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We present a novel depth from focus technique. Following prior work, our pipeline starts with a focal stack and an estimation of the amount of defocus as given by, for instance, the ring difference filter. To improve robustness to outliers while avoiding to rely on costly nonlinear optimizations, we propose an original scheme that linearly scans the profile over a fixed size window, searching for the best peak within each window using a linearized least-squares Laplace regression. As a post-process, depth estimates with low confidence are reconstructed though an adaptive moving least squares filter. We show how to objectively evaluate the performance of our approach by generating synthetic focal stacks from which the reconstructed depth maps can be compared to ground truth. Our results show that our method achieves higher accuracy than previous nonlinear Laplace regression technique, while being orders of magnitude faster. [ABSTRACT FROM AUTHOR]

The property of maintaining the lens state of the liquid crystal (LC) lens during the switching between positive and negative lens states is made use of in the fast acquirement of multi-focus images without magnification change. A depth from focus (DFF) pipeline that can generate a low-error depth map and an all-in-focus image is proposed. The depth of the scene is then obtained via DFF pipeline from the captured images. The depth sensor proposed in this paper has the advantages of simple structure, low cost, and long service life. [ABSTRACT FROM AUTHOR]

Abstract The property of maintaining the lens state of the liquid crystal (LC) lens during the switching between positive and negative lens states is made use of in the fast acquirement of multi-focus images without magnification change. A depth from focus (DFF) pipeline that can generate a low-error depth map and an all-in-focus image is proposed. The depth of the scene is then obtained via DFF pipeline from the captured images. The depth sensor proposed in this paper has the advantages of simple structure, low cost, and long service life.

Glazed ceramics are a common material analyzed through geochemistry, whether in the form of tableware collected during excavations or tiles observed as part of architectural features. Within the framework of these studies, measuring the thickness of the transparent glaze is one of the useful variables available for the characterization of the ceramic, contributing to searches for provenance as well as serialization. However, this task often requires invasive methods performed in the laboratory, which may not always be possible. This paper develops a non‐invasive and portable on‐site system for measuring the thickness of ceramic glazes. Based on the depth from focus technique, this method makes use of a classical camera, a macroscopic lens, a translation stage, and a laptop for system control. In this article, we test this method through the measurement of glaze for ten samples as compared to results obtained for sections through scanning electron microscopy. [ABSTRACT FROM AUTHOR]

The structure of nonwoven fabric has a great influence on the fabric properties, and the filtration performance of the fabric is determined by its pore structures and key parameters such as pore size, distribution and porosity, which should be described accurately and appropriately. However, at present problems exist in the methods of describing the three-dimensional structure of nonwovens, such as the high cost, slow speed and inaccurate model. This paper proposes a new method of reconstruction algorithm, inspired by the criss-cross internal structure of nonwovens. This nonwoven structure with a certain depth presents some implication of the processing information, which is similar to the formation of an anthill. The reconstructing processes are as follows. First, the multi-focused images of fibers are captured by the microscope and the images are then fused into a two-dimensional image. Second, the three-dimensional coordinate of the source point is calculated by the fiber segmentation and One-to-Many projection. Finally, the fiber axis is generated by connecting the source point according to the prescribed conditions, and then the closed three-dimensional surface is the three-dimensional path of a dynamic-radius sphere rolling along the fiber axis. The experiment results demonstrate that the proposed method can reconstruct the accurate structure of the nonwoven fabric and retain its vertical connection information, which is of great significance to the interception of harmful substances, the production and preparation of filter medium and the follow-up study of the filtration mechanism. [ABSTRACT FROM AUTHOR]

We propose a method to nonrigidly align a three-dimensional (3D) volumetric image with a two-dimensional (2D) planar image representing a projection of the deformed volume. The application in mind comes from biological studies in which 2D intravital microscopy videos of living tissue are recorded, after which the tissue is excised and a more detailed 3D volume microscopy is performed. Coregistration of both data sets allows to combine the temporal (but 2D) information with more detailed spatial 3D information. Our approach is variational and uses a hyperelastic deformation regularization, as is appropriate for biological material. As a particular feature, the out-of-plane deformation is estimated based on the out-of-focus blur inside the 2D microscopy image. The approach becomes computationally feasible through the use of a coarse-to-fine optimization strategy and higher-order optimization methods. [ABSTRACT FROM AUTHOR]

