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In this paper, we present a novel human activity recognition approach that only requires a single video example per activity. We introduce the paradigm of active video composition, which enables one-example recognition of complex activities. The idea is to automatically create a large number of semi-artificial training videos called composed videos by manipulating an original human activity video...
Wu and coworkers introduced an active basis model (ABM) for detecting generic objects in static images. A grey-value local power spectrum was utilized to find a common template and deformable templates from a set of training images and to detect an object in unknown images by template matching. In this paper, we propose a color-based active basis model (color-based ABM for short) which includes color...
In this paper we introduce a real-time traffic light recognition system for intelligent vehicles. The method proposed is fully based on image processing. Detection step is achieved in grayscale with spot light detection, and recognition is done using our generic ??adaptive templates??. The whole process was kept modular which make our TLR capable of recognizing different traffic lights from various...
In recent years, large databases of natural images have become increasingly popular in the evaluation of face and object recognition algorithms. However, Pinto et al. previously illustrated an inherent danger in using such sets, showing that an extremely basic recognition system, built on a trivial feature set, was able to take advantage of low-level regularities in popular object and face recognition...
We present a framework intended to assist users in the task of tagging pictures with content descriptors. Histogram- or correlogram features of manually indicated regions of interest are extracted from a few training images; probabilistic diffusion over these prototypes is used to analyze further images. Since speed is pivotal in interactive applications, we apply a fast algorithm for computing local...
Many interesting problems in reinforcement learning (RL) are continuous and/or high dimensional, and in this instance, RL techniques require the use of function approximators for learning value functions and policies. Often, local linear models have been preferred over distributed nonlinear models for function approximation in RL. We suggest that one reason for the difficulties encountered when using...
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