The presence of locations that possess distinct spatial-cognitive features (salient landmarks) is a fundamental necessity for supporting navigation. Embedding formal or structural variability sufficient to create such landmark locations is therefore an important consideration in the design of large urban and architectural spaces. Despite the availability of diverse theories that seek to identify the characteristics of 'a salient landmark', relatively few experimental techniques are available to empirically evaluate saliency in a given architecture plan. This study is therefore motivated by the development of an ability to measure spatial distinctiveness during the architectural design and modelling process. The information from such an analysis can prove useful for evaluating the way in which a design provides support for wayfinding and spatial appeal. Statistical summaries obtained from the three-dimensional (3D) isovists are compared using principal component analysis to differentiate monotonous regions from the more structurally distinct ones. The experiments reported in the paper demonstrate novel utilization of the isovist concept to capture spatial properties and comparison of structural saliency among two well-known architectural designs. Central contributions of the paper include the novel experimentation technique of capturing and utilizing 3D isovists, its interpretation and the quantitative methodology behind saliency computation.
This study is founded on the idea that an analysis of the visual gaze dynamics of pedestrians can increase our understanding of how important architectural features in urban environments are perceived by pedestrians. The results of such an analysis can lead to improvements in urban design. However, a technical challenge arises when trying to determine the gaze direction of pedestrians recorded on video. High "noise" levels and the subtlety of human gaze dynamics hamper precise calculations. However, as robots can be programmed and analysed more efficiently than humans this study employs them for developing and training a gaze analysis system with the aim to later apply it to human video data using the machine learning technique of manifold alignment. For the present study a laboratory was set up to become a model street scene in which autonomous humanoid robots of approximately 55cm in height simulate the behaviour of human pedestrians. The experiments compare the inputs from several cameras as the robot walks down the model street and changes its behaviour upon encountering "visually attractive objects". Overhead recordings and the robot's internal joint signals are analysed after filtering to provide "true" data against which the recorded data can be compared for accuracy testing. A central component of the research is the calculation of a torus-like manifold that represents all different 3D head directions of a robot head and which allows for ordering extracted 3D gaze vectors obtained from video sequences. We briefly describe how the obtained multidimensional trajectory data can be analysed by using a temporal behaviour analysis technique based on support vector machines that was developed separately.
Visual attention mechanisms allow humans to extract relevant and important information from raw input percepts. Many applications in robotics and computer vision have modeled human visual attention mechanisms using a bottomup data centric approach. In contrast, recent studies in cognitive science highlight advantages of a top-down approach to the attention mechanisms, especially in applications involving goal-directed search. In this paper, we propose a topdown approach for extracting salient objects/regions of space. The top-down methodology first isolates different objects in an unorganized point cloud, and compares each object for uniqueness. A measure of saliency using the properties of geodesic distance on the object's surface is defined. Our method works on 3D point cloud data, and identifies salient objects of high curvature and unique silhouette. These being the most unique features of a scene, are robust to clutter, occlusions and view point changes. We provide the details of the proposed method and initial experimental results.
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