6,303 research outputs found
Learning and Using Taxonomies For Fast Visual Categorization
The computational complexity of current visual categorization algorithms scales linearly at best with the number of categories. The goal of classifying simultaneously N_(cat) = 10^4 - 10^5 visual categories requires sub-linear classification costs. We explore algorithms for automatically building classification trees which have, in principle, log N_(cat) complexity. We find that a greedy algorithm that recursively splits the set of categories into the two minimally confused subsets achieves 5-20 fold speedups at a small cost in classification performance. Our approach is independent of the specific classification algorithm used. A welcome by-product of our algorithm is a very reasonable taxonomy of the Caltech-256 dataset
3-D Face Analysis and Identification Based on Statistical Shape Modelling
This paper presents an effective method of statistical shape representation for automatic face analysis and identification in 3-D. The method combines statistical shape modelling techniques and the non-rigid deformation matching scheme. This work is distinguished by three key contributions. The first is the introduction of a new 3-D shape registration method using hierarchical landmark detection and multilevel B-spline warping technique, which allows accurate dense correspondence search for statistical model construction. The second is the shape representation approach, based on Laplacian Eigenmap, which provides a nonlinear submanifold that links underlying structure of facial data. The third contribution is a hybrid method for matching the statistical model and test dataset which controls the levels of the model’s deformation at different matching stages and so increases chance of the successful matching. The proposed method is tested on the public database, BU-3DFE. Results indicate that it can achieve extremely high verification rates in a series of tests, thus providing real-world practicality
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Efficient smile detection by Extreme Learning Machine
Smile detection is a specialized task in facial expression analysis with applications such as photo selection, user experience analysis, and patient monitoring. As one of the most important and informative expressions, smile conveys the underlying emotion status such as joy, happiness, and satisfaction. In this paper, an efficient smile detection approach is proposed based on Extreme Learning Machine (ELM). The faces are first detected and a holistic flow-based face registration is applied which does not need any manual labeling or key point detection. Then ELM is used to train the classifier. The proposed smile detector is tested with different feature descriptors on publicly available databases including real-world face images. The comparisons against benchmark classifiers including Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) suggest that the proposed ELM based smile detector in general performs better and is very efficient. Compared to state-of-the-art smile detector, the proposed method achieves competitive results without preprocessing and manual registration
A Hybrid Real-Time Vision-Based Person Detection Method
[EN] In this paper, we introduce a hybrid real-time
method for vision-based pedestrian detection
made up by the sequential combination of two
basic methods applied in a coarse to fine fashion.
The proposed method aims to achieve an
improved balance between detection accuracy
and computational load by taking advantage
of the strengths of these basic techniques.
Haar-like features combined with Boosting
techniques, which have been demonstrated to
provide rapid but not accurate enough results in
human detection, are used in the first stage to
provide a preliminary candidate selection in the
scene. Then, feature extraction and classification
methods, which present high accuracy rates at
expenses of a higher computational cost, are
applied over boosting candidates providing the
final prediction. Experimental results show that
the proposed method performs effectively and
efficiently, which supports its suitability for real
applications.This work is supported by CASBLIP project 6-th FP\cite{RefCASBLIP}. The authors acknowledge the support of the Technological Institute of Optics, Colour and Imaging of Valencia - AIDO. Dr. Samuel Morillas acknowledges the support of Generalitat Valenciana under grant GVPRE/2008/257 and Universitat Politècnica de València under grant
Primeros Proyetos de Investigación 13202. }Oliver Moll, J.; Albiol Colomer, A.; Morillas, S.; Peris Fajarnes, G. (2011). A Hybrid Real-Time Vision-Based Person Detection Method. Waves. 86-95. http://hdl.handle.net/10251/57676S869
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