Mathematical Principles of Object 3D Reconstruction by Shape-from-Focus Methods

Shape-from-Focus (SFF) methods have been developed for about twenty years. They able to obtain the shape of 3D objects from a series of partially focused images. The plane to which the microscope or camera is focused intersects the 3D object in a contour line. Due to wave properties of light and due to finite resolution of the output device, the image can be considered as sharp not only on this contour line, but also in a certain interval of height—the zone of sharpness. SSFs are able to identify these focused parts to compose a fully focused 2D image and to reconstruct a 3D profile of the surface to be observed.

Investigation of Shape-from-Focus Precision by Texture Frequency Analysis

This paper reports the precision of shape-from-focus (SFF) imaging according to the texture frequencies and window sizes of a focus measure. SFF is one of various depth measurement techniques for optical imaging, such as microscopy and endoscopy. SFF measures the depth of an object according to focus measure, which is generally computed with a fixed window. The window size affects the performance of SFF and should be adjusted for the texture of an object. In this study, we investigated the precision difference of SFF in texture frequencies and by window size. Two experiments were performed: precision validation in texture frequencies with a fixed window size, and precision validation in various window sizes related to pixel-cycle lengths. The first experimental results showed that a smaller window size could not provide a correct focus measure, and the second results showed that a window size that is approximately equal to a pixel-cycle length of the texture could provide better precision. These findings could potentially contribute to determining the appropriate window size of focus measure operation in shape-from-focus reconstruction.

A Small and High-Speed Driving Mechanism for 3D Shape Measurement in Monocular Endoscopy

Three-dimensional (3D) shape acquisition has been widely introduced to enrich quantitative analysis with the combination of object shape and texture, for example, surface roughness evaluation in industry and gastrointestinal endoscopy in medicine. Shape from focus is a promising technique to measure substance surfaces in 3D space because no occlusion problem appears in principle, as does with stereo shape measurement, which is another commonly used option. We have been developing endoscopic shape measurement devices and shape reconstruction algorithms. In this paper, we propose a mechanism for driving an image sensor reciprocated for the shape from focus of 3D shape measurement in monocular endoscopy. It uses a stepping motor and a planar-end cam, which transforms the motor rotation to imaging sensor reciprocation, to implement the shape from focus of 3D shape measurement in endoscopy. We test and discuss the device in terms of its driving accuracy and application feasibility for endoscopic 3D shape measurement.

Roughness measurement of leaf surface based on shape from focus

Background Surface roughness has a significant effect on leaf wettability. Consequently, it influences the efficiency and effectiveness of pesticide application. Therefore, roughness measurement of leaf surface offers support to the relevant research efforts. To characterize surface roughness, the prevailing methods have drawn support from large equipment that often come with high costs and poor portability, which is not suitable for field measurement. Additionally, such equipment may even suffer from inherent drawbacks like the absence of relationship between pixel intensity and corresponding height for scanning electron microscope (SEM). Results An imaging system with variable object distance was created to capture images of plant leaves, and a method based on shape from focus (SFF) was proposed. The given space-variantly blurred images were processed with the proposed algorithm to obtain the surface roughness of plant leaves. The algorithm improves the current SFF method through image alignment, focus distortion correction, and the introduction of NaN values that allows it to be applied for precise 3d-reconstruction and small-scale surface roughness measurement. Conclusion Compared with methods that rely on optical three-dimensional interference microscope, the method proposed in this paper preserves the overall topography of leaf surface, and achieves superior cost performance at the same time. It is clear from experiments on standard gauge blocks that the RMSE of step was approximately 4.44 µm. Furthermore, according to the Friedman/Nemenyi test, the focus measure operator SML was expected to demonstrate the best performance.

A Fast Shape-from-Focus-Based Surface Topography Measurement Method

Shape from focus is an accurate, but relatively time-consuming, 3D profilometry technique (compared to e.g., laser triangulation or fringe projection). This is the case because a large amount of data that needs to be captured and processed to obtain 3D measurements. In this paper, we propose a two-step shape-from-focus measurement approach that can improve the speed with 40%. By using a faster profilometry technique to create a coarse measurement of an unknown target, this coarse measurement can be used to limit the data capture to only the required frames. This method can significantly improve the measurement and processing speed. The method was tested on a 40 mm by 40 mm custom target and resulted in an overall 46% reduction of measurement time. The accuracy of the proposed method was compared against the conventional shape from focus method by comparing both methods with a more accurate reference.

Leaf surface roughness measure based on shape from focus

BackgroundSurface roughness has a significant effect on leaf wettability, consequently influencing the efficiency and effectiveness of pesticide spraying application. Therefore, surface roughness measure of plant leaves is conducive to relevant researches. In order to characterize the surface roughness, present methods have to draw support from large apparatus, but they are generally high-cost and not portable enough for field measurement. Methods those instruments even have potentially inherent drawback such as absence of relation between pixel intensity and corresponding height for scanning electron microscope (SEM). ResultsAn imaging system with variable object distance is set up to capture images of plant leaves and a shape from focus (SFF) based method is proposed. These space-variantly blurred images are processed with the proposed algorithm to yield surface roughness of plant leaves. The algorithm mainly improves the current SFF method in image alignment, focus distortion correction, and NaN values introducing to make it applicative for precise 3d-reconstruction and surface roughness measure in small scale. ConclusionCompared with method via optical three-dimensional interference microscope, the proposed method preserves the overall topography of leaf surface and meanwhile achieves superior cost performance. Experiments on standard gauge blocks revealed the RMSE of step was approximately 4.44μm. Furthermore, the focus measure operator SML was supposed to perform best according to Friedman/Nemenyi test.