seedlings are calculated based on segmented point clouds and validation is carried out. Critically, we also demonstrate the robustness of our method for different growth conditions and species. The experimental results show that the proposed method could be used to quickly calculate the morphological parameters of each seedling and it is practical to use this approach for high-throughput seedling phenotyping.
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Priya, Sarv, Amit Agarwal, Caitlin Ward, Thomas Locke, Varun Monga, and Girish Bathla. "Survival prediction in glioblastoma on post-contrast magnetic resonance imaging using filtration based first-order texture analysis: Comparison of multiple machine learning models." Neuroradiology Journal 34, no. 4 (February 3, 2021): 355–62. http://dx.doi.org/10.1177/1971400921990766.

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Objective Magnetic resonance texture analysis (MRTA) is a relatively new technique that can be a valuable addition to clinical and imaging parameters in predicting prognosis. In the present study, we investigated the efficacy of MRTA for glioblastoma survival using T1 contrast-enhanced (CE) images for texture analysis. Methods We evaluated the diagnostic performance of multiple machine learning models based on first-order histogram statistical parameters derived from T1-weighted CE images in the survival stratification of glioblastoma multiforme (GBM). Retrospective evaluation of 85 patients with GBM was performed. Thirty-six first-order texture parameters at six spatial scale filters (SSF) were extracted on the T1 CE axial images for the whole tumor using commercially available research software. Several machine learning classification models (in four broad categories: linear, penalized linear, non-linear, and ensemble classifiers) were evaluated to assess the survival prediction performance using optimal features. Principal component analysis was used prior to fitting the linear classifiers in order to reduce the dimensionality of the feature inputs. Fivefold cross-validation was used to partition the data iteratively into training and testing sets. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance. Results The neural network model was the highest performing model with the highest observed AUC (0.811) and cross-validated AUC (0.71). The most important variable was the age at diagnosis, with mean and mean of positive pixels (MPP) for SSF = 0 being the second and third most important, followed by skewness for SSF = 0 and SSF = 4. Conclusions First-order texture features, when combined with age at presentation, show good accuracy in predicting GBM survival.
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Ausiannikau, A. V., and V. M. Kozel. "Filtration of histogram evaluation of probability density based on fuzzy data accessibility to a grouping interval." Doklady BGUIR 19, no. 4 (July 1, 2021): 13–20. http://dx.doi.org/10.35596/1729-7648-2021-19-4-13-20.

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The paper proposes a histogram estimate of the probability density based on fuzzy data belonging to a grouping interval. A methodology for constructing a histogram estimate using a histogram smoothing filter is presented. The technique of constructing such a filter is described. The main filter parameter is established – the coefficient of the statistical relationship between the amount of data falling into the grouping interval for a single inclusion function and when approaching to use the membership function. The use of an iterative procedure for a histogram filter allows for a greater “smoothness” of the histogram. The simulation results show the effectiveness of using a histogram filter for different data volumes. At the same time, the choice of the number of grouping intervals for the “correct” recognition of probability density becomes not critical. The histogram filter is a simple tool that can easily be built into any algorithm for constructing histogram estimates.
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Loquin, Kevin, and Olivier Strauss. "Histogram density estimators based upon a fuzzy partition." Statistics & Probability Letters 78, no. 13 (September 2008): 1863–68. http://dx.doi.org/10.1016/j.spl.2008.01.053.

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GUO, Hong-wei, Jiang YU, Jia-xing ZHU, and Zhi-yong LI. "Weighted mean filter based on local histogram." Journal of Computer Applications 30, no. 11 (December 14, 2010): 3019–21. http://dx.doi.org/10.3724/sp.j.1087.2010.03019.

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Wang, Baoping, Jiulun Fan, Weixin Xie, and Chengmao Wu. "Adaptive histogram-based filter for image restoration." Journal of Electronics (China) 21, no. 4 (July 2004): 306–13. http://dx.doi.org/10.1007/bf02687886.

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Jung-Hua Wang, Wen-Jeng Liu, and Lian-Da Lin. "Histogram-based fuzzy filter for image restoration." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 32, no. 2 (April 2002): 230–38. http://dx.doi.org/10.1109/3477.990880.

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Dai, Ying Meng, Lin Feng Wei, and Cong Luo. "Image Retrieval Method Based on Vision Feature of Color." Applied Mechanics and Materials 303-306 (February 2013): 1406–11. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1406.

