In this paper, an alternative approach in operational modal analysis is presented, utilizing IMAGE PROCESSING technique and transmissibility functions. Imaging sensors do not impose additional mass on the structure due to their non-contact nature, while transmissibility functions, independent of excitation type, can directly extract mode shapes. The innovation of this research lies in combining these two techniques to record dynamic responses and identify modal properties. To capture the temporal response history from video signals, the block-matching method with sub-pixel accuracy was employed. Validation was conducted by recording the response of the tip of a cantilevered steel beam subjected to impact excitation, using a high-speed camera and a laser vibrometer, simultaneously. The RMSE plots in the time domain and the PSD in the frequency domain indicate high accuracy of this method. Using this approach, the displacement time histories of various points on the structure were extracted from the video signals, and the modal properties, including natural frequencies, damping ratios, and mode shapes, were identified using the transmissibility matrix method. The results obtained from the proposed method were compared with the stochastic subspace identification (SSI) method and analytical solutions. The findings reveal the accuracy of the modal identification approach introduced in this article. The highest relative error in estimating the natural frequencies of the first and second modes, compared to the values from the laser method, are 0.19% and 0.13%, respectively, and in comparison to the analytical values, they are 0.34% and 1.5%, respectively.

Nowadays, with the increasing use of various discrete data acquisition methods such as drones and digital cameras, IMAGE PROCESSING has found wide application. However, video data alone cannot play a significant role in urban management decisions until they are transformed into statistical sequences. In this paper, a system for detecting the number of cars per unit length and time is presented. In this method, video data is converted into statistical sequences of traffic indicators. First, the IMAGEs corresponding to each frame are modeled into background IMAGEs based on the Gaussian mixture model, which are resistant to lighting changes. This operation is performed on a large number of frames to create a learned background IMAGE. In traditional traffic IMAGE PROCESSING methods, modeling the background IMAGE was not considered, and conversely, in the proposed method, this model is used to detect moving objects. Then, by comparing each input main frame with the learned background IMAGE, moving cars are detected. The information on the number of cars per unit length and time, which corresponds to the concepts of traffic volume and density, is used to estimate traffic flow. Based on the simulations performed and the comparison of the obtained results with other results from different studies, the high performance of the proposed method in car detection and accurate counting, considering proper background IMAGE training, is demonstrated. Moreover, this method can be used for PROCESSING low-quality IMAGEs.

The progress of science and using remote sensing technologies could help farmers to finds valuable information from field such as crop health, determining of the area and type of cultivation, calculating crop growth rate and various indices. Canopy cover percent is one of the vital parameters for modeling and prediction of yield production. Field observation methods of estimating CCP are expensive and time consuming. Using drones for arial imaging at field scale and IMAGE PROCESSING algorism to estimate CCP are fast and accurate. At this study, 441 arial photos was taken at height of 30 m above ground surface via DJI drone (Mavic 2 pro) for estimating maize CCP. The field was located at Alvand city-Qazvin province. Two different methods of segmentation and classification were used for assessing CCP. Region of interest separability test and linear regression between calculated data were used for result evaluation. Results showed that, although the accuracy of both methods was high, on average the segmentation methods obtained CCP 10 percent lower that classification algorism. Also, the high R-square coefficient of 97% between the data showed that the accuracy of methods based on IMAGE PROCESSING, such as segmentation, is lower than classification methods, but in case of lack of access to the required software, that are based on artificial intelligence methods, it is easy to achieve a favorable result by implementing programming codes based on segmentation methods in high-level and open-source languages, including Python.

