In volume visualization, the voxel visibitity and materials are carried out through an interactive editing of Transfer Function. In this paper, we present a two-level GPU-based labeling method that computes in times of rendering a set of labeled structures using the Adaboost machine learning classifier. In a pre-processing step, Adaboost trains a binary classifier from a pre-labeled dataset and...

Modulation scheme recognition occupies a crucial position in the civil and military application. In this paper, we present boosting algorithm as an ensemble frame to achieve a higher accuracy than a single classifier. To evaluate the effect of boosting algorithm, eight common communication signals are yet to be identified. And five kinds of entropy are extracted as the training vector. And then...

Pedestrian detection is one of the hot research problems in computer vision field. The Cascade AdaBoost System is a commonly used algorithm in this region. However, when the training datasets become larger, it is still a time consuming process to build one Adaboost classifier. In this paper we detail an implementation of the AdaBoost algorithm using the NVIDIA CUDA framework based on the haar f...

There is a large literature explaining why AdaBoost is a successful classifier. The literature on AdaBoost focuses on classifier margins and boosting's interpretation as the optimization of an exponential likelihood function. These existing explanations, however, have been pointed out to be incomplete. A random forest is another popular ensemble method for which there is substantially less expl...

Much attention has been paid to the theoretical explanation of the empirical success of AdaBoost. The most influential work is the margin theory, which is essentially an upper bound for the generalization error of any voting classifier in terms of the margin distribution over the training data. However, important questions were raised about the margin explanation. Breiman (1999) proved a bound ...

Pedestrians are the vulnerable participants in transportation system when crashes happen. It is important to detect pedestrian efficiently and accurately in many computer vision applications, such as intelligent transportation systems (ITSs) and safety driving assistant systems (SDASs). This paper proposes a two-stage pedestrian detection method based on machine vision. In the first stage, AdaB...

Gabor features has been recognized as one of the most successful representation methods, such as Elastic Graph Matching, Gabor Fisher Classifier, and AdaBoost Gabor Fisher Classifier. One of the key issues in using Gabor features is how to efficiently reduce its high dimensionality. This paper proposes a multiple Fisher classifiers combination approach based on re-grouping Gabor features select...