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Color histogram is an important technique for color database retrieving, but it often ignores color’s spatial distribution information. This paper proposes an improved color histogram algorithm based on the HSV space, whose subspaces are non-equally quantized. The algorithm first proceeds annular partition on the original image, and then uses the method presented by Aibing Rao etc. [1] to count each partition. At last, it calculates the weighted sums for the distances between distinct color histograms. Experimental results demonstrate that the algorithm reduces the feature dimensions and keeps a good accuracy as well as the spatial distribution information. Thus, a better retrieval result is obtained.
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Pan, Shengdong, Xiangjing An, and Hangen He. "OptimalO(1) Bilateral Filter with Arbitrary Spatial and Range Kernels Using Sparse Approximation." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/289517.

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A number of acceleration schemes for speeding up the time-consuming bilateral filter have been proposed in the literature. Among these techniques, the histogram-based bilateral filter trades the flexibility for achievingO(1) computational complexity using box spatial kernel. A recent study shows that this technique can be leveraged forO(1) bilateral filter with arbitrary spatial and range kernels by linearly combining the results of multiple-box bilateral filters. However, this method requires many box bilateral filters to obtain sufficient accuracy when approximating the bilateral filter with a large spatial kernel. In this paper, we propose approximating arbitrary spatial kernel using a fixed number of boxes. It turns out that the multiple-box spatial kernel can be applied in manyO(1) acceleration schemes in addition to the histogram-based one. Experiments on the application to the histogram-based acceleration are presented in this paper. Results show that the proposed method has better accuracy in approximating the bilateral filter with Gaussian spatial kernel, compared with the previous histogram-based methods. Furthermore, the performance of the proposed histogram-based bilateral filter is robust with respect to the parameters of the filter kernel.
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Schulte, Stefan, Valérie De Witte, Mike Nachtegael, Dietrich Van der Weken, and Etienne E. Kerre. "Histogram-based fuzzy colour filter for image restoration." Image and Vision Computing 25, no. 9 (September 2007): 1377–90. http://dx.doi.org/10.1016/j.imavis.2006.10.002.

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Dissertations / Theses on the topic "Histogram and partition based filter"

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Trogadas, Giorgos, and Larissa Ekonoja. "The effect of noise filters on DVS event streams : Examining background activity filters on neuromorphic event streams." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302514.

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Image classification using data from neuromorphic vision sensors is a challenging task that affects the use of dynamic vision sensor cameras in real- world environments. One impeding factor is noise in the neuromorphic event stream, which is often generated by the dynamic vision sensors themselves. This means that effective noise filtration is key to successful use of event- based data streams in real-world applications. In this paper we harness two feature representations of neuromorphic vision data in order to apply conventional frame-based image tools on the neuromorphic event stream. We use a standard noise filter to evaluate the effectiveness of noise filtration using a popular dataset converted to neuromorphic vision data. The two feature representations are the best-of-class standard Histograms of Averaged Time Surfaces (HATS) and a simpler grid matrix representation. To evaluate the effectiveness of the noise filter, we compare classification accuracies using various noise filter windows at different noise levels by adding additional artificially generated Gaussian noise to the dataset. Our performance metrics are reported as classification accuracy. Our results show that the classification accuracy using frames generated with HATS is not significantly improved by a noise filter. However, the classification accuracy of the frames generated with the more traditional grid representation is improved. These results can be refined and tuned for other datasets and may eventually contribute to on- the- fly noise reduction in neuromorphic vision sensors.
Händelsekameror är en ny typ av kamera som registrerar små ljusförändringar i kamerans synfält. Sensorn som kameran bygger på är modellerad efter näthinnan som finns i våra ögon. Näthinnan är uppbyggd av tunna lager av celler som omvandlar ljus till nervsignaler. Eftersom synsensorer efterliknar nervsystemet har de getts namnet neuromorfiska synsensorer. För att registrera små ljusförändringar måste dessa sensorer vara väldigt känsliga vilket även genererar ett elektroniskt brus. Detta brus försämrar kvalitén på signalen vilket blir en förhindrande faktor när dessa synsensorer ska användas i praktiken och ställer stora krav på att hitta effektiva metoder för brusredusering. Denna avhandling undersöker två typer av digitala framställningar som omvandlar signalen ifrån händelsekameror till något som efterliknar vanliga bilder som kan användas med traditionella metoder för bildigenkänning. Vi undersöker brusreduseringens inverkan på den övergripande noggrannhet som uppnås av en artificiell intelligens vid bildigenkänning. För att utmana AIn har vi tillfört ytterligare normalfördelat brus i signalen. De digitala framställningar som används är dels histogram av genomsnittliga tidsytor (eng. histograms of averaged time surfaces) och en matrisrepresentation. Vi visar att HATS är robust och klarar av att generera digitala framställningar som tillåter AIn att bibehålla god noggrannhet även vid höga nivåer av brus, vilket medför att brusreduseringens inverkan var försumbar. Matrisrepresentationen gynnas av brusredusering vid högre nivåer av brus.
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Sadreddini, Maryam. "Non-Uniformly Partitioned Block Convolution on Graphics Processing Units." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3243.

