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Edge Detection Based on Riesz Transform 7th

Edge Detection Based on Riesz Transform

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7th international conference on Image and Signal Processing (ICISP)

作者： Belaid, Ahror Aloui, Soraya Boukerroui, Djamal Univ Abderrahmane Mira Med Comp Lab LIMED Bejaia 06000 Algeria Mirada Med Oxford Ctr Innovat New Rd Oxford OX1 1BY England

ISBN: (纸本)9783319336183;9783319336176

In this paper, we present a new way of 2D feature extraction. We start by showing the direct link that exist between the Riesz Transform (RT) and the gradient and Laplacian operators. This formulation allows us to interpret the RT as a gradient of a smoothed image. Thus, by analogy with the classical models, the maximum gradient and the zero crossings of the divergence of the TR provide information about the position of contours. The interest of the RT is its representation that naturally sweeps the whole area of the image and allows a correct description of structures. Using different filters, our models have been tested and compared with classical models and some recent ones. The results show that our detection technique is more efficient and more accurate.

关键词： Riesz transform Edge detection Laplacian zero crossings Monogenic signal alpha scale space filter

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7th international conference on Image and Signal Processing, ICISP 2016

7th International Conference on Image and Signal Processing,...

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7th international conference on Image and Signal Processing, ICISP 2016

ISBN: (纸本)9783319336176

The proceedings contain 40 papers. The special focus in this conference is on Feature Extraction, Computer Vision and pattern recognition. The topics include: On the benefit of state separation for tracking in image space with an interacting multiple model filter;feature asymmetry of the conformal monogenic signal;edge detection based on riesz transform;otolith recognition system using a normal angles contour;a hybrid combination of multiple SVM classifiers for automatic recognition of the damages and symptoms on plant leaves;leaf classification using convexity measure of polygons;privacy preserving dynamic room layout mapping;defect detection on patterned fabrics using entropy cues;curve extraction by geodesics fusion;a chaotic cryptosystem for color image with dynamic look-up table;nonlinear estimation of chromophore concentrations and shading from hyperspectral images;a color image database for haze model and dehazing methods evaluation;collaborative unmixing hyperspectral imagery via nonnegative matrix factorization;a new method for arabic text detection in natural scene image based on the color homogeneity;measuring spectral reflectance and 3d shape using multi-primary image projector;computer vision color constancy from maximal projections mean assumption;demosaicking method for multispectral images based on spatial gradient and inter-channel correlation;single image super-resolution using sparse representation on a K-NN dictionary;super-resolved enhancement of a single image and its application in cardiac MRI;speaker classification via supervised hierarchical clustering using ICA mixture model and speaker discrimination using several classifiers and a relativistic speaker characterization.

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Single Image Super-Resolution Using Sparse Representation on a K-NN Dictionary 7th

Single Image Super-Resolution Using Sparse Representation on...

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7th international conference on Image and Signal Processing (ICISP)

作者： Ning, Liu Shuang, Liang Peking Univ Sch Math Sci Bldg Peking Univ5 Yiheyuan Rd Beijing Peoples R China

ISBN: (纸本)9783319336183;9783319336176

This paper presents a new method of generating a high-resolution image from a low-resolution image. We use a sparse representation based model for low-resolution image patches. We use large patches instead of small ones of existing methods. The size of the dictionary must be large to guarantee its completeness. For each patch in the low-resolution image, we search for similar patches in the dictionary to obtain a sub-dictionary. To define the similarity and to speed up the searching process, we present a Restricted Boltzmann Machine (RBM) based binary encoding method to get binary codes for the low-resolution patches, and use Hamming distance to describe the similarity. With the KNN dictionary of each low-resolution patch, we use a sparse representation method to get its high-resolution version. Experimental results illustrate that our method outperforms other methods.

关键词： Super-resolution Sparse representation Restricted Boltzmann machine Binary encoding

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Detection and Classification of Diseases of Grape Plant Using Opposite Colour Local Binary pattern Feature and Machine Learning for Automated Decision Support System. 3

Detection and Classification of Diseases of Grape Plant Usin...

