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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...

引用

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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Multi-task Layout analysis of Handwritten Musical Scores 9th

Multi-task Layout Analysis of Handwritten Musical Scores

引用

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

作者： Quiros, Lorenzo Toselli, Alejandro H. Vidal, Enrique Univ Politecn Valencia Pattern Recognit & Human Language Technol Res Ctr Camino Vera S-N Valencia 46022 Spain

ISBN: (纸本)9783030313210;9783030313203

Document Layout analysis (DLA) is a process that must be performed before attempting to recognize the content of handwritten musical scores by a modern automatic or semiautomatic system. DLA should provide the segmentation of the document image into semantically useful region types such as staff, lyrics, etc. In this paper we extend our previous work for DLA of handwritten text documents to also address complex handwritten music scores. This system is able to perform region segmentation, region classification and baseline detection in an integrated manner. Several experiments were performed in two different datasets in order to validate this approach and assess it in different scenarios. Results show high accuracy in such complex manuscripts and very competent computational time, which is a good indicator of the scalability of the method for very large collections.

关键词： Document Layout analysis Text region detection and classification Semantic segmentation Music document processing Music score images

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Keystroke Mobile Authentication: Performance of Long-Term Approaches and Fusion with Behavioral Profiling 9th

Keystroke Mobile Authentication: Performance of Long-Term Ap...

引用

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

作者： Acien, Alejandro Morales, Aythami Vera-Rodriguez, Ruben Fierrez, Julian Univ Autonoma Madrid Sch Engn BiDA Lab C Francisco Tomas y Valiente 11 Madrid 28049 Spain

ISBN: (纸本)9783030313210;9783030313203

In this paper we evaluate the performance of mobile keystroke authentication according to: (1) data availability to model the user;and (2) combination with behavioral-based profiling techniques. We have developed an ensemble of three behavioral based-profile authentication techniques (WiFi, GPS Location, and App usage) and a Keystroke state-of-the-art recognition approach. Algorithms based on template update are employed for profiling systems meanwhile bidirectional recurrent neuronal networks with a Siamese training setup is used for the keystroke system. Our experiments are conducted on the semi-uncontrolled UMDAA-02 database. This database comprises smartphone sensor signals acquired during natural human-mobile interaction. Our results show that it is necessary 6 days of usage data stored to achieve the best performance in average. The template update allows to improve the equal error rate of keystroke by a relative 20%-30% performance.

关键词： Mobile authentication Biometric recognition Behavioral pattern Behavioral-based profiling Keystroke dynamics

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Low-Light Face image Enhancement Based on Dynamic Face Part Selection 9th

Low-Light Face Image Enhancement Based on Dynamic Face Part ...

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

作者： Oulefki, Adel Aouache, Mustapha Bengherabi, Messaoud Ctr Dev Technol Avances CDTA POB 17 Algiers 16303 Algeria

ISBN: (纸本)9783030313210;9783030313203

A common challenge faced by face recognition community is struggling to circumvent face images that are acquired under low-light situation. The present work aims to couple the power of the popular CLAHE algorithm for face preprocessing with a Fuzzy inference system in such a way to correct the annoyance of non-uniform illumination of face images in a targeted and a precise manner. Due to the particularity of the low-light illumination problem. Firstly, the input face image is divided into two equal sub-regions. Subsequently, the degree of brightness in each sub-region and in the whole face is used for dynamic decision of whether to normalize. In the case where only one region of the face undertakes the CLAHE-Fuzzy approach is applied. Thus, the left and right face regions are grouped back followed by further processing like a blur removal and contrast enhancement (smoothing). Visual results showed that more facial features appeared in comparison with other approaches for enhancement. Besides, we quantitatively validate the accuracy of the developed Partial Fuzzy Enhancement Approach (PFEA) with four different metrics. The effectiveness of PFEA technique has been demonstrated by presenting extensive experimental results using Extended Yale-B, CMU-PIE, Mobio, and CAS-PEAL databases.

关键词： Face image enhancement Partial Fuzzy Enhancement Approach (PFEA) Blending images

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Offline Signature Verification Using Textural Descriptors 9th

Offline Signature Verification Using Textural Descriptors

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

作者： Hadjadj, Ismail Gattal, Abdeljalil Djeddi, Chawki Ayad, Mouloud Siddiqi, Imran Abass, Faycel Mohand Akli Univ Bouira Algeria Larbi Tebessi Univ Tebessa Algeria Bahria Univ Islamabad Pakistan

ISBN: (纸本)9783030313210;9783030313203

Offline signature verification has been the most commonly employed modality for authentication of an individual and, it enjoys global acceptance in legal, banking and official documents. Verifying the authenticity of a signature (genuine or forged) remains a challenging problem from the perspective of computerized solutions. This paper presents a signature verification technique that exploits the textural information of a signature image to discriminate between genuine and forged signatures. Signature images are characterized using two textural descriptors, the local ternary patterns (LTP) and the oriented basic image features (oBIFs). Signature images are projected in the feature space and the distances between pairs of genuine and forged signatures are used to train SVM classifiers (a separate SVM for each of the two descriptors). When presented with a questioned signature, the decision on its authenticity is made by combining the decisions of the two classifiers. The technique is evaluated on Dutch and Chinese signature images of the ICDAR 2011 benchmark dataset and high accuracies are reported.

关键词： Offline signature verification Local Ternary patterns (LTP) oriented Basic image Features (oBIFs) Support Vector Machine (SVM)

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Real-Time Traffic Monitoring with Occlusion Handling 9th

Real-Time Traffic Monitoring with Occlusion Handling

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

作者： Fernandez-Sanjurjo, Mauro Mucientes, Manuel Brea, Victor M. Univ Santiago de Compostela Ctr Singular Invest Tecnoloxias Intelixentes CiTI Santiago De Compostela Spain

ISBN: (纸本)9783030313210;9783030313203

Traffic surveillance through vision systems is a highly demanded task. To solve it, it is necessary to combine detection and tracking in a way that meets the requirements of operating in real time while being robust against occlusions. This paper proposes a traffic monitoring system that meets these requirements. It is formed by a deep learning-based detector, tracking through a combination of Discriminative Correlation Filter and a Kalman Filter, and data association based on the Hungarian method. The viability of the system has been proved for roundabout input/output analysis with near 1,000 vehicles in real-life scenarios.

关键词： Multiple object tracking Traffic monitoring Roundabout analysis

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