Depth estimation is a classical problem in computer vision, which typically relies on either a depth sensor or stereo matching alone. The depth sensor provides real-time estimates in repetitive and textureless regions where stereo matching is not effective. However, stereo matching can obtain more accurate results in rich texture regions and object boundaries where the depth sensor often fails....

Art on melanoma screening evolved steeply in the last two years, with the adoption of deep learning, but those models pose challenges of their own, as they are expensive to train, and complex to parameterize. We shed light at those difficulties, with an exhaustive evaluation of nine commonly found open choices faced when picking or designing deep networks for melanoma screening: model architect...

Virtual view synthesis is an essential part of computer vision and 3D applications. A high-quality depth map is the main problem with virtual view synthesis. Because as compared to the color image the resolution of the corresponding depth image is low. In this paper, an efficient and confided method based on the gradual omission of outliers is proposed to compute reliable depth values. In the p...

Reliable depth estimation is important to many autonomous robotic systems and visual control algorithms. The Microsoft Kinect is a new, low cost game controller peripheral that calculates a depth map of the environment with good accuracy and high rate. In this paper we use the calibrated output of the depth sensor to obtain an accurate absolute depth map. Subsequently by using a Randomized Houg...

RGB-D sensors are popular in the computer vision community, especially for problems of scene understanding, semantic scene labeling, and segmentation. However, most of these methods depend on reliable input depth measurements, while discarding unreliable ones. This paper studies how reliable depth values can be used to correct the unreliable ones, and how to complete (or extend) the available d...