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1. Phase- shifted 3D Imaging of Rotating Milling/Drilling tools

   Guo, Xiangyu; Lee, ChaBum (October 2020, American Society for Precision Engineering)

   null (Ed.)

   Drilling and milling operations are material removal processes involved in everyday conventional productions, especially in the high-speed metal cutting industry. The monitoring of tool information (wear, dynamic behavior, deformation, etc.) is essential to guarantee the success of product fabrication. Many methods have been applied to monitor the cutting tools from the information of cutting force, spindle motor current, vibration, as well as sound acoustic emission. However, those methods are indirect and sensitive to environmental noises. Here, the in-process imaging technique that can capture the cutting tool information while cutting the metal was studied. As machinists judge whether a tool is worn-out by the naked eye, utilizing the vision system can directly present the performance of the machine tools. We proposed a phase shifted strobo-stereoscopic method (Figure 1) for three-dimensional (3D) imaging. The stroboscopic instrument is usually applied for the measurement of fast-moving objects. The operation principle is as follows: when synchronizing the frequency of the light source illumination and the motion of object, the object appears to be stationary. The motion frequency of the target is transferring from the count information of the encoder signals from the working rotary spindle. If small differences are added to the frequency, the object appears to be slowly moving or rotating. This effect can be working as the source for the phase-shifting; with this phase information, the target can be whole-view 3D reconstructed by 360 degrees. The stereoscopic technique is embedded with two CCD cameras capturing images that are located bilateral symmetrically in regard to the target. The 3D scene is reconstructed by the location information of the same object points from both the left and right images. In the proposed system, an air spindle was used to secure the motion accuracy and drilling/milling speed. As shown in Figure 2, two CCDs with 10X objective lenses were installed on a linear rail with rotary stages to capture the machine tool bit raw picture for further 3D reconstruction. The overall measurement process was summarized in the flow chart (Figure 3). As the count number of encoder signals is related to the rotary speed, the input speed (unit of RPM) was set as the reference signal to control the frequency (f0) of the illumination of the LED. When the frequency was matched with the reference signal, both CCDs started to gather the pictures. With the mismatched frequency (Δf) information, a sequence of images was gathered under the phase-shifted process for a whole-view 3D reconstruction. The study in this paper was based on a 3/8’’ drilling tool performance monitoring. This paper presents the principle of the phase-shifted strobe-stereoscopic 3D imaging process. A hardware set-up is introduced, , as well as the 3D imaging algorithm. The reconstructed image analysis under different working speeds is discussed, the reconstruction resolution included. The uncertainty of the imaging process and the built-up system are also analyzed. As the input signal is the working speed, no other information from other sources is required. This proposed method can be applied as an on-machine or even in-process metrology. With the direct method of the 3D imaging machine vision system, it can directly offer the machine tool surface and fatigue information. This presented method can supplement the blank for determining the performance status of the machine tools, which further guarantees the fabrication process. 

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2. Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

   https://doi.org/10.3390/en14154595  

   Asadi, Parisa; Beckingham, Lauren E. (August 2021, Energies)

   null (Ed.)

   X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery. 

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3. A Wide-Field Imaging Approach for Simultaneous Super-Resolution Surface-Enhanced Raman Scattering Bioimaging and Spectroscopy

   https://doi.org/10.1021/acsmeasuresciau.2c00013  

   Shoup, Deben N.; Scarpitti, Brian T.; Schultz, Zachary D. (July 2022, ACS Measurement Science Au)

   High spatial resolution imaging and chemical specific detection in living organisms is important in a wide range of fields, from medicine to catalysis. In this work, we characterize a wide-field surface enhanced Raman scattering (SERS) imaging approach capable of simultaneously capturing images and SERS spectra from nanoparticle SERS-tags in cancer cells. By passing the image through a transmission diffraction grating before it reaches an array detector, we record the image and wavelength dispersed signal simultaneously on the camera sensor. Optimization of the experiment provides an approach with better spectral resolution and more rapid acquisition than liquid crystal tunable filters commonly used for wide-field SERS imaging. Intensity fluctuations inherent to SERS enabled localization algorithms to be applied to both the spatial and spectral domain, providing super-resolution SERS images that are correlated with improved peak positions identified in the spectrum of the SERS tag. The detected Raman signal is shown to be sensitive to the focal plane, providing 3D sectioning abilities for the detected nanoparticles. Our work demonstrates spectrally resolved super-resolution SERS imaging that has potential to be applied to complex physical and biological imaging applications. 

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4. Geometric super-resolution on push-broom hyperspectral imaging for plasma optical emission spectroscopy

   https://doi.org/10.1039/C8JA00235E  

   Shi, Songyue; Gong, Xiaoxia; Mu, Yan; Finch, Kevin; Gamez, Gerardo (January 2018, Journal of Analytical Atomic Spectrometry)

   Push-broom hyperspectral imaging (Pb-HSI) is a powerful technique for obtaining the spectral information along with the spatial information simultaneously for various applications, from remote sensing to chemical imaging. Spatial resolution improvement is beneficial in many instances; however, typical solutions suffer from the limitation of geometric extent, lowered light throughput, or reduced field-of-view (FOV). Sub-pixel shifting (SPS) acquires higher-resolution images, compared to typical imaging approaches, from the deconvolution of low-resolution images acquired with a higher sampling rate. Furthermore, SPS is particularly suited for Pb-HSI due to its scanning nature. In this study, an SPS approach is developed and implemented on a Pb-HSI system for plasma optical emission spectroscopy. The preliminary results showed that a periodic deconvolution error was generated in the final SPS Pb-HSI images. The periodic error was traced back to random noise present in the raw/convoluted SPS data and its frequency displays an inverse relationship with the number of sub-pixel samples acquired. Computer modelled data allows studying the effect of varying the relative standard deviation (RSD) in the raw/convoluted SPS data on the final reconstructed SPS images and optimization of noise filtering. The optimized SPS Pb-HSI technique was used to acquire the line-of-sight integrated optical emission maps from an atmospheric pressure micro-capillary dielectric barrier discharge (μDBD). The selected plasma species of interest (He, I, N 2 , N 2 + , and O) yield some insight into the underlying mechanisms. The SPS Pb-HSI technique developed here will allow implementing geometric super-resolution in many applications, for example, it will be used for extracting radially resolved information from Abel's inversion protocols, where improved fitting is expected due to the increase in resolution/data points. 

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5. Best Segmentation Buddies for Image-Shape Correspondence

   Lang, Itai; Lyu, Dongwei; Decatur, Dale; Hanocka, Rana (June 2026, Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition)

   Finding correspondences is a fundamental and extensively researched problem in computer vision and graphics. In this work, we examine the underexplored problem of estimating segmentation-to-segmentation correspondence between images in the wild and untextured 3D shapes. This task is highly challenging due to substantial differences in appearance, geometry, and viewpoint. Our approach bridges the cross-modality gap by linking pixels in the image segment to vertices in the corresponding semantic part of the 3D shape. To achieve this, we first distill deep visual features from a 2D vision model onto the 3D shape surface, allowing for the computation of feature similarity between image pixels and shape vertices. We then identify Best Segmentation Buddies, vertices whose most similar image pixel lies within the image segmentation region, enabling the reliable discovery of vertices in semantically corresponding shape parts. Finally, we leverage distilled 3D features from the 2D image segmentation model to segment the shape directly in 3D, bootstrapping the correspondence process. We demonstrate the generality and robustness of our approach across a wide range of image-shape pairs, showcasing accurate and semantically meaningful correspondences. 

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