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The detection of microaneurysms (MAs) in color fundus images remains an open issue in the medical image processing due to the low availability of reliable programs. In this paper, we present a system for automated detecting of MAs based with the techniques Hessian matrix and contrast limited adaptive histogram equalization (CLAHE). Thus, features extraction as optical disc, vascular tree and other...
Brain Computer Interface (BCI) has become one of the most interesting alternatives to support automatic systems able to interpret brain functions. Recently, the Motor Imagery (MI) paradigm is a widely topic of interest as a tool to develop BCI-based systems. Here, we present a relevant feature extraction methodology, termed MI discrimination using kernel relevance analysis (MIDKRA), to support MI...
We propose a new, hierarchical, aggregation-based deep neural network to learn aging features from facial images. Our deep-aging feature vector is designed to capture both local and global aging cues from facial images. A Convolutional Neural Network (CNN) is employed to extract region- specific features at the lowest level of our hierarchy. These features are then hierarchically aggregated to consecutive...
Optical Character Recognition (OCR) in video stream of flipping pages is a challenging task because flipping at random speed cause difficulties to identify frames that contain the open page image (OPI) for better readability. Also, low resolution, blurring effect shadows add significant noise in selection of proper frames for OCR. In this work, we focus on the problem of identifying the set of optimal...
Road detection is a vital task for the development of autonomous vehicles. The knowledge of the free road surface ahead of the target vehicle can be used for autonomous driving, road departure warning, as well as to support advanced driver assistance systems like vehicle or pedestrian detection. Using vision to detect the road has several advantages in front of other sensors: richness of features,...
Vision-based road detection is important in different areas of computer vision such as autonomous driving, car collision warning and pedestrian crossing detection. However, current vision-based road detection methods are usually based on low-level features and they assume structured roads, road homogeneity, and uniform lighting conditions. Therefore, in this paper, contextual 3D information is used...
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