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Vision-Based Tracking for a Human Following Mobile Robot
Published in Laxmidhar Behera, Swagat Kumar, Prem Kumar Patchaikani, Ranjith Ravindranathan Nair, Samrat Dutta, Intelligent Control of Robotic Systems, 2020
Laxmidhar Behera, Swagat Kumar, Prem Kumar Patchaikani, Ranjith Ravindranathan Nair, Samrat Dutta
The pinhole camera model describes the mathematical relationship between the coordinates of a 3-D point and its projection onto the image plane of an ideal pinhole camera, where the camera aperture is described as a point and no lenses are used to focus light [496]. Let (cxt, cyt, h) is the position vector of the human center Pt with respect to the frame {Rc}, h is a known positive constant, (αt, βt) is the image coordinate of the point projected on the image plane, and f is the focal length of the camera. Using the pinhole model for the camera as shown in Figure 16.14, the following relationships are obtained: cxtf=cytαt=hβt
Introduction to Computer Vision and Basic Concepts of Image Formation
Published in Manas Kamal Bhuyan, Computer Vision and Image Processing, 2019
The Pinhole camera pinhole camera is a very simple camera model. The pinhole camera consists of a closed box with a small opening on the front through which light can enter. The incoming light forms an image on the opposite wall. As shown in Figure 1.24, an inverted image of the scene is formed. The geometric properties of the pinhole camera are very straightforward and simple. In the camera, the optical axis runs through the pinhole perpendicular to the image plane. The concept of how the 2-dimensional image of a 3-dimensional real world scene is formed can be explained with the help of the simplest basic pinhole camera model. This setup is similar to the human eye where pinhole and image plane correspond to pupil and retina, respectively. The pinhole camera lies in between the observed world scene and the image plane. Any ray reflected from a surface of the scene is constrained to pass through the pinhole and impinges on the image plane. Therefore, as seen from the image plane through pinhole, for each area in the image there is a corresponding area in the real world. Thus, the image formed is a linear transformation from the 3-dimensional projective space R3 to the 2-dimensional projective space R2. In a digital camera, a sensor array is available in place of the film.
P
Published in Phillip A. Laplante, Dictionary of Computer Science, Engineering, and Technology, 2017
perspective projection the complete projection model of a scene onto an image plane via a pinhole camera model. The perspective projection of any set of parallel lines which are not parallel to the projection plane converge to a vanishing point. In 3D, the parallel lines meet only at infinity and there is an infinity of vanishing points, one for each of the infinity of directions in which a line can be oriented.
An automatic calibration algorithm for endoscopic structured light sensors in cylindrical environment
Published in Nondestructive Testing and Evaluation, 2023
Mohand Alzuhiri, Zi Li, Jiaoyang Li, Adithya Rao, Yiming Deng
The magnitude of is set to have a large positive value that ensures the intersection of the with the cylinder (Empirically, was always set to be larger than 10). To calculate (), the ray-cone intersection () algorithm is used. finds the intersection points between the input rays () and the estimated cone model. A detailed description of the ray-cone intersection () process is described in Section 6.1. The estimated camera points () are then created by projecting from the 3D space to image coordinates with a pinhole camera model (PHM). The pinhole camera is a projection camera model that describes the projection of the points from the 3D world to the 2D coordinates of the camera sensor (assuming a distortion-free imaging lens). The pinhole camera model concept is explained later in the paper and is mathematically described in Eq. 10. With both the input data and estimated data in the camera image domain, the minimisation problem can be described by
Artificial intelligence (AI) in augmented reality (AR)-assisted manufacturing applications: a review
Published in International Journal of Production Research, 2021
Chandan K. Sahu, Crystal Young, Rahul Rai
Within single image strategies, Bogdan et al. (2018) present DeepCalib, a Convolutional Neural Network (CNN) with Inception-V3 architecture (Szegedy et al. 2016) that estimates the intrinsic parameters of the camera. The CNN is trained using omnidirectional images from the Internet. Their method does not require motion estimation, a calibration target, consecutive frame inputs, or information about the structure of the scene. The AI-based self-calibration method by Zhuang et al. (2019) finds camera intrinsics and radial distortion for Simultaneous Localization and Mapping (SLAM) systems using two-view epipolar constraints. In this work, the CNN is trained on a set of images with varying intrinsics and radial distortion. The solution by Lopez et al. (2019) finds both intrinsic and extrinsic camera parameters from single images with radial distortion. They utilise a CNN that is trained to perform regression on varying extrinsic and intrinsic parameters. Donné et al. (2016) present ML for adaptive calibration template detection or MATE. A CNN is trained using standard checkerboard calibration patterns in order to detect these calibration patterns in single images. After the calibration pattern is detected, other methods can be used to derive the camera parameters. He, Wang, and Hu (2018) estimate depth and focal length from a single image. While this method cannot be used to find all of the intrinsic parameters of the camera, it effectively finds the focal length of an image. Hold-Geoffroy et al. (2018) utilise a deep CNN (DCNN) to infer the focal length and camera orientation parameters of a camera from a single image. The DCNN is trained using the SUN360 database (Xiao et al. 2012), a large-scale panorama dataset. A simple pinhole camera model is utilised, so distortion parameters are omitted.
Efficient collection and automatic annotation of real-world object images by taking advantage of post-diminished multiple visual markers*
Published in Advanced Robotics, 2019
Takuya Kiyokawa, Keita Tomochika, Jun Takamatsu, Tsukasa Ogasawara
To remove the markers, we overwrite all of the pixels of the extracted pedestal region with a background image as shown in the procedure overview of Figure 10. The detailed procedure of the background-masking process is as follows. Obtain the pixel position of the object of in the image. Based on a pinhole camera model using the camera parameters , the 3D object position in is projected into the pixel position of in the image.Calculate the pixel positions of the points on the pedestal boundary as shown in Figure 4. We obtain the 3D boundary positions using the known pedestal radius in . Then we project the 3D position to the pixel position in the same process as step 1.Estimate the more pixel positions of the pedestal boundary in the image by elliptic approximation [40] of these points.Calculate the pixel positions of the points on the object boundary in the image, which is the shape boundary of the approximate shape prepared beforehand.Create the pedestal mask image as shown in the fourth picture from the left of Figure 10, where is between the pedestal boundary (calculated in step 2) and the object boundary (calculated in step 3).Fill the pedestal region with the background image using the pedestal mask image.