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Affordance learning in general, is to identify the purpose, use, and ways to interact with an object, based on information gained from observing the object. Most of the existing affordance learning approaches assume the object target has been cropped individually from images. However, the object could not be easily separated from others due to occlusion or noise. Actually, two or more neighboring...
Light signal recognition is one of the key issues in autonomous vehicle. Unlike normal object detection and recognition, which can be done by using different sensors, light signal recognition is naturally a computer vision problem. Although commercialized ADAS (Advanced Driving Assistance System) products, such as mobileye, could be used for rear-end collision warning, a cost-effective approach is...
In this paper, a new method based on deep learning for robotics autonomous navigation is presented. Different from the most traditional methods based on fixed models, a convolutional neural network (CNN) modelling technique in Deep learning is selected to extract the feature inspired by the working pattern of the biological brain. This neural network model has muti-layer features where the ambient...
Most of the previous work on video action recognition use complex hand-designed local features, such as SIFT, HOG and SURF, but these approaches are implemented sophisticatedly and difficult to be extended to other sensor modalities. Recent studies discover that there are no universally best hand-engineered features for all datasets, and learning features directly from the data may be more advantageous...
Hyperspectral image (HSI) classification is a hot topic in the remote sensing community. This paper proposes a new framework of spectral-spatial feature extraction for HSI classification, in which for the first time the concept of deep learning is introduced. Specifically, the model of autoencoder is exploited in our framework to extract various kinds of features. First we verify the eligibility of...
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