article Free AccessDirect construction of polynomial surfaces from dense range images through region growing Authors: Nickolas S. Sapidis National Technical Univ. of Athens, Athens, Greece National Technical Univ. of Athens, Athens, GreeceView Profile , Paul J. Besl General Motors Research Labs., Warren, MI General Motors Research Labs., Warren, MIView Profile Authors Info & Claims ACM Transactions on GraphicsVolume 14Issue 2pp 171–200https://doi.org/10.1145/221659.221672Published:01 April 1995Publication History 59citation1,120DownloadsMetricsTotal Citations59Total Downloads1,120Last 12 Months43Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

The authors describe a general-purpose, representation-independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces. The method handles the full six degrees of freedom and is based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point. The ICP algorithm always converges monotonically to the nearest local minimum of a mean-square distance metric, and the rate of convergence is rapid during the first few iterations. Therefore, given an adequate set of initial rotations and translations for a particular class of objects with a certain level of 'shape complexity', one can globally minimize the mean-square distance metric over all six degrees of freedom by testing each initial registration. One important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model, prior to shape inspection. Experimental results show the capabilities of the registration algorithm on point sets, curves, and surfaces.>

The authors deal with the principal patch representation of visible surfaces in a scene, an algorithm for creating a stable surface model in this representation, and an algorithm for extending this model into subsequent views. The representation is based on the fact that the lines of minimum and maximum curvature on a generic smooth surface are orthogonal at almost every point. An engineering solution is presented for creating global principal patch networks. By computing the lines of curvature from a range image, it will be possible to construct a mesh data structure and repatch the surface with tensor product surfaces that are virtually free of tensor product artifacts and allow application of intersurface continuity algorithms. The authors focus on the computational difficulties encountered in building a quadrilateral mesh based on integrated lines of curvature.>

It is a common practice in computer vision and image processing to convolve rectangular constant coefficient windows with digital images to perform local smoothing and derivative estimation for edge detection and other purposes. If all data points in each image window belong to the same statistical population, this practice is reasonable and fast. But, as is well known, constant coefficient window operators produce incorrect results if more than one statistical population is present within a window, for example, if a gray-level or gradient discontinuity is present. This paper shows one way to apply the theory of robust statistics to the data smoothing and derivative estimation problem. A robust window operator is demonstrated that preserves gray-level and gradient discontinuities in digital images as it smooths and estimates derivatives.

The task of automating the visual inspection of pin-in-hole solder joints is addressed. Two approaches are explored: statistical pattern recognition and expert systems. An objective dimensionality-reduction method is used to enhance the performance of traditional statistical pattern recognition approaches by decorrelating feature data, generating feature weights, and reducing run-time computations. The expert system uses features in a manner more analogous to the visual clues that a human inspector would rely on for classification. Rules using these cues are developed, and a voting scheme is implemented to accumulate classification evidence incrementally. Both methods compared favorably with human inspector performance.>

The solution of the segmentation problem requires a mechanism for partitioning the image array into low-level entities based on a model of the underlying image structure. A piecewise-smooth surface model for image data that possesses surface coherence properties is used to develop an algorithm that simultaneously segments a large class of images into regions of arbitrary shape and approximates image data with bivariate functions so that it is possible to compute a complete, noiseless image reconstruction based on the extracted functions and regions. Surface curvature sign labeling provides an initial coarse image segmentation, which is refined by an iterative region-growing method based on variable-order surface fitting. Experimental results show the algorithm's performance on six range images and three intensity images.>

Geometric matching algorithms and geometric representations are examined for point sets, curves, surfaces, volumes, and their space-time trajectories. The author focuses mainly on general-purpose techniques. Geometric matching benefits greatly from taking advantage of symmetries and special features, e.g. dihedral edges and circles or any three reliably identifiable feature points. Volume matching and representation techniques are also presented. Texture and fractal representations are briefly described.>

This paper describes an approach for automatic inspection of solder joints on printed circuit boards using gray-scale images. Common defects in solder joints are recognized using features computed from segmented solder joint subimages. Unacceptable joints are assigned to one of several defective classes. Defect classification, rather than just detection of defective joints, is motivated by the desire to automatically take corrective action on the assembly line. The features used for classification are based on characteristics of intensity surfaces. It is shown that features derived from surface facets are effective in the classification of solder joints using a minimum-distance classification algorithm.

An approach is described for the automatic inspection of solder joints on printed circuit boards. Common defects are identified in solder joints and a joint is classified as being good or belonging to one of the defective classes. The motivation for this classification is not just the detection of defective joints, but the desire to automatically take corrective action on the assembly line. The features used for classification are based on characteristics of intensity surfaces. It is shown that features derived from facets and Gaussian curvature are effective in the classification of solder joints using a minimum-distance classification algorithm. Class separation plots are shown to be useful for quickly studying individual effectiveness of a feature or pair of features in classification. Results show the efficacy of the described approach.