scholarly journals Gradient characteristics of a detection field for image data

2018 ◽  
Vol 19 (12) ◽  
pp. 741-744
Author(s):  
Zbigniew Czapla
Keyword(s):  
Image Data ◽  
Vehicle Detection ◽  
Image Sequence ◽  
Video Stream ◽  
Binary Form ◽  
Vehicle Classification ◽  
Source Image ◽  
Vehicle Speed ◽  
Method Of Determination

The paper presents a method of determination of gradient characteristics describing a detection field. The presented method is destined for image data. Image data are in the form of a source image sequence. Frames taken from a video stream, obtained at a measurement station, create a source image sequence. The same detection field is defined for all images of the source image sequence. The source image sequence is converted into binary form. Conversion is carried out on the basis of analysis of source images gradients. Layout of obtained binary vales of target images is in accordance with a content of source images. In the area of detection field, arithmetic and averaging sums of binary values are appropriately calculated. On the bases of averaging sums of binary values, gradient characteristics of the detection field are determined. Gradient characteristics of detection field are intended for vehicle detection and also can be utilized for vehicle speed determination or vehicle classification..

scholarly journals Method of Determination of RMS Exposure of Ex-tended Source Image by Microphotometry

2016 ◽  
Vol 0 (1) ◽  
pp. 124
Author(s):  
Ігор Генріхович Чиж ◽  
Олександр Олексійович Голембовський
Keyword(s):  
Source Image ◽  
Method Of Determination

scholarly journals An Analytical Framework for Accurate Traffic Flow Parameter Calculation from UAV Aerial Videos

2020 ◽  
Vol 12 (22) ◽  
pp. 3844
Author(s):  
Ivan Brkić ◽  
Mario Miler ◽  
Marko Ševrović ◽  
Damir Medak
Keyword(s):  
Traffic Flow ◽  
Low Cost ◽  
Vehicle Detection ◽  
Analytical Framework ◽  
Satellite System ◽  
Percentage Error ◽  
Vehicle Speed ◽  
Dual Frequency ◽  
Flow Parameters

Unmanned Aerial Vehicles (UAVs) represent easy, affordable, and simple solutions for many tasks, including the collection of traffic data. The main aim of this study is to propose a new, low-cost framework for the determination of highly accurate traffic flow parameters. The proposed framework consists of four segments: terrain survey, image processing, vehicle detection, and collection of traffic flow parameters. The testing phase of the framework was done on the Zagreb bypass motorway. A significant part of this study is the integration of the state-of-the-art pre-trained Faster Region-based Convolutional Neural Network (Faster R-CNN) for vehicle detection. Moreover, the study includes detailed explanations about vehicle speed estimation based on the calculation of the Mean Absolute Percentage Error (MAPE). Faster R-CNN was pre-trained on Common Objects in COntext (COCO) images dataset, fine-tuned on 160 images, and tested on 40 images. A dual-frequency Global Navigation Satellite System (GNSS) receiver was used for the determination of spatial resolution. This approach to data collection enables extraction of trajectories for an individual vehicle, which consequently provides a method for microscopic traffic flow parameters in detail analysis. As an example, the trajectories of two vehicles were extracted and the comparison of the driver’s behavior was given by speed—time, speed—space, and space—time diagrams.

scholarly journals Intelligent compression for synchrotron radiation source image

2021 ◽  
Vol 251 ◽  
pp. 03073
Author(s):  
Shiyuan Fu ◽  
Lu Wang ◽  
Yaodong Cheng ◽  
Gang Chen
Keyword(s):  
Deep Learning ◽  
Synchrotron Radiation ◽  
Image Data ◽  
Lossless Compression ◽  
Image Sequence ◽  
Mapping Method ◽  
Scientific Data ◽  
Frame Rate ◽  
Source Image ◽  
The Difference

Synchrotron radiation sources (SRS) produce a huge amount of image data. This scientific data, which needs to be stored and transferred losslessly, will bring great pressure on storage and bandwidth. The SRS images have the characteristics of high frame rate and high resolution, and traditional image lossless compression methods can only save up to 30% in size. Focus on this problem, we propose a lossless compression method for SRS images based on deep learning. First, we use the difference algorithm to reduce the linear correlation within the image sequence. Then we propose a reversible truncated mapping method to reduce the range of the pixel value distribution. Thirdly, we train a deep learning model to learn the nonlinear relationship within the image sequence. Finally, we use the probability distribution predicted by the deep leaning model combined with arithmetic coding to fulfil lossless compression. Test result based on SRS images shows that our method can further decrease 20% of the data size compared to PNG, JPEG2000 and FLIF.

