Object detection and recognition are important problems in computer vision. Since these
problems are meta-heuristic, despite a lot of research, practically usable, intelligent, real …

This paper proposes an automated visual classification framework in which a novel analysis
method (LSTMS-B) of EEG signals guides the selection of multiple networks that leads to the …

Random ferns—as a less known example of ensemble learning—have been successfully
applied in many computer vision applications ranging from keypoint matching to object …

We describe a method for eye pupil localization based on an ensemble of randomized
regression trees and use several publicly available datasets for its quantitative and …

Text localization from scene images is a challenging task that finds application in many
areas. In this work, we propose a novel hybrid text localization approach that exploits Multi …

In last years, most human action recognition works have used dense trajectories features, to
achieve state-of-the-art results. Histograms of Oriented Gradients (HOG), Histogram of …

The safety helmet wearing of workers is extremely important to their safety in construction
scenarios, and it is very meaningful for computer vision, pattern recognition and artificial …

We present a robust learning based instance recognition framework from single view point
clouds. Our framework is able to handle real-world instance recognition challenges, ie …

Tracking registration is a key issue in augmented reality applications, particularly where
there are no artificial identifier placed manually. In this paper, an efficient markerless …

Visual categorization problems, such as object classification or action recognition, are
increasingly often approached using a detection strategy: a classifier function is first applied …