Is currently a postdoctoral researcher at the University of Caen in France. He obtained his Ph. D. in Computer Vision (Visual Object Detection) from the University of Grenoble in 2011 under the supervision of Dr. Bill Triggs. His main research interests include visual recognition (image classification and object detection) and machine learning.
Machine Learning Methods for Visual Object Detection
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- Nombre de pages160
- FormatPoche
- PrésentationBroché
- Poids0.245 kg
- Dimensions15,0 cm × 22,0 cm × 0,9 cm
- ISBN978-3-8417-9068-2
- EAN9783841790682
- Date de parution06/03/2012
- CollectionOMN.UNIV.EUROP.
- ÉditeurUniv Européenne
Résumé
This book presents practical methods for detecting common object classes such as people, cars, cows, chairs, etc. , in real world images. In particular, it discusses a range of visual representations (Histograms of Oriented Gradients (HOG), Local Binary Patterns (LBP), Local Ternary Patterns (LTP), Local Quantized Patterns (LQP) and similar techniques), dimensionality reduction (Partial Least Squares and SVM Weight Sparsification) and learning methods (Latent and Non-Latent Support Vector Machines) for the problem of object detection.
These methods are presented from a practical perspective and shown to give state-of-the-art performance on a range of challenging public datasets.
These methods are presented from a practical perspective and shown to give state-of-the-art performance on a range of challenging public datasets.