A simple strategy for calibrating the geometry of light sources

We present a methodology for calibrating multiple light source locations in 3D from images. The procedure involves the use of a novel calibration object that consists of three spheres at known relative positions. The process uses intensity images to find the positions of the light sources. We conducted experiments to locate light sources in 51 different positions in a laboratory setting. Our data shows that the vector from a point in the scene to a light source can be measured to within 2.7±4° at α=.05 (6 percent relative) of its true direction and within 0.13±.02 m at α=.05 (9 percent relative) of its true magnitude compared to empirically measured ground truth. Finally, we demonstrate how light source information is used for color correction     

Recognizing plankton images from the shadow image particle profiling evaluation recorder

We present a system to recognize underwater plankton images from the shadow image particle profiling evaluation recorder (SIPPER). The challenge of the SIPPER image set is that many images do not have clear contours. To address that, shape features that do not heavily depend on contour information were developed. A soft margin support vector machine (SVM) was used as the classifier. We developed a way to assign probability after multiclass SVM classification. Our approach achieved approximately 90% accuracy on a collection of plankton images. On another larger image set containing manually unidentifiable particles, it also provided 75.6% overall accuracy. The proposed approach was statistically significantly more accurate on the two data sets than a C4.5 decision tree and a cascade correlation neural network. The single SVM significantly outperformed ensembles of decision trees created by bagging and random forests on the smaller data set and was slightly better on the other data set. The 15-feature subset produced by our feature selection approach provided slightly better accuracy than using all 29 features. Our probability model gave us a reasonable rejection curve on the larger data set.     

Dynamic-scale model construction from range imagery

The construction of a surface model from range data may be undertaken at any point in a continuum of scales that reflects the level of detail of the resulting model. This continuum relates the construction parameters to the scale of the model. We propose methods to dynamically reprocess range data at different scales. The construction result from a single scale is automatically evaluated, causing reconstruction at a different scale when user-defined criteria are not met. We demonstrate our methods in constructing a planar b-rep space envelope (a scene representation) for over 400 range images. The experiments demonstrate the ability to construct 100 percent valid models, with the scale of detail within specified requirements     

A Scalable Framework For Segmenting Magnetic Resonance Images

A fast, accurate and fully automatic method of segmenting magnetic resonance images of the human brain is introduced. The approach scales well allowing fast segmentations of fine resolution images. The approach is based on modifications of the soft clustering algorithm, fuzzy c-means, that enable it to scale to large data sets. Two types of modifications to create incremental versions of fuzzy c-means are discussed. They are much faster when compared to fuzzy c-means for medium to extremely large data sets because they work on successive subsets of the data. They are comparable in quality to application of fuzzy c-means to all of the data. The clustering algorithms coupled with inhomogeneity correction and smoothing are used to create a framework for automatically segmenting magnetic resonance images of the human brain. The framework is applied to a set of normal human brain volumes acquired from different magnetic resonance scanners using different head coils, acquisition parameters and field strengths. Results are compared to those from two widely used magnetic resonance image segmentation programs, Statistical Parametric Mapping and the FMRIB Software Library (FSL). The results are comparable to FSL while providing significant speed-up and better scalability to larger volumes of data.     

Model-based force-driven nonrigid motion recovery from sequences of range images without point correspondences

In this article we propose a new method for accurate nonrigid motion analysis when point correspondence data is not available. Nonlinear finite element models are constructed by integrating range data and prior knowledge about an object's properties. The motion sequence is recovered given an initial alignment of the model with the first frame of the sequence. The main idea of the method is to find the forces that are responsible for the motion or shape deformation of the given object. The task is broken into subtasks of finding the forces for each frame. Both absolute values and directions of these forces are taken into consideration and iteratively varied not only for each frame, but also between the frames. Experimental results demonstrate the success of the proposed algorithm. The method is applied to man-made elastic materials and human hand modeling. It allows for recovery of single and multiple forces using restricted (elastic-articulated) and completely unrestricted (elastic) models. Our work demonstrates the possibility of accurate nonrigid motion analysis and force recovery from range image sequences containing nonrigid objects and large motion without interframe point correspondences.     

Fast accurate fuzzy clustering through data reduction

Clustering is a useful approach in image segmentation, data mining, and other pattern recognition problems for which unlabeled data exist. Fuzzy clustering using fuzzy c-means or variants of it can provide a data partition that is both better and more meaningful than hard clustering approaches. The clustering process can be quite slow when there are many objects or patterns to be clustered. This paper discusses the algorithm brFCM, which is able to reduce the number of distinct patterns which must be clustered without adversely affecting the partition quality. The reduction is done by aggregating similar examples and then using a weighted exemplar in the clustering process. The reduction in the amount of clustering data allows a partition of the data to be produced faster. The algorithm is applied to the problem of segmenting 32 magnetic resonance images into different tissue types and the problem of segmenting 172 infrared images into trees, grass and target. Average speed-ups of as much as 59-290 times a traditional implementation of fuzzy c-means were obtained using brFCM, while producing partitions that are equivalent to those produced by fuzzy c-means.     

A curvature-based approach to terrain recognition

The authors describe an algorithm which uses a Gaussian and mean curvature profile for extracting special points on terrain and then use these points for recognition of particular regions of the terrain. The Gaussian and mean curvatures are chosen because they are invariant under rotation and translation. In the Gaussian and mean curvature image, the points of maximum and minimum curvature are extracted and used for matching. The stability of the position of those points in the presence of noise and with resampling is investigated. The input for this algorithm consists of 3-D digital terrain data. Curvature values are calculated from the data by fitting a quadratic surface over a square window and calculating directional derivatives of this surface. A method of surface fitting which is invariant to coordinate system transformation is suggested and implemented. The algorithm is tested with and without the presence of noise, and its performance is described     

The use of three- and four-dimensional surface harmonics for rigidand nonrigid shape recovery and representation

The use of spherical harmonics for rigid and nonrigid shape representation is well known. This paper extends the method to surface harmonics defined on domains other than the sphere and to four-dimensional spherical harmonics. These harmonics enable us to represent shapes which cannot be represented as a global function in spherical coordinates, but can be in other coordinate systems. Prolate and oblate spheroidal harmonics and cylindrical harmonics are examples of surface harmonics which we find useful. Nonrigid shapes are represented as functions of space and time either by including the time-dependence as a separate factor or by using four-dimensional spherical harmonics. This paper compares the errors of fitting various surface harmonics to an assortment of synthetic and real data samples, both rigid and nonrigid. In all cases we use a linear least-squares approach to find the best fit to given range data. It is found that for some shapes there is a variation among geometries in the number of harmonics functions needed to achieve a desired accuracy. In particular, it was found that four-dimensional spherical harmonics provide an improved model of the motion of the left ventricle of the heart