| | SLO | ENG | Cookies and privacy

Bigger font | Smaller font

Search the digital library catalog Help

Query: search in
search in
search in
search in
* old and bologna study programme

Options:
  Reset


1 - 8 / 8
First pagePrevious page1Next pageLast page
1.
Estimating the size of plants by using two parallel views
Barbara Videc, Jurij Rakun, 2017, original scientific article

Abstract: This paper presents a method of estimating the size of plants by using two parallel views of the scene, taken by a common digital camera. The approach relays on the principle of similar triangles with the following constraints: the resolution of the camera is known; the object is always in parallel to the camera sensor and the intermediate distance between the two concessive images is available. The approach was first calibrated and tested using one artificial object in a controlled environment. After that real examples were taken from agriculture, where we measured the distance and the size of a vine plant, apple and pear tree. By comparing the calculated values to measured values, we concluded that the average absolute error in distance was 0.11 m or around 3.7 %, and the absolute error in high was 0.09 m or 4.6 %.
Keywords: digital image processing, size, digital camera, pixels, similar triangles
Published: 10.10.2018; Views: 540; Downloads: 227
.pdf Full text (727,92 KB)
This document has many files! More...

2.
Determining the grain size distribution of granular soils using image analysis
Nihat Dipova, 2017, original scientific article

Abstract: Image-processing technology includes storing the images of objects in a computer and processing them with the computer for a specified purpose. Image analysis is the numerical expression of the images of objects by means of mimicking the functioning of the human visual system and the generation of numerical data for calculations that will be made later. Digital image analysis provides the capability for rapid measurement, which can be made in near-real time, for numerous engineering parameters of materials. Recently, image analysis has been used in geotechnical engineering practices. Grain size distribution and grain shape are the most fundamental properties used to interpret the origin and behaviour of soils. Mechanical sieving has some limitations, e.g., it does not measure the axial dimension of a particle, particle shape is not taken into consideration, and especially for elongated and flat particles a sieve analysis will not yield a reliable measure. In this study the grain size distribution of sands has been determined following image-analysis techniques, using simple apparatus, non-professional cameras and open-code software. The sample is put on a transparent plate that is illuminated with a white backlight. The digital images were acquired with a CCD DSLR camera. The segmentation of the particles is achieved by image thresholding, binary coding and particle labeling. The geometrical measurements of each particle are obtained using an automated pixel-counting technique. Local contacts or limited overlaps were overcome using a watershed split. The same sample was tested by traditional sieve analysis. An image-analysis-based grain size distribution has been compared with a sieve-analysis distribution. The results show that the grain size distribution of the image-based analysis and the sieve analysis are in good agreement.
Keywords: image analysis, image processing, grain size, sand
Published: 18.06.2018; Views: 483; Downloads: 63
.pdf Full text (1,27 MB)
This document has many files! More...

3.
An overview of image analysis algorithms for license plate recognition
Khalid Aboura, Rami Al-Hmouz, 2017, original scientific article

Abstract: Background and purpose: We explore the problem of License Plate Recognition (LPR) to highlight a number of algorithms that can be used in image analysis problems. In management support systems using image object recognition, the intelligence resides in the statistical algorithms that can be used in various LPR steps. We describe a number of solutions, from the initial thresholding step to localization and recognition of image elements. The objective of this paper is to present a number of probabilistic approaches in LPR steps, then combine these approaches together in one system. Most LPR approaches used deterministic models that are sensitive to many uncontrolled issues like illumination, distance of vehicles from camera, processing noise etc. The essence of our approaches resides in the statistical algorithms that can accurately localize and recognize license plate. Design/Methodology/Approach: We introduce simple and inexpensive methods to solve relatively important problems, using probabilistic approaches. In these approaches, we describe a number of statistical solutions, from the initial thresholding step to localization and recognition of image elements. In the localization step, we use frequency plate signals from the images which we analyze through the Discrete Fourier Transform. Also, a probabilistic model is adopted in the recognition of plate characters. Finally, we show how to combine results from bilingual license plates like Saudi Arabia plates. Results: The algorithms provide the effectiveness for an ever-prevalent form of vehicles, building and properties management. The result shows the advantage of using the probabilistic approached in all LPR steps. The averaged classification rates when using local dataset reached 79.13%. Conclusion: An improvement of recognition rate can be achieved when there are two source of information especially of license plates that have two independent texts.
Keywords: image analysis, probabilistic modeling, signal processing, license plate recognition
Published: 28.11.2017; Views: 605; Downloads: 274
.pdf Full text (1,01 MB)
This document has many files! More...

