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Zagros forests are one of the important and strategic areas of the country. Forest inventories in areas like Zagros is expensive and time-consuming due to the difficult access and low density of trees. Laser scanning methods to prepare point clouds and produce maps provide valuable and significant information in forest management. With the release of the LiDAR short-range light detection sensor in 2020 by Apple, it became possible to use alternative scanning methods. In the present study, in order to check the accuracy of iPhone LiDAR in preparing point clouds of tree trunks, 37 Iranian oak trees were selected and specific longitudinal and transverse distances were marked on their trunks. The high accuracy of the point clouds for these distances resulted in a high correlation between the generated point clouds and the real distances. The RMSE% was obtained for longitudinal distances of 1.33 cm and for transverse distances of 2.2 cm respectively, which proves the high accuracy of the mentioned technology and its high capability in extracting quantitative components of tree trunks.

UAV-based images are now widely used to generate large-scale orthophoto mosaics. The generation of an orthophoto mosaic is divided into two stages: image registration and image stitching. Using the DSM associated with each image, orthophotos are generated throughout the image registration. The single orthophotos generated are then joined to each other step by step in the second stage, utilizing various methods of image stitching. Image stitching methods depend on the complex and challenging processes of image matching and seamline determination. In addition, the registration and stitching of images in UAV-based mapping projects with a significant number of images is a time-consuming process. In this study, a straightforward method is provided for creating large-scale true orthophoto mosaics from UAV-based images without the requirement to generate single orthophotos, image registration and seamline network determination. Instead of registrating the images and then stitching them step-by-step, this method processes the DSM of the entire area and all of the images simultaneously. First, for each DSM point, the optimal image is determined from among all the visible images based on an optimization procedure. Optimization is based on two criteria: distance from nadir point and distance from the projection center. Using the determined optimal images, the differential rectification procedure is then run, and the orthophoto mosaic cells are filled. The results of this investigation demonstrated that the proposed method yielded a mosaic with minimal changes along the seamline. In addition, the proposed method is compared with the conventional orthophoto mosaic production method, which is based on image matching and determination of seamlines. Evaluations indicate that the proposed method is able to increase the production rate of orthophoto mosaic by 39% and 45% in dataset 1 and 2 respectively. Additionally, the geometric accuracy calculated using the checkpoints in the orthophoto mosaic generated by the suggested method has decreased by an average of 2 cm, indicating more precise results.

In order to establish a system for managing road pavement, it is mandatory to prepare information components based on various perspectives of pavement management. One of the most important information components in these systems is quality assessment regarding road pavement status. Apart from causing vehicle depreciation and damage, maintenance costs, and reducing the useful lifespan of the pavement structure, road pavement failures also lead to accidents and reduce road safety. Bearing in mind that the most important surface damages in road pavement are related to cracks with longitudinal, transverse, oblique, alligator and block types, and as such cracks and defect can be visually and non-destructively assessed and evaluated, imaging-based approaches and techniques can provide details such as the type of defect, its severity, extent, and location and prove to be highly useful. UAV has been proposed as a complementary approach aimed at providing information on defects caused by cracks in the country road pavement management system. According to the author, the output of UAV products will significantly improve if the system parameters are adjusted. Consequently, through presenting a procedure to investigate the optimal parameters in the design of a UAV network, further, attempts were made for the implementation of an automated algorithm based on image processing operations & classifier decision tree which is independent of scale and image dimensions. Hence, after removing the road edges and determining the asphalt area, a pixel detection operation was carried out to reveal the cracks. An accuracy of 96% was determined for the orthophotomosaic.

Micro-Aerial Vehicles (MAVs) are ideal platforms for indoor and outdoor applications because to their small size and light weight [1, 2]. Obstacles, on the other hand, may cause MAVs to crash. Cameras collect a lot of evidence about their surroundings. Through the use of grayscale values [3], point features [4], and edge details [5], vision-based techniques detect obstacles. They are divided into monocular and stereo types. There are four types of monocular approaches: appearance-based [6], motion-based [7], depth-based [8], and expansion-based [4]. Expansion-based techniques are based on the same principle as human vision. The majority of expansion-based systems identify obstacles by recognizing object points [4, 5, 9-11]. However, relying solely on points may not be sufficient; as a result, the MAV may collide with unseen impediments. To overcome this obstacle, we describe a novel technique based on the same concept but employing region-enlarging rates.