Introduction: In sugar factories, control of sugar crystals growth in the granulation stages is very important to produce sugar grains with a special and required size. Machine vision systems can determine the size of sugar crystals. The main challenge of IMAGE PROCESSING systems is the lack of capable ALGORITHMS to separate contact and overlap crystals accurately. So far, various ALGORITHMS have been developed to detect crystals and remove their overlapping. However, these methods have not been able to fully detect and separate the overlap of crystals. The purpose of this study was to provide an appropriate IMAGE PROCESSING algorithm for determining the size of crystals in sugar baking solution (massecuite), which gives us the characteristics of size and shape for the particles in the baking pot instantly to evaluate and improve the quality of the final product.Materials and Methods: The massecuite samples were provided from Debal Khozaei Sugarcane Agro-industrial Company, Ahvaz, Iran. After preparation of the sugar crystals samples under lam and lamer (microscope slides), a digital camera with two Megapixel resolution, attached to a ZS9 Olympus microscope, was used for IMAGE capturing. Then, using MATLAB IMAGE PROCESSING toolbox, the color IMAGE (RGB) transferred to grey scale. A mixture of structural operations such as erosion and expansion with spatial filters including median filter were used to remove the IMAGE noises. The function of histogram local adjustment was used to improve IMAGE contrast. Three methods of segmentation including convexity, fuzzy clustering, and multiplicative intrinsic component optimization (MICO), along with their combination were used to segment the IMAGE of massecuite crystals. A reference IMAGE was used to determine accuracy of the IMAGE PROCESSING ALGORITHMS. To do this, the massecuite crystals IMAGE was manually segmented by IMAGE J software. All segmentation ALGORITHMS were applied on the reference IMAGE, and seven geometrical parameters, including the mean aperture (MA), coefficient of variation (CV), and standard deviation (SD) were calculated for all the sugar particles in the IMAGE. Finally, the percent of MA measurement error was calculated for each sugar crystal to find the best algorithm.Results and Discussion: In manual segmentation, the number of sugar crystals in the selected IMAGE was 26. In the manually segmented IMAGE, the average of MA, SD and CV for sugar grains in the IMAGE were 0.422 mm, 0.157 mm and 37.18% respectively. The relatively large CV of the calculated geometrical parameters indicated the non-uniformity of the sugar particles size inside the massecuite. The convexity method was able to perform well in some areas of the IMAGE, and in some other areas, it could not detect the contact between the crystals. The value of the SD and CV of all the geometric parameters determined by the convexity method were greater than the reference values determined by the manual segmentation. This indicates the weaker performance of this method in determining the sugar crystals size compared to the manual method. The values of SD and CV of all geometric parameters determined by the combined fuzzy-convexity method were greater than the reference values, but lower than the values of the convexity method alone. So, the combination of the fuzzy clustering method with the convexity method improved the segmentation performance of crystal IMAGEs. The SD and CV values of all geometric parameters determined by the combined MICO-convexity method were greater than the reference values, but lower than the values of convexity and fuzzy-convexity methods. This point shows the better performance of the combined MICO-convexity method in segmenting the IMAGEs of sugar crystals compared to the other two methods. The average of MA, SD and CV for sugar crystals in the IMAGE were 0.382 mm, 0.150 mm and 39.23% respectively and had no significant difference with the reference method values in 5% probability level. The mean error of MA determined by the combined MICO-convexity algorithm was 13.24% and Pearson correlation factor was 0.88. As a result, the combined MICO-convexity method was proposed to determine the size of sugar crystals in massecuite.Conclusion: After applying different ALGORITHMS on the selected IMAGE of sugar crystals in massecuite, it was found that the combined MICO-convexity method can separate sugar crystals well. Also, the CV obtained for this IMAGE segmentation algorithm was not much different from the CV of the manual reference method, so this algorithm can be used in the IMAGE PROCESSING system of the massecuite crystals.

This paper presents a new methodology for the automated inspection of pipes. Standard inspection systems are based on closed-circuit television cameras which are mounted on remotely controlled robots and connected to remote video recording devices. The main problems of such camera-based inspection systems are: 1) the lack of visibility in the interior of the pipes and 2) the poor quality of the obtained IMAGEs because of difficult lighting conditions. The focus of this research is the automated detection and location of defects in the internal surface of pipes.The proposed optical system is an assembly of a CCD camera and a laser diode to create a ring-shaped pattern. The camera obtains IMAGEs of the light projections on the pipe wall. A novel method for extracting and analyzing intensity variations in the obtained IMAGEs is described. The IMAGE data analysis is based on IMAGE PROCESSING ALGORITHMS. Finally, an IMAGE of the pipe wall is generated by extracting the intensity information existing in the pipe pictures. Defects and anomalies can be detected using this extracted IMAGE.

