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To be effective, state of the art machine learning technology needs large amounts of annotated data. There are numerous compelling applications in healthcare that can benefit from high performance automated decision support systems provided by deep learning technology, but they lack the comprehensive data resources required to apply sophisticated machine learning models. Further, for economic reasons,...
Among the multiple advantages and applications of remote sensing, one of the most important uses is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this letter, we propose a novel bat algorithm (BA)-based clustering approach for solving crop type...
An avatar is a representation of a real person in a virtual world and a user considers it as his/her secondary personal identity. With the spread of Virtual Reality(VR) technologies, there has been a user's growing interest in how to make an avatar to describe his/her identity well. Recent commercial games also support powerful avatar customization services to satisfy customer's various needs, but...
Feature selection aims to find the most important information from a given set of features. As this task can be seen as an optimization problem, the combinatorial growth of the possible solutions may be in-viable for a exhaustive search. In this paper we propose a new nature-inspired feature selection technique based on the bats behaviour, which has never been applied to this context so far. The wrapper...
In many applications which require real-time keypoint recognition such as Augmented Reality, Random Ferns (RF) is widely used due to its runtime performance. It relies on an offline training phase during which runtime computational burdens are delegated. This leads to robust, accurate, and framerate performance. However, it requires significant amounts of memory, and this has been an obstacle to its...
One of the general objectives of visual surveillance is to recognise abnormal activities from images. Current object detection/tracking techniques cannot directly classify such activities as fighting and snatching, while they reliably recognise primitive actions, such as walking and running. We represent each target activity as ground, weighted and undirected trees, Markov logic networks (MLNs), starting...
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