This work introduces a real-time full-resolution depth estimation device, which allows integral displays to be fed with a real-time light-field. The core principle of the technique is a high-speed focal stack acquisition method combined with an efficient implementation of the depth estimation algorithm, allowing the generation of real time, high resolution depth maps. As the procedure does not depend on any custom hardware, if the requirements are met, the described method can turn any high speed camera into a 3D camera with true depth output. The concept was tested with an experimental setup consisting of an electronically variable focus lens, a high-speed camera, and a GPU for processing, plus a control board for lens and image sensor synchronization. The comparison with other state of the art algorithms shows our advantages in computational time and precision. [ABSTRACT FROM AUTHOR]

Depth-from-focus methods can estimate the depth from a set of images taken with different focus settings. We recently proposed a method that uses the relationship of the ratio between the luminance value of a target pixel and the mean value of the neighboring pixels. This relationship has a Poisson distribution. Despite its good performance, the method requires a large amount of memory and computation time because it needs to store focus measurement values for each depth and each window radius on a pixel-wise basis, and filtering to compute the mean value, which is performed twice, makes the relationship among neighboring pixels too strong to parallelize the pixel-wise processing. In this paper, we propose an approximate calculation method that can give almost the same results with a single time filtering operation and enables pixel-wise parallelization. This pixel-wise processing does not require the aforementioned focus measure values to be stored, which reduces the amount of memory. Additionally, utilizing the pixel-wise processing, we propose a method of determining the process window size that can improve noise tolerance and in depth estimation in texture-less regions. Through experiments, we show that our new method can better estimate depth values in a much shorter time. [ABSTRACT FROM AUTHOR]

Human visual system relies on both monocular focusness cues and binocular stereo cues to gain effective 3D perception. Correspondingly, depth from focus/defocus (DfF/DfD) and stereo matching are two most studied passive depth sensing schemes, which are traditionally solved in separate tracks. However, the two techniques are essentially complementary: the monocular cue from DfF/DfD can robustly handle repetitive textures and occlusion that are problematic for stereo matching whereas the binocular cue from stereo matching is insensitive to defocus blurs and can resolve large depth range. In this paper, we emulate human perception and present unified learning-based techniques to conduct hybrid DfF/DfD and stereo matching. We first construct a comprehensive focal stack dataset synthesized by depth-guided light field rendering. Next, we propose different network architectures to suit various inputs, including focal stack, stereo image pair, binocular focal stack, a focus-defocus image pair and defocus-stereo image triplet. We also exploit different connection methods between the separate networks for integrating them into an optimized solution to produce high fidelity disparity maps. For experiment, we further explore different hardware setup to capture both monocular and binocular depth cues. Results show that our new learning-based hybrid techniques can significantly improve accuracy and robustness in depth estimation. [ABSTRACT FROM AUTHOR]

This work introduces a real-time full-resolution depth estimation device, which allows integral displays to be fed with a real-time light-field. The core principle of the technique is a high-speed focal stack acquisition method combined with an efficient implementation of the depth estimation algorithm, allowing the generation of real time, high resolution depth maps. As the procedure does not depend on any custom hardware, if the requirements are met, the described method can turn any high speed camera into a 3D camera with true depth output. The concept was tested with an experimental setup consisting of an electronically variable focus lens, a high-speed camera, and a GPU for processing, plus a control board for lens and image sensor synchronization. The comparison with other state of the art algorithms shows our advantages in computational time and precision.

