A technology that utilizes penetrating rays is one of the oldest nondestructive testing methods. Nowadays, the process of radiogram analysis is performed by qualified human operators and automatic systems are still under development. In this work we present advanced algorithms for automatic segmentation of radiographic images of welded joints. The goal of segmentation of a radiogram is to change and simplify representation of the image into a form that is more meaningful and easier to analyse automatically. The radiogram is divided into parts containing the weld line, image quality indicators, lead characters, and possible defects. Then, each part is analysed separately by specialized algorithms within the framework of the Intelligent System for Radiogram Analysis.

A modification of the descriptor in a human detector using Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) is presented. The proposed modification requires inserting the values of average cell brightness resulting in the increase of the descriptor length from 3780 to 3908 values, but it is easy to compute and instantly gives ≈ 25% improvement of the miss rate at 10‒4 False Positives Per Window (FPPW). The modification has been tested on two versions of HOG-based descriptors: the classic Dalal-Triggs and the modified one, where, instead of spatial Gaussian masks for blocks, an additional central cell has been used. The proposed modification is suitable for hardware implementations of HOG-based detectors, enabling an increase of the detection accuracy or resignation from the use of some hardware-unfriendly operations, such as a spatial Gaussian mask. The results of testing its influence on the brightness changes of test images are also presented. The descriptor may be used in sensor networks equipped with hardware acceleration of image processing to detect humans in the images.

This paper presents an algorithm for restoring telescope images corrupted by turbulence effects and readout noise of a telescope system in order to determine centroid positions of stars, especially the position of a reference star. A computation method employing an accurate centroid estimation algorithm reconstructing a point spread function (PSF) from the recorded astronomical images has been used. Minimisation of turbulence effects and telescope control system noise in long exposure images acquired and recorded by the ground telescope is proposed. As a solution of the distortion error a minimisation signal is dedicated to GoTo calibration procedures built in control mechanisms of the electromechanical telescope system. The proposed method has been verified in the Matlab environment for real deep sky images recorded by the ground telescope system.

Image processing techniques (band rationing, color composite, Principal Component Analyses)

are widely used by many researchers to describe various mines and minerals. The primary aim of

this study is to use remote sensing data to identify iron deposits and gossans located in Kaman,

Kırşehir region in the central part of Anatolia, Turkey. Capability of image processing techniques is

proved to be highly useful to detect iron and gossan zones. Landsat ETM+ was used to create remote

sensing images with the purpose of enhancing iron and gossan detection by applying ArcMap image

processing techniques. The methods used for mapping iron and gossan area are 3/1 band rationing,

3/5 : 1/3 : 5/7 color composite, third PC and PC4 : PC3 : PC2 as RG B which obtained result from

Standard Principal Component Analysis and third PC which obtained result from Developed Selected

Principal Component Analyses (Crosta Technique), respectively. Iron-rich or gossan zones were mapped

through classification technique applied to obtained images. Iron and gossan content maps were

designed as final products. These data were confirmed by field observations. It was observed that iron

rich and gossan zones could be detected through remote sensing techniques to a great extent. This

study shows that remote sensing techniques offer significant advantages to detect iron rich and gossan

zones. It is necessary to confirm the iron deposites and gossan zones that have been detected for the

time being through field observations.

Based on the mould temperature measured by thermocouples during slab continuous casting, a difference of temperature thermograph is developed to detect slab cracks. In order to detect abnormal temperature region caused by longitudinal crack, the suspicious regions are extracted and divided by virtue of computer image processing algorithms, such as threshold segmentation, connected region judgement and boundary tracing. The abnormal regions are then determined and labeled with the eight connected component labeling algorithm. The boundary of abnormal region is also extracted to depict characteristics of longitudinal crack. Based on above researches, longitudinal crack with abnormal temperature region can be detected and is different from other abnormalities. Four samples of temperature drop are picked up to compare with longitudinal crack on the abnormal region formation, length, width, shape, et al. The results show that the abnormal region caused by longitudinal crack has a linear and vertical shape. The height of abnormal region is more than the width obviously. The ratio of height to width is usually larger than that of other temperature drop regions. This method provides a visual and easy way to detect longitudinal crack and other abnormities. Meanwhile it has a positive meaning to the intelligent and visual mould monitoring system of continuous casting.

