Nowadays, robots are dominating the manufacturing, entertainment, and healthcare
industries. Robot vision aims to equip robots with the capabilities to discover information …

Regular machine learning and data mining techniques study the training data for future
inferences under a major assumption that the future data are within the same feature space …

The inconsistency between textual features and visual contents can cause poor image
search results. To solve this problem, click features, which are more reliable than textual …

With advances in machine vision systems (eg, artificial eye, unmanned aerial vehicles,
surveillance monitoring) scene semantic recognition (SSR) technology has attracted much …

Land-use classification using remote sensing images covers a wide range of applications.
With more detailed spatial and textural information provided in very high resolution (VHR) …

Recent advances in visionary technologies impacted multi-object recognition and scene
understanding. Such scene-understanding tasks are a demanding part of several …

Face images captured in unconstrained environments usually contain significant pose
variation, which dramatically degrades the performance of algorithms designed to recognize …

Learning to manipulate 3D objects in an interactive environment has been a challenging
problem in Reinforcement Learning (RL). In particular, it is hard to train a policy that can …

Scene classification is a significant aspect of computer vision. Convolutional neural
networks (CNNs), a development of deep learning, are a well-understood tool for image …

Band selection, as a special case of the feature selection problem, tries to remove redundant
bands and select a few important bands to represent the whole image cube. This has …