Neil A. Clark

An assessment of the utility of a non-metric digital camera for measuring standing trees

Abstract Images acquired with a commercially available digital camera were used to make measurements on 20 red oak (Quercus spp.) stems. The ranges of diameter at breast height (DBH) and height to a 10 cm upper-stem diameter were 16–66 cm and 12–20 m, respectively. Camera stations located 3, 6, 9, 12, and 15 m from the stem were studied to determine the best distance to be used with the maximum wide angle setting on the camera. Geometric mean diameter estimates from the 12 and 15 m distances were within ±4 cm at any height (95% χ2). Though unbiased, measurement variation was found to increase with stem height. Using camera derived heights and diameters, volumes were found to be within 8% of volumes calculated using taped measurements of individual stems two times out of three — an improvement over existing DBH-height volume equations. This preliminary work demonstrates the ability of using a digital camera to acquire stem diameters and heights. Some limitations of the current technology are also noted. By combining equipment and procedural modifications with improved data flow from imagery to information, terrestrial digital imagery may revolutionize stem or even plot level data collection.

Active contours on statistical manifolds and texture segmentation

A new approach to active contours on statistical manifolds is presented. The statistical manifolds are 2-dimensional Riemannian manifolds that are statistically defined by maps that transform a parameter domain onto a set of probability density functions. In this novel framework, color or texture features are measured at each image point and their statistical characteristics are estimated. This is different from statistical representation of bounded regions. A modified Kullback-Leibler divergence, that measures dissimilarity between two density distributions, is added to the statistical manifolds so that a geometric interpretation of the manifolds becomes possible. With this framework, we can formulate a metric tensor on the statistical manifolds. Then, a geodesic active contour is evolved with the aid of the metric tensor. We show that the statistical manifold framework provides more robust and accurate texture segmentation results.

Spline curve matching with sparse knot sets: applications to deformable shape detection and recognition

Splines can be used to approximate noisy data with a few control points. This paper presents a new curve matching method for deformable shapes using two-dimensional splines. In contrast to the residual error criterion [F.S. Cohen et al., 1992], which is based on relative locations of corresponding knot points such that is reliable primarily for dense point sets, we use deformation energy of thin-plate-spline mapping between sparse knot points and normalized local curvature information. This method has been tested successfully for the detection and recognition of deformable shapes.

A Shape Representation for Planar Curves by Shape Signature Harmonic Embedding

This paper introduces a new representation for planar curves. From the well-known Dirichlet problem for a disk, the harmonic function embedded in a circular disk is solely dependent on specified boundary values and can be obtained from Poisson’s integral formula. We derive a discrete version of Poisson’s formula and assess its harmonic properties. Various shape signatures can be used as boundary values, whereas only the corresponding Fourier descriptors are needed for the framework. The proposed approach is similar to a scale space representation but exhibits greater generality by accommodating using any type of shape signature. In addition, it is robust to noise and computationally efficient, and it is guaranteed to have a unique solution. In this paper, we demonstrate that the approach has strong potential for shape representation and matching applications.

Diffusion on Statistical Manifolds

This paper presents a new diffusion scheme on statistical manifolds for the detection of texture boundaries. The technique derives from our previous work, in which 2-dimensional Riemannian manifolds were statistically defined by maps that transform a parameter domain onto a set of probability density functions. In the earlier approach, a modified Kullback-Leibler divergence, measuring dissimilarity between two density distributions, was added to the statistical manifolds so that a geometric interpretation of the manifolds becomes possible. Although the previous framework produced good segmentation results, the approach led to offsets in texture boundaries for some situations. This paper introduces a diffusion scheme on statistical manifolds that leads to substantially improved localization accuracy in segmentation of textured images.