Various steps of our obstacle detection technique above can be summarised in (a) data acquisition and preparation, (b) region extraction and their area calculation, and (c) obstacle detection.

 We took four pairs of images with an LG 360 CAM fisheye camera, two with the camera moving forward and two with the camera moving to the sides. In the forward direction, recall accuracy is 82% for the first data and 52% for the second data. The new technique detects only a portion of the obstacle region. This problem emerges because some regions lack at least three matching points. While moving to the right, recall accuracy for the third and fourth data is 69% and 39%, respectively. This accuracy is lower in the fourth data set than in the other data sets due to the aforementioned description, the absence of at least three corresponding points, and the possibility of inaccurate corresponding points, particularly along the fisheye image's edges, which have a low quality in these places.

 The proposed method extracts regions of close obstacles from outdoor fisheye images. The findings demonstrate the method's efficacy in a variety of complex environments. Thus, on average, 60% of the obstacles are detected in two modes of forward movement and right movement. Additionally, a comparison of the suggested method to that of Al-Kaff et al. (2017) [4] demonstrates that it is more efficient than the existing algorithm.The proposed algorithm, however, has some gaps in terms of obstacle detection.One of these limitations is that certain regions lack at least three corresponding points. Also, the presence of incorrect corresponding points causes incorrect detection of obstacles. The second limitation is that the obstacle is the same color as the background, which leads to errors in the correct detection of obstacles. The third constraint is the long processing time required by the suggested approach. These constraints can be solved in the future with the use of more accurate and faster algorithms.

In recent years, the use of unmanned aerial vehicles (UAVs) that has been introduced to UAVs has been widely used in surveying engineering under the name of UAV photogrammetry. Different urban and non-urban scales on different scales provide the conditions for examining and evaluating the geometric accuracy of these high-resolution spatial images in the production and delivery of large-scale maps and coverage scales. One of the most important geometric parameters in UAV images is the determination of the spatial resolution, which is known as the criterion for detecting the smallest distance between two adjacent objects that can be distinguished in the images. There are several ways to accurately measure the spatial resolution of images; in this study, the Siemens star was used as one of the most widely used artificial targets in measuring spatial segregation. The purpose of this paper is to provide an automatic method for detecting the radius of ambiguity and calculating the spatial resolution limit in images taken from UAVs. The results of this study showed that firstly, according to the flight altitude and the amount of blurring of the image, the Siemens Star Target should be used with appropriate dimensions and number of arms, and secondly, the rate of reduction of image resolution in the tested drones was between 1.2 and 3.7.

For complete 3D modeling of a desired area, using by terrestrial laser scanner point cloud, it is necessary moving the set and increase the occupation points to measure the occlusions. But it takes more time and money for field measurements and in result will be increased time and calculations cost. Thus, the initial planning for selecting the optimal locations for the device in order to complete 3D model is essential and the computing field and office costs in a reasonable period decreased. In this paper, particle swarm optimization algorithm to achieve this objective has been used. In the proposed method, an approximate model of the scan region needs for the candidate deployment positions, and makes the algorithm’s search space. Each particle is set of the selected candidate points and a set of particles is considered as a groups. Cost function was considered with two goals, a reduction in occlusions and pick a least possible number of selected points. Algorithms starts with a set of multiple random selection of points, as an initial response and moving particles in the search space, during successive iterations, the algorithm answers locating the optimal laser scanner. In this process, the optimal choice system is automatic and repetitive and ensure proper alignment with the minimum number of points required for a complete measurement of region is achieved. The results show that the particle swarm optimization algorithm in a large number of the candidate can optimize the laser scanner selected points for the establishment.

Terrestrial Laser Scanner (TLS) technology, have altered quickly data acquisition for map production in surveying. In many cases, it is impossible to complete surveying of the desired area without TLS displacement in one station to another. Occlusion is innate in data acquisition, with this type of device. To solve this problem, TLS devices should be placed in different locations and scanning operation to be performed. Increase the number of scan stations cause data redundancy and on the other hand will be increases the computational and monetary cost of project. Aim of this paper is presents a novel method for selecting a better place and localization of TLS. Thus, the mechanism of data acquisition was considered by TLS. Also parameters affecting the choice of a place and a station were investigated. Point cloud data investigations show that these parameters have a major impact in reducing the time needed to completeness of data collection. Occlusion as one of the most important parameters has been discussed in this paper. Using data from one station with combination of image processing method, the area of hidden parts to be estimated. Due to the size and area of the various occlusion set, determines to be appropriate locations for new data collection station. After evaluating candidate points according to given criteria, conditional on the project, the next point will be selected for acquisition. By continuous using this method, reduce a ground operation volume in this type of projects. Reducing time of land surveying in many field projects is very important. While ensuring the quality of data and decreased project costs is essential.

