scholarly journals Estimating camera parameters from starry night photographs

2020 ◽  
Vol 6 (4) ◽  
pp. 445-454
Author(s):  
Naoto Ishikawa ◽  
Yoshinori Dobashi
Keyword(s):  
Input Image ◽  
Previous Method ◽  
Estimation Methods ◽  
Specific Method ◽  
Star Catalog ◽  
Geometric Calculation ◽  
Camera Parameters ◽  
Celestial Sphere

AbstractWe propose an efficient, specific method for estimating camera parameters from a single starry night image. Such an image consists of a collection of disks representing stars, so traditional estimation methods for common pictures do not work. Our method uses a database, a star catalog, that stores the positions of stars on the celestial sphere. Our method computes magnitudes (i.e., brightnesses) of stars in the input image and uses them to find the corresponding stars in the star catalog. Camera parameters can then be estimated by a simple geometric calculation. Our method is over ten times faster and more accurate than a previous method.

scholarly journals Effective Three-Stage Demosaicking Method for RGBW CFA Images Using The Iterative Error-Compensation Based Approach

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3908
Author(s):  
Kuo-Liang Chung ◽  
Tzu-Hsien Chan ◽  
Szu-Ni Chen
Keyword(s):  
Error Compensation ◽  
Color Image ◽  
Ground Truth ◽  
Input Image ◽  
Previous Method ◽  
Color Filter ◽  
Second Stage ◽  
Full Color ◽  
Iterative Error

As the color filter array (CFA)2.0, the RGBW CFA pattern, in which each CFA pixel contains only one R, G, B, or W color value, provides more luminance information than the Bayer CFA pattern. Demosaicking RGBW CFA images I R G B W is necessary in order to provide high-quality RGB full-color images as the target images for human perception. In this letter, we propose a three-stage demosaicking method for I R G B W . In the first-stage, a cross shape-based color difference approach is proposed in order to interpolate the missing W color pixels in the W color plane of I R G B W . In the second stage, an iterative error compensation-based demosaicking process is proposed to improve the quality of the demosaiced RGB full-color image. In the third stage, taking the input image I R G B W as the ground truth RGBW CFA image, an I R G B W -based refinement process is proposed to refine the quality of the demosaiced image obtained by the second stage. Based on the testing RGBW images that were collected from the Kodak and IMAX datasets, the comprehensive experimental results illustrated that the proposed three-stage demosaicking method achieves substantial quality and perceptual effect improvement relative to the previous method by Hamilton and Compton and the two state-of-the-art methods, Kwan et al.’s pansharpening-based method, and Kwan and Chou’s deep learning-based method.

Optimization of guide star catalog for daytime star sensors

2019 ◽  
Vol 34 (01n03) ◽  
pp. 2040065
Author(s):  
Feng Wu ◽  
Xifang Zhu ◽  
Qingquan Xu ◽  
Ruxi Xiang ◽  
Qiuyang Yu ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Simple Calculation ◽  
High Signal ◽  
Star Sensor ◽  
Infrared Band ◽  
Star Catalog ◽  
Celestial Sphere ◽  
Guide Star ◽  
Star Pattern Identification ◽  
Navigation Information

Daytime star sensor provides accuracy navigation information to air vehicles near the ground in the daytime by observing stars. It has been an important development of modern star sensors. In order to achieve a high signal-to-noise ratio, daytime star sensors work in the infrared band to avoid interferences from sky background. Daytime star sensors output accurate attitudes by identifying the observed stars in the field of view (FOV) according to the loaded guide star catalog. Guide stars are usually required to be distributed uniformly on the celestial sphere to improve the performance of star pattern identification. The parameters including limiting magnitude and FOV are determined by processing the 2MASS star catalog as the original star data and performing star distribution statistics. After constellation features are discussed, the idea of distributing stars in the local FOV to constellations is put forward by using the star pair angular separations. An optimization algorithm to build the guide star catalog for daytime stars is proposed to achieve evenly distributed guide stars. The guide star catalog is established and analyzed, proving that the proposed algorithm has simple calculation and easy realization. The Boltzmann entropy of obtained guide star catalog drops two orders of magnitude. Guide stars are distributed more uniformly.

scholarly journals Object Distance Estimation Using a Single Image Taken from a Moving Rolling Shutter Camera