Reliable, remote pulse rate measurement is potentially very important for medical diagnostics and screening. In this paper the Videoplethysmography was analyzed especially to verify the possible use of signals obtained for the YUV color model in order to estimate the pulse rate, to examine what is the best pulse estimation method for short video sequences and finally, to analyze how potential PPG-signals can be distinguished from other (e.g. background) signals. The presented methods were verified using data collected from 60 volunteers.

Two low-cost methods of estimating the road surface condition are presented in the paper, the first one

based on the use of accelerometers and the other on the analysis of images acquired from cameras installed

in a vehicle. In the first method, miniature positioning and accelerometer sensors are used for evaluation of

the road surface roughness. The device designed for installation in vehicles is composed of a GPS receiver

and a multi-axis accelerometer. The measurement data were collected from recorded ride sessions taken

place on diversified road surface roughness conditions and at varied vehicle speeds on each of examined

road sections. The data were gathered for various vehicle body types and afterwards successful attempts

were made in constructing the road surface classification employing the created algorithm. In turn, in the

video method, a set of algorithms processing images from a depth camera and RGB cameras were created.

A representative sample of the material to be analysed was obtained and a neural network model for classification

of road defects was trained. The research has shown high effectiveness of applying the digital image

processing to rejection of images of undamaged surface, exceeding 80%. Average effectiveness of identification

of road defects amounted to 70%. The paper presents the methods of collecting and processing the

data related to surface damage as well as the results of analyses and conclusions.

The paper presents a technique for measuring membrane displacements with one motionless camera. The method consists in measuring the distance to an object based on one image obtained from a motionless camera with a fixed-focus lens. The essence of the proposed measurement technique is to determine changes of the distance between a membrane and a video camera based on analysis of changes in the focus view of a marker placed on the membrane plane. It is proven that the used technique allows to monitor the frequency and amplitude of the membrane vibration. The tests were performed for the oscillation frequency in the range from 0.5 Hz to 6 Hz and deviations from the neutral position in the range of ±3 mm.

The study of the different engineering materials according to their mechanical and dynamic characteristics has become an area of research interest in recent years. Several studies have verified that the mechanical properties of the material are directly affected by the distribution and size of the particles that compose it. Such is the case of asphalt mixtures. For this reason, different digital tools have been developed in order to be able to detect the structural components of the elements in a precise, clear and efficient manner. In this work, a segmentation model is developed for different types of dense-graded asphalt mixtures with grain sizes from 9.5 mm to 0.0075 mm, using sieve size reconstruction of the laboratory production curve. The laboratory curve is used to validate the particles detection model that uses morphological operations for elements separation. All this with the objective of developing a versatile tool for the analysis and study of pavement structures in a non-destructive test. The results show that the model presented in this work is able to segment elements with an area greater than 0.0324 mm2 and reproduce the sieve size curves of the mixtures with a high percentage of precision.

Glucose concentration measurement is essential for diagnosis, monitoring and treatment of various medical conditions like diabetes mellitus, hypoglycemia, etc. This paper presents a novel image-processing and machine learning based approach for glucose concentration measurement. Experimentation based on Glucose oxidase - peroxidase (GOD/POD) method has been performed to create the database. Glucose in the sample reacts with the reagent wherein the concentration of glucose is detected using colorimetric principle. Colour intensity thus produced, is proportional to the glucose concentration and varies at different levels. Existing clinical chemistry analyzers use spectrophotometry to estimate the glucose level of the sample. Instead, this developed system uses simplified hardware arrangement and estimates glucose concentration by capturing the image of the sample. After further processing, its Saturation (S) and Luminance (Y) values are extracted from the captured image. Linear regression based machine learning algorithm is used for training the dataset consists of saturation and luminance values of images at different concentration levels. Integration of machine learning provides the benefit of improved accuracy and predictability in determining glucose level. The detection of glucose concentrations in the range of 10–400 mg/dl has been evaluated. The results of the developed system were verified with the currently used spectrophotometry based Trace40 clinical chemistry analyzer. The deviation of the estimated values from the actual values was found to be around 2- 3%.

