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Добірка наукової літератури з теми "Linear camera"

Автор: Grafiati

Опубліковано: 24 липня 2022

Оновлено: 8 серпня 2022

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Статті в журналах з теми "Linear camera"

Jun Chu, Jun Chu, Li Wang Li Wang, Ruina Feng Ruina Feng, and Guimei Zhang Guimei Zhang. "Linear camera calibration and pose estimation from vanishing points." Chinese Optics Letters 10, s1 (2012): S11007–311011. http://dx.doi.org/10.3788/col201210.s11007.

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KINOSITA, Jr., Kazuhiko, Katsuyuki SHIROGUCHI, Tetsuaki OKAMOTO, Kengo ADACHI, Yasuhiro ONOUE, and Hiroyasu ITOH. "Is Your Video Camera Linear?" Seibutsu Butsuri 45, no. 4 (2005): 216–18. http://dx.doi.org/10.2142/biophys.45.216.

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Potapov, A. I., V. E. Makhov, Ya G. Smorodinskii, and E. Ya Manevich. "Smart-Camera–Based Linear Sizing." Russian Journal of Nondestructive Testing 55, no. 7 (2019): 524–32. http://dx.doi.org/10.1134/s1061830919070064.

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Purnama, Sevia Indah, Irmayatul Hikmah, Mas Aly Afandi, and Elsa Sri Mulyani. "OPTIMASI PEMBACAAN SUHU KAMERA TERMAL MENGGUNAKAN REGRESI LINIER." BAREKENG: Jurnal Ilmu Matematika dan Terapan 15, no. 1 (2021): 127–36. http://dx.doi.org/10.30598/barekengvol15iss1pp127-136.

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Fever is one of the symptoms of a person with Covid-19. Body temperature must be checked e before entering crowded areas such as schools, offices, shops, and hospitals. It is a mandatory protocol that must be done. One of the tools that can be used to check body temperature is a thermal camera. Thermal cameras have the disadvantage of a high temperature reading error. This is because the thermal camera used has a low resolution. This study aims to reduce the value of the temperature reading error on the thermal camera using the linear regression method. The linear regression method is able to reduce the error rate of temperature readings by 5.27% at 36 ° C reading. The reduction in reading error also occurred by 5.27% at 37 ° C and 6.44% at 38 ° C. Based on the results obtained, this study shows that linear regression can be applied to thermal cameras and provides a decrease in the error rate of temperature readings on thermal cameras

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Zwanenberg, Oliver van, Sophie Triantaphillidou, Robin Jenkin, and Alexandra Psarrou. "Camera System Performance Derived from Natural Scenes." Electronic Imaging 2020, no. 9 (2020): 241–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.9.iqsp-241.

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The Modulation Transfer Function (MTF) is a wellestablished measure of camera system performance, commonly employed to characterize optical and image capture systems. It is a measure based on Linear System Theory; thus, its use relies on the assumption that the system is linear and stationary. This is not the case with modern-day camera systems that incorporate non-linear image signal processes (ISP) to improve the output image. Nonlinearities result in variations in camera system performance, which are dependent upon the specific input signals. This paper discusses the development of a novel framework, designed to acquire MTFs directly from images of natural complex scenes, thus making the use of traditional test charts with set patterns redundant. The framework is based on extraction, characterization and classification of edges found within images of natural scenes. Scene derived performance measures aim to characterize non-linear image processes incorporated in modern cameras more faithfully. Further, they can produce ‘live’ performance measures, acquired directly from camera feeds.

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Fritz, Gerhard, and Alexander Bergmann. "SAXS instruments with slit collimation: investigation of resolution and flux." Journal of Applied Crystallography 39, no. 1 (2006): 64–71. http://dx.doi.org/10.1107/s002188980503966x.

