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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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Detection of malaria parasites in thin red blood smear using a segmentation approach with U-Net 37

Detection of malaria parasites in thin red blood smear using...

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37th International conference on Biomedical Engineering, IBIOMED 2020

作者： Nautre, Adrien Nugroho, Hanung Adi Frannita, Eka Legya Nurfauzi, Rizki Ecole des Mines de Saint-Etienne Mathematical Imaging and Spatial Pattern Analysis Saint-Etienne France Universitas Gadjah Mada Department of Electrical and Information Engineering Yogyakarta Indonesia

ISBN: (纸本)9781728171562

Malaria is infected by Plasmodium parasite which was a plague in many countries in the world. An alternative way to decrease the number of deaths caused by malaria parasites was that conducting advanced diagnostic for detecting malaria parasite. However, this diagnostic required expert skills and was inclined to human errors. In this situation, automation of this process could greatly help lot of laboratory to handle the disease. In this paper, we presented an automated way to segment the Plasmodium parasites infected in red blood cells using U-net. We conducted our training approach in the three different color spaces which are RGB, HSV and GGB. The proposed method achieved accuracy of 0.9940, 0.9936 and 0.9947 in RGB, HSV and GGB color space respectively. © 2020 IEEE.

关键词： image segmentation

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3rd International Workshop on Computer Vision for analysis of Underwater imagery, CVAUI 2018, 7th International Workshop on Computational Forensics, IWCF 2018, and International Workshop on Multimedia Information Processing for Personality and Social Networks analysis, MIPPSNA 2018 held at 24th International conference on pattern recognition, ICPR 2018

3rd International Workshop on Computer Vision for Analysis o...

引用

3rd International Workshop on Computer Vision for analysis of Underwater imagery, CVAUI 2018, 7th International Workshop on Computational Forensics, IWCF 2018, and International Workshop on Multimedia Information Processing for Personality and Social Networks analysis, MIPPSNA 2018 held at 24th International conference on pattern recognition, ICPR 2018

ISBN: (纸本)9783030057916

The proceedings contain 16 papers. The special focus in this conference is on Computer Vision for analysis of Underwater imagery. The topics include: Categorization of Document image Tampering Techniques and How to Identify Them;overview of the Multimedia Information Processing for Personality & Social Networks analysis Contest;handwritten Texts for Personality Identification Using Convolutional Neural Networks;recognition of Apparent Personality Traits from Text and Handwritten images;multimodal Database of Emotional Speech, Video and Gestures;from Text to Speech: A Multimodal Cross-Domain Approach for Deception Detection;marine Snow Removal Using a Fully Convolutional 3D Neural Network Combined with an Adaptive Median Filter;strategies for Tackling the Class Imbalance Problem in Marine image Classification;an Online Platform for Underwater image Quality Evaluation;tracking Sponge Size and Behaviour with Fixed Underwater Observatories;enhancement of Low-Lighting Underwater images Using Dark Channel Prior and Fast Guided Filters;Underwater-GAN: Underwater image Restoration via Conditional Generative Adversarial Network;single image Plankton 3D Reconstruction from Extended Depth of Field Shadowgraph;a Novel Method for Race Determination of Human Skulls.

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9th iberian conference on pattern recognition and image analysis, IbPRIA 2019

9th Iberian Conference on Pattern Recognition and Image Anal...

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9th iberian conference on pattern recognition and image analysis, IbPRIA 2019

ISBN: (纸本)9783030313319

The proceedings contain 99 papers. The special focus in this conference is on iberian conference on pattern recognition and image analysis. The topics include: 3D Reconstruction of Archaeological Pottery from Its Point Cloud;Geometric Interpretation of CNNs’ Last Layer;re-Weighted l1 Algorithms within the Lagrange Duality Framework: Bringing Interpretability to Weights;a Note on Gradient-Based Intensity Normalization;Blind Robust 3-D Mesh Watermarking Based on Mesh Saliency and QIM Quantization for Copyright Protection;using Copies to Remove Sensitive Data: A Case Study on Fair Superhero Alignment Prediction;weighted Multisource Tradaboost;a Proposal of Neural Networks with Intermediate Outputs;addressing the Big Data Multi-class Imbalance Problem with Oversampling and Deep Learning Neural Networks;interactive-Predictive Neural Multimodal Systems;reinforcement Learning and Neuroevolution in Flappy Bird Game;description and recognition of Activity patterns Using Sparse Vector Fields;instance Selection for the Nearest Neighbor Classifier: Connecting the Performance to the Underlying Data Structure;Modified DBSCAN Algorithm for Microscopic image analysis of Wood;Automatic Detection of Tuberculosis Bacilli from Microscopic Sputum Smear images Using Faster R-CNN, Transfer Learning and Augmentation;Detection of Stone Circles in Periglacial Regions of Antarctica in UAV Datasets;lesion Detection in Breast Ultrasound images Using a Machine Learning Approach and Genetic Optimization;evaluating the Impact of Color Information in Deep Neural Networks;Diatom Classification Including Morphological Adaptations Using CNNs;deep Learning of Visual and Textual Data for Region Detection Applied to Item Coding;uncertainty Estimation for Black-Box Classification Models: A Use Case for Sentiment analysis;single View Facial Hair 3D Reconstruction.