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Real time convolution has many applications among others simulating room reverberation in audio processing. Non-uniformly partitioning filters could satisfy the both desired features of having a low latency and less computational complexity for an efficient convolution. However, distributing the computation to have an uniform demand on Central Processing Unit (CPU) is still challenging. Moreover, computational cost for very long filters is still not acceptable. In this thesis, a new algorithm is presented by taking advantage of the broad memory on Graphics Processing Units (GPU). Performing the computations of a non-uniformly partitioned block convolution on GPU could solve the problem of work load on CPU. It is shown that the computational time in this algorithm reduces for the filters with long length.
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Lai, Bo-Syun, and 賴柏勳. "Classified-Filter-based Post-Compensation Scheme for Color Filter Array Demosaicing and Speed-Up Parametric-Oriented Histogram Equalization." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/8ty86r.

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碩士
國立臺灣科技大學
電機工程系
100
In this thesis, two contributions are delivered, including Classified-Filter-based Post-Compensation Scheme for Color Filter Array Demosaicing, and Speed-Up Parametric-Oriented Histogram Equalization. In the first half, a classified-filter-based post-compensation scheme for color filter array (CFA) demosaicing is proposed. This technique can be used for improving the image quality of the interpolated result obtained by any former demosaicing method. First, each pixel is classified according to its neighborhood’s magnitude and angle. Then, different Least-Mean-Square (LMS) filters are trained for dealing pixels of various characteristics. As documented in the experimental results, the proposed scheme can substantially boost the image quality; in addition, a better visual perception can be obtained. Notably, the proposed method can be considered as effective post-compensation by applying for any former schemes to yield an even better image quality. In the second half, two local contrast enhancement methods, namely Parametric-Oriented Histogram Equalization (POHE) and the Correct POHE (CPOHE), are proposed to effectively acquire the enhanced results while maintaining high accuracy on the contrast. The grayscale distribution of a specific region in an image can be modeled with a kernel function such as the Gaussian, thus the corresponding estimated cdf can be regarded the transformation function for contrast enhancement. However, the required parameters are still required by accessing all of the pixels, and thus consuming additional computations. To cope with this, the concept of integral image is adopted to effectively derive the required parameters. For further improving the local contrast, the distortion induced from the aforementioned cdf is analyzed, and it is further corrected by the proposed CPOHE through the concepts of classification and regression. In the experimental results, some former well-known methods are adopted for comparison, and it also demonstrates that the proposed methods provide high practical value for some active territories such as medical imaging and computer vision.
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Book chapters on the topic "Histogram and partition based filter"

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Hussain, Ayyaz, M. Arfan Jaffar, Abdul Basit Siddiqui, Muhammad Nazir, and Anwar M. Mirza. "Modified Histogram Based Fuzzy Filter." In Computer Vision/Computer Graphics CollaborationTechniques, 277–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01811-4_25.

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He, Wenguang, Gangqiang Xiong, and Yaomin Wang. "Reversible Data Hiding Based on Dynamic Image Partition and Multilevel Histogram Modification." In Advances in Computer and Computational Sciences, 503–10. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3773-3_49.

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Wang, Zhenhua, Fuyuan Hu, Shaohui Si, Yajun Gu, Ze Li, and Zhengtian Wu. "Fast Image Filter Based on Adaptive-Weight and Joint-Histogram Algorithm." In Lecture Notes in Computer Science, 551–63. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23989-7_56.

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Sahoo, Narayan, Ganeswara Padhy, Nilamani Bhoi, and Pranati Rautaray. "Automatic Localization of Pupil Using Histogram Thresholding and Region Based Mask Filter." In Soft Computing Techniques in Vision Science, 55–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25507-6_6.