Blood parasites including Theileria, Babesia and Anaplasma are causative agents for theileriosis, babesiosis and anaplasmosis, respectively, which are responsible for tremendous economic loss to sheep farming industry worldwide. These parasites are mainly transmitted by hard ticks and carrier sheep are responsible for resistance of parasites in affected regions. Dezful city, south-west of Iran, has warm and humid climate and parasitic diseases including Haemoparasites seems to be prevalent among herds in this area. The purpose of this study was to evaluate prevalence of blood parasites in sheep of Dezful area using direct microscopy and PCR methods. For this reason, in August 2016, in a cluster sampling, a total of 200 blood samples from sheep of 4 regions of Dezful were collected randomly. Blood films were prepared and evaluated by microscopy. PCR was performed on 112 randomly selected samples. Prevalence of Babesia, Theileria and Anaplasma was 1.5%, 26% and 33% using microscopy, respectively while 72.72% and 0% of sheep were PCR positive for Theileria spp. and Babesia spp., respectively. No other blood parasite was observed in microscopy. The results of this study indicate high prevalence of Theileria and Anaplasma and low prevalence of Babesia among sheep of Dezful area. Regarding the results of this study, measures such as promoting knowledge of breeders regarding parasitic diseases of livestock, and in particular blood parasites and raising awareness of economic loss from these diseases through decrement in livestock production and increment in the costs of treatment, insecticide spraying of livestock places and bathing of animals against ecto-parasites are necessary for reduction of this parasitic infection.

Auto-navigating of unmanned aerial vehicles (UAV) in the outdoor environment is performed by using the Global positioning system (GPS) receiver. The power of the GPS signal on the earth surface is very low. This can affect the performance of GPS receivers in the environments contaminated with the other source of radio frequency interference (RFI). GPS jamming and spoofing are the most serious and intentional RFI attacks. Due to the jamming attacks, the positioning accuracy of UAV will be degraded. This positioning error can be reached to tens of kilometers off the true location as was reported in some research articles. The detection of GPS jamming is the first step to mitigate this attack. Most of jamming detection methods are based on GPS signals processing. If a jamming detection system just relies on GPS signal processing to detect and confront RFI threats, it will be capable of failure in the use of the more advanced hardware and sophisticated methods by invaders intentional. The camera is a common sensor almost in all of UAVs which is a passive sensor and resistant to the jamming signals. Here, the use of image-based navigation methods for GPS jamming detection will be helpful due to the insensitivity of camera sensors to jamming signals. GPS jamming attacks can be detected in UAV navigation, independently of the signal processing methods, using a comparison of two flight trajectories assigned for a UAV, determined from visual navigation and GPS positioning data. For this purpose, the trajectory descriptor of the Normalized Distance of the Consecutive Points (NDCP) and trajectory descriptor of the Consecutive Directions Angles (CDA) is used. These descriptors are independent of the coordinate system of the trajectory but are not independent of the number of trajectory points. As a result of the jamming attacks, the GPS receiver may not be able to receive the signal and the positioning cannot be performed. Therefore, two trajectories of UAV from GPS and visual navigation may have a different number of points. So, the NDCP and CDA cannot be used for these trajectories in all the time. The trajectory descriptor of Histogram of the Oriented Displacements (HOD) is independent of the number of trajectory points, but it is not independent of the coordinate system of the trajectory. In this paper, a method is developed to allow the use of the HOD trajectory descriptor to detect the occurrence of the GPS jamming attacks. For this purpose, first, the coordinate system of the UAV trajectory from GPS data is transformed to the coordinate system of the UAV trajectory from visual navigation. Here, a sliding window-based approach is used for determining of the jamming location. The performance of the HOD trajectory descriptor in detecting jamming attacks versus the NDCP and CDA trajectory descriptors were compared. The results show that the HOD trajectory descriptor has a significant advantage in detecting the jamming attacks concerning NDCP and CDA trajectory descriptors, especially in the positioning errors of more than ten meters due to jamming attacks, which can be more reliable. This descriptor can be used to detect the occurrence of the jamming attacks. The ability of the HOD trajectory descriptor is significant in detecting positioning errors greater than five meters, compared to the other two trajectory descriptors.

