Context Based Visual Content Verification

In this paper the intermediary visual content verification method based on multi-level co-occurrences is studied. The co-occurrence statistics are in general used to determine relational properties between objects based on information collected from data. As such these measures are heavily subject to relative number of occurrences and give only limited amount of accuracy when predicting objects in real world. In order to improve the accuracy of this method in the verification task, we include the context information such as location, type of environment etc. In order to train our model we provide new annotated dataset the Advanced Attribute VOC (AAVOC) that contains additional properties of the image. We show that the usage of context greatly improve the accuracy of verification with up to 16% improvement.Comment: 6 pages, 6 Figures, Published in Proceedings of the Information and Digital Technology Conference, 201

Understanding Predication in Conceptual Spaces

We argue that a cognitive semantics has to take into account the possibly partial information that a cognitive agent has of the world. After discussing Gärdenfors's view of objects in conceptual spaces, we offer a number of viable treatments of partiality of information and we formalize them by means of alternative predicative logics. Our analysis shows that understanding the nature of simple predicative sentences is crucial for a cognitive semantics

What are natural concepts? A design perspective

Conceptual spaces have become an increasingly popular modeling tool in cognitive psychology. The core idea of the conceptual spaces approach is that concepts can be represented as regions in similarity spaces. While it is generally acknowledged that not every region in such a space represents a natural concept, it is still an open question what distinguishes those regions that represent natural concepts from those that do not. The central claim of this paper is that natural concepts are represented by the cells of an optimally designed similarity space

Modelling Dynamic Scenes at Unsignalised Road Intersections

Understanding dynamic scenes at road intersections is both crucial and challenging for intelligent vehicles. In order to detect potentially dangerous situations, algorithms are needed that can interpret the behaviour of the actors in the scene and predict its likely evolution. The difficulty of this task arises from the large number of possible scenarios. The conventional answer to this issue is to discard vehicle interactions in the manoeuvre prediction process, i.e. to infer the manoeuvre performed by each vehicle from its past and current behaviour, independently from the other vehicles in the scene. In this paper we show how this affects collision risk estimation in very common scenarios, making it unusable in practice for Advanced Driver Assistance Systems (ADAS) applications. As an alternative we propose a probabilistic model for vehicles traversing unsignalised intersections that accounts for the mutual influence between vehicle manoeuvres. The focus is on the utilisation of contextual information (i.e. layout of the intersection, presence of other vehicles and traffic rules) to interpret a vehicle's behaviour. We show how the model can be used for accurate situation and risk assessment

A Survey on Artificial Intelligence and Robotics

Today many multi-national companies or organizations are adopting the use of automation. Automation means replacing the human by intelligent robots or machines which are capable to work as human (may be better than human). Artificial intelligence is a way of making machines, robots or software to think like human. As the concept of artificial intelligence is use in robotics, it is necessary to understand the basic functions which are required for robots to think and work like human. These functions are planning, acting, monitoring, perceiving and goal reasoning. These functions help robots to develop its skills and implement it. Since robotics is a rapidly growing field from last decade, it is important to learn and improve the basic functionality of robots and make it more useful and user-friendly