引用

3rd international conference on Signal Processing and Integrated Networks (SPIN)

作者： Waghmare, Harshal Kokare, Radha Dandawate, Yogesh Vishwakanna Inst Informat Technol Dept Elect & Telecommun Engn Pune Maharashtra India

ISBN: (纸本)9781467391979

Plant diseases cause major economic and production losses as well as curtailment in both quantity and quality of agricultural production. Now a day's, for supervising large field of crops there is been increased demand for plant leaf disease detection system. The critical issue here is to monitor the health of the plants and detection of the respective diseases. Studies show that most of the plant disease can be diagnosed from the properties of the leaf. Thus leaf based disease analysis for plants is an exciting new domain. The technique proposed for identification of plant disease through the leaf texture analysis and pattern recognition. In this work we focus on Grapes plant leaf disease detection system. The system takes a single leaf of a plant as an input and segmentation is performed after background removal. The segmented leaf image is then analyzed through high pass filter to detect the diseased part of the leaf. The segmented leaf texture is retrieved using unique fractal based texture feature. Fractal based features are locally invariant in nature and therefore provides a good texture model. The texture of every independent disease will be different. The extracted texture pattern is then classified using multiclass SVM. The work classifies focus on major diseases commonly observed in Grapes plant which are downy mildew & black rot. The proposed approach avails advice of agricultural experts easily to farmers with the accuracy of 96.6%.

关键词： Plant disease Decision Support System (DSS) HSV (Hue Saturation Value) Support Vector Machine (SVM) Texture analysis OC-LBP

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Scalable Sequential pattern Mining Based on PrefixSpan for High Dimensional Data 3

Scalable Sequential Pattern Mining Based on PrefixSpan for H...

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3rd international conference on Data and Software Engineering (ICoDSE)

作者： Akbar, Muhammad Nur Saptawati, G. A. Putri Inst Technol Bandung Sch Elect Engn & Informat Bandung Indonesia

ISBN: (纸本)9781509056712

The phenomenon of data explosion makes analysis to find insights inside the data become more difficult. A problem that often occurs in the application of pattern recognition in the real-world domain is not only caused by the large size of data but also the high-dimensional data. Data analysis demands that a large and complex data can be processed quickly and optimally to support decision making. This study offered a scalable sequential patterns extraction to gain more insight from the data using PrefixSpan implemented on the Spark platform as a distributed system. The goal is to overcome the problem of increasing the amount of data (scalability) in complex and high dimensional data effectively and in a relatively quick performance. The experiments show that this method can make full use of cluster computing resources to accelerate the mining process, reduces the time of scanning database and build projected database with an increasing number of worker on the Spark platform.

关键词： PrefixSpan Spark sequential patterns mining scalability high dimensional data

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Unsupervised feature selection algorithm based on sparse representation 3

Unsupervised feature selection algorithm based on sparse rep...

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2016 3rd international conference on Systems and Informatics, ICSAI 2016

作者： Cui, Guoqing Yang, Jie Zareapoor, Masoumeh Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Minhang District Shanghai China Jiechen Wang Complex Network and Control Lab Shanghai Jiao Tong University Minhang District Shanghai China

ISBN: (纸本)9781509055210

Feature selection, as a preprocessing step to machine learning, plays a pivotal role in removing irrelevant data, reducing dimensionality and improving performance evaluations. Recent years, sparse representation has become a useful tool for both supervised and unsupervised feature selection. So far, most of these algorithms still have many problems such as large computation load, performance with poor stability. Thus, this paper proposes a new unsupervised feature selection algorithm via sparse representation (UFSSR), with respect to efficiency and effectiveness. Firstly, this paper reconstructs part of data matrix via sparse representation, which makes the proposed algorithm be robust and independent of domain knowledge. Then, to reduce the reconstruction error, a new feature evaluation function is given to rank all features. Theoretical analysis and experiments compared with many popular algorithms on a set of datasets demonstrate the improvements brought by UFSSR. © 2016 IEEE.

关键词： Feature Selection

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Process Mining of Interactions During Computer-Based Testing for Detecting and Modelling Guessing Behavior 3rd

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3rd international conference on Learning and Collaboration Technologies (LCT) Held as Part of 18th international conference on Human-Computer Interaction (HCI international)

作者： Papamitsiou, Zacharoula Economides, Anastasios A. Univ Macedonia IPPS Informat Syst Thessaloniki Greece

ISBN: (纸本)9783319394831;9783319394824

Detecting, recognizing and modelling patterns of observed examinee behaviors during assessment is a topic of great interest for the educational research community. In this paper we investigate the perspectives of process-centric inference of guessing behavior patterns. The underlying idea is to extract knowledge from real processes (i.e., not assumed nor truncated), logged automatically by the assessment environment. We applied a three-step process mining methodology on logged interaction traces from a case study with 259 undergraduate university students. The analysis revealed sequences of interactions in which low goal-orientation students answered quickly and correctly on difficult items, without reviewing them, while they submitted wrong answers on easier items. We assumed that this implies guessing behavior. From the conformance checking and performance analysis we found that the fitness of our process model is almost 85 %. Hence, initial results are encouraging towards modelling guessing behavior. Potential implications and future work plans are also discussed.