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Automatic Medicinal Plants Classification using Multi-channel Modified Local Gradient pattern with SVM Classifier 8

Automatic Medicinal Plants Classification using Multi-channe...

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Joint 8th International conference on Informatics, Electronics and Vision (ICIEV) / 3rd International conference on Imaging, Vision and pattern recognition (icIVPR) / International conference on Activity and Behavior Computing (ABC)

作者： Habiba, Umme Howlader, Md Rasel Islam, Md Aminul Faisal, Rahat Hossain Rahman, Md Mostafijur Univ Barisal Dept Comp Sci & Engn Barisal Bangladesh

ISBN: (纸本)9781728107882

Most of the people in the world rely on traditional medicine which is made from medicinal plants. However, very few works concentrate on automatic classification. Therefore, the automatic classification of medicinal plants demands more investigation which is an important issue for conservation, authentication, and production of medicines. In this paper, for automatically classifying medicinal plants, we present a Multi-channel Modified Local Gradient pattern (MCMLGP), a new texture-based feature descriptor that uses different channels of color images for extracting more significant features to improve the performance of classification. We have trained our proposed approach using SVM classifier with various kernels such as linear, polynomial and HI. In addition, we have used different feature descriptors for comparative experimental analysis with MCMLGP by conducting the rigorous experiment on our own medicinal plants dataset. The proposed approach gain higher accuracy (96.11%) than other techniques, and significantly valuable for exploration and evolution of medicinal plants classification.

关键词： image Classification Medicinal plant LGP Adaptive Threshold SVM

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State-of-the-Art Review of Deep Learning for Medical image analysis

State-of-the-Art Review of Deep Learning for Medical Image A...

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International conference on Intelligent Sustainable Systems (ICISS)

作者： Shankey Garg Pradeep Singh NIT Raipur Chhattisgarh India

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

Medical image analysis involves solving medical issues by the analysis of images fetched by medical practice. Main objective is to fetch the effective and efficient data in order to perform diagnosis in a better way. Improvements in the area of deep learning especially convolutional neural networks have become the choice of researchers for the medical image analysis field. Deep convolutional neural networks are mostly applied as compared to other available deep learning methods for the medical image analysis tasks. Deep learning and pattern recognition tools are utilized broadly in medical image analysis. In this paper a concise discussion of different techniques that are being deployed using deep learning for medical imaging such as classification, detection, & segmentation is investigated.

关键词： Deep learning Medical diagnostic imaging Task analysis Feature extraction Lesions Diseases Computational modeling

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Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing 1

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33rd International conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE)

作者： Gorji, Saeed Rahimi Granmo, Ole-Christoffer Glimsdal, Sondre Edwards, Jonathan Goodwin, Morten Univ Agder Ctr Artificial Intelligence Res Grimstad Norway Temporal Comp Newcastle Upon Tyne Tyne & Wear England

ISBN: (数字)9783030557898

ISBN: (纸本)9783030557881;9783030557898

The Tsetlin Machine (TM) is a machine learning algorithm founded on the classical Tsetlin Automaton (TA) and game theory. It further leverages frequent pattern mining and resource allocation principles to extract common patterns in the data, rather than relying on minimizing output error, which is prone to overfitting. Unlike the intertwined nature of pattern representation in neural networks, a TM decomposes problems into self-contained patterns, represented as conjunctive clauses. The clause outputs, in turn, are combined into a classification decision through summation and thresholding, akin to a logistic regression function, however, with binary weights and a unit step output function. In this paper, we exploit this hierarchical structure by introducing a novel algorithm that avoids evaluating the clauses exhaustively. Instead we use a simple look-up table that indexes the clauses on the features that falsify them. In this manner, we can quickly evaluate a large number of clauses through falsification, simply by iterating through the features and using the look-up table to eliminate those clauses that are falsified. The look-up table is further structured so that it facilitates constant time updating, thus supporting use also during learning. We report up to 15 times faster classification and three times faster learning on MNIST and Fashion-MNIST image classification, and IMDb sentiment analysis.

关键词： Tsetlin machines pattern recognition Propositional logic Frequent pattern mining Learning automata pattern indexing

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A Method for the Evaluation and Classification of the Orange Peel Effect on Painted Injection Moulded Part Surfaces 9th

A Method for the Evaluation and Classification of the Orange...

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

作者： Jafari-Tabrizi, Atae Luise Lichtenegger, Hannah Gruber, Dieter P. Polymer Competence Ctr Leoben GmbH Roseggerstr 12 A-8700 Leoben Austria

ISBN: (纸本)9783030313210;9783030313203

Orange peel effect, the wavy appearance on the surface, is one of the frequently encountered defects on the painted injection moulded parts. In this work, a method for evaluation and classification of the orange peel effect on a black-painted high-gloss surface using image processing and frequency analysis is presented. A monochrome camera is used to acquire images from the surface of the part, while an LED-bar is illuminating it. Because the part is complex shaped, in order to inspect the whole surface, a robotic manipulator is used to handle the part in front of the camera. After taking images, the region of interest (the region in the image illuminated by the light source) is selected manually. Based on the result of the image processing and frequency analysis assessing the waviness of the ROI, a score to evaluate and classify the intensity of the orange peel effect is calculated. Finally, the outcomes of this method are compared with the subjective evaluation of the experts, in order to examine the reliability of the evaluation method.