 

In the past few decades, urban environments have expanded much larger than before. One of the most important problems in most metropolises and even small cities is the management of the transportation system. An advanced monitoring system of urban vehicles allows for overcoming the traffic problems. With the development of unmanned aerial vehicles (UAVs), continuous and accurate monitoring of urban environments has been provided for the users. In this research, an efficient method is presented that detects the vehicle in the UAV images. The proposed method is effective in terms of computational speed and accuracy.  

   The foundation of the proposed method is based on the characteristics of the local features in the UAV images. The presented method consists of two main stages of training classification model and detecting vehicles. In the first part, local features are extracted and described by the SIFT algorithm. The SIFT algorithm is one of the most powerful algorithms for extracting and describing local features that are used in various photogrammetry and machine vision applications. This algorithm is robust to geometric and radiometric changes of the images. Due to the high dimensions of extracted features from all the training samples, the BOVW (Bag of Visual Word) model has been applied. This model is used to reduce the dimensions of the features and display the images. Simple and efficient computing is one of the significant features of the BOVW model. At this stage, after producing a library of features, the SVM classification model is trained. In the detection part of the algorithm, the images are entered into the algorithm and the local features are extracted in all images by the SIFT algorithm. The BOVW model is often used to display an image patch. In most researches, this model is implemented by applying a search window to the whole image. This type of methods has a higher confidence level in detection, but it is a very time consuming process and increases the volume of the computations. For this purpose, the approach of points clustering and their representation by the BOVW model is proposed. In this method, features that are within a certain range are considered as a cluster. Euclidean distance is used in image space for clustering. Then, the clusters produced by the BOVW model are displayed. Then, a feature vector is constructed for each cluster. The trained SVM is applied to each of the production vectors and each cluster is classified as a vehicle and non-vehicle. If the cluster is detected as a car, the position of the center of that cluster is marked on the image.  

   The proposed method was implemented on 8 images with a number of different car targets. Also, considering the use of the search window approach in many researches, our results were compared with the results obtained by other researchers. The results show that the calculation time of the proposed method is 82 seconds, while the search window method takes 2496 seconds to run. In order to verify the accuracy of the algorithm, two criteria were used. The first criterion is the “Producer's accuracy”, which represents the proportion of correct detections of the vehicle to the entire vehicles existing in the images. This criterion is 75.79% for the proposed method. The second criterion is the “User's accuracy”. This criterion is obtained by dividing the correctly detected samples into the sum of the correctly and incorrectly detected samples. The User's accuracy criterion has been reported to be 59.50%.  

The value of the Producer's accuracy criterion is greater for the search window method which has led to a more accurate detection of vehicles compared to our method.  This is due to the small moving steps of the search window in the entire image. However, the search window method has increased the amount of the time spent on the calculations. The User's accuracy criterion shows that the proposed method has less incorrect detections. The results indicate that our method has a higher degree of reliability. The average of these two criteria indicates the superiority of the proposed method in terms of the accuracy of the calculations. On the other hand, the proposed algorithm has a great advantage in terms of computational speed compared to the search window method.

The relative position and orientation between two cameras in a stereo pair are included within the Essential matrix, E. The decomposition of this matrix into a rotation matrix, R, and a skew-symmetric matrix, S, is an efficient tool for retrieving the relative position and orientation of the cameras. In this paper, a new method is proposed to recover the relative position and orientation of the cameras in a stereo pair using the singular value decomposition (SVD) of the Essential matrix. First, the existing formulas in the decomposition of the Essential matrix into a rotation matrix and a skew-symmetric matrix using the SVD decomposition are directly proved using the SVD properties. Then, based on these results, a new method in the decomposition of the Essential matrix using SVD will be presented. The Essential matrix decomposition in this method is accomplished by extracting the base vector of the left null space of the Essential matrix and then by SVD decomposition of the skew-symmetric matrix corresponding to this base vector. In this method, the initial mapping of the Essential matrix, recovered from the erroneous coordinates of the corresponding image points in two images, into the space of Essential matrices does not require. This mapping is performed by determining the skew-symmetric matrix, S. The proposed numerical analysis shows that the results of the new presented method are correct and identical with the results of the existing formulas.

