Abstract Feature curves are largely adopted to highlight shape features, such as sharp lines, or to divide surfaces into meaningful segments, like convex or concave regions. Extracting these curves is not sufficient to convey prominent and meaningful information about a shape. We have first to separate the curves belonging to features from those caused by noise and then to select the lines, which describe non-trivial portions of a surface. The automatic detection of such features is crucial for the identification and or annotation of relevant parts of a given shape. To do this, the Hough transform (HT) is a feature extraction technique widely used in image analysis, computer vision and digital image processing, while, for 3D shapes, the extraction of salient feature curves is still an open problem. Thanks to algebraic geometry concepts, the HT technique has been recently extended to include a vast class of algebraic curves, thus proving to be a competitive tool for yielding an explicit representation of the diverse feature lines equations. In the paper, for the first time we apply this novel extension of the HT technique to the realm of 3D shapes in order to identify and localize semantic features like patterns, decorations or anatomical details on 3D objects (both complete and fragments), even in the case of features partially damaged or incomplete. The method recognizes various features, possibly compound, and it selects the most suitable feature profiles among families of algebraic curves.
In 3D, other variants of HT have been introduced and used but as far as we know none of them exploits the huge variety of algebraic plane curves (for this we refer again to the surveys @cite_49 and @cite_20 ). For instance, in @cite_10 the HT has been employed to identify recurring straight line elements on the walls of buildings. In that application, the HT is applied only to planar point sets and line elements are clustered according to their angle with respect to a main wall direction; in this sense, the Hough aggregator is used to select the feature line directions (horizontal, vertical, slanting) one at a time.
@cite_20 @cite_10 @cite_49
1998447437 1964005581 1976952084 2073534752
Abstract The Hough transform (HT) is commonly used in machine vision applications for detecting discontinuous patterns in noisy images. The process of using the HT to detect lines in an image involves the computation of the HT for the entire image, accumulating votes in an accumulator array and searching the array for peaks which hold information of potential lines present in the input image. The peaks provide only the length of the normal to the line and the angle that the normal makes with the x -axis. They do not provide any information regarding the length, position or end points of the line segments. However, the butterfly shaped [1] spread of votes in the accumulator array, generated by the process of peak formation, holds vital information like the length and position of the input line segment. Some authors [2] have used this property to develop an algorithm to determine the coordinates of the end points, the length, and the normal parameters of straight lines. A limitation of this method, making it unsuitable for application to a real machine vision problem, is that it would yield erroneous results if applied to an image consisting of anything more than a single line segment. Moreover, the precision of this algorithm is dependent on the sharpness of the peak. In this paper, new techniques which address the above mentioned shortcomings have been described. This paper details the method developed to provide complete line segment description for an image consisting of multiple line segments. In addition, the developed techniques are more robust and accurate than the previously proposed methods as they do not depend upon the sharpness of the peak. Hough transform (HT) is a well-known technique for extracting lines. However, it is difficult for most existing HT methods to extract line segments robustly from complicated images, mainly because the influence from various objects other than line segments are not taken into account. This paper proposes an accurate and robust evaluator that dynamically removes contributions of backgrounds and analyzes voting patterns around peaks in the accumulator space. In the experiments, four peak detection algorithms are tested against seven images completely automatically. Results show that our method is superior to existing methods in terms of accuracy and robustness while there are no clear differences in execution time. The proposed evaluator detects peaks after the HT and hence it can be applied to any HT that keeps the basic characteristics of the voting process. We propose a novel approach for the reconstruction of urban structures from 3D point clouds with an assumption of Manhattan World (MW) building geometry; i.e., the predominance of three mutually orthogonal directions in the scene. Our approach works in two steps. First, the input points are classified according to the MW assumption into four local shape types: walls, edges, corners, and edge corners. The classified points are organized into a connected set of clusters from which a volume description is extracted. The MW assumption allows us to robustly identify the fundamental shape types, describe the volumes within the bounding box, and reconstruct visible and occluded parts of the sampled structure. We show results of our reconstruction that has been applied to several synthetic and real-world 3D point data sets of various densities and from multiple viewpoints. Our method automatically reconstructs 3D building models from up to 10 million points in 10 to 60 seconds. The morphological hit-or-miss transform (HMT) was found to be efficient for the detection of buildings in panchromatic bands of very high spatial resolution images. The use of multispectral information was judged to be necessary to improve the results. The application of morphological operators to multispectral images is problematic, as no universal strategy for ordering the multivalued pixels of these images has been widely adopted. In this paper, we propose a new method to detect building locations based on a recently developed concept for the HMT to handle noise, called percentage occupancy HMT (POHMT). The parameters for the POHMT were defined with the aid of the top-hat by reconstruction transformation. To eliminate irrelevant locations, we applied a vegetation mask and verified locations by their proximity to shadows. The novelty of the method consists in the proposed vector-based strategy that allows for the application of the POHMT to multispectral images in order to detect building locations. Moreover, an original technique to automatically define the parameters for the POHMT was proposed. The method was tested on subsets from a pan-sharpened Ikonos image and from raw GeoEye and WorldView-2 images. The experimental results are promising.