Search results for "AdaBoost"
showing 3 items of 13 documents
Penalized linear discriminant analysis and Discrete AdaBoost to distinguish human hair metal profiles: The case of adolescents residing near Mt. Etna
2016
The research focus of the present paper was twofold. First, we tried to document that human intake of trace elements is influenced by geological factors of the place of residence. Second, we showed that the elemental composition of human hair is a useful screening tool for assessing people's exposure to potentially toxic substances. For this purpose, we used samples of human hair from adolescents and applied two robust statistical approaches. Samples from two distinct geological and environmental sites were collected: the first one was characterized by the presence of the active volcano Mt. Etna (ETNA group) and the second one lithologically made up of sedimentary rocks (SIC group). Chemica…
Optimized Parallel Implementation of Face Detection based on GPU component
2015
Display Omitted An algorithm for face detection has been implemented on CPU.An acceleration of this algorithm on GPU migration.Performance of GPU implementation shows the effectiveness of this implementation.Another optimization method on GPU are operated. Face detection is an important aspect for various domains such as: biometrics, video surveillance and human computer interaction. Generally a generic face processing system includes a face detection, or recognition step, as well as tracking and rendering phase. In this paper, we develop a real-time and robust face detection implementation based on GPU component. Face detection is performed by adapting the Viola and Jones algorithm. We hav…
A Comparative Study on Feature Selection for Retinal Vessel Segmentation Using FABC
2009
This paper presents a comparative study on five feature selection heuristics applied to a retinal image database called DRIVE. Features are chosen from a feature vector (encoding local information, but as well information from structures and shapes available in the image) constructed for each pixel in the field of view (FOV) of the image. After selecting the most discriminatory features, an AdaBoost classifier is applied for training. The results of classifications are used to compare the effectiveness of the five feature selection methods.