Research Article: A robust system framework has been developed that takes into account bubble depth when estimating bubble parameters using a single camera. • A single camera method for depth determination from blur was developed • In situ calibration allows the system to be very robust to differing fluids • The method allows for better bubble size estimation Current non-intrusive bubble parameter measurement techniques fail to consider bubble depth when extracting parameters from 2-D images, resulting in inaccurate measurements. A bubble depth determination method is therefore proposed here that uses a correlation between depth and image focus to estimate depth using the edge intensity gradient ("blur") with simultaneous depth calibration. Images of bubbles at various depths in two-phase and three-phase systems were analysed to correlate bubble depth and blur. To increase the reliability of the methodology across various fluids and conditions, an in-situ calibration method is demonstrated. Bubble depth can be correlated from blur with a fair degree of reliability and the calibration procedure was effective in validating the bubble depth model, even across differing fluid opacity, bubble concentration, and fluid conditions. This article outlines a versatile and robust system that requires a single camera and which enables per image calibration to compensate for experimental configurational changes, making the technique suitable for variable applications. [ABSTRACT FROM AUTHOR]

Estimating the 3D shape of a scene from differently focused set of images has been a practical approach for 3D reconstruction with color cameras. However, reconstructed depth with existing depth from focus (DFF) methods still suffer from poor quality with textureless and object boundary regions. In this paper, we propose an improved depth estimation based on depth from focus iteratively refining 3D shape from uniformly focused image set (UFIS). We investigated the appearance changes in spatial and frequency domains in iterative manner. In order to achieve sub-frame accuracy in depth estimation, optimal location of focused frame in DFF is estimated by fitting a polynomial curve on the dissimilarity measurements. In order to avoid wrong depth values on texture-less regions we propose to build a confidence map and use it to identify erroneous depth estimations. We evaluated our method on public and our own datasets obtained from different types of devices, such as smartphones, medical, and normal color cameras. Quantitative and qualitative evaluations on various test image sets show promising performance of the proposed method in depth estimation. [ABSTRACT FROM AUTHOR]

Optical microscopy is an essential tool in biology and medicine. Imaging thin, yet non-flat objects in a single shot (without relying on more sophisticated sectioning setups) remains challenging as the shallow depth of field that comes with high-resolution microscopes leads to unsharp image regions and makes depth localization and quantitative image interpretation difficult. Here, we present a method that improves the resolution of light microscopy images of such objects by locally estimating image distortion while jointly estimating object distance to the focal plane. Specifically, we estimate the parameters of a spatially-variant Point Spread Function (PSF) model using a Convolutional Neural Network (CNN), which does not require instrument- or object-specific calibration. Our method recovers PSF parameters from the image itself with up to a squared Pearson correlation coefficient of 0.99 in ideal conditions, while remaining robust to object rotation, illumination variations, or photon noise. When the recovered PSFs are used with a spatially-variant and regularized Richardson-Lucy (RL) deconvolution algorithm, we observed up to 2.1 dB better Signal-to-Noise Ratio (SNR) compared to other Blind Deconvolution (BD) techniques. Following microscope-specific calibration, we further demonstrate that the recovered PSF model parameters permit estimating surface depth with a precision of $\mathrm {2~ \mu \text {m} }$ and over an extended range when using engineered PSFs. Our method opens up multiple possibilities for enhancing images of non-flat objects with minimal need for a priori knowledge about the optical setup. [ABSTRACT FROM AUTHOR]

Depth from focus (DfF) is a method of estimating the depth of a scene by using information acquired through changes in the focus of a camera. Within the DfF framework of, the focus measure (FM) forms the foundation which determines the accuracy of the output. With the results from the FM, the role of a DfF pipeline is to determine and recalculate unreliable measurements while enhancing those that are reliable. In this paper, we propose a new FM, which we call the “ring difference filter” (RDF), that can more accurately and robustly measure focus. FMs can usually be categorized as confident local methods or noise robust non-local methods. The RDF’s unique ring-and-disk structure allows it to have the advantages of both local and non-local FMs. We then describe an efficient pipeline that utilizes the RDF’s properties. Part of this pipeline is our proposed RDF-based cost aggregation method, which is able to robustly refine the initial results in the presence of image noise. Our method is able to reproduce results that are on par with or even better than those of state-of-the-art methods, while spending less time in computation. [ABSTRACT FROM AUTHOR]

