When I started work on beads, the first task was to register over a thousand strings of them in the Petrie Collection. At that time, I did not know what I might need in the later stage of my work, let alone what other people in the field or museum my require for their purposes. The obvious course naturally resorted to is to follow some system or other with certain modifications. As stated by Beck, “to describe a bead fully it is necessary, to state its form, perforation, colour, material, and decoration” But for the registration of whole strings of beads, there are some other informations which should be recorded as well.

Method of Registration

A new edge-based approach for efficient image registration is proposed. The proposed approach applies wavelet transform to extract a number of feature points as the basis for registration. Each selected feature point is an edge point whose edge response is the maximum within a neighborhood. By using a line-fitting model, all the edge directions of the feature points are estimated from the edge outputs of a transformed image. In order to estimate the orientation difference between two partially overlapping images, a so-called “angle histogram” is calculated. From the angle histogram, the rotation angle which can be used to compensate for the difference between two target images can be decided by seeking the angle that corresponds to the maximum peak in the histogram. Based on the rotation angle, an initial matching can be performed. During the real matching process, we check each candidate pair in advance to see if it can possibly become a correct matching pair. Due to this checking, many unnecessary calculations involving cross-correlations can be screened in advance. Therefore, the search time for obtaining correct matching pairs is reduced significantly. Finally, based on the set of correctly matched feature point pairs, the transformation between two partially overlapping images can be decided. The proposed method can tolerate roughly about 10% scaling variation and does not restrict the position and orientation of images. Further, since all the selected feature points are edge points, the restriction can significantly reduce the search space and, meanwhile, speed up the matching process. Compared with conventional algorithms, the proposed scheme is a great improvement in efficiency as well as reliability for the image registration problem.

Image Registration Using a New Edge-Based Approach

3D point cloud data is used for 3D model acquisition, geometry processing and 3D inspection. Registration of

3D point cloud data is crucial for each field. The difference between 2D image registration and 3D point cloud

registration is that the latter requires several things to be considered: translation on each plane, rotation, tilt

and etc. This paper describes a method of registering 3D point cloud data with noise. The relationship between

the two sets of 3D point cloud data can be obtained by Affine transformation. In order to calculate 3D Affine

transformation matrix, corresponding points are required. To find the corresponding points, we use the height

map which is projected from 3D point cloud data onto XY plane. We formulate the height map matching as a

cost function and estimate the corresponding points. To find the proper 3D Affine transformation matrix, we

formulate a cost function which uses the relationship of the corresponding points. Also the proper 3D Affine

transformation matrix can be calculated by minimizing the cost function. The experimental results show that

the proposed method can be applied to various objects and gives better performance than the previous work.

Robust Registration Method of 3D Point Cloud Data

Demonstration Structure for a 3D Holographic Sensor

In recent years, machine vision has played an important role in product surface quality detection. The promotion and use of this technology have largely avoided the subjectivity caused by human detection and improved detection efficiency and accuracy. Different from the image data commonly used in machine vision, point cloud can describe the spatial structure of an object, provide more information than image data, and have the ability to expand the data to build multi-dimensional data models. Due to the strong anti-interference ability of point cloud sensors and the high accuracy of three-dimensional positioning information point cloud, nondestructive testing technology based on point cloud has received more and more attention. This paper summarizes the research progress of product surface quality detection methods based on 3D point cloud in recent years. According to different data processing methods, the detection research is divided into five categories: based on point cloud contour, based on local geometric feature, based on template matching, based on multimodal point cloud, and based on deep learning. The five methods are reviewed and summarized respectively. Finally, the key problems of each detection method and the future trend of product surface quality detection technology based on 3D point cloud are discussed.