关键词： Assessment analytics Educational data mining Guessing behavior pattern recognition Process mining Student interaction analysis

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3rd international conference on Advances in Speech and Language Technologies for Iberian Languages, IberSPEECH 2016

3rd International Conference on Advances in Speech and Langu...

引用

3rd international conference on Advances in Speech and Language Technologies for Iberian Languages, IberSPEECH 2016

ISBN: (纸本)9783319491684

The proceedings contain 27 papers. The special focus in this conference is on Speech Production, Analysis, Coding, Synthesis, Automatic Speech recognition, Paralinguistic Speaker Trait Characterization and Language Technologies in Different Application Fields. The topics include: Study of the effect of reducing training data in speech synthesis adaptation based on frequency warping;a dynamic FEC for improved robustness of CELP-based codec;objective comparison of four GMM-based methods for PMA-to-speech conversion;adding singing capabilities to unit selection TTS through HNM-based conversion;a novel error mitigation scheme based on replacement vectors and FEC codes for speech recovery in loss-prone channels;language-independent acoustic cloning of HTS voices;prosodic break prediction with RNNs;surgery of speech synthesis models to overcome the scarcity of training data;an analysis of deep neural networks in broad phonetic classes for noisy speech recognition;automatic speech recognition with deep neural networks for impaired speech;detection of publicity mentions in broadcast radio;deep neural network-based noise estimation for robust ASR in dual-microphone smartphones;crowdsourced video subtitling with adaptation based on user-corrected lattices;acoustic analysis of anomalous use of prosodic features in a corpus of people with intellectual disability;detecting psychological distress in adults through transcriptions of clinical interviews;automatic detection of hyperarticulated speech;acoustic-prosodic automatic personality trait assessment for adults and children and evaluating different non-native pronunciation scoring metrics with the Japanese speakers of the sample corpus.

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Facebook traffic pattern analytics 2016

Facebook traffic pattern analytics

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3rd Multidisciplinary international Social Networks conference, MISNC 2016, 5th ASE international conference on Social Informatics, SocialInformatics 2016 and 7th ASE international conference on Data Science, DS 2016

作者： Chen, C. Iglasias, J. Lin, X. Li, J.J. Morreale, P. Ness, Linda Computer Science Kean University 1000 Morris Avenue UnionNJ07083 United States DIMACS Rutgers University 96 Frelinghuysen Road PiscatawayNJ08854-8018 United States

ISBN: (纸本)9781450341295

This paper presents a study of applying a new data pattern recognition approach, called Product Coefficients (PCs), to discover patterns of Facebook network and service traffic. It includes three parts: 1) collection of service response time and packages as the initial data set, 2) preprocessing of the data sets and discovers of PCs values as patterns of the data set. 3) displaying of the patterns for simple outlier detection. Additional research is ongoing related to the post processing of PCs values for prediction, e.g. using machine learning to decide if outliers are indicators of a security attack. The key contribution of this work is the application of PCs method to some practical Facebook traffic data. © 2016 ACM.

关键词： Social networking (online)

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Proceedings - AIMS 2015, 3rd international conference on Artificial Intelligence, Modelling and Simulation

Proceedings - AIMS 2015, 3rd International Conference on Art...

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3rd international conference on Artificial Intelligence, Modelling and Simulation, AIMS 2015

ISBN: (纸本)9781467386753

The proceedings contain 76 papers. The topics discussed include: a machine learning-based approach to estimate the CPU-burst time for processes in the computational grids;a nature inspired heuristic optimization algorithm based on lightning;optimizing cluster of questions by using dynamic mutation in genetic algorithm;local binary patterns for gender classification;power energy management for a grid-connected pv system using rule-base fuzzy logic;minutiae matching algorithm using artificial neural network for fingerprint recognition;design and evaluation of a multi-model, multi-level artificial neural network for eczema skin lesion detection;a CMOS analog current-mode direct and complementary membership function circuit for fuzzy logic controller applications;a clustering algorithm for WSN to optimize the network lifetime using type-2 fuzzy logic model;an idiotypic solution sieve for selecting the best performing solutions in real-world distributed intelligence;a new variant of arithmetic mean iterative method for fourth order integro-differential equations solution;protein map of control mice exposed to context fear using a novel implementation of granger causality;and pattern matching performance comparisons as big data analysis recommendations for hepatitis C virus (HCV) sequence DNA.

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