Malignant melanomas are the most deadly type of skin cancer, yet detected early have high chances of successful treatment. In the last twenty years, the interest in automatic recognition and classification of melanoma dynamically increased, partly because of appearing public datasets with dermatoscopic images of skin lesions. Automated computer-aided skin cancer detection in dermatoscopic images is a very challenging task due to uneven sizes of datasets, huge intra-class variation with small interclass variation, and the existence of many artifacts in the images. One of the most recognized methods of melanoma diagnosis is the ABCD method. In the paper, we propose an extended version of this method and an intelligent decision support system based on neural networks that uses its results in the form of hand-crafted features. Automatic determination of the skin features with the ABCD method is difficult due to the large diversity of images of various quality, the existence of hair, different markers and other obstacles. Therefore, it was necessary to apply advanced methods of pre-processing the images. The proposed system is an ensemble of ten neural networks working in parallel, and one network using their results to generate a final decision. This system structure enables to increase the efficiency of its operation by several percentage points compared with a single neural network. The proposed system is trained on over 5000 and tested afterwards on 200 skin moles. The presented system can be used as a decision support system for primary care physicians, as a system capable of self-examination of the skin with a dermatoscope and also as an important tool to improve biopsy decision making.

We present a novel quantum algorithm for the classification of images. The algorithm is constructed using principal component analysis and von Neuman quantum measurements. In order to apply the algorithm we present a new quantum representation of grayscale images.

In this paper, the climate and environmental datasets were processed by the scripts of Generic Mapping Tools (GMT) and R to evaluate changes in climate parameters, vegetation patters and land cover types in Burkina Faso. Located in the southern Sahel zone, Burkina Faso experiences one of the most extreme climatic hazards in sub-saharan Africa varying from the extreme floods in Volta River Basin, to desertification and recurrent droughts.. The data include the TerraClimate dataset and satellite images Landsat 8-9 Operational Land Imager (OLI) and Thermal Infrared (TIRS) C2 L1. The dynamics of target climate characteristics of Burkina Faso was visualised for 2013-2022 using remote sensing data. To evaluate the environmental dynamics the TerraClimate data were used for visualizing key climate parameter: extreme temperatures, precipitation, soil moisture, downward surface shortwave radiation, vapour pressure deficit and anomaly. The Palmer Drought Severity Index (PDSI) was modelled over the study area to estimate soil water balance related to the soil moisture conditions as a prerequisites for vegetation growth. The land cover types were mapped using the k-means clustering by R. Two vegetation indices were computed to evaluate the changes in vegetation patterns over recent decade. These included the Normalized Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI) The scripts used for cartographic workflow are presented and discussed. This study contributes to the environmental mapping of Burkina Faso with aim to highlight the links between the climate processes and vegetation dynamics in West Africa.

The research investigates the possibility of applying Sentinel-2, PlanetScope satellite imageries, and LiDAR data for automation of land cover mapping and 3D vegetation characteristics in post-agricultural areas, mainly in the aspect of detection and monitoring of the secondary forest succession. The study was performed for the tested area in the Biskupice district (South of Poland), as an example of an uncontrolled forest succession process occurring on post-agricultural lands. The areas of interest were parcels where agricultural use has been abandoned and forest succession has progressed. This paper indicates the possibility of automating the process of monitoring wooded and shrubby areas developing in post-agricultural areas with the help of modern geodata and geoinformation methods. It was verified whether the processing of Sentinel-2, PlanetScope imageries allows for reliable land cover classification as an identification forest succession area. The airborne laser scanning (ALS) data were used for deriving detailed information about the forest succession process. Using the ALS point clouds vegetation parameters i.e., height and canopy cover were determined and presented as raster maps, histograms, or profiles. In the presented study Sentinel-2, PlanetScope imageries, and ALS data processing showed a significant differentiation of the spatial structure of vegetation. These differences are visible in the surface size (2D) and the vertical vegetation structure (3D).