In this paper, a multi-focus image stack captured by varying positions of the imaging plane is processed to synthesize an all-in-focus (AIF) image and estimate its corresponding depth map. Compared with traditional methods (e.g., pixel- and block-based techniques), our focus-based measures are calculated based on irregularly shaped regions that have been refined or split in an iterative manner, to adapt to different image contents. An initial all-focus image is first computed, which is then segmented to get a region map. Spatial-focal property for each region is then analyzed to determine whether a region should be iteratively split into sub-regions. After iterative splitting, the final region map is used to perform regionally best focusing, based on the Winner-take-all (WTA) strategy, i.e., choosing the best focused pixels from image stack. The depth image can be easily converted from the resulting label image, where the label for each pixel represents the image index from which the pixel with the best focus is chosen. Regions whose focus profiles are not confident in getting a winner of the best focus will resort to spatial propagation from neighboring confident regions. Our experiments show that the adaptive region-splitting algorithm outperforms other state-of-the-art methods or commercial software in synthesis quality (in terms of a well-known Q metric), depth maps (in terms of subjective quality), and processing speed (with a gain of 17.81~40.43%). [ABSTRACT FROM AUTHOR]

Photogrammetry can be used for the measurement of small objects with micro-features, with good results, low costs, and the possible addition of texture information to the 3D models. The performance of this technique is strongly affected by the scaling method, since it retrieves a model that must be scaled after its elaboration. In this paper, a fully automated multi-step scaling system is presented, which is based on machine vision algorithms for retrieving blurred areas. This method allows researchers to find the correct scale factor for a photogrammetric micro model and is experimentally compared to the existing manual method basing on the German guideline VDI/VDE 2634, Part 3. The experimental tests are performed on millimeter-sized certified workpieces, finding micrometric errors, when referred to reference measurements. As a consequence, the method is candidate to be used for measurements of micro-features. The proposed tool improves the performance of the manual method by eliminating operator-dependent procedures. The software tool is available online as supplementary material and represents a powerful tool to face scaling issues of micro-photogrammetric activities. [ABSTRACT FROM AUTHOR]

• The processing range of the femtosecond laser platform was limited by shifting of the focus. • A defocus compensation scheme (DCS) for femtosecond laser direct writing was proposed. • The DCS comprises of an optimized DFF method and the bilinear interpolation algorithm. • A Laplace-DWT evaluation function combining Laplace and DWT functions was proposed. The optical path error, the position-independent geometric error, and the form and position tolerance of the substrate will lead to position deviations of the laser focus resulting in the inconsistency of the large area micro-structure and limiting the processing range of the femtosecond laser fabrication platform. To realize continuous defocus compensation along the processing trajectory, an autofocus scheme consisting of an optimized depth from focus (DFF) method based on grid division and the bilinear interpolation algorithm is proposed in this paper. The optimized DFF method includes the Laplace-DWT evaluation function combining the advantages of spatial domain and frequency domain sharpness evaluation function, an adaptive focusing window selection method, and an adaptive step length three-step hill-climbing search method. The optimized DFF method was used to obtain the best focus position of each grid point. Compared with the traditional DFF method, the optimized algorithm has higher focusing precision and avoids the disadvantage of time-consuming focusing. Then, to compensate for interpolating points of the processing trajectory, the bilinear interpolation algorithm was introduced to achieve online focusing during two-photon lithography processing. Finally, the feasibility and effectiveness of the auto-focusing scheme were validated by experiments. Through defocus compensation, the sharpness evaluation value of the focus image on the processing trajectory improved by 90%, which indicated the successful focus of the laser beam on the workpiece surface. Moreover, a high-precision and large-area encoded micro-polarizer array with consistent structure were fabricated successfully. This paper provides a simple way to identify and compensate the defocus errors online with high accuracy and efficiency. The proposed method can improve the machining accuracy and quality of the femtosecond laser direct writing. [ABSTRACT FROM AUTHOR]

