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Automatic Recognition of Engineering Drawings and Maps
Published in Rangachar Kasturi, Mohan M. Trivedi, Image Analysis Applications, 2020
Masakazu Ejiri, Shigeru Kakumoto, Takafumi Miyatake, Shigeru Shimada, Kazuaki Iwamura
The first step in automatic drawing recognition is to obtain a stable and precise image of the drawing using a scanner. There are basically two types of scanners: a drum type and a flatbed type. The drum scanner uses a rotating drum and a point-sensor to traverse the drawing in the direction of the drum axis and acquire two-dimensional information from the drawing. The flatbed-type scanner uses a linear sensor mounted on a servo-controlled carrier. The carrier is also mounted on a servo-controlled arm, thus providing X and Y motion in the plane of the flat table on which the drawing is set. The carrier is usually controlled to move at a constant speed to obtain two-dimensional information from a narrow area on the drawing, whose width corresponds to the length of the linear sensor. Then, the arm is moved stepwise to another area after the carrier finishes one full stroke of motion.
Photomask Fabrication
Published in Robert Doering, Yoshio Nishi, Handbook of Semiconductor Manufacturing Technology, 2017
All scanning probe microscope (SPMs) consist of a probe tip, a sensor that accurately locates the vertical position of the tip, a feedback system that controls the vertical position of the tip and a piezoelectric scanner that moves the tip relative to the sample in a raster pattern. A computer system drives the scanner, measures the data and converts it into an image. The tip is linked to a cantilever. The most common method to detect the position of the probe tip is by employing optical sensor. In one such scheme, a laser beam bounces of the back of the cantilever onto a position sensitive photo-detector [84,85]. During the scans the tip follows the contour of the surface which is monitored by a laser/detector system or any such precision motion detector system Scanning modes are referred as contact mode, alternative or intermittent contact mode, and non-contact mode.
Video-Image-Text Content Mining
Published in Wahiba Ben Abdessalem Karaa, Nilanjan Dey, Mining Multimedia Documents, 2017
OCR (optical character recognition) is a technique that converts different types of scanned images that are captured by digital camera of documents, (PDF files, sales receipts, mail, handwritten, typewritten, or any number of printed records) into searchable and editable data. It is widely used for extracting textual metadata, that is, machine-encoded text. (www.abbyy.com) states that the recognized document by OCR looks like the original. Therefore, these textual data can be used in machine processes such as machine translation, text to speech, and text mining. The OCR software allows saving a lot of time and effort spent in creating and processing and repurchasing various documents. OCR is a field of research in pattern recognition, artificial intelligence, and computer vision [12,13].
A Deep Learning Approach to Detecting Objects in Underwater Images
Published in Cybernetics and Systems, 2023
Kalaiarasi G, Ashok J, Saritha B, Manoj Prabu M
Figure 2 depicts a flowchart for anticipating underwater images. A convolutional neural network-based underwater object prediction is designed using deep learning. Using additional number arrays in the model, this network detects images after encoding them into numeric arrays. Images are recorded and sent to the internet software as the user adds various characteristics of the forecast into web form and the model with TensorFlow and scaled it down from its original enormous size. The numerical values of the various objects are entered into the data collection by this model.To identify object prediction from photographs found online, the proposed technique leverages a CNN object recognition model. Image acquisition, image preprocessing, segmentation, feature extraction, and grading are five primary stages of object identification. Scanners are used for image processing tasks such as picture enhancement, segmenting a photograph into separate sections, locating infection foci, and extracting features that aid in image classification.
Morphological characterisation of ANSYS 3-D modelled aggregates
Published in International Journal of Pavement Engineering, 2022
Iqbal Marie, M. Mahdi, Randa Oqab Mujalli
Many types of scanners exist; X-ray scanner, Laser Scanner or Structured Light Scanner. These scanners can scan 3D aggregate shapes, from which a database of aggregates can be produced. X-ray computed tomography scanning is a widely used 3D scanning method to scan aggregates and concrete samples to generate the mesostructure of concrete (Thilakarathna et al. (2020); Liu et al. (2018)). However, it is a time-consuming and costly technique in addition to safety concerns (Anochie-Boateng et al., 2013). Another technique uses 3D laser scanning to scan and acquire 3D aggregate shapes (Mazzucco et al., 2018). However, the 3D laser scanners are relatively slow in comparison to the structured light scanners. Moreover, structured light scanners are safer than X-ray scanners and laser scanners (Thilakarathna et al., 2021).
Allocation and scheduling of digital dentistry services in a dental cloud manufacturing system
Published in International Journal of Computer Integrated Manufacturing, 2021
Siavash Valizadeh, Omid Fatahi Valilai, Mahmoud Houshmand
Generally, the digital process of producing dental prostheses or templates consists of four stages: digitalization, design, process planning and production that are done by scanners, CAD, CAM and CNC (Computer Numerical Control)/3D printer, respectively (Beuer, Schweiger, and Edelhoff 2008; Heister and Anderl 2014; Lebon et al. 2016a; Tapie et al. 2015) (Figure 3a). First, two types of scanners may be used: extraoral scanners and oral scanners. Traditionally, after tooth preparation, a physical impression is made in a dentist office, the impression is sent to a dental laboratory and the technician makes a physical model from the impression. The physical model is called Master Cast, which is a copy of the state of the patient’s teeth and gums. Subsequently, the master cat is converted to a digital cast by an extraoral scanner (Figure 3b). This stage is also called digitalisation which is a type of reverse engineering (Raja and Fernandes 2007). Digitalization is enhanced by the introduction of oral scanners that produce digital casts directly after tooth preparation without making an impression and physical model (Figure 3b). Using the oral scanner eliminates the physical modeling steps that make scanning more accurate and quicker while purchasing an oral scanner creates more investment for dentists (Grünheid, McCarthy, and Larson 2014; Logozzo et al. 2014a, 2014b).