Thermal-imaging systems respond to infrared radiation that is naturally emitted by objects. Various multispectral and hyperspectral devices are available for measuring radiation in discrete sub-bands and thus enable a detection of differences in a spectral emissivity or transmission. For example, such devices can be used to detect hazardous gases. However, their operation principle is based on the fact that radiation is considered a scalar property. Consequently, all the radiation vector properties, such as polarization, are neglected. Analysing radiation in terms of the polarization state and the spatial distribution of thereof across a scene can provide additional information regarding the imaged objects. Various methods can be used to extract polarimetric information from an observed scene. We briefly review architectures of polarimetric imagers used in different wavebands. First, the state-of-the-art polarimeters are presented, and, then, a classification of polarimetric-measurement devices is described in detail. Additionally, the data processing in Stokes polarimeters is given. Emphasis is laid on the methods for obtaining the Stokes parameters. Some predictions in terms of LWIR polarimeters are presented in the conclusion.

This study is focused on the image analysis of motionless hydraulic mixing process, for which pressure changes were the driving force. To improve the understanding of hydraulic mixing, mixing efficiency was assessed with dye introduction, which resulted in certain challenges. In order to overcome them, the framework and methodology consisting of three main steps were proposed and applied to an experimental case study. The experiments were recorded using a camera and then processed according to the proposed framework and methodology. The main outputs from the methodology which were based only on the recorded movie were liquid heights and colour changes during the process time. In addition, considerable attention has also been given to issues related to other colour systems and the hydrodynamic description of the process.

In this paper methods and their examination results for automatic segmentation and parameterization of vessels based on spectral domain optical coherence tomography (SD-OCT) of the retina are presented. We present three strategies for morphologic image processing of a fundus image reconstructed from OCT scans. A specificity of initial image processing for fundus reconstruction is analysed. Then, the parameterization step is performed based on the vessels segmented with the proposed algorithm. The influence of various methods on the vessel segmentation and fully automatic vessel measurement is analysed. Experiments were carried out with a set of 3D OCT scans obtained from 24 eyes (12 healthy volunteers) with the use of an Avanti RTvue OCT device. The results of automatic vessel segmentation were numerically compared with those prepared manually by the medical doctor experts.

The Edge detection is a customarily task. Edge detection is the main task to perform as it gives clear information about the images. It is a tremendous device in photograph processing gadgets and computer imaginative and prescient. Previous research has been done on moving window approach and genetic algorithms. In this research paper new technique, Bacterial Foraging Optimization (BFO) is applied which is galvanized through the social foraging conduct of Escherichia coli (E.coli). The Bacterial Foraging Optimization (BFO) has been practice by analysts for clarifying real world optimization problems arising in different areas of engineering and application domains, due to its efficiency. The Brightness preserving bi-histogram equalization (BHEE) is another technique that is used for edge enhancement. The BFO is applied on the low level characteristics on the images to find the pixels of natural images and the values of F-measures, recall(r) and precision (p) are calculated and compared with the previous technique. The enhancement technique i.e. BBHE is carried out to improve the information about the pictures.

Finger tapping is one of the standard tests for Parkinson's disease diagnosis performed to assess the motor function of patients' upper limbs. In clinical practice, the assessment of the patient's ability to perform the test is carried out visually and largely depends on the experience of clinicians. This article presents the results of research devoted to the objectification of this test. The methodology was based on the proposed measurement method consisting in frame processing of the video stream recorded during the test to determine the time series representing the distance between the index finger and the thumb. Analysis of the resulting signals was carried out in order to determine the characteristic features that were then used in the process of distinguishing patients with Parkinson's disease from healthy cases using methods of machine learning. The research was conducted with the participation of 21 patients with Parkinson's disease and 21 healthy subjects. The results indicate that it is possible to obtain the sensitivity and specificity of the proposed method at the level of approx. 80 %. However, the patients were in the so-called ON phase when symptoms are reduced due to medication, which was a much greater challenge compared to analyzing signals with clearly visible symptoms as reported in related works.

In this article, the authors focused on the widely used aluminium extrusion technology, where the die quality and durability are the essential factors. In this study, detailed solutions in the three-key area have been presented. First is applying marking technology, where a laser technique was proposed as a consistent light source of high power in a selected, narrow spectral range. In the second, an automated and reliable identification method of alphanumeric characters was investigated using an advanced machine vision system and digital image processing adopted to the industrial conditions. Third, a proposed concept of online tool management was introduced as an efficient process for properly planning the production process, cost estimation and risk assessment. In this research, the authors pay attention to the designed vision system’s speed, reliability, and mobility. This leads to the practical, industrial application of the proposed solutions, where the influence of external factors is not negligible.

