Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (eBook, PDF)

23rd Iberoamerican Congress, CIARP 2018, Madrid, Spain, November 19-22, 2018, Proceedings
Redaktion: Vera-Rodriguez, Ruben; Morales, Aythami; Fierrez, Julian

• Format: PDF

Produktbeschreibung

This book constitutes the refereed post-conference proceedings of the 23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018, held in Madrid, Spain, in November 2018

The 112 papers presented were carefully reviewed and selected from 187 submissions

The program was comprised of 6 oral sessions on the following topics: machine learning, computer vision, classification, biometrics and medical applications, and brain signals, and also on: text and character analysis, human interaction, and sentiment analysis

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Produktdetails

• Verlag: Springer-Verlag GmbH
• Seitenzahl: 987
• Erscheinungstermin: 2. März 2019
• Englisch
• ISBN-13: 9783030134693
• Artikelnr.: 55401337

Inhaltsangabe

Machine Learning.- Data Augmentation via Variational Auto-Encoders.- LSTM-based Multi-scale Model for WindSpeed Forecasting.-Using deep learning to classify class imbalanced gene-expression microarrays datasets.-A data representation approach to support imbalanced data classification beased in TWSVM.- Improving regression models by dissimilarity representation of bio-chemical data.- A Comparative Study on Unsupervised Domain Adaption for Coffee Crop Mapping.- Analytical Comparison of Histogram Distance Measures.- Gaussian processes regression with multiple annotors.- Linear projection learned from hybrid CKA for enhancing distance-based classifiers.- Evaluation of Bag-of-Word performance for time series classification using discriminative SIFT-based mid-level representations.- Color classification methods for perennial weed detection in cereal crops.- Hierarchical graph-based segmentation in detection of object-related regions.- Dealingwith heterogeneous Google Earth images on Building Area Detection task.- Evaluation of scale-aware realignments of hierarchical image segmentation.