Interactive Visualization of Graph Pyramids

Hierarchies of plane graphs, called graph pyramids, can be used for collecting, storing and analyzing geographical information based on satellite images or other input data. The visualization of graph pyramids facilitates studies about their structure, such as their vertex distribution or height in relation of a specific input image. Thus, a researcher can debug algorithms and ask for statistical information. Furthermore, it improves the better understanding of geographical data, like landscape properties or thematical maps. In this paper, we present an interactive 3D visualization tool that supports several coordinated views on graph pyramids, subpyramids, level graphs, thematical maps, etc. Additionally, some implementation details and application results are discussed

Spatial reasoning by active observation

The problem of deriving spatial relationships between objects in general requires high lever\u27 abstract representation, and it would pose difficulties even for human observer. Based on a formalism for spatial layouts proposed earlier, we present methods for deducing spatial relations between objects by an active, sighted agent in a large-scale environment. The deduction of spatial relations is based on simple visual clues, and thus this technique is more feasible than schemes that rely on complex object recognition.<br /

Are shadows transparent? An investigation on white, shadows and transparency in pictures

Shadow perception and transparency perception appear to use very similar rules, to the point that from the perceptual point of view shadows have been considered an instance of transparent objects. We claim that in spite of the similarities, shadows ought not to be considered transparent entities. The discussion has consequences both for the issue of the location of shadows, and for the conceptualization of transparency, and it provides insight into the inner complexities of the conceptual structure of visual concepts

Spatial reasoning via active observation

This paper presents a model for space in which an autonomous agent acquires information about its environment. The agent uses a predefined exploration strategy to build a map allowing it to navigate and deduce relationships between points in space. The shapes of objects in the environment are represented qualitatively. This shape information is deduced from the agent\u27s motion. Normally, in a qualitative model, directional information degrades under transitive deduction. By reasoning about the shape of the environment, the agent can match visual events to points on the objects. This strengthens the model by allowing further relationships to be deduced. In particular, points that are separated by long distances, or complex surfaces, can be related by line-of-sight. These relationships are deduced without incorporating any metric information into the model. Examples are given to demonstrate the use of the model

The Computation of Surface Lightness in Simple and Complex Scenes

The present thesis examined how reflectance properties and the complexity of surface mesostructure (small-scale surface relief) influence perceived lightness in centresurround displays. Chapters 2 and 3 evaluated the role of surface relief, gloss, and interreflections on lightness constancy, which was examined across changes in background albedo and illumination level. For surfaces with visible mesostructure (“rocky” surfaces), lightness constancy across changes in background albedo was better for targets embedded in glossy versus matte surfaces. However, this improved lightness constancy for gloss was not observed when illumination varied. Control experiments compared the matte and glossy rocky surrounds to two control displays, which matched either pixel histograms or a phase-scrambled power spectrum. Lightness constancy was improved for rocky glossy displays over the histogram-matched displays, but not compared to phase-scrambled variants of these images with equated power spectrums. The results were similar for surfaces rendered with 1, 2, 3 and 4 interreflections. These results suggest that lightness perception in complex centre-surround displays can be explained by the distribution of contrast across space and scale, independently of explicit information about surface shading or specularity. The results for surfaces without surface relief (“homogeneous” surfaces) differed qualitatively to rocky surfaces, exhibiting abrupt steps in perceived lightness at points at which the targets transitioned from being increments to decrements. Chapter 4 examined whether homogeneous displays evoke more complex mid-level representations similar to conditions of transparency. Matching target lightness in a homogeneous display to that in a textured or rocky display required varying both lightness and transmittance of the test patch on the textured display to obtain the most satisfactory matches. However, transmittance was only varied to match the contrast of targets against homogeneous surrounds, and not to explicitly match the amount of transparency perceived in the displays. The results suggest perceived target-surround edge contrast differs between homogeneous and textured displays. Varying the mid-level property of transparency in textured displays provides a natural means for equating both target lightness and the unique appearance of the edge contrast in homogeneous displays

The Computation of Surface Lightness in Simple and Complex Scenes

The present thesis examined how reflectance properties and the complexity of surface mesostructure (small-scale surface relief) influence perceived lightness in centresurround displays. Chapters 2 and 3 evaluated the role of surface relief, gloss, and interreflections on lightness constancy, which was examined across changes in background albedo and illumination level. For surfaces with visible mesostructure (“rocky” surfaces), lightness constancy across changes in background albedo was better for targets embedded in glossy versus matte surfaces. However, this improved lightness constancy for gloss was not observed when illumination varied. Control experiments compared the matte and glossy rocky surrounds to two control displays, which matched either pixel histograms or a phase-scrambled power spectrum. Lightness constancy was improved for rocky glossy displays over the histogram-matched displays, but not compared to phase-scrambled variants of these images with equated power spectrums. The results were similar for surfaces rendered with 1, 2, 3 and 4 interreflections. These results suggest that lightness perception in complex centre-surround displays can be explained by the distribution of contrast across space and scale, independently of explicit information about surface shading or specularity. The results for surfaces without surface relief (“homogeneous” surfaces) differed qualitatively to rocky surfaces, exhibiting abrupt steps in perceived lightness at points at which the targets transitioned from being increments to decrements. Chapter 4 examined whether homogeneous displays evoke more complex mid-level representations similar to conditions of transparency. Matching target lightness in a homogeneous display to that in a textured or rocky display required varying both lightness and transmittance of the test patch on the textured display to obtain the most satisfactory matches. However, transmittance was only varied to match the contrast of targets against homogeneous surrounds, and not to explicitly match the amount of transparency perceived in the displays. The results suggest perceived target-surround edge contrast differs between homogeneous and textured displays. Varying the mid-level property of transparency in textured displays provides a natural means for equating both target lightness and the unique appearance of the edge contrast in homogeneous displays

A COMPREHENSIVE GEOSPATIAL KNOWLEDGE DISCOVERY FRAMEWORK FOR SPATIAL ASSOCIATION RULE MINING

Continuous advances in modern data collection techniques help spatial scientists gain access to massive and high-resolution spatial and spatio-temporal data. Thus there is an urgent need to develop effective and efficient methods seeking to find unknown and useful information embedded in big-data datasets of unprecedentedly large size (e.g., millions of observations), high dimensionality (e.g., hundreds of variables), and complexity (e.g., heterogeneous data sources, space–time dynamics, multivariate connections, explicit and implicit spatial relations and interactions). Responding to this line of development, this research focuses on the utilization of the association rule (AR) mining technique for a geospatial knowledge discovery process. Prior attempts have sidestepped the complexity of the spatial dependence structure embedded in the studied phenomenon. Thus, adopting association rule mining in spatial analysis is rather problematic. Interestingly, a very similar predicament afflicts spatial regression analysis with a spatial weight matrix that would be assigned a priori, without validation on the specific domain of application. Besides, a dependable geospatial knowledge discovery process necessitates algorithms supporting automatic and robust but accurate procedures for the evaluation of mined results. Surprisingly, this has received little attention in the context of spatial association rule mining. To remedy the existing deficiencies mentioned above, the foremost goal for this research is to construct a comprehensive geospatial knowledge discovery framework using spatial association rule mining for the detection of spatial patterns embedded in geospatial databases and to demonstrate its application within the domain of crime analysis. It is the first attempt at delivering a complete geo-spatial knowledge discovery framework using spatial association rule mining