High Resolution Optical Metrology And Edge Detection Using A Pc-Controlled Smart CCD Camera

1987 ◽  
Author(s):  
M. T. Gale ◽  
P. Seitz
Keyword(s):  
High Resolution ◽  
Edge Detection ◽  
Ccd Camera ◽  
Optical Metrology

On-line alignment and astigmatism correction using a TV and personal computer

1993 ◽  
Vol 51 ◽  
pp. 202-203
Author(s):  
F. Hosokawa ◽  
Y. Kondo ◽  
T. Honda ◽  
Y. Ishida ◽  
M. Kersker
Keyword(s):  
High Resolution ◽  
Personal Computer ◽  
Block Diagram ◽  
Incident Beam ◽  
Ccd Camera ◽  
Alignment Procedure ◽  
Astigmatism Correction ◽  
Transmission Electron ◽  
Alignment System ◽  
On Line

High-resolution transmission electron microscopy must attain utmost accuracy in the alignment of incident beam direction and in astigmatism correction, and that, in the shortest possible time. As a method to eliminate this troublesome work, an automatic alignment system using the Slow-Scan CCD camera has been introduced recently. In this method, diffractograms of amorphous images are calculated and analyzed to detect misalignment and astigmatism automatically. In the present study, we also examined diffractogram analysis using a personal computer and digitized TV images, and found that TV images provided enough quality for the on-line alignment procedure of high-resolution work in TEM. Fig. 1 shows a block diagram of our system. The averaged image is digitized by a TV board and is transported to a computer memory, then a diffractogram is calculated using an FFT board, and the feedback parameters which are determined by diffractogram analysis are sent to the microscope(JEM- 2010) through the RS232C interface. The on-line correction system has the following three modes.

scholarly journals Improved Mask R-CNN for Rural Building Roof Type Recognition from UAV High-Resolution Images: A Case Study in Hunan Province, China

2022 ◽  
Vol 14 (2) ◽  
pp. 265
Author(s):  
Yanjun Wang ◽  
Shaochun Li ◽  
Fei Teng ◽  
Yunhao Lin ◽  
Mengjie Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Edge Detection ◽  
Hunan Province ◽  
Building Roof ◽  
Rural Buildings ◽  
High Resolution Images ◽  
Rgb Images ◽  
Sobel Edge Detection ◽  
Model Recognition

Accurate roof information of buildings can be obtained from UAV high-resolution images. The large-scale accurate recognition of roof types (such as gabled, flat, hipped, complex and mono-pitched roofs) of rural buildings is crucial for rural planning and construction. At present, most UAV high-resolution optical images only have red, green and blue (RGB) band information, which aggravates the problems of inter-class similarity and intra-class variability of image features. Furthermore, the different roof types of rural buildings are complex, spatially scattered, and easily covered by vegetation, which in turn leads to the low accuracy of roof type identification by existing methods. In response to the above problems, this paper proposes a method for identifying roof types of complex rural buildings based on visible high-resolution remote sensing images from UAVs. First, the fusion of deep learning networks with different visual features is investigated to analyze the effect of the different feature combinations of the visible difference vegetation index (VDVI) and Sobel edge detection features and UAV visible images on model recognition of rural building roof types. Secondly, an improved Mask R-CNN model is proposed to learn more complex features of different types of images of building roofs by using the ResNet152 feature extraction network with migration learning. After we obtained roof type recognition results in two test areas, we evaluated the accuracy of the results using the confusion matrix and obtained the following conclusions: (1) the model with RGB images incorporating Sobel edge detection features has the highest accuracy and enables the model to recognize more and more accurately the roof types of different morphological rural buildings, and the model recognition accuracy (Kappa coefficient (KC)) compared to that of RGB images is on average improved by 0.115; (2) compared with the original Mask R-CNN, U-Net, DeeplabV3 and PSPNet deep learning models, the improved Mask R-CNN model has the highest accuracy in recognizing the roof types of rural buildings, with F1-score, KC and OA averaging 0.777, 0.821 and 0.905, respectively. The method can obtain clear and accurate profiles and types of rural building roofs, and can be extended for green roof suitability evaluation, rooftop solar potential assessment, and other building roof surveys, management and planning.

Infrastructure Inspection Using an Unmanned Aerial System (UAS) With Metamodeling-Based Image Correction

2016 ◽  
Author(s):  
Chen-Ming Kuo ◽  
Chung-Hsin Kuo ◽  
Shu-Ping Lin ◽  
Mark Christian E. Manuel ◽  
Po Ting Lin ◽  
...  
Keyword(s):  
High Resolution ◽  
Ccd Camera ◽  
Kriging Model ◽  
Image Correction ◽  
Railway Transport ◽  
Truss Bridges ◽  
Aerial Vehicle ◽  
High Resolution Images ◽  
Infrastructure Inspection ◽  
Steel Truss Bridges

Public infrastructures such as bridges are common civil structures for road and railway transport. In Poland, many of the steel truss bridges were constructed in the 1950s or earlier. The aging managements and damage assessments are required to ensure safe operations of these old bridges. The first step of damage assessment is usually done via visual inspection. The said inspection procedure can be expensive, laborious and dangerous as it is often performed by trained personnel. As a solution to this, we have developed and used a custom-designed, modular aerial robot equipped with a CCD camera for the collection of high-resolution images. The images were merged into one single, high-resolution facade map that will be the basis for subsequent evaluation by bridge inspectors. It was observed that the collected images had encountered irregularities which decreases the reliability of the facade map. We have conducted experiments to estimate the correction of image perspective in terms of attitude and position of unmanned aerial vehicle (UAV). A Kriging model was utilized to parametrically model the aforementioned nonlinear relationship. The image reliability is then evaluated based on the variance of the parametric model. The generated information is further used for high fidelity automated image correction and stitching.

