Statistical-learning-based object detection is an important topic in computer vision. It learns
visual representation from annotated exemplars to identify semantic defined objects in …

Augmented reality (AR) allows to seamlessly insert virtual objects in an image sequence. In
order to accomplish this goal, it is important that synthetic elements are rendered and …

With recent advances in mobile computing, the demand for visual localization or landmark
identification on mobile devices is gaining interest. We advance the state of the art in this …

This paper introduces recent progress on techniques of object detection and pose tracking
with a monocular camera for augmented reality applications. To visually merge a virtual …

Mobile phones have evolved into powerful image and video processing devices equipped
with high-resolution cameras, color displays, and hardware-accelerated graphics. They are …

High-resolution remote-sensing images are widely used for object detection but are affected
by various factors. During the detection process, the orientation sensitivity of the image …

This work focuses on a slice through the field-Computer Vision Metrics–from the view of
feature description metrics, or how to describe, compute and design the macro-features and …

The efficiency, robustness and distinctiveness of a feature descriptor are critical to the user
experience and scalability of a mobile Augmented Reality (AR) system. However, existing …

The histogram of oriented gradients (HOG) is widely used for image description and proves
to be very effective. In many vision problems, rotation-invariant analysis is necessary or …

Feature extraction and matching is at the base of many computer vision problems, such as
object recognition or structure from motion. Current methods for assessing the performance …