![]() ![]() Xu, Y., Guo, D., Zheng, T., Cheng, A.: Research on camera calibration methods of the machine vision. Yang, J., Xu, R., Ding, Z., Lv, H.: 3D character recognition using binocular camera for medical assist. Lasers 37, 1613–1617 (2010)įrancisco, A.: Continuous principal distance change for binocular depth perception. Yao, J., Yan, H., Zhang, X., Jiang, Y.: Image registration and superposition for improving ranging accuracy of imaging laser radar. Steinvall, O.: Effects of target shape and reflection on laser radar cross sections. In: Laser Radar Technology and Applications IX (2004) Johnson, S., Nichols, T., Gatt, P., Klausutis, T.: Range precision of direct-detection laser radar systems. Jia, T., Shi, Y., Zhou, Z., Chen, D.: 3D depth information extraction with omni-directional camera. 21, 1143–1156 (2013)Ĭharalampous, K., Kostavelis, I., Gasteratos, A.: Thorough robot navigation based on SVM local planning. Ghommam, J., Mehrjerdi, H., Saad, M.: Robust formation control without velocity measurement of the leader robot. ![]() Mendes, N., Neto, P.: Indirect adaptive fuzzy control for industrial robots: a solution for contact applications. Hoang, N., Kang, H.: Neural network-based adaptive tracking control of mobile robots in the presence of wheel slip and external disturbance force. Kong, L.F., Pei-Liang, W.U., Xian-Shan, L.I.: Object depth estimation using translations of hand-eye system with uncalibrated camera. Tan, K.: Squirrel cage induction generator system using wavelet petri fuzzy neural network control for wind power applications. Hachmon, G., Mamet, N., Sasson, S., Barkai, T., Hadar, N., Abu-Horowitz, A., Bachelet, I.: A non-Newtonian fluid robot. ![]() The algorithm shows that our method could effectively locate the target depth. Under the condition of the height of the camera and the height of the target are known, the target depth analysis is carried out. According to the imaging model of the camera, the imaging parameters of the camera (such as focal length, field of view, equivalent focal length, etc.), and the basic principle of analog signal to digital signal, we derived a relationship model between the target depth, field of view, equivalent focal length and the camera resolution. In this paper, a new method of target depth measurement for machine monocular vision is proposed. These two technologies achieve the precise positioning by increasing the amount of data and sacrificing processing speed. If it is in “mm” why not use something that people are more used to (50mm as standard) than 57mm? and change the way its calculated to do “expected” behavior.Most of the existing machine vision positioning technology focused on the technology of double camera geometric positioning, or use a single camera plus non-visual sensor technology for positioning.I do not remember what SketchUp displays in unit when it ships.You can adjust the FOV between 1 and 120 degrees (the default is 35 degrees in SketchUp and 30 degrees in SketchUp Pro).” States that “One is when the field of view (FOV) is set very wide. ![]() In Clipping & missing faces - Clipping and missing faces | SketchUp Help I believe these two are default value of SketchUp 2015 Pro. Therefore, it shows 57mm as default field of view, but if i use “deg” unit and restart sketchup, it would display “35.00 deg.” I also have noticed that SketchUp seems to remember what unit that I have used for the last time. I have noticed that I can type “35mm” or “35 deg” in field of view VCB. A SketchUp view with a FOV of 50mm is considerably more wide-angled than in a camera with a 50 mm focal length lens for a view where the width is bigger than the height. In SketchUp the FOV is measured vertically across the model window, while the shape of the window varies, whereas in a camera the measurement applies to a diagonal of the picture frame that has fixed 2:3 proportions. Just a note about the field of view in SketchUp: The FOV numbers do not match the ones given to a 35 mm camera lens. Confused about Field of View Camera Settings ![]()
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