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3860
Author(s):  
Namhoon Kim ◽  
Junsu Bae ◽  
Cheolhwan Kim ◽  
Soyeon Park ◽  
Hong-Gyoo Sohn
Keyword(s):  
Mathematical Model ◽  
Focal Length ◽  
Estimation Error ◽  
Distance Estimation ◽  
Estimation Methods ◽  
Image Size ◽  
Single Image ◽  
Object Distance ◽  
Camera Parameters ◽  
The Mathematical Model

This paper proposes a technique to estimate the distance between an object and a rolling shutter camera using a single image. The implementation of this technique uses the principle of the rolling shutter effect (RSE), a distortion within the rolling-shutter-type camera. The proposed technique has a mathematical strength compared to other single photo-based distance estimation methods that do not consider the geometric arrangement. The relationship between the distance and RSE angle was derived using the camera parameters (focal length, shutter speed, image size, etc.). Mathematical equations were derived for three different scenarios. The mathematical model was verified through experiments using a Nikon D750 and Nikkor 50 mm lens mounted on a car with varying speeds, object distances, and camera parameters. The results show that the mathematical model provides an accurate distance estimation of an object. The distance estimation error using the RSE due to the change in speed remained stable at approximately 10 cm. However, when the distance between the object and camera was more than 10 m, the estimated distance was sensitive to the RSE and the error increased dramatically.

scholarly journals Unsupervised Monocular Depth Estimation Based on Residual Neural Network of Coarse–Refined Feature Extractions for Drone

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1179 ◽  
Author(s):  
Tao Huang ◽  
Shuanfeng Zhao ◽  
Longlong Geng ◽  
Qian Xu
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Depth Map ◽  
Ground Truth ◽  
Depth Estimation ◽  
Input Image ◽  
Superior Performance ◽  
Estimation Methods ◽  
Depth Information ◽  
Monocular Depth

To take full advantage of the information of images captured by drones and given that most existing monocular depth estimation methods based on supervised learning require vast quantities of corresponding ground truth depth data for training, the model of unsupervised monocular depth estimation based on residual neural network of coarse–refined feature extractions for drone is therefore proposed. As a virtual camera is introduced through a deep residual convolution neural network based on coarse–refined feature extractions inspired by the principle of binocular depth estimation, the unsupervised monocular depth estimation has become an image reconstruction problem. To improve the performance of our model for monocular depth estimation, the following innovations are proposed. First, the pyramid processing for input image is proposed to build the topological relationship between the resolution of input image and the depth of input image, which can improve the sensitivity of depth information from a single image and reduce the impact of input image resolution on depth estimation. Second, the residual neural network of coarse–refined feature extractions for corresponding image reconstruction is designed to improve the accuracy of feature extraction and solve the contradiction between the calculation time and the numbers of network layers. In addition, to predict high detail output depth maps, the long skip connections between corresponding layers in the neural network of coarse feature extractions and deconvolution neural network of refined feature extractions are designed. Third, the loss of corresponding image reconstruction based on the structural similarity index (SSIM), the loss of approximate disparity smoothness and the loss of depth map are united as a novel training loss to better train our model. The experimental results show that our model has superior performance on the KITTI dataset composed by corresponding left view and right view and Make3D dataset composed by image and corresponding ground truth depth map compared to the state-of-the-art monocular depth estimation methods and basically meet the requirements for depth information of images captured by drones when our model is trained on KITTI.

The mystery of Copernicus’ star catalog (Part one)

2019 ◽  
Vol 945 (3) ◽  
pp. 37-47
Author(s):  
S.A. Tolchelnikova
Keyword(s):  
Solar System ◽  
20Th Century ◽  
Foreseeable Future ◽  
Vernal Equinox ◽  
Astronomical Observations ◽  
Star Catalog ◽  
The World ◽  
Solar System Bodies ◽  
Celestial Sphere

The Copernican catalog differs from the catalogs of previous epochs by transferring the reference point of ecliptic longitudes from the vernal equinox to the star γ Aries. This violation of the tradition which did not influence the catalogs of subsequent epochs, is regarded by N. I. Idelson as an anachronism, and in the opinion of E. P. Fedorov, this idea of Copernicus was ahead of time. Since the contradictions in the evaluation of Copernicus’ works are inherent in the literature of the 20th century, it is necessary to recall the pre-Copernican astronomy and refer to the text of his works. Our study consists of three parts (papers). The first one is devoted to the period from the studying the motion of heavenly bodies upon celestial sphere to passing to the World structure and the movements of the Solar system bodies. Copernicus’s heliocentric theory made invaluable contribution into the solution of this problem, impossible without determining the distances. The previous basis of astronomical observations was increased by 20 thousand times. A similar advance in the determination of distances by a mathematically exact method is hardly possible in the foreseeable future.

