Cognitive visual tracking and camera control

Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision

The Semantics and Acquisition of Time in Language

This dissertation is about the structure of temporal semantics and children’s acquisition of temporal language. It argues for the importance of investigating semantics both at the abstract level of linguistic structures and at the concrete level of the time-course of acquisition, as these two levels provide natural constraints for each other. With respect to semantics, it provides a computationally inspired analysis of tense, grammatical aspect and lexical aspect that uses finite state automata to dynamically calculate the progress of an event over a time interval. It is shown that the analysis can account for many well-known temporal phenomena, such as the different entailments of telic and atelic predicates in the imperfective aspect (the imperfective paradox), and the various unified and serial interpretations of sentences involving a cardinally quantified phrase, such as Three Ringlings visited Florida. With respect to children’s acquisition of temporal language, the dissertation investigates the Aspect First hypothesis which states that children initially use tense and grammatical aspect morphology to mark the lexical aspect property of telicity. Two forced-choice comprehension experiments were conducted with children aged 2.5 to 5 years old to test children’s understanding of tense and grammatical aspect morphology; in a control condition, open class cues were used to test children’s conceptual competence with tense and grammatical aspect information independently of their competence with the relevant morphology (e.g., in the middle of and in a few seconds were the open class cues for imperfective aspect and future tense, respectively). Results showed that even the youngest children understood the concepts underlying tense and grammatical aspect as measured by their performance with the open class cues but they did not demonstrate adult competence with the closed class morphology for grammatical aspect and did so only marginally for tense. Comprehension of tense morphology preceded that of grammatical aspect morphology and in particular, children showed an early facility with markers of the future tense

Neural-Symbolic Learning and Reasoning: Contributions and Challenges

The goal of neural-symbolic computation is to integrate robust connectionist learning and sound symbolic reasoning. With the recent advances in connectionist learning, in particular deep neural networks, forms of representation learning have emerged. However, such representations have not become useful for reasoning. Results from neural-symbolic computation have shown to offer powerful alternatives for knowledge representation, learning and reasoning in neural computation. This paper recalls the main contributions and discusses key challenges for neural-symbolic integration which have been identified at a recent Dagstuhl seminar

Grammaticalization of kes construction in Korean: Grammaticalization path and emergence of (inter)subjective meanings

Ph.D