The Unmanned Aerial Vehicles (UAV) positioning in the outdoor environment is usually done by the Global Positioning System (GPS). Due to the low power of the GPS signal at the earth surface, its performance disrupted in the contaminated environments with the jamming attacks. The UAV positioning and its accuracy using GPS will be degraded in the jamming attacks. A positioning error about tens of kilometers off the true location was reported in the research articles, due to jamming. Assessing the performance accuracy of UAV’s GPS receiver during the jamming attacks has great importance in maintaining the UAV’s flight safety, especially in the military applications. Currently, the developed and presented methods in jamming detection and mitigation relies on the analysis of the GPS signals. On the other side, many study works are being performed for effective and successful jamming attacks for security, military and intelligence purposes. To detect the occurrence of errors in the GPS positioning, therefore, it is necessary to research and introduce some new independent techniques of the GPS signal processing methods. The presented method in this paper to detect the occurrence of the jamming attacks and to assess the UAV's GPS performance is an independent method from the GPS signals processing. This method is an image-based approach which uses the UAV images taken from its flight path. As the camera is a passive sensor, its performance will not be affected by the external signals and electronic warfare such as jamming attacks. The UAV's flight trajectory is extracted using visual odometry (VO) from the UAV images, and then this trajectory is compared with the UAV's trajectory derived from the corresponding GPS data. In this method, there is no need for the initialization of the coordinate system parameters of VO as it is a relative method in agent positioning. Also, to reduce the drift error in VO only a few images within a selection window is used in the computation of the relative and absolute trajectories of UAV. For reduction of drift-error of visual odometry within this selection window, sliding widow bundle adjustment technique is used here. This process repeated for each UAV position from the begin to the end of the UAV trajectory. For each selection window, two trajectory descriptors correspond to the UAV trajectory from VO and its replica from GPS data are computed.  Then these trajectories compared using their trajectory descriptors. Two trajectory descriptors were defined here to compare these trajectories: The Normalized Distance of Consecutive Points (NDCP) trajectory descriptor and the Consecutive Directions Angle (CDA) trajectory descriptor. These trajectory descriptors are the functions of the UAV’s positions in its flight path within each selection window. The comparison of these trajectory descriptors is done using the cosine similarity index (CSI). CSI takes into account the normalized angle between two codimension normalized vectors to compare them. If there is a problem in the UAV's GPS performance, the cosine similarity index will differ from its ideal number one or 100%. The results show that the proposed method, using these trajectory descriptors, can detect the occurrence of jamming attacks and to assess the accuracy of the UAV's GPS performance during its flight.

Electronic tourism is one of the newest types of tourism. The virtual tour is part of the e-tourism, which aims to eliminate temporal and spatial constraints. Panoramic photos are one of the main components for making virtual tours. Nowadays, given the fact that technology has made a lot of progress, there are few panoramic photos from different regions, and usually special people create panoramic photos for the particular usage. On the other hand, given the high quality of photos by advanced technology, these photos have created the potential for producing panoramic photos. Panoramic photos can create by combining multiple images together by matching algorithms. Each of these algorithms has a different accuracy in different situations, and most of these methods are not evaluated for panoramic photos. The purpose of this paper is to investigate various methods and algorithms for categorization of these methods and to find the appropriate method for the preparation of panoramas in tourism. In this regard, how these algorithms function in different situations between images such as transition, rotation and scaling is investigated in order to get the panoramic photos of the same region and choose the best method of evaluation. For this purpose, different matching methods were first investigated. Then, four algorithms were evaluated on panoramic photographs in imposed by different conditions such as fish eye distortion, salt and pepper noise and various scaling. The results show that the SURF algorithm in most situations obtained better results in less time compare to the other three algorithms.

