Extraction of main and secondary roads in VHR images using a higher-order phase field model.

This paper addresses the issue of extracting main and secondary road networks in dense urban areas from very high resolution (VHR, ~0.61m) satellite images. The difficulty with secondary roads lies in the low discriminative power of the grey-level distributions of road regions and the background, and the greater effect of occlusions and other noise on narrower roads. To tackle this problem, we use a previously developed higher-order active contour (HOAC) phase field model and augment it with an additional non-linear non-local term. The additional term allows separate control of road width and road curvature; thus more precise prior knowledge can be incorporated, and better road prolongation can be achieved for the same width. Promising results on QuickBird panchromatic images at reduced resolutions and comparisons with other models demonstrate the role and the efficiency of our new model

A phase field model incorporating generic and specific prior knowledge applied to road network extraction from VHR satellite images.

We address the problem of updating road maps in dense urban areas by extracting the main road network from a very high resolution (VHR) satellite image. Our model of the region occupied by the road network in the image is innovative. It incorporates three different types of prior geometric knowledge: generic boundary smoothness constraints, equivalent to a standard active contour prior; knowledge of the geometric properties of road networks (i.e. that they occupy regions composed of long, low-curvature segments joined at junctions), equivalent to a higher-order active contour prior; and knowledge of the road network at an earlier date derived from GIS data, similar to other ‘shape priors’ in the literature. In addition, we represent the road network region as a ‘phase field’, which offers a number of important advantages over other region modelling frameworks. All three types of prior knowledge prove important for overcoming the complexity of geometric ‘noise’ in VHR images. Promising results and a comparison with several other techniques demonstrate the effectiveness of our approach

Attentional Narrowing: Triggering, Detecting and Overcoming a Threat to Safety.

In complex safety-critical domains, such as aviation or medicine, considerable multitasking requirements and attentional demands are imposed on operators who may, during off-nominal events, also experience high levels of anxiety. High task load and anxiety can trigger attentional narrowing – an involuntary reduction in the range of cues that can be utilized by an operator. As evidenced by numerous accidents, attentional narrowing is a highly undesirable and potentially dangerous state as it hampers information gathering, reasoning, and problem solving. However, because the problem is difficult to reproduce in controlled environments, little is known about its triggers, markers and possible countermeasures. Therefore, the goals of this dissertation were to (1) identify reliable triggers of attentional narrowing in controlled laboratory settings, (2) identify real-time markers of attentional narrowing that can also distinguish that phenomenon from focused attention – another state of reduced attentional field that, contrary to attentional narrowing, is deliberate and often desirable, (3) develop and test display designs that help overcome the narrowing of the attentional field. Based on a series of experiments in the context of a visual search task and a multi-tasking environment, novel unsolvable problems were identified as the most reliable trigger of attentional narrowing. Eye tracking was used successfully to detect and trace the phenomenon. Specifically, three eye tracking metrics emerged as promising markers of attentional narrowing: (1) the percentage of fixations, (2) dwell duration and (3) fixation duration in the display area where the novel problem was presented. These metrics were used to develop an algorithm capable of detecting attentional narrowing in real time and distinguishing it from focused attention. A command display (as opposed to status) was shown to support participants in broadening their attentional field and improving their time sharing performance. This dissertation contributes to the knowledge base in attentional narrowing and, more generally, attention management. A novel eye tracking based technique for detecting the attentional state and a promising countermeasure to the problem were developed. Overall, the findings from this research contribute to improved safety and performance in a range of complex high-risk domains.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135773/1/jprinet_1.pd