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For this reason, the strong classifiers are arranged in a cascade in order of complexity, where each successive classifier is trained only on those selected samples which pass through the preceding classifiers. If at any stage in the cascade a classifier rejects the sub-window under inspection, no further processing is performed and continue on ...
Cascade classifiers are available in OpenCV, with pre-trained cascades for frontal faces and upper body. Training a new cascade in OpenCV is also possible with either haar_training or train_cascades methods. This can be used for rapid object detection of more specific targets, including non-human objects with Haar-like features. The process ...
In the Viola–Jones object detection framework, the Haar-like features are therefore organized in something called a classifier cascade to form a strong learner or classifier. The key advantage of a Haar-like feature over most other features is its calculation speed. Due to the use of integral images, a Haar-like feature of any size can be ...
The generalized Haar wavelets represent the next highest performing approach: they produced roughly a 0.01 miss rate at a 10 −4 false positive rate on the MIT set, and roughly a 0.3 miss rate on the INRIA set. The PCA-SIFT descriptors and shape context descriptors both performed fairly poorly on both data sets.
Instead of decision trees, linear models have been proposed and evaluated as base estimators in random forests, in particular multinomial logistic regression and naive Bayes classifiers. [ 37 ] [ 38 ] [ 39 ] In cases that the relationship between the predictors and the target variable is linear, the base learners may have an equally high ...
OpenCV's Cascade Classifiers support LBPs as of version 2. VLFeat, an open source computer vision library in C (with bindings to multiple languages including MATLAB) has an implementation. LBPLibrary is a collection of eleven Local Binary Patterns (LBP) algorithms developed for background subtraction problem. The algorithms were implemented in ...
This page was last edited on 9 April 2016, at 12:19 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may ...
The scale-invariant feature transform (SIFT) is a computer vision algorithm to detect, describe, and match local features in images, invented by David Lowe in 1999. [1] ...