Estimating the 3D shape of a scene from differently focused set of images has been a practical approach for 3D reconstruction with color cameras. However, reconstructed depth with existing depth from focus (DFF) methods still suffer from poor quality with textureless and object boundary regions. In this paper, we propose an improved depth estimation based on depth from focus iteratively refining 3D shape from uniformly focused image set (UFIS). We investigated the appearance changes in spatial and frequency domains in iterative manner. In order to achieve sub-frame accuracy in depth estimation, optimal location of focused frame in DFF is estimated by fitting a polynomial curve on the dissimilarity measurements. In order to avoid wrong depth values on texture-less regions we propose to build a confidence map and use it to identify erroneous depth estimations. We evaluated our method on public and our own datasets obtained from different types of devices, such as smartphones, medical, and normal color cameras. Quantitative and qualitative evaluations on various test image sets show promising performance of the proposed method in depth estimation.

Copyright of Industria Textila is the property of Institutul National de Cercetare-Dezvoltare pentru Textile si Pielarie and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Measuring 3D micro-features is a challenging task that is usually performed using high-cost systems generally based on technologies with narrow working ranges and very accurate control of the sensor positions. Well-known image analysis methods, such as Photogrammetry, would likely lower the costs of 3D inspection of micro-features and add texture information to the 3D models; however, the behaviour of Photogrammetry is strongly affected by the scaling method because it retrieves a model that must be scaled after its computation. In this paper, an experimental study of the validity of a hybrid 3D image processing method for measuring micro-features is presented. This method exploits the Depth-from-Focus method to retrieve the correct scale for a photogrammetric model. The measurement of properly-designed and manufactured specimens was performed by following and adapting the German guideline VDI/VDE 2634, Part 3 to validate the method using calibrated specimens. The proposed system has been demonstrated to be very promising can achieve an error of less than 10 μm. [ABSTRACT FROM AUTHOR]

By using depth from focus (DFF) alone to obtain the 3D wear area reconstruction of grinding wheel, the depth values in the 3D wear area image are not continuous while by using shape from shading (SFS) alone, the 3D wear area cannot be fully obtained. A novel method that uses Fourier transform to fuse the methods of DFF and SFS is proposed in this paper. The proposed method can achieve depth continuity in tool wear images. After performing high-pass filtering and low-pass filtering for the 3D reconstructed images of DFF and SFS, respectively, in frequency domain, the 3D wear area reconstruction image of grinding wheel can then be obtained by Fourier inversion. Experimental results showed that the 3D wear area reconstruction image obtained by the proposed method can achieve better accuracy for the tool wear volume than using DFF or SFS alone. And the efficiency of the proposed method is proved to be better than microscopes. [ABSTRACT FROM AUTHOR]

Copyright of Industria Textila is the property of Institutul National de Cercetare-Dezvoltare pentru Textile si Pielarie and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, new methodologies for the estimation of the depth of a target with unknown dimensions, based on depth from focus strategies, are proposed. The measurements are extracted from images acquired with a single camera, resorting to the minimization of a new functional, deeply rooted on the optical characteristics of the lens system. The analysis and synthesis of two complementary filters and a linear parametrically varying observer are discussed in detail. These estimators use information present on the boundary of the target, which is assumed to be on a plane parallel to the camera sensor, and whose dimensions are considered to remain constant over time. This paper complements a single pan and tilt camera-based indoor positioning and tracking system. To assess the performance of the proposed solutions, a series of indoor experimental tests for a range of operation of up to ten meters, which included tracking and localizing a small unmanned aerial vehicle with unknown dimensions, was carried out. Depth estimates with accuracies on the order of a few centimeters were obtained. [ABSTRACT FROM PUBLISHER]