Investigation of the Effects of Design and Process Parameters on the Mechanical Properties of Biodegradable Bone Scaffolds, Fabricated Using Pneumatic Microextrusion Process

2020 ◽  
Author(s):  
Mohan Yu ◽  
Ye Jien Yeow ◽  
Logan Lawrence ◽  
Pier Paolo Claudio ◽  
James B. Day ◽  
...  
Keyword(s):  
High Resolution ◽  
Process Parameters ◽  
Testing Machine ◽  
Ccd Camera ◽  
Biological Tissues ◽  
Morphological Properties ◽  
Color Temperature ◽  
Layer Width ◽  
Bone Scaffolds ◽  
Four Levels

Abstract Pneumatic micro-extrusion (PME) is a direct-write additive manufacturing process, which has emerged as a robust, high-resolution method for the fabrication of a broad spectrum of biological tissues and organs. In the PME process, a high-pressure flow is injected into a cartridge, which contains a bioink material, resulting in pressure-driven material deposition on a free surface via a converging conical micro-capillary. In this study, PCL powder was loaded into the cartridge, maintained at 120 °C. The flow pressure was set to 550 kPa. Laminar molten PCL flow was deposited on a glass surface (steadily and uniformly kept at 45 °C), using a 200 μm nozzle. A porous, cylindrical scaffold was designed (honeycomb-filled), having a diameter and height of 10 mm and 3 mm, respectively. To investigate the effects of the design and process parameters, a series of experiments were designed and conducted where print speed was varied at four levels in the range of 0.30–0.45 mm/s with 0.05 mm/s increments. In addition, similarly, layer height and layer width were changed at four levels in the range of 125–200 μm with 25μm increments. Finally, infill density was set at four levels in the range of 0.20–0.35 with 5% increments. As a result, 16 experimental runs were characterized, each replicated four times. Of each of the PME-fabricated samples, an image was acquired (both horizontally and vertically) using a high-resolution CCD camera. Illumination was provided by an LED ring light (being of a brightness in the range of 30,000–40,000 Lux as well as a color temperature of 6000 K). Subsequently, the acquired images were analyzed using in-house digital image processing algorithms, forwarded with the aim to characterize both the diameter and the height of the fabricated bone scaffolds. The veracity of the image-based measurements was corroborated, using offline caliper measurements. Furthermore, the compression properties of the fabricated bone scaffolds were measured using a compression testing machine; the samples were subjected to a compression load, applied with a velocity of 0.08 mm/s. Overall, the results of this study pave the way for future investigation of PME-deposited PCL scaffolds with optimal mechanical and morphological properties for incorporation of hBMSCs toward the treatment of osseous fractures and defects.

Real-time spectral interleaved electro-optic dual-comb spectroscopy

2020 ◽  
Author(s):  
Bingxin Xu ◽  
Xinyu Fan ◽  
Shuai Wang ◽  
Zuyuan He
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Figure Of Merit ◽  
Frequency Comb ◽  
Sampling Rate ◽  
Optical Metrology ◽  
Broad Bandwidth ◽  
Frequency Combs ◽  
Electro Optic ◽  
Spectral Sampling

Abstract Optical frequency comb with evenly spaced lines over a broad bandwidth has revolutionized the fields of optical metrology and spectroscopy. Here, we propose an electro-optic dual-comb spectroscopy to real-time interleave the spectrum with high resolution, in which two electro-optic frequency combs are seed by swept light source. An interleaved spectrum with a high resolution is real-time recorded by the sweeping probe comb without gap time, which is multi-heterodyne detected by the sweeping local comb. The proposed scheme measures a spectrum spanning 304 GHz in 1.6 ms with a resolution of 1 MHz, and reaches a spectral sampling rate of 1.9*108 points/s under Nyquist-limitation. A reflectance spectrum is measured with a calculated figure-of-merit of 4.2*108, which shows great prospect for fast and high-resolution applications.

Enhancement of Differential Interference Contrast Images of Live Cells Obtained With Digital Cameras

1999 ◽  
Vol 5 (S2) ◽  
pp. 1112-1113
Author(s):  
M.V. Parthasarathy
Keyword(s):  
High Resolution ◽  
Contrast Enhancement ◽  
Dynamic Range ◽  
Ccd Camera ◽  
Video Camera ◽  
Live Cells ◽  
Photographic Emulsion ◽  
Digital Cameras ◽  
Structural Detail ◽  
Ccd Cameras

The usefulness of Differential Contrast Interference (DIC) light microscopy for observing fine details within transparent specimens is well known. However, when viewed by the eye or by recording with photographic emulsion, fine structural detail at the limit of resolution is often not visible because of lack of contrast. To overcome this problem, electronic contrast enhancement capabilities of video cameras have been used to enhance structural details that would otherwise be invisible. The technique, commonly referred to as VE-DIC (Video Enhanced DIC), uses first analog contrast enhancement of the image with a video camera followed by a real-time digital image processor to further enhance the image with. We are exploring the feasibility of achieving fine structural detail of live cells by directly acquiring digital images of them with a high resolution CCD camera.High resolution cooled slow-scan 12-bit CCD cameras are well suited for DIC microscopy because of their greater dynamic range than video CCD cameras that are normally 8-bits or lower.

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