Minimum spanning trees for valley and ridge characterization in digital elevation maps

Texture synthesis employs neighbourhood matching to generate appropriate new content. Terrain synthesis has the added constraint that new content must be geographically plausible. The profile recognition and polygon breaking algorithm (PPA) [Chang et al. 1998] provides a robust mechanism for characterizing terrain as systems of valley and ridge lines in digital elevation maps. We exploit this to create a terrain characterization metric that is robust, efficient to compute and is sensitive to terrain properties.

Terrain regions are characterized as a minimum spanning tree derived from a graph created from the sample points of the elevation map which are encoded as weights in the edges of the graph. This formulation allows us to provide a single consistent feature definition that is sensitive to the pattern of ridges and valleys in the terrain Alternative formulations of these weights provide richer characteristicmeasures and we provide examples of alternate definitions based on curvature and contour measures.

We show that the measure is robust, with a significant portion derived directly from information local to the terrain sample. Global terrain characteristics introduce the issue of over- and underconnected valley/ridge lines when working with sub-regions. This is addressed by providing two graph construction strategies, which respectively provide an upper bound on connectivity as a single spanning tree, and a lower bound as a forest of trees.

Efficient minimum spanning tree algorithms are adapted to the context of terrain data and are shown to provide substantially better performance than previous PPA implementations. In particular, these are able to characterize valley and ridge behaviour at every point even in large elevation maps, providing a measure sensitive to terrain features at all scales.

The resulting graph based formulation provides an efficient and elegant algorithm for characterizing terrain features. The measure can be calculated efficiently, is robust under changes of neighbourhood position, size and resolution and the hybrid measure is sensitive to terrain features both locally and globally.

Ridge regression for two dimensional locality preserving projection

Two Dimensional Locality Preserving Projection (2D-LPP) is a recent extension of LPP, a popular face recognition algorithm. It has been shown that 2D-LPP performs better than PCA, 2D-PCA and LPP. However, the computational cost of 2D-LPP is high. This paper proposes a novel algorithm called Ridge Regression for Two Dimensional Locality Preserving Projection (RR- 2DLPP), which is an extension of 2D-LPP with the use of ridge regression. RR-2DLPP is comparable to 2DLPP in performance whilst having a lower computational cost. The experimental results on three benchmark face data sets - the ORL, Yale and FERET databases - demonstrate the effectiveness and efficiency of RR-2DLPP compared with other face recognition algorithms such as PCA, LPP, SR, 2D-PCA and 2D-LPP.

Face recognition using kernel ridge regression

In this paper, we present novel ridge regression (RR) and kernel ridge regression (KRR) techniques for multivariate labels and apply the methods to the problem of face recognition. Motivated by the fact that the regular simplex vertices are separate points with highest degree of symmetry, we choose such vertices as the targets for the distinct individuals in recognition and apply RR or KRR to map the training face images into a face subspace where the training images from each individual will locate near their individual targets. We identify the new face image by mapping it into this face subspace and comparing its distance to all individual targets. An efficient cross-validation algorithm is also provided for selecting the regularization and kernel parameters. Experiments were conducted on two face databases and the results demonstrate that the proposed algorithm significantly outperforms the three popular linear face recognition techniques (Eigenfaces, Fisherfaces and Laplacianfaces) and also performs comparably with the recently developed Orthogonal Laplacianfaces with the advantage of computational speed. Experimental results also demonstrate that KRR outperforms RR as expected since KRR can utilize the nonlinear structure of the face images. Although we concentrate on face recognition in this paper, the proposed method is general and may be applied for general multi-category classification problems.

Cigarette smoke-induced changes to alveolar macrophage phenotype and function are improved by treatment with procysteine

Defective efferocytosis may perpetuate inflammation in smokers with or without chronic obstructive pulmonary disease (COPD). Macrophages may phenotypically polarize to classically activated M1 (proinflammatory; regulation of antigen presentation) or alternatively activated M2 (poor antigen presentation; improved efferocytosis) markers. In bronchoalveolar lavage (BAL)–derived macrophages from control subjects and smoker/ex-smoker COPD subjects, we investigated M1 markers (antigen-presenting major histocompatibility complex [MHC] Classes I and II), complement receptors (CRs), the high-affinity Fc receptor involved with immunoglobulin binding for phagocytosis (Fc-gamma receptor, FcγR1), M2 markers (dendritic cell–specific intercellular adhesion molecule-grabbing nonintegrin [DC-SIGN] and arginase), and macrophage function (efferocytosis and proinflammatory cytokine production in response to LPS). The availability of glutathione (GSH) in BAL was assessed, because GSH is essential for both M1 function and efferocytosis. We used a murine model to investigate macrophage phenotype/function further in response to cigarette smoke. In lung tissue (disaggregated) and BAL, we investigated CRs, the available GSH, arginase, and efferocytosis. We further investigated the therapeutic effects of an oral administration of a GSH precursor, cysteine l-2-oxothiazolidine-4-carboxylic acid (procysteine). Significantly decreased efferocytosis, available GSH, and M1 antigen–presenting molecules were evident in both COPD groups, with increased DC-SIGN and production of proinflammatory cytokines. Increased CR-3 was evident in the current-smoker COPD group. In smoke-exposed mice, we found decreased efferocytosis (BAL and tissue) and available GSH, and increased arginase, CR-3, and CR-4. Treatment with procysteine significantly increased GSH, efferocytosis (BAL: control group, 26.2%; smoke-exposed group, 17.66%; procysteine + smoke-exposed group, 27.8%; tissue: control group, 35.9%; smoke-exposed group, 21.6%; procysteine + smoke-exposed group, 34.5%), and decreased CR-4 in lung tissue. Macrophages in COPD are of a mixed phenotype and function. The increased efferocytosis and availability of GSH in response to procysteine indicates that this treatment may be useful as adjunct therapy for improving macrophage function in COPD and in susceptible smokers.