A novel method is proposed to obtain the target depth. A sequence of images is taken by linear camera arrays spreading perpendicularly to the line of sight. The image from the camera at the intersection of the linear camera array and the line of sight is selected as the reference image. The measurable range is divided into several distance segments. For each segment, a parallax correction stack image is calculated, then the regional similarity of the reference image and the stack image is got. The target distance is in the segment which has maximum similarity of the reference image and the stack image. Without image matching, depth from synthetic aperture focus has good robustness and can not be influenced by single image error or deviation. Simulation results verify the efficacy of this scheme. Moreover, the range precision is also analyzed. Without electromagnetic pollution, the method has the advantages of concealment. [ABSTRACT FROM AUTHOR]

Purpose A three-dimensional (3-D) shape measurement endoscopic technique is proposed to provide depth information, which is lacking in current endoscopes, in addition to the conventional surface texture information. The integration of surface texture and 3-D shapes offers effective analytical data and can be used to detect unusual tissues. We constructed a prototype endoscope to validate our method. Methods A 3-D measurement endoscope using shape from focus is proposed in this paper. It employs a focusing part to measure both texture and 3-D shapes of objects. Image focusing is achieved with a single-lens system. Results A prototype was made in consideration of proper endoscope sizes. We validated the method by experimenting on artificial objects and a biological object with the prototype. First, the accuracy was evaluated using artificial objects. The RMS errors were 0.87 mm for a plate and 0.64 mm for a cylinder. Next, inner wall of pig stomach was measured in vitro to evaluate the feasibility of the proposed method. Conclusion The proposed method was efficient for 3-D measurement with endoscopes in the experiments and is suitable for downsizing because it is a single-lens system. [ABSTRACT FROM AUTHOR]

At the Ilmenau University of Technology, work has been continuing on nanopositioning and nanomeasuring machines (NPMMs), which allow users to perform highly exact dimensional and traceable positioning with sub-nanometre resolution and nanometre uncertainty within a measuring volume of 25 mm × 25 mm × 5 mm. The NPMM can work with various probe systems operating in the z-direction, including atomic force microscopes, focus sensors and tactile probes. However, point-by-point measurement of the entire 25 mm × 25 mm area would take a prohibitively long time. This paper presents two area-based optical sensors as an alternative scanning approach. The use of these sensors can significantly reduce the time required for measurements of the entire scannable area. The methods used are white light interferometry and depth from focus. The use of the NPMM for these tasks adds the ability for high-accuracy positioning, which is necessary in order to use several special stitching methods which can reduce the propagation of uncertainty normally introduced by standard stitching. [ABSTRACT FROM AUTHOR]

We transform an LCD into a display that supports both 2D multi-touch and unencumbered 3D gestures. Our BiDirectional (BiDi) screen, capable of both image capture and display, is inspired by emerging LCDs that use embedded optical sensors to detect multiple points of contact. Our key contribution is to exploit the spatial light modulation capability of LCDs to allow lensless imaging without interfering with display functionality. We switch between a display mode showing traditional graphics and a capture mode in which the backlight is disabled and the LCD displays a pinhole array or an equivalent tiled-broadband code. A large-format image sensor is placed slightly behind the liquid crystal layer. Together, the image sensor and LCD form a mask-based light field camera, capturing an array of images equivalent to that produced by a camera array spanning the display surface. The recovered multi-view orthographic imagery is used to passively estimate the depth of scene points. Two motivating applications are described: a hybrid touch plus gesture interaction and a light-gun mode for interacting with external light-emitting widgets. We show a working prototype that simulates the image sensor with a camera and diffuser, allowing interaction up to 50 cm in front of a modified 20.1 inch LCD. [ABSTRACT FROM AUTHOR]

We present confocal stereo, a new method for computing 3D shape by controlling the focus and aperture of a lens. The method is specifically designed for reconstructing scenes with high geometric complexity or fine-scale texture. To achieve this, we introduce the confocal constancy property, which states that as the lens aperture varies, the pixel intensity of a visible in-focus scene point will vary in a scene- independent way, that can be predicted by prior radiometric lens calibration. The only requirement is that incoming radiance within the cone subtended by the largest aperture is nearly constant. First, we develop a detailed lens model that factors out the distortions in high resolution SLR cameras (12MP or more) with large-aperture lenses (e.g., f1.2). This allows us to assemble an A× F aperture-focus image (AFI) for each pixel, that collects the undistorted measurements over all A apertures and F focus settings. In the AFI representation, confocal constancy reduces to color comparisons within regions of the AFI, and leads to focus metrics that can be evaluated separately for each pixel. We propose two such metrics and present initial reconstruction results for complex scenes, as well as for a scene with known ground-truth shape. [ABSTRACT FROM AUTHOR]