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Gao, Shasha, Liang Zhou, and Qiang Xie. "An Improved Particle Filter Target Tracking Algorithm Based on Color Histogram and Convolutional Network." In Lecture Notes in Computer Science, 149–55. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97310-4_17.

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Yu, Hai-Yan, and Jiu-Lun Fan. "Three-Level Image Segmentation Based on Maximum Fuzzy Partition Entropy of 2-D Histogram and Quantum Genetic Algorithm." In Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, 484–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85984-0_58.

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U., Janasruti, Kavya S., Merwin A., and Vanithamani Rangasamy. "Deep Learning-Based Approach to Detect Leukemia, Lymphoma, and Multiple Myeloma in Bone Marrow." In Advances in Bioinformatics and Biomedical Engineering, 259–82. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3947-0.ch014.

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Bone marrow cancer is one of the life-threatening diseases which may cause death to many individuals. Leukemia, lymphoma, multiple myeloma, and other cancers that form in the blood-forming stem cells of the bone marrow constitute bone marrow cancer. Early detection can increase the chance for remission. Accurate and rapid segmentation techniques can assist physicians to identify diseases and provide better treatment at the right time. CAD systems can be useful for the early discovery of bone marrow cancer. It features the latest updated algorithm that combines deep learning with MATLAB for health assessment. This can assist in the early detection of leukemia, lymphoma, and multiple myeloma. For denoising histopathological images, new K-SVD and fast non-local mean filter algorithms are employed. For pre-processing, algorithms like multilayer perceptron and novel hybrid histogram-based soft covering rough k-means clustering techniques are employed. Three classifiers, namely R-CNN, ResNet 50, and LSTM, are used to classify, and the performance is compared based on the accuracy.
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Haefner, Michael, Alfred Gangl, Michael Liedlgruber, A. Uhl, Andreas Vecsei, and Friedrich Wrba. "Pit Pattern Classification Using Multichannel Features and Multiclassification." In Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications, 335–50. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-314-2.ch022.

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Wavelet-, Fourier-, and spatial domain-based texture classification methods have been used successfully for classifying zoom-endoscopic colon images according to the pit pattern classification scheme. Regarding the wavelet-based methods, statistical features based on the wavelet coefficients as well as structural features based on the wavelet packet decomposition structures of the images have been used. In the case of the Fourier-based method, statistical features based on the Fourier-coefficients in ring filter domains are computed. In the spatial domain, histogram-based techniques are used. After reviewing the various methods employed we start by extracting the feature vectors for the methods from one color channel only. To enhance the classification results the methods are then extended to utilize multichannel features obtained from all three color channels of the respective color model used. Finally, these methods are combined into one multiclassifier to stabilize classification results across the image classes.
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Deng, Feng, Zhong Su, Rui Wang, Jun Liu, and Yanzhi Wang. "A High-Performance Infrared Imaging System with Adaptive Contrast Enhancement." In Proceedings of CECNet 2021. IOS Press, 2021. http://dx.doi.org/10.3233/faia210450.

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Most of the existing infrared imaging systems employ the scheme of FPGA/FPGA+DSP with numerous peripheral circuits, which leads to complex hardware architecture, limited system versatility, and low computing performance. It has become an intriguing technical problem worldwide to simplify the system structure while improving the imaging performance. In this paper, we present a novel real-time infrared imaging system based on the Rockchip’s RV1108 visual processing SoC (system on chip). Moreover, to address the problem of low contrast and dim details in infrared images with a high dynamic range, an adaptive contrast enhancement method based on bilateral filter is proposed and implemented on the system. First, the infrared image is divided into a base layer and a detail layer through bilateral filter, then the base layer is compressed by an adaptive bi-plateau histogram equalization algorithm, and finally a linear-weighted method is used to integrate the detail layer to obtain the image with enhanced details. The experimental results indicate that compared with traditional algorithms, our method can effectively improve the overall contrast of the image, while effectively retaining the image details without noise magnification. For an image of 320*240 pixels, the real-time processing rate of the system is 68 frames/s. The system has the characteristics of simplified structure, perceptive image details, and high computing performance.
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Viswanath, K., and R. Gunasundari. "Modified Distance Regularized Level Set Segmentation Based Analysis for Kidney Stone Detection." In Medical Imaging, 693–710. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0571-6.ch027.

