A CNN-based automatic vulnerability detection

Automatic MRI Prostate Segmentation based CNN-ASM

CNN-Based Automatic Urinary Particles Recognition

The urine sediment analysis of particles in microscopic images can assist physicians in evaluating patients with renal and urinary tract diseases. Manual urine sediment examination is labor-intensive, subjective and time-consuming, and the traditional automatic algorithms often extract the hand-crafted features for recognition. Instead of using the hand-crafted features, in this paper, we explo...

Automatic Pavement Crack Detection Based on Aerial Imagery

Road health information is an important indicator for assessing the status of the road in management systems. Identifying the abandonment of surfaces is an important process in maintaining roads and traffic safety, which is traditionally conducted on the basis of field surveys. Today, remote sensing methods, especially photogrammetric imaging, are presented. In this article, based on by UAVs im...

Automatic Road Detection and Extraction From MultiSpectral Images Using a New Hierarchical Object-based Method

Road detection and Extraction is one of the most important issues in photogrammetry, remote sensing and machine vision. A great deal of research has been done in this area based on multispectral images, which are mostly relatively good results. In this paper, a novel automated and hierarchical object-based method for detecting and extracting of roads is proposed. This research is based on the M...

Detection of Symmetry Axis by a CNN-based Algorithm

In this paper a method for symmetry axis detection in binary images is presented. The method exploits the nonlinear dynamic behavior of Cellular Neural Networks (CNNs), in particular the propagation of bipolar waves. The image is represented in polar form, transforming the symmetry with respect to an arbitrarily oriented axis in a vertical symmetry: the position of the vertical axis corresponds...