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检索条件"任意字段=3rd Iberian Conference on Pattern Recognition and Image Analysis"

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analysis of Gender Identification in Bahasa Indonesia using Supervised Machine Learning Algorithm

Analysis of Gender Identification in Bahasa Indonesia using ...

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International conference on Information and Communications Technology (ICOIACT)

作者： Evawaty Tanuar Edi Abdurachman Ford Lumban Gaol Lukas School of Computer Science Bina Nusantara University Jakarta Indonesia BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia

The following topics are dealt with: learning (artificial intelligence); feature extraction; image classification; convolutional neural nets; pattern classification; support vector machines; medical image processing; ... 详细信息

ISBN: (数字)9781728173566

ISBN: (纸本)9781728173573

关键词： Support vector machines Machine learning algorithms Biometrics (access control) Signal processing algorithms Artificial neural networks Speech recognition Classification algorithms

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Denoising of Smart-phone based Fringe Projection image using Curvelet Transform 2019

Denoising of Smart-phone based Fringe Projection Image using...

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3rd International conference on Digital Signal Processing (ICDSP) / 2nd International conference on Computer Graphics and Virtuality (ICCGV)

作者： Wang, Pingzheng Shan, Mingguang Yin, Zhihu Harbin Engn Univ Coll Informat & Commun Engn 145 Nantong St Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781450362047

Fourier transform profilometry (FTP) can enable three-dimensional(3-D) surface profiles through single-shot imaging, which make it one of the popular high-throughput, non-contact topography methods. Here, we developed a portable FTP using a smart phone and a micro projector. The micro projector casts a fringe pattern onto the measured object surface and the smart phone captures the resulting deformed fringe image. However, the recorded image has distortion and noise, which seriously degrade the reconstructed quality. In addition, the traditional transform, such as Fourier and wavelet, can suppress parts of high frequencies yielded by sharp changes in shape, resulting in image blurring. We then introduce Curvelet transform to inhibit the noise and preserve the high frequencies owing to its multiscale and multi-direction analysis in image processing capacities. Simulations and the experimental results show that the Curvelet transform has an excellent performance in image denoising and Curvelet transform denoising achieves high-precision 3D reconstruction.

关键词： Curvelet transform image denoising Portable fringe image acquisition system Single fringe pattern

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pattern recognition in the differentiated image for the powder and granulated materials particle size classification 3

Pattern recognition in the differentiated image for the powd...

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3rd International conference on Information Processing and Control Engineering (ICIPCE)

作者： Yunovidov, D. V. Nadezhin, M. N. Shabalov, V. A. JSC NIUIF Severnoe Shosse 75 Cherepovets 162622 Russia Cherepovets State Univ Off 203Lunacharsky Str 5 Cherepovets 162622 Russia

The paper shows and investigates the technique of classifying the particle size of powder and granulated materials. The objects of the study are industrially produced mineral fertilizers. Samples with different composition (about five types of mineral fertilizers) and various degrees of particle size (less than 100 mu m, less than 500 mu m and granules of 2-5 mm) were examined. The samples particles have an irregular shape, close to spherical (in the case of granules) or cubic (in the case of powders). To improve the accuracy and eliminate the particle shape influence on analysis, the preliminary pressing of samples on a boric acid substrate was used. The keynote of the proposed technique is to obtain an optoelectronic image of an object with a resolution of at least 640x480 pixels (a three-dimensional matrix of pixel intensity in the Red-Green-Blue (RGB) system). Next, the area of analysis is separated from the obtained image and transformed into grayscale (a two-dimensional matrix of pixel intensities with a resolution of at least 200x200 pixels). The influence of external illumination (gradient, temperature and brightness) is eliminated by the grayscale image differentiation. The result is the "surface map" of the sample, which reflects defects in the pressed structure (patterns, which are responsible for the size of the particles). According to the found patterns, the samples are classified according to their particle size. Four classification algorithms were investigated (linear, linear with L1 and L2 regularization, and a nonlinear "random forest"). All proposed approaches are automated and implemented in the Python 3.6 programming language. There is provided the selection of the operating parameters of all the described algorithms.

关键词： Particle size

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image Based Estimation of Fruit Phytopathogenic Lesions Area 9th

Image Based Estimation of Fruit Phytopathogenic Lesions Area

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9th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Marcal, Andre R. S. Santos, Elisabete M. D. S. Tavares, Fernando Univ Porto Fac Ciencias Porto Portugal INESC TEC Porto Portugal Univ Porto CIBIO Ctr Invest Biodiversidade & Recursos Genet Lab Associado InBIO Vairao Portugal

ISBN: (纸本)9783030313210;9783030313203

A method was developed to measure the surface area of walnut fruit phytopathogenic lesions from images acquired with a basic calibration target. The fruit is modelled by a spheroid, established from the 2D view ellipse using an iterative process. The method was tested with images of colour circular marks placed on a wooden spheroid. It proved effective in the estimation of the spheroid semi-diameters (average relative errors of 0.8% and 1.0%), spheroid surface (1.77%) and volume (2.71%). The computation of the colour mark surface area was within the expected error, considering the image resolution (up to about 4%), for 22 out of 28 images tested.