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The abnormalities of the kidney can be identified by ultrasound imaging. The kidney may have structural abnormalities like kidney swelling, change in its position and appearance. Kidney abnormality may also arise due to the formation of stones, cysts, cancerous cells, congenital anomalies, blockage of urine etc. For surgical operations it is very important to identify the exact and accurate location of stone in the kidney. The ultrasound images are of low contrast and contain speckle noise. This makes the detection of kidney abnormalities rather challenging task. Thus preprocessing of ultrasound images is carried out to remove speckle noise. In preprocessing, first image restoration is done to reduce speckle noise then it is applied to Gabor filter for smoothening. Next the resultant image is enhanced using histogram equalization. The preprocessed ultrasound image is segmented using distance regularized level set segmentation (DR-LSS), since it yields better results. It uses a two-step splitting methods to iteratively solve the DR-LSS equation, first step is iterating LSS equation, and then solving the Sign distance equation. The second step is to regularize the level set function which is the obtained from first step for better stability. The DR is included for LSS for eliminating of anti-leakages on image boundary. The DR-LSS does not require any expensive re-initialization and it is very high speed of operation. The RD-LSS results are compared with distance regularized level set evolution DRLSE1, DRLSE2 and DRLSE3. Extracted region of the kidney after segmentation is applied to Symlets (Sym12), Biorthogonal (bio3.7, bio3.9 & bio4.4) and Daubechies (Db12) lifting scheme wavelet subbands to extract energy levels. These energy level gives an indication about presence of stone in that particular location which significantly vary from that of normal energy level. These energy levels are trained by Multilayer Perceptron (MLP) and Back Propagation (BP) ANN to identify the type of stone with an accuracy of 98.6%.
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Conference papers on the topic "Histogram and partition based filter"

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Zhang, Dongni, Won-Jae Park, Seung-Jun Lee, Kang-A. Choi, and Sung-Jea Ko. "Histogram partition based gamma correction for image contrast enhancement." In 2012 IEEE 16th International Symposium on Consumer Electronics - (ISCE 2012). IEEE, 2012. http://dx.doi.org/10.1109/isce.2012.6241687.

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Lee, Jae-Yeong, and Wonpil Yu. "Visual tracking by partition-based histogram backprojection and maximum support criteria." In 2011 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2011. http://dx.doi.org/10.1109/robio.2011.6181739.

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Fotouhi, M., A. R. Gholami, and S. Kasaei. "Particle Filter-Based Object Tracking Using Adaptive Histogram." In 2011 7th Iranian Conference on Machine Vision and Image Processing (MVIP). IEEE, 2011. http://dx.doi.org/10.1109/iranianmvip.2011.6121612.

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Pandeeswari, P., and S. Murugeswari. "A partition based bloom filter for fastest data search." In 2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT). IEEE, 2016. http://dx.doi.org/10.1109/icaccct.2016.7831649.

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Wei, Zhiqiang, Caiyan Duan, Shuming Jiang, Yuanyuan Zhang, Jianfeng Zhang, and Lianpeng Zhu. "The Improved Winner Filter Image Restoration Based on Partition." In 2013 6th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2013. http://dx.doi.org/10.1109/iscid.2013.163.

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Lahraichi, Mohammed, Khalid Housni, and Samir Mbarki. "Particle Filter Object Tracking Based on Color Histogram and Gabor Filter Magnitude." In BDCA'17: 2nd international Conference on Big Data, Cloud and Applications. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3090354.3090434.

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Patro, Ram Narayan, Harish Kumar Sahoo, and Pradyut Kumar Biswal. "Dual Histogram Based RVIN detector and Hybrid Gaussian Filter." In 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON). IEEE, 2021. http://dx.doi.org/10.1109/gucon50781.2021.9573672.

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Farina, Marcello, and Ruggero Carli. "Plug and play partition-based state estimation based on Kalman filter." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7402692.

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Shao, Dangguo, and Dong C. Liu. "Local Histogram Matching Based Bilateral Filter to Ultrasound Speckle Reduction." In 2011 5th International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2011. http://dx.doi.org/10.1109/icbbe.2011.5780214.

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Zhang, Tao, and Lili Wang. "Tracking algorithm based on color correlation histogram using particle filter." In 2015 27th Chinese Control and Decision Conference (CCDC). IEEE, 2015. http://dx.doi.org/10.1109/ccdc.2015.7161799.

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