关键词： image processing Orange peel effect Surface inspection

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image Processing Method for Epidermal Cells Detection and Measurement in Arabidopsis Thaliana Leaves 9th

Image Processing Method for Epidermal Cells Detection and Me...

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

作者： Forero, Manuel G. Perdomo, Sammy A. Quimbaya, Mauricio A. Perez, Guillermo F. Univ Ibague Ibague Colombia Serv Nacl Aprendizaje SENA ASTIN Cali Colombia Pontificia Univ Javeriana Dept Nat Sci & Math Cali Colombia

ISBN: (纸本)9783030313210;9783030313203

Arabidopsis thaliana is the most important model specie employed for genetic analysis in plants. As it has been extensively proven, the first pair of extended leaves and its cellular and morphological changes during Arabidopsis development, is and accurate model to understand the molecular and physiological events that control cell cycle progression in plants. Nevertheless, cell analysis on leaves depends significantly on images acquired from a microscopy coupled to a drawing tube, where cells are traced by hand for posterior digitalization and analysis. This process is tedious, inaccurate and highly temporally inefficient. A new image processing method for cell detection in leaves of Arabidopsis thaliana is presented. Using complementary image processing techniques, we introduce a good way to obtain the original cell contour shapes, surpassing the limitations given by factors like noise, stomata, blurred edges, and non-uniform illumination. Results show the new methodology minimizes considerably the time of cell detection compared with the microscopy coupled tube method, and produces matching percentages over 80%.

关键词： Arabidopsis thaliana Cell drawings Epidermal cells image detection image analysis method

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A Deep pattern recognition Approach for Inferring Respiratory Volume Fluctuations from fMRI Data 23rd

A Deep Pattern Recognition Approach for Inferring Respirator...

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23rd International conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Bayrak, Roza G. Salas, Jorge A. Huo, Yuankai Chang, Catie Department of Electrical Engineering and Computer Science Vanderbilt University NashvilleTN37215 United States Department of Biomedical Engineering Vanderbilt University NashvilleTN37215 United States Vanderbilt University Institute of Imaging Science NashvilleTN37215 United States

ISBN: (纸本)9783030597276

Functional magnetic resonance imaging (fMRI) is one of the most widely used non-invasive techniques for investigating human brain activity. Yet, in addition to local neural activity, fMRI signals can be substantially influenced by non-local physiological effects stemming from processes such as slow changes in respiratory volume (RV) over time. While external monitoring of respiration is currently relied upon for quantifying RV and reducing its effects during fMRI scans, these measurements are not always available or of sufficient quality. Here, we propose an end-to-end procedure for modeling fMRI effects linked with RV, in the common scenario of missing respiration data. We compare the performance of multiple deep learning models in reconstructing missing RV data based on fMRI spatiotemporal patterns. Finally, we demonstrate how the inference of missing RV data may improve the quality of resting-state fMRI analysis by directly accounting for signal variations associated with slow changes in the depth of breathing over time. © 2020, Springer Nature Switzerland AG.

关键词： pattern recognition

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Wavelet-Energy-Weighted Local Binary pattern analysis for Tire Tread pattern Classification 3

Wavelet-Energy-Weighted Local Binary Pattern Analysis for Ti...

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

作者： Liu, Ying Zhang, Shuai Wang, Fuping Lim, Kengpang Liu, Qiqi Lei, Yanbo Gong, Yanchao Lu, Jin Minist Publ Secur Natl Key Lab Elect Informat Proc Applicat Crime S Beijing Peoples R China Xian Univ Posts & Telecommun Ctr Image & Informat Proc Xian Peoples R China

ISBN: (纸本)9781728136639

Tire tread pattern image classification plays an important role in crime scene and traffic accident investigation. Due to the lack of standard test dataset, there is little work done in this area. For efficient texture feature description, inherent characteristic of tire patterns need to be considered. Leveraging on the directionality characteristics of tread patterns, a novel texture feature extraction algorithm is proposed based on adaptive weighted feature fusion with the weights defined by sub-band energy ratio. The proposed approach consists of: (1) discrete wavelet decomposition of tire tread image to obtain low frequency, horizontal, vertical and diagonal sub-bands;(2) extraction of rotation-invariant uniform local binary pattern features from the sub-band images;(3) concatenating the tread pattern directional features, weighted by their corresponding sub-band energies. Applying SVM for tire tread pattern classification, experimental results on real-world tire tread patterns show that the proposed texture feature extraction algorithm is outperforms other prior methods.

关键词： Tire tread pattern image classification texture directional feature local binary pattern adaptive weighting feature fusion

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