Applied Graph Theory in Computer Vision and Pattern Recognition

This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.

Assessing the function of the fronto-parietal attention network: Insights from resting-state fMRI and the attentional network test

In the recent past, various intrinsic connectivity networks (ICN) have been identified in the resting brain. It has been hypothesized that the fronto-parietal ICN is involved in attentional processes. Evidence for this claim stems from task-related activation studies that show a joint activation of the implicated brain regions during tasks that require sustained attention. In this study, we used functional magnetic resonance imaging (fMRI) to demonstrate that functional connectivity within the fronto-parietal network at rest directly relates to attention. We applied graph theory to functional connectivity data from multiple regions of interest and tested for associations with behavioral measures of attention as provided by the attentional network test (ANT), which we acquired in a separate session outside the MRI environment. We found robust statistical associations with centrality measures of global and local connectivity of nodes within the network with the alerting and executive control subfunctions of attention. The results provide further evidence for the functional significance of ICN and the hypothesized role of the fronto-parietal attention network. Hum Brain Mapp , 2013. © 2013 Wiley Periodicals, Inc.

Neutral biodiversity theory can explain the imbalance of phylogenetic trees but not the tempo of their diversification

Numerous evolutionary studies have sought to explain the distribution of diversity across the limbs of the tree of life. At the same time, ecological studies have sought to explain differences in diversity and relative abundance within and among ecological communities. Traditionally, these patterns have been considered separately, but models that consider processes operating at the level of individuals, such as neutral biodiversity theory (NBT), can provide a link between them. Here, we compare evolutionary dynamics across a suite of NBT models. We show that NBT can yield phylogenetic tree topologies with imbalance closely resembling empirical observations. In general, metacommunities that exhibit greater disparity in abundance are characterized by more imbalanced phylogenetic trees. However, NBT fails to capture the tempo of diversification as represented by the distribution of branching events through time. We suggest that population-level processes might therefore help explain the asymmetry of phylogenetic trees, but that tree shape might mislead estimates of evolutionary rates unless the diversification process is modeled explicitly.

Herbivores differentially limit the seedling growth and sapling recruitment of two dominant rain forest trees

Resource heterogeneity may influence how plants are attacked and respond to consumers in multiple ways. Perhaps a better understanding of how this interaction might limit sapling recruitment in tree populations may be achieved by examining species’ functional responses to herbivores on a continuum of resource availability. Here, we experimentally reduced herbivore pressure on newly established seedlings of two dominant masting trees in 40 canopy gaps, across c. 80 ha of tropical rain forest in central Africa (Korup, Cameroon). Mesh cages were built to protect individual seedlings, and their leaf production and changes in height were followed for 22 months. With more light, herbivores increasingly prevented the less shade-tolerant Microberlinia bisulcata from growing as tall as it could and producing more leaves, indicating an undercompensation. The more shade-tolerant Tetraberlinia bifoliolata was much less affected by herbivores, showing instead near to full compensation for leaf numbers, and a negligible to weak impact of herbivores on its height growth. A stage-matrix model that compared control and caged populations lent evidence for a stronger impact of herbivores on the long-term population dynamics of M. bisulcata than T. bifoliolata. Our results suggest that insect herbivores can contribute to the local coexistence of two abundant tree species at Korup by disproportionately suppressing sapling recruitment of the faster-growing dominant via undercompensation across the light gradient created by canopy disturbances. The functional patterns we have documented here are consistent with current theory, and, because gap formations are integral to forest regeneration, they may be more widely applicable in other tropical forest communities. If so, the interaction between life-history and herbivore impact across light gradients may play a substantial role in tree species coexistence.