Gentle AdaBoost Classifier Research Articles

Intelligent Measurement of Spinal Curvature Using Cascade Gentle AdaBoost Classifier and Region-Based DRLSE

For spinal curvature measurements, because of the anatomical complexity of the spine CT image, developing an automated method to avoid manual landmark is a challenging task. In this study, we propose an intelligent framework that integrates the cascade AdaBoost classifier and region-based distance regularized level set evolution (DRLSE) with the vertebral centroid measurement. First, the histogram-of-oriented-gradients based cascade gentle AdaBoost classifier is used to detect automatically and localize vertebral bodies from computer tomography (CT) spinal images. Considering these vertebral pathological images enables us to produce a diverse training dataset. Then, the DRLSE method introduces the local region information to converge the vertebral boundary quickly. The located bounding box is regarded as an accurate initial contour. This avoids the negative impact of manual initialization. Finally, we perform vertebral centroid extraction and spinal curve fitting. The spinal curvature angle is determined by calculating the angle between two tangents to the curve. We verified the effectiveness of the proposed method on 10 spine CT volumes. Quantitative comparison against the ground-truth centroids yielded a detection accuracy rate of 98.3% and a mean centroid location error of 1.15 mm. The comparative results with existing methods demonstrate that the proposed method can accurately detect and segment vertebral bodies. Furthermore, the spinal curvature can be automatically measured without manual landmark.

Gentle Adaboost algorithm based on multi‐feature fusion for face detection

There are few types of Haar-like rectangle features, which leads to the problem that the classifier training time is too long due to the large number of feature quantities required in the description of the face. Local binary patterns (LBP) are used to describe the local texture features of the face image. Considering the inadequacy of basic LBP features in face detection, unified MB-LBP features and unified rotation-invariant LBP features are used to describe local texture features of faces. Considering the shortcomings of MB-LBP feature and rotation-invariant LBP feature on face edge information, the edge azimuth field feature based on Canny operator is combined with the above two features to describe face information. Finally, the Gentle Adaboost classifier was designed to classify all the extracted features. The experimental results show that the unified MB-LBP feature and the unified rotation invariant LBP feature and the edge azimuth field feature based on Canny operator can not only describe the face information from the local but also the whole, which greatly improves the detection rate and detection speed of the face with multiple poses and different rotation modes.

Optimized UAV object tracking framework based on Integrated Particle filter with ego-motion transformation matrix

Vision based object tracking problem still a hot and important area of research specially when the tracking algorithms are performed by the aircraft unmanned vehicle (UAV). Tracking with the UAV requires special considerations due to the flight maneuvers, environmental conditions and aircraft moving camera. The ego motion calculations can compensate the effect of the moving background resulted from the moving camera. In this paper an optimized object tracking framework is introduced to tackle this problem based on particle filter. It integrates the calculated ego motion transformation matrix with the dynamic model of the particle filter during the prediction stage. Then apply the correction stage on the particle filter observation model which based on two kinds of features includes Haar-like Rectangles and edge orientation histogram (EOH) features. The Gentle AdaBoost classifier is used to select the most informative features as a preliminary step. The experimental results achieved more than 94.6% rate of successful tracking during different scenarios of the VIVID database in real time tracking speed.

BENCHMARK EVALUATION OF HOG DESCRIPTORS AS FEATURES FOR CLASSIFICATION OF TRAFFIC SIGNS

The purpose of this paper is to analyze the performance of the Histograms of Oriented Gradients (HOG) as descriptors for traffic signs recognition. The test dataset consists of speed limit traffic signs because of their high inter-class similarities. HOG features of speed limit signs, which were extracted from different traffic scenes, were computed and a Gentle AdaBoost classifier was invoked to evaluate the different features. The performance of HOG was tested with a dataset consisting of 1727 Swedish speed signs images. Different numbers of HOG features per descriptor, ranging from 36 features up 396 features, were computed for each traffic sign in the benchmark testing. The results show that HOG features perform high classification rate as the Gentle AdaBoost classification rate was 99.42%, and they are suitable to real time traffic sign recognition. However, it is found that changing the number of orientation bins has insignificant effect on the classification rate. In addition to this, HOG descriptors are not robust with respect to sign orientation.