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Six small-angle X-ray cameras with block collimation systems were simulated, namely the original Kratky camera, a high-flux version of the Kratky camera, a SAXSess (Anton Parr) camera with a focusing mirror in a linear collimation setup and in a pin-hole setup, as well as a similar camera with a parallelizing mirror in a linear and a pin-hole setup. Their performance was examined using Monte Carlo ray-tracing. The Kratky and the SAXSess camera gave resolutions of 64–65 nm, the high-flux Kratky camera gave a resolution of 44 nm, and the camera with parallelizing mirror gave a resolution of 32 nm. The flux of the camera with parallelizing mirror was 1.47 times higher than for the SAXSess camera, and 18.6 times the flux of the Kratky camera. On changing the alignment, the camera with parallelizing mirror exhibited the best performance up to a resolution of 44 nm; the SAXSess camera was better for higher resolutions. Experimental flux measurements agree if no collimation system is added. Measurements of beam profiles and flux including collimation systems show only qualitative agreement because of user-dependent factors during alignment.

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Moussa, Carol, Louis Hardan, Cynthia Kassis, et al. "Accuracy of Dental Photography: Professional vs. Smartphone’s Camera." BioMed Research International 2021 (December 15, 2021): 1–7. http://dx.doi.org/10.1155/2021/3910291.

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There is a scant literature on the accuracy of dental photographs captured by Digital Single-Lens Reflex (DSLR) and smartphone cameras. The aim was to compare linear measurements of plaster models photographed with DSLR and smartphone’s camera with digital models. Thirty maxillary casts were prepared. Vertical and horizontal reference lines were marked on each tooth, with exception to molars. Then, models were scanned with the TRIOS 3 Basic intraoral dental scanner (control). Six photographs were captured for each model: one using DSLR camera (Canon EOS 700D) and five with smartphone (iPhone X) (distance range 16-32 cm). Teeth heights and widths were measured on scans and photographs. The following conclusions could be drawn: (1) the measurements of teeth by means of DSLR and smartphone cameras (at distances of at least 24 cm) and scan did not differ. (2) The measurements of anterior teeth by means of DSLR and smartphone cameras (at all distances tested) and scan exhibited no difference. For documentational purposes, the distortion is negligeable, and both camera devices can be applied. Dentists can rely on DSLR and smartphone cameras (at distances of at least 24 cm) for smile designs providing comparable and reliable linear measurements.

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Antuña-Sánchez, Juan C., Roberto Román, Victoria E. Cachorro, et al. "Relative sky radiance from multi-exposure all-sky camera images." Atmospheric Measurement Techniques 14, no. 3 (2021): 2201–17. http://dx.doi.org/10.5194/amt-14-2201-2021.

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Abstract. All-sky cameras are frequently used to detect cloud cover; however, this work explores the use of these instruments for the more complex purpose of extracting relative sky radiances. An all-sky camera (SONA202-NF model) with three colour filters narrower than usual for this kind of cameras is configured to capture raw images at seven exposure times. A detailed camera characterization of the black level, readout noise, hot pixels and linear response is carried out. A methodology is proposed to obtain a linear high dynamic range (HDR) image and its uncertainty, which represents the relative sky radiance (in arbitrary units) maps at three effective wavelengths. The relative sky radiances are extracted from these maps and normalized by dividing every radiance of one channel by the sum of all radiances at this channel. Then, the normalized radiances are compared with the sky radiance measured at different sky points by a sun and sky photometer belonging to the Aerosol Robotic Network (AERONET). The camera radiances correlate with photometer ones except for scattering angles below 10∘, which is probably due to some light reflections on the fisheye lens and camera dome. Camera and photometer wavelengths are not coincident; hence, camera radiances are also compared with sky radiances simulated by a radiative transfer model at the same camera effective wavelengths. This comparison reveals an uncertainty on the normalized camera radiances of about 3.3 %, 4.3 % and 5.3 % for 467, 536 and 605 nm, respectively, if specific quality criteria are applied.

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Gao Junchai, 高俊钗, 雷志勇 Lei Zhiyong, and 王泽民 Wang Zemin. "Image Correction of Linear Array Camera." Laser & Optoelectronics Progress 47, no. 9 (2010): 091501. http://dx.doi.org/10.3788/lop47.091501.

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Long Quan and Zhongdan Lan. "Linear N-point camera pose determination." IEEE Transactions on Pattern Analysis and Machine Intelligence 21, no. 8 (1999): 774–80. http://dx.doi.org/10.1109/34.784291.

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