Laura Antanas

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Understanding complex, dynamic scenes of real-world activities from low-level sensor data is of central importance for intelligent systems. The main difficulty lies in the fact that complex scenes are best described in high-level, logical formalisms, while sensor data usually consists of many low-level features. We first propose a method to obtain a logical(More)
Histological image analysis plays a key role in understanding the effects of disease and treatment responses at the cellular level. However, evaluating histology images by hand is time-consuming and subjective. While semi-automatic and automatic approaches for image segmentation give acceptable results in some branches of histological image analysis, until(More)
Real-world scenes involve many objects that interact with each other in complex semantic patterns. For example, a bar scene can be naturally described as having a variable number of chairs of similar size, close to each other and aligned horizontally. This high-level interpretation of a scene relies on semantically meaningful entities and is most generally(More)
While relational representations have been popular in early work on syntactic and structural pattern recognition, they are rarely used in contemporary approaches to computer vision due to their pure symbolic nature. The recent progress and successes in combining statistical learning principles with relational representations motivates us to reinvestigate(More)
Robot grasping is a critical and difficult problem in robotics. The problem of simply finding a stable grasp is difficult enough, but to perform a useful grasp, we must also consider other aspects of the task: the object, its properties, and any task-related constraints. The choice of grasping region is highly dependent on the category of object, and the(More)
While grasps must satisfy the grasping stability criteria, good grasps depend on the specific manipulation scenario: the object, its properties and functionalities, as well as the task and grasp constraints. In this paper, we consider such information for robot grasping by leveraging manifolds and symbolic object parts. Specifically, we introduce a new(More)
Augmenting vision systems with high-level knowledge and reasoning can improve lower-level vision processes, such as object detection, with richer and more structured information. In this paper we tackle the problem of delimiting conceptual elements of street views based on spatial relations between lowerlevel components, e.g. the element ‘house’ is composed(More)