Current research work on the development of different depth-detection methods for supporting pick-and-place manipulations of carbon nanotubes (CNTs) in a scanning electron microscope (SEM) is presented. A nanorobot station capable of performing this manipulation task in the SEM’s vacuum chamber has been developed, consisting of two cooperating nanorobots. One robot carries an electrothermal microgripper and also performs the coarse approach between microgripper and CNT. The second robot serves as sample holder and realizes the fine positioning of the CNT sample. For a reliable fine approach between microgripper and CNT as well as an intended future automation of handling tasks, depth-detection methods are required in order to precisely measure and align the z-position of microgripper and CNT. Two different methods for z-position estimation are proposed. The depth from focus method uses the focusing information of SEM images whereas the touchdown sensor concept relies on a bimorph piezo bending actuator. The feasibility of both approaches is shown and first experimental results are discussed. Future work has to show how the proposed methods will increase the efficiency of nanorobotic manipulation and how these methods can advance the automation of nanohandling tasks. [ABSTRACT FROM AUTHOR]

By using a passive autofocus system through a charge-coupled device (CCD) camera, this paper presents a new Yuan-Ze University (YZU) algorithm to detect the position of the sharpest image from a rough surface of an industry component quickly and accurately. To prove the performance of the YZU algorithm, it is compared to some well-known methods like fast Fourier transform (FFT), amplitude, Laplacian, discreet cosine transform (DCT), and conventional edge operators. Moreover, a new dynamic search method that implements this algorithm to produce real time digital image systems with fast response, accuracy, and robustness is proposed. The experiment results show that this technique is applicable to practical 3D measurement. In the application, two specimens a gauge block and an integrated circuit (IC) leadframe are tested to demonstrate the validity of 3D reconstruction from this YZU algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a high-speed digital processed microscopic observational system for telemicrooperation is proposed with a dynamic focusing system and a high-speed digital-processing system using the “depth from focus” criterion. In our previous work [10], we proposed a system that could simultaneously obtain an “all-in-focus image” as well as the “depth” of an object. In reality, in a microoperation, it is not easy to obtain good visibility of objects with a microscope focused at a shallow depth, especially in microsurgery and DNA studies, among other procedures. In this sense, the all-in-focus image, which keeps an in-focus texture over the entire object, is useful for observing microenvironments with the microscope. However, one drawback of the all-in-focus image is that there is no information about the objects’ depth. It is also important to obtain a depth map and show the 3D microenvironments at any view angle in real time to actuate the microobjects. Our earlier system with a dynamic focusing lens and a smart sensor could obtain the all-in-focus image and the depth in 2 s. To realize real-time microoperation, a system that could process at least 30 frames per second (60 times faster than the previous system) would be required. This paper briefly reviews the depth from focus criterion to Simultaneously achieve the all-in-focus image and the reconstruction of 3D microenvironments. After discussing the problem inherent in our earlier system, a frame-rate system constructed with a high-speed video camera and FPGA (field programmable gate array) hardware is discussed. To adapt this system for use with the microscope, new criteria to solve the “ghost problem” in reconstructing the all-in-focus image are proposed. Finally, microobservation shows the validity of this system. [ABSTRACT FROM AUTHOR]

