Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning.

As the size of components mounted on printed circuit boards (PCBs) decreases, defect detection becomes more important. The first step in an inspection involves recognizing and inspecting characters printed on parts attached to the PCB. In addition, since industrial fields that produce PCBs can change very rapidly, the style of the collected data may vary between collection sites and collection periods. Therefore, flexible learning data that can respond to all fields and time periods are needed. In this paper, large amounts of character data on PCB components were obtained and analyzed in depth. In addition, we proposed a method of recognizing characters by constructing a dataset that was robust with various fonts and environmental changes using a large amount of data. Moreover, a coreset capable of evaluating an effective deep learning model and a base set using n-pick sampling capable of responding to a continuously increasing dataset were proposed. Existing original data and the EfficientNet B0 model showed an accuracy of 97.741%. However, the accuracy of our proposed model was increased to 98.274% for the coreset of 8000 images per class. In particular, the accuracy was 98.921% for the base set with only 1900 images per class.

Depth extraction of three-dimensional objects using block matching for slice images in synthetic aperture integral imaging.

We describe a computational method for depth extraction of three-dimensional (3D) objects using block matching for slice images in synthetic aperture integral imaging (SAII). SAII is capable of providing high-resolution 3D slice images for 3D objects because the picked-up elemental images are high-resolution ones. In the proposed method, the high-resolution elemental images are recorded by moving a camera; a computational reconstruction algorithm based on ray backprojection generates a set of 3D slice images from the recorded elemental images. To extract depth information of the 3D objects, we propose a new block-matching algorithm between a reference elemental image and a set of 3D slice images. The property of the slices images is that the focused areas are the right location for an object, whereas the blurred areas are considered to be empty space; thus, this can extract robust and accurate depth information of the 3D objects. To demonstrate our method, we carry out the preliminary experiments of 3D objects; the results indicate that our method is superior to a conventional method in terms of depth-map quality.

Image quality enhancement of computational integral imaging reconstruction for partially occluded objects using binary weighting mask on occlusion areas.

This paper presents an image quality enhancement of computational integral imaging reconstruction (CIIR) method by using a binary weighting mask on occlusion areas in elemental images. The proposed method utilizes a block-matching algorithm to estimate the occlusion areas in elemental images. Then, a binary weighting mask generated from the estimated occlusion area is applied to our CIIR method. This minimizes the overlapping effect of occluding objects in the reconstructed plane images and thus improves visual quality dramatically. To show the usefulness of our proposed scheme, we conduct several experiments and present the results. The experimental results indicate that our method is superior to the existing methods.

Simple correction method of distorted elemental images using surface markers on lenslet array for computational integral imaging reconstruction.

In this paper, we propose a simple correction method of distorted elemental images for computational integral imaging reconstruction (CIIR) method by using surface markers on lenslet array. The position information of surface markers is extracted from distorted elemental images with geometric misalignments such as skew, rotation and so on. Then the elemental images can be corrected simply when applying linear transformation calculated from the extracted positions. Therefore, the proposed method can simply correct geometric misalignments such as skew and rotation. The corrected elemental images can provide the precise reconstruction of 3D plane images in CIIR. To show the usefulness of the proposed method, the preliminary experiments are carried out and the experimental results are presented. To the best of our knowledge, this is the first report to deal with compensating for the distorted elemental images recorded by using computational integral imaging.

Resolution-enhanced three-dimensional image reconstruction by use of smart pixel mapping in computational integral imaging.

Computational integral imaging method can digitally provide a series of plane images of three-dimensional (3D) objects. However, the resolution of 3D reconstructed images is dramatically degraded as the distance from the lenslet array increases. In this paper, to overcome this problem, we propose a novel computational integral imaging reconstruction (CIIR) method based on smart pixel mapping (SPM). Since SPM is a computational process in which elemental images recorded at long distances are convertible to ones recorded near lenslet array, this can give us the improved resolution of plane images for 3D objects located at a long distance range from a lenslet array. For the effective use of the SPM-based CIIR method, we design a novel two-stage CIIR method by the combined use of the conventional CIIR and the SPM-based one. The conventional CIIR method is applied over a short distance range, while the SPM-based CIIR is used over a long distance range. We carry out some experiments to verify the performance of the two-stage CIIR system. From the experimental results, the proposed system can provide a substantial gain over a long distance range in terms of the resolution of reconstructed plane images.

Occlusion removal method of partially occluded 3D object using sub-image block matching in computational integral imaging.

In this paper, we propose an occlusion removal method using sub-image block matching for improved recognition of partially occluded 3D objects in computational integral imaging (CII). When 3D plane images are reconstructed in CII, occlusion degrades the resolution of reconstructed images. To overcome this problem, we apply the sub-image transform to elemental image array (EIA) and these sub-images are employed using block matching method for depth estimation. Based on the estimated depth information, we remove the unknown occlusion. After completing the occlusion removal for all sub-images, we obtain the modified EIA without occlusion information through the inverse sub-image transform. Finally, the 3D plane images are reconstructed by using a computational integral imaging reconstruction method with the modified EIA. The proposed method can provide a substantial gain in terms of the visual quality of 3D reconstructed images. To show the usefulness of the proposed method we carry out some experiments and the results are presented.