Mineral segmentation in thin sections based on IMAGE PROCESSING ALGORITHMS is one of the popular research topics geosciences. Rocks are the main information resource for geological studies, and mounting thin section from rocks is the most popular method for studying them. Mineral segmentation in thin sections is also the pre-step for further studying on thin sections such as mineral identification and measuring the size of minerals. In this paper, a new method for mineral segmentation based on IMAGE PROCESSING and clustering ALGORITHMS is proposed for mineral segmentation in thin sections. In order to segment minerals, using a polarizer microscope, two IMAGEs in plane and cross polarized lights are captured from each thin sections, and by extracting the color features from the IMAGEs, minerals inside each thin section are segmented. Therefore, initially, the color features including RGB and HSI components are extracted for each pixels for both IMAGEs, and then using IMAGE PROCESSING and clustering ALGORITHMS the pixels are clustered and each cluster is related to a segmented mineral. Experimental results indicate that the proposed algorithm produces accurate and reliable results, especially for those thin sections containing altered minerals. The proposed algorithm can be used in such applications as petroleum geology, mineralogy training and NASA mars exploration.

Knowledge on sunflower seed physical characteristics can play an important role in the proper procedures during harvesting, transport, drying, sorting, seed peeling and storage. In order to study the effect of removing leaf and seed in different plant densities on seed physical and chemical properties of sunflower hybrids, an experiment was conducted as a factorial split plot based on completely randomized block design with three replications in 2009 at Aboureihan Research Field of University of Tehran. Azargol and new Iranian hybrid SHF81-90 and three plant densities 60000, 80000 and 100000 plants/ha were main plots and five levels of change in source and sink size (removed 50% lower leaves, removed 50% upper leaves, removed 50% seeds, removed 25% seeds) and control (without removing leaf and seed) were sub plots. Photographs were taken from the seeds, by a digital camera, and analyzed using MATLAB programming language. Some morphological features were extracted by this software. Results indicated that the hybrids had significant difference in Perimeter and Area, Major and Minor Axis, Elongation, Compactness, Roundness, Solidity, seed weight and seed coat to seed ratio at 5% and 1% probability levels. Also the effects of plant density and source and sink manipulation were significant on all traits. Oil percent increased along with the plant density increase. Correlation between oil percent with seed weight (r=+0.53) and seed coat to seed ratio (r=-0.27) was significant at 1% probability level. Also the results of this study showed that using IMAGE PROCESSING technique, which is the new technique in agricultural researches, the physical properties of seed cultivars in different environmental conditions can be identified more accurately and this will be helpful in management of different planting, harvesting and postharvest stages.

A canopy cover percentage is considered one of the most important evaluation criteria for evaluating the ‎simultaneous effects of impressive factors on water efficiency. Since digital cameras are developed and ‎widely available, the use of discrimination indices in the visible spectrum is making it possible to calculate ‎the leaf area index and chlorophyll content of vegetation covers. Therefore, in this study, the ‎performance of five plant Vegetation Discrimination Indices (VIDs) and a variety of thresholding ‎ALGORITHMS was compared in order to distinguish the sugar beet's vegetation cover from its background, ‎among which two new indices were introduced. In comparison with the old VID of Excess Green minus ‎excess Red (ExGR), using the new VID of Excess Green minus excess Blue (ExGB) and Riddler-Calvard's ‎thresholding algorithm resulted in a 29.54 percent increase in vegetation cover segmentation accuracy. ‎Following this step, we determined which function would best predictdry beet weight from vegetation ‎cover percentage, and the power function did the best. In order to estimate the yield, the segmentation ‎method based on Riddler-Calvard thresholding and the New Canopy Index of Vegetation Extraction ‎‎(CIVEn) had an error of 12.09 Kg. With an error of 41.25 Kg, the segmentation method based on Otsu ‎thresholding and ExGR index performed worst.‎