Pattern Recognition Research Papers - Academia.edu (original) (raw)

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- Geology, Geochemistry, Geophysics, Volcanology
- by Dylan S Davis
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- Archaeology, Geophysics, Remote Sensing, Machine Learning
- by AJAY KUMAR
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- Principal Component Analysis, Pattern Recognition, Neural Networks, Machine Vision

7th International Conference on Artificial Intelligence and Applications (AI 2021) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference
looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and
share cutting-edge development in the field. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

9
th International Conference of Artificial Intelligence and Fuzzy Logic (AI & FL 2021)
provides a forum for researchers who address this issue and to present their work in a peerreviewed forum. Authors are solicited to contribute to the conference by submitting articles that
illustrate research results, projects, surveying works and industrial experiences that describe
significant advances in the following areas, but are not limited to these topics only.
Authors are solicited to contribute to this conference by submitting articles that illustrate
research results, projects, surveying works and industrial experiences that describe significant
advances in the areas of Artificial Intelligence & applications

- by Jukka Heikkonen
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- Computer Vision, Pattern Recognition, Segmentation, Calibration

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.

- by Andrew Wong
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- Machine Learning, Graph Theory, Motor Learning, Data Analysis

Identification of flow pattern during the simultaneous flow of two immiscible liquids requires knowledge of the flow rate of each fluid as well as knowledge of other physical parameters like conduit inclination, pipe material, pipe diameter, viscosity of the oil, wetting characteristics of the pipe, design of the entry mixer, and fluid-fluid interfacial tension. This article presents an artificial neural

- by Gargi Das
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- Engineering, Chemical Engineering, Liquid Crystals, Pattern Recognition
- by Francesc Serratosa
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- Robot Vision, Pattern Recognition, Image Analysis, Image segmentation

Emotion recognition is one of the important highlights of human emotional intelligence and has long been studied to be incorporated with machine intelligence argued to make machines even more intelligent. This paper aims to contribute to this field of study by enabling machines to recognize emotion from facial electromyogram (EMG) signals. This includes a compilation of the groups attempt to recognize basic facial expressions namely happy, angry, and sad through the use of EMG signals from facial muscles. The group extracted features from the three EMG signals from the face of two human subjects, a male and a female, and analyzed these features to serve as feature templates. Using a minimum-distance classifier, recognition rates exceeded the target accuracy - 85 percent - reaching 94.44 percent for both the male and female subjects.

- by Grace Briones
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- Computer Science, Artificial Intelligence, Emotional intelligence, Biosensors

We present both a computational and an experimental approach to the problem of biological aerosol characterization, joining the expertises reached in the field of theoretical optical scattering by complex, arbitrary shaped particles... more

- by Rosalba Saija
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- Optics, Pattern Recognition, Feature Extraction, Electromagnetic Field

We report our recent work on the recognition of scene text captured by mobile cameras, which we have named Kannada Pado. The text region is currently manually cropped using a user-friendly interface, which permits repeated croppings from the captured image in a hierarchical fashion. The scene text segment is then binarized using the algorithm, midline analysis, and propagation for segmentation. The segmented binary text image is recognized using Lipi Gnani Kannada OCR. The recognized text can be transcribed in Roman, Devanagari, and other principal Indian scripts. Such tools will be of immense use in metropolitan cities such as Bengaluru for business visitors and tourists to be able to read important textual information using their mobile itself. The entire implementation is of low computational complexity and hence, runs fully on the mobile itself, without any backend computation. Currently, text recognition accuracy is the bottleneck, which, when improved, will make the app immediately usable by people. Then, it will be made available to the public from Google Playstore.