In this paper, an observational sensor system for tele-micro-operation is proposed with a dynamic focusing lens and a smart vision sensor using the 'depth from focus' criteria. Recently, micro-operations, such as for micro-surgery, DNA manipulations, etc., have gained in importance. However, the small depth of focus of the microscope produces poor observability. For example, if the focus is on the object, the actuator cannot be seen with the microscope. On the other hand, if the focus is on the actuator, the object cannot be observed. In this sense, the 'all-in-focus image', which holds the in-focused texture all over the image, is useful to observe the micro-environments with a microscope. One drawback of the all-in-focus image is that there is no information about the depth of objects. It is also important to obtain the depth map and show the three-dimensional (3D) micro virtual environments in real-time to actuate the micro objects intuitively. First, this paper reviews the criteria of 'depth from focus' to achieve the all-in-focus image and the 3D micro environments' simultaneous reconstruction. After evaluating the validity of this criteria with off-line simulation, a real-time virtual reality (VR) micro camera system is proposed to achieve the micro VR environments with the 'depth from focus' criteria. This system is constructed with a dynamic focusing lens, which can change its focal distance at a high frequency, and a smart vision system, which is capable of capturing and processing the image data in high speed with SIMD architecture. [ABSTRACT FROM AUTHOR]

Depth from Focus (DFF) and Depth from Defocus (DFD) methods are theoretically unified with the geometric triangulation principle. Fundamentally, the depth sensitivities of DFF and DFD are not different than those of stereo (or motion) based systems having the same physical dimensions. Contrary to common belief, DFD does not inherently avoid the matching (correspondence) problem. Basically, DFD and DFF do not avoid the occlusion problem any more than triangulation techniques, but they are more stable in the presence of such disruptions. The fundamental advantage of DFF and DFD methods is the two-dimensionality of the aperture, allowing more robust estimation. We analyze the effect of noise in different spatial frequencies, and derive the optimal changes of the focus settings in DFD. These results elucidate the limitations of methods based on depth of field and provide a foundation for fair performance comparison between DFF/DFD and shape from stereo (or motion) algorithms. [ABSTRACT FROM AUTHOR]

Consider situations where the depth at each point in the scene is multi-valued, due to the presence of a virtual image semi-reflected by a transparent surface. The semi-reflected image is linearly superimposed on the image of an object that is behind the transparent surface. A novel approach is proposed for the separation of the superimposed layers. Focusing on either of the layers yields initial separation, but crosstalk remains. The separation is enhanced by mutual blurring of the perturbing components in the images. However, this blurring requires the estimation of the defocus blur kernels. We thus propose a method for self calibration of the blur kernels, given the raw images. The kernels are sought to minimize the mutual information of the recovered layers. Autofocusing and depth estimation in the presence of semi-reflections are also considered. Experimental results are presented. [ABSTRACT FROM AUTHOR]

The problem of reconstructing a depth map from a sequence of differently focused images (focal stack) is called Depth from focus. The core idea of this method is to analyze the sharpness of each pixel and compare it along the z axis of the focal stack to estimate the true depth value. This approach has two main drawbacks: it depends on the optics of the camera and on the focus measure operator. Recent advances in deep learning techniques show promising results in this way, however, still have problems generalizing to different scenes, cameras, optics and focal stack focus positions. In this paper we propose a novel deep learning based method to approach this problem. Firstly, we propose to estimate distances relative to the focal stack focus position instead of estimating true or invariant depths, allowing us to generalize different scenes and optical setups without losing the possibility to extract real distances. Secondly, we present our novel architecture: a 2D siamese encoder–3D decoder with a differentiable argmax regression that is able to compute depth from stacks of variable sizes. Finally, we compare our method with 2 other depth from focus algorithms and with a monocular depth estimation method. [Display omitted] • A novel deep learning based method able to extract depth maps from focal stacks with values relative to focus positions. • A model architecture independent from the number of planes of the focal stack. • A quantitative comparison along with a qualitative one with different cameras, lenses and focus configurations. [ABSTRACT FROM AUTHOR]

Images taken of 3D objects are generally defocused. Depth-from-focus techniques use the defocus information to determine range. However, quantitative measurement of focus is difficult and requires accurate modeling of the point-spread function (PSF). We describe a new method that determines depth using the symmetry and smoothness of focus gradient with respect to the focus position. The technique is passive and uses a monocular imaging system. The performance for estimating range is experimentally demonstrated. [ABSTRACT FROM AUTHOR]