Novel guide star optimal selection algorithm for star sensors based on star clustering

2018 ◽  
Vol 32 (34n36) ◽  
pp. 1840089
Author(s):  
Feng Wu ◽  
Xifang Zhu ◽  
Ruxi Xiang ◽  
Qiuyang Yu ◽  
Tingting Huang ◽  
...  
Keyword(s):  
Mean Shift ◽  
Simple Calculation ◽  
Field Of View ◽  
Space Vehicles ◽  
Optimal Selection ◽  
Selection Algorithm ◽  
Mean Shift Algorithm ◽  
Star Catalog ◽  
Celestial Sphere ◽  
Guide Star

Modern space vehicles face the challenges to obtain more and more accurate attitudes in order to complete the demanding tasks. Onboard star sensors which identify the observed stars in the field of view according to the loaded guide star catalog and output accurate attitude have attracted most interests. Guide stars are usually required to distribute uniformly on the celestial sphere to improve the performance of the star pattern identification. An optimal selection algorithm is proposed to achieve an even distribution of guide stars in this paper. Constellation features are discussed. The mean shift algorithm is analyzed. The idea that distributes stars in the local field of view to constellations is proposed by using the star pair angular separations according to the star positions in the inertial coordinate system. The optimal selection algorithm of guide stars based on star clustering is developed. Its detailed implement procedures are introduced completely. The guide star optimal selection experiment in visible band by using SAO star catalog as the original star data is implemented. It proves that the proposed algorithm has the virtue of simple calculation and easy realization. The obtained guide star distribution is superior to the regression selection algorithm and the magnitude weighted method.

scholarly journals End-to-End Monocular Range Estimation for Forward Collision Warning

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5941
Author(s):  
Jie Tang ◽  
Jian Li
Keyword(s):  
Estimation Methods ◽  
Target Range ◽  
Range Estimation ◽  
Estimation Task ◽  
Collision Warning ◽  
Training Stage ◽  
Camera Parameters ◽  
End To End ◽  
Forward Collision Warning ◽  
Object Level

Estimating range to the closest object in front is the core component of the forward collision warning (FCW) system. Previous monocular range estimation methods mostly involve two sequential steps of object detection and range estimation. As a result, they are only effective for objects from specific categories relying on expensive object-level annotation for training, but not for unseen categories. In this paper, we present an end-to-end deep learning architecture to solve the above problems. Specifically, we represent the target range as a weighted sum of a set of potential distances. These potential distances are generated by inverse perspective projection based on intrinsic and extrinsic camera parameters, while a deep neural network predicts the corresponding weights of these distances. The whole architecture is optimized towards the range estimation task directly in an end-to-end manner with only the target range as supervision. As object category is not restricted in the training stage, the proposed method can generalize to objects with unseen categories. Furthermore, camera parameters are explicitly considered in the proposed method, making it able to generalize to images taken with different cameras and novel views. Additionally, the proposed method is not a pure black box, but provides partial interpretability by visualizing the produced weights to see which part of the image dominates the final result. We conduct experiments to verify the above properties of the proposed method on synthetic and real-world collected data.

The Impact of Missing and Error-Prone Auxiliary Information on Sparse-Matrix Sub-Population Parameter Estimates

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 89-99 ◽  
Author(s):  
Leslie Rutkowski ◽  
Yan Zhou
Keyword(s):  
Sparse Matrix ◽  
Small Body ◽  
Auxiliary Information ◽  
Poor Quality ◽  
Quality Data ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Population Parameter ◽  
Conditioning Model ◽  
The Impact

Abstract. Given a consistent interest in comparing achievement across sub-populations in international assessments such as TIMSS, PIRLS, and PISA, it is critical that sub-population achievement is estimated reliably and with sufficient precision. As such, we systematically examine the limitations to current estimation methods used by these programs. Using a simulation study along with empirical results from the 2007 cycle of TIMSS, we show that a combination of missing and misclassified data in the conditioning model induces biases in sub-population achievement estimates, the magnitude and degree to which can be readily explained by data quality. Importantly, estimated biases in sub-population achievement are limited to the conditioning variable with poor-quality data while other sub-population achievement estimates are unaffected. Findings are generally in line with theory on missing and error-prone covariates. The current research adds to a small body of literature that has noted some of the limitations to sub-population estimation.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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