- by Sasidharan Ayyavu
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- Machine Learning, Pattern Recognition, Kannada, Tamil
- by Marcos Faundez-Zanuy
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- Cognitive Science, Pattern Recognition, Modeling, Biometrics

Study of the relationship between mutagenicity and molecular structure for a data set of nitrogenous cyclic compounds is reported. A computerized SAR system (ADAPT) was utilized to classify a data set of 114 nitrogenous cyclic compounds with 19 molecular descriptors. All of the descriptors represented at least 10% of the compounds in the data sets. The average correct predictability of the data base was calculated to be 89% after evaluating 100 training/prediction subsets. The actual predictive ability of the discriminants generated by the ADAPT system was demonstrated by predicting the mutagenicity of structurally similar compounds not in the data set. Weight vectors generated in the pattern recognition programs were used to predict the bacterial mutagenicity of 10 compounds which were not included in the data set. All of the compounds were predicted correctly which was actually better than the 89% calculated by the system. This displayed the ability of the system of classify compo...

- by Larry D Claxton
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- Pattern Recognition, Software, Mutation, Salmonella Typhimurium

Understanding text captured in real-world scenes is a challenging problem in the field of visual pattern recognition and continues to generate a significant interest in the OCR (Optical Character Recognition) community. This paper proposes a novel method to recognize scene texts avoiding the conventional character segmentation step. The idea is to scan the text image with multi-scale windows and apply a robust recognition model, relying on a neural classification approach, to every window in order to recognize valid characters and identify non valid ones. Recognition results are represented as a graph model in order to determine the best sequence of characters. Some linguistic knowledge is also incorporated to remove errors due to recognition confusions. The designed method is evaluated on the ICDAR 2003 database of scene text images and outperforms state-of-the-art approaches.

- by Pascale Sebillot
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- Pattern Recognition, Character Recognition, Neural Network, Design method
- by Brian Butterworth
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- Mathematics, Cognition, Pattern Recognition, Language
- by T. Kauranne and +1
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- Pattern Recognition, Electrical And Electronic Engineering
- by AR Publication
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- Artificial Intelligence, Image Processing, Machine Learning, Pattern Recognition
- by YZ Zeng
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- Algorithms, Artificial Intelligence, Materials Science, Crystallography

Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of measurements capable of expressing the most relevant topological features. This article presents a survey of such measurements. It includes general considerations about complex network characterization, a brief review of the principal models, and the ...

- by Francisco Rodrigues
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- Physics, Condensed Matter Physics, Quantum Physics, Multivariate Statistics

Abstract: This paper proves that non-convex quadratically constrained quadratic programs have an exact semidefinite relaxation when their underlying graph is acyclic, provided the constraint set satisfies a certain technical condition. When the condition is not satisfied, we propose a heuristic to obtain a feasible point starting from a solution of the relaxed problem. These methods are then demonstrated to provide exact solutions to a richer class of optimal power flow problems than previously solved.

- by deepak kumar
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- Mathematics, Functional Analysis, Computer Science, Machine Learning
- by Jean-Louis Roujean
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- Cartography, Artificial Intelligence, Machine Learning, Pattern Recognition

Face Recognition has been identified as one of the attracting research areas and it has drawn the attention of many researchers due to its varying applications such as security systems, medical systems, entertainment, etc. Face recognition is the preferred mode of identification by humans: it is natural, robust and non-intrusive. A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user and no one else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. In this paper we have developed and illustrated a recognition system for human faces using a novel Kohonen self-organizing map (SOM) or Self-Organi...

It is common practice to utilize evidence from biological and psychological vision experiments to develop computational models for low-level feature extraction. The receptive profiles of simple cells in mammalian visual systems have been... more

- by Kamesh Namuduri
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- Mathematics, Computer Science, Computer Vision, Image Processing
- by robert haralick
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- Computer Science, Pattern Recognition, Optimization, Performance Evaluation
- by Kamran Khakpour
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- Expert Systems, Control Engineering, Pattern Recognition, Robust control
- by Chinmaya Misra
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- Information Retrieval, Computer Vision, Image Processing, Pattern Recognition
- by Hamid Pilevar
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- Cognitive Science, Data Mining, Pattern Recognition, Algorithm

