Automatic pavement crack segmentation plays a critical role in the field of defect inspection.
Although recent segmentation-based CNNs studies showed a promising pavement crack …

Automatic and accurate pavement crack detection is essential for cost-effective road
maintenance. Deep convolutional neural networks (DCNNs) are widely used in recent …

Deep convolutional neural networks (DCNNs) trained on the pixel-level annotated images
have achieved improvements in semantic segmentation. Due to the high cost of labeling …

Weakly supervised semantic segmentation under image-level label supervision has
undergone impressive improvements over the past years. These approaches can …

Hierarchical image segmentation is a prominent trend in the literature as a way to improve
the segmentation quality. Generally, meaningful objects in an image are described by …

The segmentation of building from satellite and airborne images is necessary for high‐
resolution buildings maps generation and it is still challenging. On annotated pixel‐level …

Text similarity is critical in a variety of applications, including word processing, signal
processing, imagery, data mining, wireless sensor networks, etc., where text similarity …

Automatic building semantic segmentation is the most critical and relevant task in several
geospatial applications. Methods based on convolutional neural networks (CNNs) are …

Tracking a specific person in environments with non-uniform illumination is a difficult task for
mobile robots. Image information such as color is essential to identify a target person …

Automatic skin lesion segmentation is the most critical and relevant task in computer‐aided
skin cancer diagnosis. Methods based on convolutional neural networks (CNNs) are mainly …