Present paper is a continuation of works on evaluation of red, green, blue (RGB) to hue, saturation, intensity (HSI) colour space transformation in regard to digital image processing application in optical measurements methods. HSI colour space seems to be the most suitable domain for engineering applications due to its immunity to non-uniform lightning. Previous stages referred to the analysis of various RGB to HSI colour space transformations equivalence and programming platform configuration influence on the algorithms execution. The main purpose of this step is to understand the influence of computer processor architecture on the computing time, since analysis of images requires considerable computer resources. The technical development of computer components is very fast and selection of particular processor architecture can be an advantage for fastening the image analysis and then the measurements results. In this paper the colour space transformation algorithms, their complexity and execution time are discussed. The most common algorithms were compared with the authors own one. Computing time was considered as the main criterion taking into account a technical advancement of two computer processor architectures. It was shown that proposed algorithm was characterized by shorter execution time than in reported previously results.

The aim of this research is to evaluate the performance of four UAV image processing software for the automatic estimation of volumes based on estimated volume accuracy, spatial accuracy, and execution time, with and without Ground Control Points (GCPs). A total of 52 images of a building were captured using a DJI Mavic Air UAV at 60m altitude and 80% forward and side overlap. The dataset was processed with and without GCPs using Pix4DMapper, Agisoft Metashape Pro, Reality Capture, and 3DF Zephyr. The UAV-based estimated volume generated from the software was compared with the true volume of the building generated from its as-built 3D building information modeled in Revit 2018 environment. The resulting percentage difference was computed. The average volumes estimated from the four software with the use of GCPs were 4757.448 m3 (3.87%), 4728.1 m3 (2.54%), 4291.561 m3 (11.5%), and 4154.938 m3 (14.35%), respectively. Similarly, when GCPs were not used for the image processing, average volumes of 4631.385 m3 (4.52%), 4773.025 m3 (1.6%), 4617.899 m3 (4.89%), and 4420.403 m3 (8.92%) were obtained in the same order. In addition to the volume estimation analysis, other parameters, including execution time, positional RMSE, and spatial resolution, were evaluated. Based on these parameters, Agisoft Metashape Pro proved to be more accurate, time-efficient, and reliable for volumetric estimations from UAV images compared to the other investigated software. The findings of this study can guide decision-making in selecting the appropriate software for UAV-based volume estimation in different applications.

This paper presents and assesses an inverse heat conduction problem (IHCP) solution procedure which was developed to determine the local convective heat transfer coefficient along the circumferential coordinate at the inner wall of a coiled pipe by applying the filtering technique approach to infrared temperature maps acquired on the outer tube’s wall. The data−processing procedure filters out the unwanted noise from the raw temperature data to enable the direct calculation of its Laplacian which is embedded in the formulation of the inverse heat conduction problem. The presented technique is experimentally verified using data that were acquired in the laminar flow regime that is frequently found in coiled−tube heat−exchanger applications. The estimated convective heat transfer coefficient distributions are substantially consistent with the available numerical results in the scientific literature.

In the current smart tech world, there is an immense need of automating tasks and processes to avoid human intervention, save time and energy. Nowadays, mobile phones have become one of the essential things for human beings either to call someone, connect to the internet, while driving people need mobile phones to receive or make a call, use google maps to know the routes and many more. Normally in cars, mobile holders are placed on the dashboard to hold the mobile and the orientation of the phone needs to be changed according to the driver's convenience manually, but the driver may distract from driving while trying to access mobile phone which may lead to accidents. To solve this problem, an auto adjustable mobile holder is designed in such a way that it rotates according to the movement of the driver and also it can even alert the driver when he feels drowsiness. Image Processing is used to detect the movement of the driver which is then processed using LabVIEW software and NI myRIO hardware. NI Vision development module is used to perform face recognition and servo motors are used to rotate the holder in the required position. Simulation results show that the proposed system has achieved maximum accuracy in detecting faces, drowsiness and finding the position coordinates.