One of the most famous cultural heritages in Indonesia is batik. Batik is a specially made drawing cloth by writing Malam (wax) on the cloth, then processed in a certain way. The diversity of motifs both in Indonesia and the allied countries raises new research topics in the field of information technology, both for conservation, storage, publication and the creation of new batik motifs. In computer science research area, studies about Batik pattern have been done by researchers and some algorithms have been successfully applied in Batik pattern recognition. This study was focused on Batik motif recognition using texture fusion feature which is Gabor, Log-Gabor, and GLCM; and using PCA feature reduction to improve the classification accuracy and reduce the computational time. To improve the accuracy, we proposed a Deep Neural Network model to recognise batik pattern and used batch normalisation as a regularises to generalise the model and to reduce time complexity. From the experiments, the feature extraction, selection, and reduction gave better accuracy than the raw dataset. The feature selection and reduction also reduce time complexity. The DNN+BN significantly improve the accuracy of the classification model from 65.36% to 83.15%. BN as a regularization has successfully made the model more general, hence improve the accuracy of the model. The parameters tuning also improved accuracy from 83.15% to 85.57%.

Scene text recognition brings various new challenges occurs in recent years. Detecting and recognizing text in scenes entails some of the equivalent problems as document processing, but there are also numerous novel problems to face for recognizing text in natural scene images. Recent research in these regions has exposed several promise but present is motionless much effort to be entire in these regions. Most existing techniques have focused on detecting horizontal or near-horizontal texts. In this paper, we propose a new scheme which detects texts of arbitrary directions in natural scene images. Our algorithm is equipped with two sets of characteristics specially designed for capturing both the natural characteristics of texts using MSER regions using Otsu method. To better estimate our algorithm and compare it with other existing algorithms, we are using existing MSRA Dataset, ICDAR Dataset, and our new dataset, which includes various texts in various real-world situations. Experiments results on these standard datasets and the proposed dataset shows that our algorithm compares positively with the modern algorithms when using horizontal texts and accomplishes significantly improved performance on texts of random orientations in composite natural scenes images.

- by Erik Rodner
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- Pattern Recognition, Binary Classification
- by Nilson Souza
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- Engineering, Computer Science, Information Retrieval, Music
- by akshaya sharma
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- Finance, Pattern Recognition, Technical Analysis, Statistical Inference
- by Joel Gannett and +1
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- Machine Learning, Pattern Recognition, Photonics, Support Vector Machines

The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content.

- by Gemma Sánchez
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- Pattern Recognition, User Interface, Performance Evaluation, Document Analysis
- by M. Bestehorn
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- Pattern Recognition, Fluid, Mathematical Sciences, Physical sciences

Total polyphenol contents, estimated by Folin–Ciocalteu method, and CIELab chromatic parameters were determined in Basque and French ciders with the aim of developing a classification system to confirm the authenticity of ciders. A preliminary study of data structure was performed by a multivariate data analysis using chemometric techniques such as cluster analysis and principal component analysis. Supervised pattern recognition methods,

- by Blanca Gallo and +1
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- Principal Component Analysis, Pattern Recognition, Food Chemistry, Multivariate Data Analysis
- by Ian Fasel and +2
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- Machine Learning, Pattern Recognition, Facial expression, Feature Selection
- by Jan Sijbers
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- Iterative Methods, Pattern Recognition, Image Classification, Image Reconstruction
- by Luis angel Alvarado Hernandez
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- Engineering, Pattern Recognition, Clustering, Physical sciences

In this paper, we present a high compression and collision resistant algorithm for images either suitable to extract an indexing pattern of the image and to detect deformations applied to original image. Some transforms are extracting characteristics invariant against geometrical deformations (rotation and scalling). Among them, the Radon transform, largely used in magnetic resonance imaging, is also robust against image processing basic attacks (like compression, filtering, blurring, etc...) and strong attacks (Stirmark). This transformation allows to caracterize easily features of geometrical transforms. It permits also an easy extraction of an indexing vector of the image.

- by Jean-Didier Legat
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- Pattern Recognition, Hash Function

The selection of the optimal feature subset and the classification has become an important issue in the field of iris recognition. In this paper we propose several methods for iris feature subset selection and vector creation. The deterministic feature sequence is extracted from the iris image by using the contourlet transform technique. Contourlet transform captures the intrinsic geometrical structures of

- by Amir Azizi
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- Computer Science, Pattern Recognition, Feature Extraction, Support vector machine