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Object detection is an important and challenging problem in the field of computer vision. Classical object detection approaches such as background subtraction and saliency detection do not require manual collection of training samples, but can be easily affected by noise factors, such as luminance changes and cluttered background. On the other hand, supervised learning based approaches such as Boosting...
Moving objects detection and recognition around an intelligent vehicle are active research fields. A great number of approaches have been proposed in recent decades. This paper proposes a novel approach based solely on spatial information to solve this problem. Moving objects detection is achieved in conjunction with an egomotion estimation by sparse matched feature points. For objects recognition,...
In this paper we present a new method for automatic object detection in images and video sequences. As a classifier the popular AdaBoost algorithm is used, that combines several weak classifiers into one strong classifier. To create a detector based on this classifier, the weak classifiers are set into relation during boosting by using a geometric model. All votes of the weak detectors are evaluated...
We propose a novel method to improve the training efficiency and accuracy of boosted classifiers for object detection. The key step of the proposed method is a sample pre-mapping on original space by referring to the selected `reference sample' before feeding into weak classifiers. The reference sample corresponds to an approximation of the optimal separating hyper-plane in an implicit high dimensional...
We present a method to detect characters on signboards in natural scene images. For many applications, both classifier with small computational cost and the efficient feature set, which gives rise to accurate recognition are required. Texture based features are often used for target detection. It has been also shown that the shape of the intensity distribution is often useful for character extraction...
Pedestrian detection in still image should handle the large appearance and pose variations arising from the articulated structure and various clothing of human bodies as well as view points. So it is difficult to design effective classifier for this problem. In this paper, we address these variations in detection via multiple instance learning, specifically logistic multiple instance boosting (LMIB)...
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