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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...
There are many computer vision algorithms developed for visual (scene and object) recognition. Some systems focus on involved learning algorithms, some leverage millions of training images, and some systems focus on modeling relevant information (features) with the goal of effective recognition. However, none of these systems come close to human capabilities. If we study human responses on similar...
When designing intelligence for a car many different tasks can be performed. Some of these tasks cannot easily be performed by conventional algorithms in comparison with the human brain. Recently, such intelligence has often been reached by using probability based systems. In this paper, Hierarchical Temporal Memory (HTM) is used to implement one of these tasks, namely traffic sign recognition. In...
In this paper we propose a novel nonparametric approach for object recognition and scene parsing using dense scene alignment. Given an input image, we retrieve its best matches from a large database with annotated images using our modified, coarse-to-fine SIFT flow algorithm that aligns the structures within two images. Based on the dense scene correspondence obtained from the SIFT flow, our system...
It is now fairly routine to quasi-automatically generate acoustic bathymetry and optical mosaics from properly instrumented Autonomous Underwater Vehicles (AUVs). However, further analysis and interpretation of gathered data is needed to address tasks such as habitat characterization and monitoring. This analysis stage is performed by human experts which limits the amount and speed of data processing...
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