Information and estimation in image processing

1987 ◽  
Vol 45 ◽  
pp. 14-17
Author(s):  
B. Roy Frieden
Keyword(s):  
Image Processing ◽  
Optical System ◽  
Large Amplitude ◽  
Communication Theory ◽  
Image Data ◽  
Direct Approach ◽  
Ill Posed

Despite the skill and determination of electro-optical system designers, the images acquired using their best designs often suffer from blur and noise. The aim of an “image enhancer” such as myself is to improve these poor images, usually by digital means, such that they better resemble the true, “optical object,” input to the system. This problem is notoriously “ill-posed,” i.e. any direct approach at inversion of the image data suffers strongly from the presence of even a small amount of noise in the data. In fact, the fluctuations engendered in neighboring output values tend to be strongly negative-correlated, so that the output spatially oscillates up and down, with large amplitude, about the true object. What can be done about this situation? As we shall see, various concepts taken from statistical communication theory have proven to be of real use in attacking this problem. We offer below a brief summary of these concepts.

Development of a method for multielemental determination in water by EDXRF with radioisotopic source of 238Pu.

2019 ◽  
Vol 7 (2A) ◽  
Author(s):  
Camilo Fuentes Serrano ◽  
Juan Reinaldo Estevez Alvares ◽  
Alfredo Montero Alvarez ◽  
Ivan Pupo Gonzales ◽  
Zahily Herrero Fernandez ◽  
...  
Keyword(s):  
Thin Layer ◽  
Expanded Uncertainty ◽  
Considerable Decrease ◽  
X Ray ◽  
Precipitation Efficiency ◽  
Method Of Determination ◽  
Sensitivity Curves ◽  
Order Of Magnitude ◽  
Metrological Parameters

A method for determination of Cr, Fe, Co, Ni, Cu, Zn, Hg and Pb in waters by Energy Dispersive X Ray Fluorescence (EDXRF) was implemented, using a radioisotopic source of 238Pu. For previous concentration was employed a procedure including a coprecipitation step with ammonium pyrrolidinedithiocarbamate (APDC) as quelant agent, the separation of the phases by filtration, the measurement of filter by EDXRF and quantification by a thin layer absolute method. Sensitivity curves for K and L lines were obtained respectively. The sensitivity for most elements was greater by an order of magnitude in the case of measurement with a source of 238Pu instead of 109Cd, which means a considerable decrease in measurement times. The influence of the concentration in the precipitation efficiency was evaluated for each element. In all cases the recoveries are close to 100%, for this reason it can be affirmed that the method of determination of the studied elements is quantitative. Metrological parameters of the method such as trueness, precision, detection limit and uncertainty were calculated. A procedure to calculate the uncertainty of the method was elaborated; the most significant source of uncertainty for the thin layer EDXRF method is associated with the determination of instrumental sensitivities. The error associated with the determination, expressed as expanded uncertainty (in %), varied from 15.4% for low element concentrations (2.5-5 μg/L) to 5.4% for the higher concentration range (20-25 μg/L).

Method of determination of aerodynamic axial force acting on vehicle body and stabilizer on it

2001 ◽  
Vol 7 (1s) ◽  
pp. 89-92
Author(s):  
E.A. Ermolenko ◽  
◽  
Yu.V. Kamenchuk ◽  
Keyword(s):  
Axial Force ◽  
Vehicle Body ◽  
Method Of Determination

Bioimpedance Spectroscopy as a Non-invasive Method of Determination of the Hydration Status in Hemodialysis Patients

2010 ◽  
Vol 14 (2) ◽  
pp. 72-77
Author(s):  
J. Rosina ◽  
Jana Vranova ◽  
Ondrej Remes ◽  
Katarina Nehezova ◽  
I. Rychlik
Keyword(s):  
Hemodialysis Patients ◽  
Hydration Status ◽  
Bioimpedance Spectroscopy ◽  
Non Invasive ◽  
Method Of Determination ◽  
Invasive Method
Sign in / Sign up

Export Citation Format

Share Document