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Detectors, Sampling, and Image Processing and Analysis
Published in Bethe A. Scalettar, James R. Abney, Cyan Cowap, Introductory Biomedical Imaging, 2022
Bethe A. Scalettar, James R. Abney, Cyan Cowap
Image processing also encompasses procedures, such as conversion to a binary format, which produce fairly drastic visual changes. Binary images are simple digital images that contain one bit per pixel and thus two gray levels – completely black and completely white. Binary images are of interest in large part because conversion of a grayscale or color image into a high-contrast, black-and-white version often is a useful method of separating interesting features in an image from those that are uninteresting. In particular, binary conversion is a common way to “segment” an image into regions containing pixels with similar properties, usually with a goal of highlighting and identifying objects of interest (Fig. 9.26). Additional processing operations that commonly are applied to binary images are described in Box 9.3.
The Role(s) of Computers
Published in F. Brent Neal, John C. Russ, Measuring Shape, 2017
In order to measure objects to determine size, shape, position, or brightness, or even simply to count them, it is necessary to isolate them from their surroundings. The most common way to accomplish this is by thresholding—selecting a range of brightness or color values that represent the objects and clearing all of the other pixels to some background color, usually white but sometimes black or transparent. The pixels corresponding to the objects may either be set to a contrasting color or left unchanged. When the thresholded image consists of just two possible pixel values, such as black for objects and white for background, it is called a binary image. The thresholding operation may be performed manually or using automatic algorithms based on the image histogram. Some methods also take into account the values of neighboring pixels or use independent knowledge such as the permissible size range of objects.
Digital Image Processing Systems
Published in Scott E. Umbaugh, Digital Image Processing and Analysis, 2017
Binary images are the simplest type of images and can take on two values, typically black and white, or “0” and “1.” A binary image is referred to as a 1 bit per pixel image, because it takes only 1 binary digit to represent each pixel. These types of images are most frequently used in computer vision applications where the only information required for the task is general shape, or outline, information. Examples include positioning a robotic gripper to grasp an object, checking a manufactured object for shape deformations, transmission of facsimile images, or in optical character recognition (OCR).
Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model
Published in Structure and Infrastructure Engineering, 2019
Chang-Su Shim, Ngoc-Son Dang, Sokanya Lon, Chi-Ho Jeon
If the inspection work, especially the creation of a RE-based surface model, is used the commercial tools due to their breakthrough advantages in term of data capturing and generating, the damage detection process is suggested to progress using an open-source programing language. Nowadays, the open-source resource is unlimited for the image processing task. Image segmentation is produced which can be considered as the key issue for the photo-based damage detection. The damage photo is converted to greyscale, and the thresholding algorithm is applied to create a binary image. This work aims to simplify or partition the damage photo into multiple segments, in this case are sets of pixels, which is analysable. The binary image, therefore, has only two possible values (black or white) for each pixel.
Production key figures for planning the mining of manganese nodules
Published in Marine Georesources & Geotechnology, 2018
Sebastian Ernst Volkmann, Felix Lehnen
Kuhn, Rühlemann, and Wiedicke-Hombach (2012) created a binary image on the basis of hydroacoustic backscatter data and slope angles for a part of E1. A “binary image” is a digital black-and-white image. This requires that raster data are translated into binary information. Hereinafter, a valid (white) image pixel represents a mineable raster unit, whereas a nonmineable raster unit is represented by an invalid (black) image pixel. A valid pixel (white; mineable) refers to a backscatter value between 70 and 140 (>10 kg/m2, wet weight) and a slope ≤3°. An invalid pixel (black; nonmineable) is outside this range or/and >3° (Kuhn, Rühlemann, and Wiedicke-Hombach 2012). The slope angle (in degrees) is derived from raster bathymetry data.
A survey on vision guided robotic systems with intelligent control strategies for autonomous tasks
Published in Cogent Engineering, 2022
Abhilasha Singh, V. Kalaichelvi, R. Karthikeyan
In the case of binary images, it has two-pixel elements 0 and 1 where 0 refers to black and 1 refers to white. In 8-bit format, images are also called the grayscale image has 256 colors where 0 stands for Black, 255 stands for white, and 127 stands for gray. Finally, the 16-bit color format also called RGB format has 65,536 different colors in it which are divided into three channels namely Red, Green, and Blue (Jackson, 2016). Based on these image formats, there are different image processing techniques to extract the useful features and prepare the datasets suitable to the algorithm so that robots are not distorted. The common process flow of data processing to extract the features from the image acquired from the vision sensor is shown in Figure 8. The process involves image acquisition from a vision sensor followed by an image preprocessing step where the images are scaled, rotated, binarized, and segmented to extract the useful features (Patel & Bhavsar, 2021; Premebida et al., 2018). Once the preprocessing is done, the datasets can be used for any kind of applications like object tracking, motion estimation, object recognition and so on (Garibotto et al., 2013; Song et al., 2018). Though the process sounds simple there are a lot of factors that affect the processing stage. The most common factors are high illumination and noise in images. Also with high-resolution cameras, large datasets need a lot of memory and powerful processing devices. Working with large videos poses serious problems where better encryption and compression techniques are needed. To address these issues, still many research works are being conducted.