4.
The accuracy of the germination rate of seeds based on image processing and artificial neural networks
Uroš Škrubej, Črtomir Rozman, Denis Stajnko, 2015, original scientific article

Abstract: This paper describes a computer vision system based on image processing and machine learning techniques which was implemented for automatic assessment of the tomato seed germination rate. The entire system was built using open source applications Image J, Weka and their public Java classes and linked by our specially developed code. After object detection, we applied artificial neural networks (ANN), which was able to correctly classify 95.44% of germinated seeds of tomato (Solanum lycopersicum L.).
Keywords: image processing, artificial neural networks, seeds, tomato
Published: 14.11.2017; Views: 759; Downloads: 256
.pdf Full text (353,43 KB)
This document has many files! More...

5.
Intelligent system for prediction of mechanical properties of material based on metallographic images
Matej Paulič, David Močnik, Mirko Ficko, Jože Balič, Tomaž Irgolič, Simon Klančnik, 2015, original scientific article

Abstract: This article presents developed intelligent system for prediction of mechanical properties of material based on metallographic images. The system is composed of two modules. The first module of the system is an algorithm for features extraction from metallographic images. The first algorithm reads metallographic image, which was obtained by microscope, followed by image features extraction with developed algorithm and in the end algorithm calculates proportions of the material microstructure. In this research we need to determine proportions of graphite, ferrite and ausferrite from metallographic images as accurately as possible. The second module of the developed system is a system for prediction of mechanical properties of material. Prediction of mechanical properties of material was performed by feed-forward artificial neural network. As inputs into artificial neural network calculated proportions of graphite, ferrite and ausferrite were used, as targets for training mechanical properties of material were used. Training of artificial neural network was performed on quite small database, but with parameters changing we succeeded. Artificial neural network learned to such extent that the error was acceptable. With the oriented neural network we successfully predicted mechanical properties for excluded sample.
Keywords: artificial neural network, factor of phase coherence between the surfaces, fracture toughness, image processing, mechanical properties, metallographic image, ultimate tensile strength, yield strength
Published: 12.07.2017; Views: 694; Downloads: 340
.pdf Full text (2,02 MB)
This document has many files! More...

6.
7.
Computer-based workpiece detection on CNC milling machine tools using optical camera and neural networks
Simon Klančnik, Jernej Šenveter, 2010, original scientific article

Abstract: In this paper, system for optical determining the workpiece origin on the CNC machine is presented. Similar high sophisticated systems are commercially available but in most cases they are very expensive and so their purchase is economically unjustified. The purpose of our research is to develop an inexpensive system for non-contact determination of the workpiece origin, which is also sufficiently precise for practical use. The system is implemented on a three-axis CNC milling machine Lakos 150 G, which is primarily designed for good machinability materials. Calibration procedure using feed-forward neural networks was developed. With this method the calibration procedure is simplified and the mathematical derivation of camera model is avoided. Learned neural network represents the camera calibration model. After neural network learning is complete, we can begin using the system for determining the workpiece origin. This developed system was through a number of tests proved to be reliable and suitable for use in practice. In the paper, working of system is illustrated with a practical example, which confirms the effectiveness of the implemented system in actual use on machine.
Keywords: neural networks, image processing, milling, workpiece detection
Published: 01.06.2012; Views: 1378; Downloads: 31
URL Link to full text

8.
Progressive method for color selective edge detection
Peter Rulić, Iztok Kramberger, Zdravko Kačič, 2007, original scientific article

Abstract: Edge detection plays an important role in image analysis systems. We present acolor selective edge detection technique, which consists of two image processing steps. The first step represents pixel-based color detection and the second progressive block-oriented edge detection. The combination of these two steps defines a selective edge detection technique, which enables fast and simple processing of those images captured using arbitrary cameras incomplex scenes with nonstandard illumination. The proposed method was implemented for the detecting of skin color objects and tested on real scene images.
Keywords: image analysis system, image processing, color detection, skin color
Published: 31.05.2012; Views: 1325; Downloads: 112
URL Link to full text

Search done in 0.25 sec.
Back to top
Logos of partners University of Maribor University of Ljubljana University of Primorska University of Nova Gorica