Surface Orientation Information Research Articles

Geostructural stability assessment of cave using rock surface discontinuity extracted from terrestrial laser scanning point cloud

The use of terrestrial laser scanning (TLS) in the caves has been growing drastically over the last decade. However, TLS application to cave stability assessment has not received much attention of researchers. This study attempted to utilize rock surface orientations obtained from TLS point cloud collected along cave passages to (1) investigate the influence of rock geostructure on cave passage development, and (2) assess cave stability by determining areas susceptible to different failure types. The TLS point cloud was divided into six parts (Entry hall, Chamber, Main hall, Shaft 1, Shaft 2 and Shaft 3), each representing different segments of the cave passages. Furthermore, the surface orientation information was extracted and grouped into surface discontinuity joint sets. The computed global mean and best–fit planes of the entire cave show that the outcrop dips 290° with a major north-south strike. But at individual level, the passages with dip angle between 26° and 80° are featured with dip direction of 75°–322°. Kinematic tests reveal the potential for various failure modes of rock slope. Our findings show that toppling is the dominant failure type accounting for high-risk rockfall in the cave, with probabilities of 75.26%, 43.07% and 24.82% in the Entry hall, Main hall and Shaft 2, respectively. Unlike Shaft 2 characterized by high risk of the three failure types (32.49%, 24.82% and 50%), the chamber and Shaft 3 passages are not suffering from slope failure. The results also show that the characteristics of rock geostructure considerably influence the development of the cave passages, and four sections of the cave are susceptible to different slope failure types, at varying degrees of risk.

Joint segmentation of color and depth data based on splitting and merging driven by surface fitting

This paper proposes a segmentation scheme based on the joint usage of color and depth data together with a 3D surface estimation scheme. Firstly a set of multi-dimensional vectors is built from color, geometry and surface orientation information. Normalized cuts spectral clustering is then applied in order to recursively segment the scene in two parts thus obtaining an over-segmentation. This procedure is followed by a recursive merging stage where close segments belonging to the same object are joined together. At each step of both procedures a NURBS model is fitted on the computed segments and the accuracy of the fitting is used as a measure of the plausibility that a segment represents a single surface or object. By comparing the accuracy to the one at the previous step, it is possible to determine if each splitting or merging operation leads to a better scene representation and consequently whether to perform it or not. Experimental results show how the proposed method provides an accurate and reliable segmentation.

Polarization-Based Method for Object Surface Orientation Information in Passive Millimeter-Wave Imaging

Surface orientation information is essential to depicting the 3-D structure of an object. This paper analyzes the linear polarization characteristics of several typical objects by simulations and measurements. Then, a polarization-based method is presented to acquire the object surface orientation information from three different linear polarization brightness temperature images by using a 94-GHz imaging radiometer. The experiments are conducted outside, where a wooden plate is placed obliquely on a metal box. Experimental results indicate that our method is capable of achieving the surface orientation information that is beneficial to recognizing the object. This method suggests possible applications for terrain models and object detections.

Driving 3D morphable models using shading cues

In this paper we show how surface orientation information inferred using shape-from-shading can be used to aid the process of fitting a 3D morphable model to an image of a face. We consider the problem of model dominance and show how shading constraints can be used to refine morphable model shape estimates, offering the possibility of exceeding the maximum possible accuracy of the model. We use this observation to motivate an optimisation scheme based on surface normal error. This ensures the fullest possible use of the information conveyed by the shading in an image. Moreover, our framework allows estimation of per-vertex albedo and bump maps which are not constrained to lie within the span of the model. This means the recovered model is capable of describing shape and reflectance phenomena not present in the training set. We show reconstruction and synthesis results and demonstrate that the shape and albedo estimates can be used for illumination insensitive recognition using only a single gallery image.

Range Image Segmentation Into Planar and Quadric Surfaces Using an Improved Robust Estimator and Genetic Algorithm

This paper presents a novel range image segmentation method employing an improved robust estimator to iteratively detect and extract distinct planar and quadric surfaces. Our robust estimator extends M-estimator Sample Consensus/Random Sample Consensus (MSAC/RANSAC) to use local surface orientation information, enhancing the accuracy of inlier/outlier classification when processing noisy range data describing multiple structures. An efficient approximation to the true geometric distance between a point and a quadric surface also contributes to effectively reject weak surface hypotheses and avoid the extraction of false surface components. Additionally, a genetic algorithm was specifically designed to accelerate the optimization process of surface extraction, while avoiding premature convergence. We present thorough experimental results with quantitative evaluation against ground truth. The segmentation algorithm was applied to three real range image databases and competes favorably against eleven other segmenters using the most popular evaluation framework in the literature. Our approach lends itself naturally to parallel implementation and application in real-time tasks. The method fits well, into several of today's applications in man-made environments, such as target detection and autonomous navigation, for which obstacle detection, but not description or reconstruction, is required. It can also be extended to process point clouds resulting from range image registration.