关键词： Fruit phytopathogenic lesion 3D modeling Spheroid Volume Surface area

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2019 3rd International conference on Imaging, Signal Processing and Communication, ICISPC 2019

2019 3rd International Conference on Imaging, Signal Process...

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3rd International conference on Imaging, Signal Processing and Communication, ICISPC 2019

ISBN: (纸本)9781728136639

The proceedings contain 32 papers. The topics discussed include: real-time classification of green coffee beans by using a convolutional neural network;color image quality assessment based on VIF;tag: nucleus detection in colorectal adenocarcinomas histology images using local texture, appearance, and gradient features;wavelet-energy-weighted local binary pattern analysis for tire tread pattern classification;image interpolation based on adaptive neuro-fuzzy inference system;and graph-based automatic modulation classifier for M-ary generalized QAM signals.

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A New Automatic Cancer Colony Forming Units Counting Method 9th

A New Automatic Cancer Colony Forming Units Counting Method

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9th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Roldan, Nicolas Rodriguez, Lizeth Hernandez, Andrea Cepeda, Karen Ondo-Mendez, Alejandro Cancino Suarez, Sandra Liliana Forero, Manuel G. Lopez, Juan M. Escuela Colombiana Ingn Julio Garavito Dept Biomed Engn Bogota Colombia Univ Rosario Sch Med & Hlth Sci Dept Biomed Engn Bogota Colombia Univ Rosario Sch Med & Hlth Sci Clin Res Grp Bogota Colombia Univ Ibague Fac Ingn Ibague Colombia

ISBN: (纸本)9783030313210;9783030313203

Clonogenic assays are an essential tool to evaluate the survival of cancer cells that have been exposed to a certain dose of radiation. Its result can be used in the generation of strategies for the optimization of radiotherapy treatments. The analysis of this type of data requires that the specialist performs the manual counting of colony forming units (CFU), i.e., find every cell that retains the ability to produce a large progeny. This task is time consuming, prone to errors and the results are not reproducible due to specialist subjective assessment. Digital image processing tools can deal with the flaws described above. This article presents a new technique for automatic CFU counting. The proposed technique extracts the regions of interest (ROIs), where a local segmentation algorithm finds and labels the CFUs in order to quantify them. Results show good sensitivity and specificity performance compared to state-of-the-art software used for CFU detection and counting.

关键词： CFU Colony counting Cell counting Cancer Cell proliferation image analysis

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Retinal Blood Vessel Segmentation: A Semi-supervised Approach 9th

Retinal Blood Vessel Segmentation: A Semi-supervised Approac...

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9th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Ghosh, Tanmai K. Saha, Sajib Rahaman, G. M. Atiqur Abu Sayed, Md Kanagasingam, Yogesan Khulna Univ Computat Color & Spectral Image Anal Lab Comp Sci & Engn Discipline Khulna Bangladesh Commonwealth Sci & Ind Res Org CSIRO Australian E Hlth Res Ctr Perth WA Australia

ISBN: (纸本)9783030313210;9783030313203

Segmentation of retinal blood vessels is an important step in several retinal image analysis tasks. State-of-the-art papers are still incapable to segment retinal vessels correctly, especially, in presence of pathology. In this paper an innovative descriptor named Robust Feature Descriptor (RFD) is proposed to describe vessel pixels more uniquely in the presence of pathology. For accurate segmentation of blood vessels, the method combines both supervised and unsupervised approaches. Extensive experiments have been conducted on three publicly available datasets namely DRIVE, STARE and CHASE_DB1;and the method has been compared with other state-of-the-art methods. The proposed method achieves an overall segmentation accuracy of 0.961, 0.960 and 0.955 respectively on DRIVE, STARE and CHASE_DB1 datasets, which are better than the state-of-the-art methods in comparison. The sensitivity, specificity and area under curve (AUC) of the method are respectively 0.737, 0.981, 0.859 on DRIVE dataset;0.805, 0.972, 0.889 on STARE dataset;and 0.763, 0.969, 0.866 on CHASE_DB1 dataset.

