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BECCA (a Brain-Emulating Cognition and Control Architecture software package) was developed in order to perform general reinforcement learning, that is, to enable unmodeled embodied systems operating in unstructured environments to perform unfamiliar tasks. It accomplishes this through automatic paired feature creation and reinforcement learning algorithms. This paper describes an implementation of...
This paper presents a hierarchal, two-layer, connectionist-based human-action recognition system (CHARS) as a first step towards developing socially intelligent robots. The first layer is a K-nearest neighbor (K-NN) classifier that categorizes human actions into two classes based on the existence of locomotion, and the second layer consists of two multi-layer recurrent neural networks that distinguish...
In this work we address the problem of feature extraction for object recognition in the context of cameras providing RGB and depth information (RGB-D data). We consider this problem in a bag of features like setting and propose a new, learned, local feature descriptor for RGB-D images, the convolutional k-means descriptor. The descriptor is based on recent results from the machine learning community...
The studies on mirror neurons observed in monkeys indicate that recognition of other's actions activates neural circuits that are also responsible for generating the very same actions in the animal. The mirror neuron hypothesis argues that such an overlap between action generation and recognition can provide a shared worldview among individuals and be a key pillar for communication. Inspired by these...
Successful state-of-the-art object recognition techniques from images have been based on powerful methods, such as sparse representation, in order to replace the also popular vector quantization (VQ) approach. Recently, sparse coding, which is characterized by representing a signal in a sparse space, has raised the bar on several object recognition benchmarks. However, one serious drawback of sparse...
We experimentally evaluated the efficacy of various autonomous supervised classification techniques for detecting transient geophysical phenomena. We demonstrated methods of detecting volcanic plumes on the planetary satellites Io and Enceladus using spacecraft images from the Voyager, Galileo, New Horizons, and Cassini missions. We successfully detected 73–95% of known plumes in images from all four...
This paper presents a real-time emotion recognition system (RTERS) as a first step towards developing a socially intelligent robot. The RTERS first localizes faces in a sequence of images, then features are extracted and passed to a recognition engine that codes facial expressions into one of seven different emotional states: happiness, sadness, fear, disgust, anger, surprise, and neutrality.We propose...
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