A methodology for extracting objective color from images

We present a methodology for correcting color images taken in practical indoor environments, such as laboratories, factories, and studios, that explicitly models illuminant location, surface reflectance and geometry, and camera responsivity. We explicitly model surfaces by taking our color images with corresponding registered three-dimensional (3-D) range images, which provide surface orientation and location information for every point in the scene. We automatically detect regions where color correction should not be applied, such as specularities, coarse texture regions, and jump edges. This correction results in objective color measures of the imaged surfaces. This kind of integrated, comprehensive system of color correction has not existed until now. i.e., it is the first of its kind in computer vision. We demonstrate results of applying this methodology to real images for applications in photorealistic rerendering, skin lesion detection, burn scar color measurement, and general color image enhancement. We also have tested the method under different lighting configurations and with three different range scanners.

EXTRACTING MEANINGFUL SLOPES FROM TERRAIN CONTOURS

Good quality terrain models are becoming more and more important, as applications such as runoff modelling are being developed that demand better surface orientation information than is available from traditional interpolation techniques. A consequence is that poor-quality elevation grids must be massaged before they provide useable runoff models. Rather than using direct data acquisition, this project concentrated on using available contour data because, despite modern techniques, contour maps are still the most available form of elevation information. Recent work on the automatic reconstruction of curves from point samples, and the generation of medial axis transforms (skeletons) has greatly helped in expressing the spatial relationships between topographic sets of contours. With these techniques the insertion of skeleton points into a TIN model guarantees the elimination of all "flat triangles" where all three vertices have the same elevation. Additional assumptions about the local uniformity of slopes give us enough information to assign elevation values to these skeleton points. In addition, various interpolation techniques were compared using the enriched contour data. Examination of the quality and consistency of the resulting maps indicates the required properties of the interpolation method in order to produce terrain models with valid slopes. The result provides us with a surprisingly realistic model of the surface - that is, one that conforms well to our subjective interpretation of what a real landscape should look like.

Shape from shading. II. Geodesic bisection and alignment.

Pattern-acuity tasks have provided valuable information about the precision with which the visual system can make judgments about relative spatial position in two-dimensional images. However, outside the laboratory the visual system is habitually faced with the more difficult task of making positional judgments within a three-dimensional spatial environment. Thus our perceptual systems for representing surface shape also need to support the recovery of the location and disposition of features in a three-dimensional space. An investigation of the precision of three-dimensional position judgments in two spatial-judgment tasks, arc length bisection along geodesics and geodesic alignment, is reported. The spatial-judgment tasks were defined with reference to a sphere rendered by means of ray-casting techniques. The presence of shading and texture cues had no effect on discrimination thresholds in either task. Observers' constant errors were generally less than the just noticeable distance, demonstrating that the observers can perform these positional judgment tasks without substantial bias. It is argued that there is no explicit computation of arc length on the basis of shading and texture information and that surface-orientation information cannot be used as a reference in geodesic-alignment tasks. The results raise questions about the utility of a representation of surface orientation in the human visual system.

Segmentation of noisy range images using the small-scale planarity of man-made vehicles

Segmentation ofvehicles in natural backgrounds using absolute range imagery is considered. The segmentation technique exploits the small-scale planarity of man-made vehicles without explicit use of surface orientation information. A planarity test is used as the initial step in partitioning man-made vehicles from natural backgrounds. The planarity test is based on an estimate of the statistics of the absolute value of the error associated with fitting planes to small regions of the Cartesian coordinates of range image pixels. The planarity test is developed as a function of the standard deviation of the noise associated with range measurements. Heuristics used in conjunction with this algorithm are also presented. This algorithm has been evaluated on a database of 57 real range images, and results are presented. Application of the algorithm to two specific cases is shown in detail.

Specular surface inspection using structured highlight and Gaussian images

The structured highlight inspection method uses an array of point sources to illuminate a specular object surface. The point sources are scanned, and highlights on the object surface resulting from each source are used to derive local surface orientation information. The extended Gaussian image (EGI) is obtained by placing at each point on a Gaussian sphere a mass proportional to the area of elements on the object surface that have a specific orientation. The EGI summarizes shape properties of the object surface and can be efficiently calculated from structured highlight data without surface reconstruction. Features of the estimated EGI including areas, moments, principal axes, homogeneity measures, and polygonality can be used as the basis for classification and inspection. The structured highlight inspection system (SHINY) has been implemented using a hemisphere of 127 point sources. The SHINY system uses a binary coding scheme to make the scanning of point sources efficient. Experiments have used the SHINY system and EGI features for the inspection and classification of surface-mounted-solder joints. >