Review: Research on product surface quality inspection technology based on 3D point cloud

As a part of the HDD manufacturing process, HDD stamped base, an exterior container, is one of the most essential components in which various parts become assembled to compose a hard disk drive (HDD). Height errors that are caused by pressing, breaking or cracking can occur on the base, because it is designed by a stamping method. In order to detect the height errors, the inspection process is essential in the production fields. In the current industry, CMM (Coordinate Measurement Machine) is one of the representative machines that inspect certain regions on the product. The machine probes a designated point by an operator and judges the defect by comparing the height of the point to the originally designed height. However, the method takes much time to inspect each designated point resulting in a total of 17 minutes. In order to reduce the total inspection time, we propose an inspection method using 3D point cloud data acquired from a holographic sensor. To compare the height from acquired 3D point cloud data with the one from the originally designed CAD data, the exact point cloud registration is important. There are differences between 2D image registration and 3D point cloud registration, such as translation on each plane, rotation, tilt, and nonlinear transformations. The relationship between the acquired 3D point cloud data and the originally designed CAD data can be obtained by projective transformation. If the projective transformation matrix between the two is obtained, 3D point cloud data registration can be performed. In order to calculate 3D projective transformation matrix, corresponding points between 3D point cloud data and CAD data are required. To find the corresponding points, we use the height map which is projected from 3D point cloud data onto XY plane. The height map has pixel intensity from the height value of each point. If the height maps from 3D point cloud data and CAD data are matched, corresponding points can be estimated. As one of the features of the HDD stamped base, there are multiple circles on the base. In this paper, we find the corresponding points between 3D point cloud data and CAD data using circle fitting, and obtain 2D Affine transformation matrix from the corresponding points. By applying 2D Affine transformation matrix to height map, the corresponding points on the 3D coordinate can be obtained. Using such points, we propose the method designed to achieve 3D projective transformation matrix. To find the proper 3D projective transformation matrix, we formulate a cost function which uses the relationship of the corresponding points. Also the proper 3D projective transformation matrix can be calculated by minimizing the cost function. Then the 3D point cloud data can be matched to CAD data and the height values of each point of 3D point cloud can be compared to the CAD data.

Registration of point cloud data for HDD stamped base inspection

In this paper we propose a novel template matching algorithm for visual inspection of bare printed circuit board (PCB). 1 In the conventional template matching for PCB inspection, the matching score and its relevant offsets are acquired by calculating the maximum value among the convolutions of template image and camera image. While the method is fast, the robustness and accuracy of matching are not guaranteed due to the gap between a design and an implementation resulting from defects and process variations. To resolve this problem, we suggest a new method which uses run-length encoding (RLE). For the template image to be matched, we accumulate data of foreground and background, and RLE data for each row and column in the template image. Using the data, we can find the x and y offsets which minimize the optimization function. The efficiency and robustness of the proposed algorithm are verified through a series of experiments. By comparing the proposed algorithm with the conventional approach, we could realize that the proposed algorithm is not only fast but also more robust and reliable in matching results.

Robust template matching using run-length encoding

FCB Bump Coin System is a machine that makes the bump height on the front side of FCB (Flip Chip Ball Grid Array) PCB uniform by pressing with high temperature and pressure vessels. In the process of pressing, defects such as scratches in SR (Solder Regist) area, breakages in MLCC (Multi Layer Ceramic Capacitor), etc. can occur on the backside of the product. FCB bump coin system inspects for defects on the backside of the product. It is essential for the inspection algorithm in the system to align inspection images with reference image. For fast and accurate inspection, the alignment algorithm is the most crucial element. And the image size the system provides is 4072x 3096 for which widely used methods take much time to process due to its big size. This paper introduces a method of fast image alignment for inspection of FCB bump coin system. It includes a global alignment method and a specific area selection method for fast image alignment. While the proposed method is simple, the experiment shows that it gives a fast and accurate image alignment result for FCB bump coin inspection.

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Hansang Cho

Papers

Registration of point cloud data for HDD stamped base inspection

Robust Registration Method of 3D Point Cloud Data

Robust template matching using run-length encoding

Fast Image Alignment for Inspection of FCB Bump Coin System