关键词： Retinal image Vessel segmentation Multi-scale line detector Robust Feature Descriptor Random forest

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Mosquito Larvae image Classification Based on DenseNet and Guided Grad-CAM 9th

Mosquito Larvae Image Classification Based on DenseNet and G...

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9th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Garcia, Zaira Yanai, Keiji Nakano, Mariko Arista, Antonio Cleofas Sanchez, Laura Perez, Hector Inst Politecn Nacl Mexico City DF Mexico Univ Electrocommun Tokyo Japan

ISBN: (纸本)9783030313210;9783030313203

The surveillance of Aedes aegypti and Aedes albopictus mosquito to avoid the spreading of arboviruses that cause Dengue, Zika and Chikungunya becomes more important, because these diseases have greatest repercussions in public health in the significant extension of the world. Mosquito larvae identification methods require special equipment, skillful entomologists and tedious work with considerable consuming time. In comparison with the short mosquito lifecycle, which is less than 2 weeks, the time required for all surveillance process is too long. In this paper, we proposed a novel technological approach based on Deep Neural Networks (DNNs) and visualization techniques to classify mosquito larvae images using the comb-like figure appeared in the eighth segment of the larva's abdomen. We present the DNN and the visualization technique employed in this work, and the results achieved after training the DNN to classify an input image into two classes: Aedes and Non-Aedes mosquito. Based on the proposed scheme, we obtain the accuracy, sensitivity and specificity, and compare this performance with existing technological approaches to demonstrate that the automatic identification process offered by the proposed scheme provides a better identification performance.

关键词： Mosquito larvae Classification Deep Neural Network Mosquito control Mosquito surveillance

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Sausage Colony Detection Based on Hyperspectral image analysis via Machine Learning 19

Sausage Colony Detection Based on Hyperspectral Image Analys...

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3rd International conference on Deep Learning Technologies (ICDLT)

作者： Xiao, Hongbing Guo, Peiyuan Chen, Xiaodong Beijing Technol & Business Univ Sch Informat Engn Beijing Peoples R China

ISBN: (纸本)9781450371605

Sausage is a kind of food that people often eat in our lives because it is cheap and delicious. However, in the process of sausage production and transportation, if the quality control is not strict, it will cause deterioration and decay. If people eat these sausages, they will be sick and even die. Existing detection methods, such as physical and chemical detection, need a strict testing environment, and the sensory detection is inaccurate and incomplete. Colonies number is a key quality measure of sausage. Therefore, we planed to study a method to achieve rapid, non-destructive and accurate detection of sausage colonies number. In this paper, a method for detecting sausage colonies number based on hyperspectral technique and image features was proposed. Firstly, the sausage hyperspectral image and spectral information were collected. Then, the N-Dimensional Visualizer was used to extract features from the hyperspectral image. Moreover, the characteristic samples of sausage surface colony information were obtained by studying the colony surface characteristic spectrum curve and the color statistics of sample graphics. At last, the colony spectrum library was established and the two feature data were fused via the machine learning algorithm Support Vector Machine (SVM) to classify sausage quality. According to the established surface colony characteristic model, the samples of validation set were detected, and the fast detection of sausage colony quality was realized. The experimental results showed that the pixel proportion of the colony information in the image was consistent with the actual colony information measured in the physical and chemical experiments, which proved that the method is feasible to study the colony on the sausage surface. The method in this study can also be further extended to the detection of other related meat products.

关键词： hyperspectral image pattern recognition machine learning SVM

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Incremental Learning for Football Match Outcomes Prediction 9th

Incremental Learning for Football Match Outcomes Prediction

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9th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Domingues, Jose Lopes, Bernardo Mihaylova, Petya Georgieva, Petia Univ Aveiro Dept Elect Telecommun & Informat Aveiro Portugal Tech Univ Sofia Sofia Bulgaria

ISBN: (纸本)9783030313210;9783030313203

Generating predictions for football match results is an expanding research area due to the commercial assets involved in the betting process. Traditionally, the results of the matches are predicted using statistical models verified by domain experts. Nowadays, this approach is challenged by the increasing amount of diverse football related information that need to be processed. In this paper, we propose an incremental learning method to predict the football match outcome category (home win, draw, away win) based on prior to the game publicly available information. The proposed framework is illustrated with data for the Portuguese first division football teams for 2017/2018 season. Factor analysis was applied to extract most discriminating features which allowed gradual convergence of the prediction error to 32.4% after accumulation of about one third of the season games. Our approach outperforms traditional models in the gambling industry today and implies potential financial opportunities. The proposed prediction model is useful for researchers, football betting crowd, bookmakers, sport managers.

关键词： Football match outcome prediction Betting market Sport data mining Factor analysis Machine learning

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