Although close range photogrammetry has not been a common approach in the Civil Engineering field especially the displacement monitoring of large scale constructions, many project successfully used this approach and as a result this method have a high potential in this application. Nowadays, as a result of improving the computer vision and photogrammetry techniques implemented in many software and high resolution images captured by the off-the-shelf digital cameras, the use of progressive photogrammetric techniques instead of traditional displacement methods become more resendable. This paper aims to explain a novel method for monitoring of the large scale constructions based on visual inspection. This method can specify the displacement of the constructions based on photogrammetric and computer vision methods using drones. The evaluation of the proposed system is done on long wall in two epochs with a short time interval. Having known the zero displacement for this construction, the difference between the coordinates of some sample points obtained in first epoch and the second epoch, 1.89 mm, shows the accuracy of the proposed system in detecting displacement. Moreover, the accuracy of this photogrammetric method was investigated by developing a tool which manually provides a known displacement between two points in the second epoch. The displacements of these points were estimated using the proposed method and compared with the known displacement. The accuracy for this method, less than 2 mm, can confirm the capability of this method for such applications.

Nowadays, automatic detection and recognition of traffic signs is one of the interest topics to researchers. Researches rely on the automatic extraction of the signs with the desirable speed and accuracy. The algorithms are developed in such a way that the system can detect and recognize traffic signs in a highly complex urban environment. A lot of research has been done on this issue in recent decades, but with this extensive research, this topic is still attractive to researchers for future research. The reason is the existence of some challenges that reduce the accuracy of existing algorithms. These are intensive changes in light condition, shadow, the existence of obstacles, scale and orientation changes, the existence of a complex field, the speed of the algorithm, and the cheapness of the system.
Generally, the process of detecting and identifying traffic signs is performed in three separate sections, consisting of image segmentation, detection and recognition of signs. In each of these processing steps, the above challenges may cause the identification process to be difficult. For example, in the segmentation step, the intense light changes of the imaging environment affect the processing results. Also, in detection step, the presence of obstacle against the signs in the accrued images cased Detection of the shape of the sign is difficult. Hence, in this paper, an overview of the existing methods for each of the three steps of detection of traffic signs and also their challenges are discussed.

Vehicle detection is a significant step for many applications such as automatic monitoring of vehicles, traffic control, transportation, etc. In these systems, a fixed camera is usually installed in some places such as highway, street and parking. Images captured by the camera firstly will be processed, and then the vehicles in it will be identified. Generally, in these systems, cars are the favourite’s parts in a video frame. Therefore, providing methods that can increase overall accuracy and also the efficiency in vehicle detection is very important. Several methods have been proposed by researchers to detect cars in images. Frame subtraction is one of the most known in which every change between the current frame and the background image is regarded as a moving car. Since car shadow is identified as variable pixels, so there is a connection between the shadow of a car and that car, which causes two problems in these systems: Firstly, the actual shape and appearance of the car can disappear because of this conjunction. As a result, it is difficult to detect the location of the vehicle and to segment the image based on cars. Secondly, the shadow of one or more cars may interfere, and all of them are identified as a vehicle. These items cause many problems in monitoring systems and traffic control systems such as car counting, or vehicle location estimates and behaviour analysis. So shadows need to be identified. To solve this problem, recently, Karami has developed a method based on the region growing to remove the shadow of the vehicle. First of all, we should find the seed point, and then, based on the Euclidean distance, we will consider the distance from the seed point to each pixel of the image in the feature space as a weight. We will use these weights to separate the shadow of the car. The main problems about that are the complexity and computational speed, low precision and also a strong dependency on grey-grade changes. Therefore, in this research, we want to solve the above problems by improving the method of Background subtraction by weighting the pixels of the image by combining several texture features to remove the shadows of vehicles. To do this, each pixel in the background image and the current frame is weighted based on a combination of textural features. This makes the shadows and the background (asphalt) pixels to have very close values and thus removed in subtraction. The proposed method is evaluated on four datasets based on OA, HR, FAR, MODP and MOTP criteria.
Using these criteria, the proposed method is compared and evaluated with region growing method, median and averaging methods, and several other methods in shadow detection.
However, in general, according to the results, the proposed method outperforms than other algorithms by 2 to 15 percent improvement in mentioned criteria. To continue and complete the research in the field of vehicles detection and remove of shadows recommended:1.Using several different data, which are obtained in different situations and at different times.
2.2. Using other non-statistical colour and texture features.
3.Detection of vehicles based on geometric structure.

One of the ways to overcome human disabilities is the use of artificial parts, for which modeling plays an important role. This process is usually is done manually and, thus, is time consuming and error prone. Human’s body lacks suitable texture. Thus, the modeling technique should be one that does not require such information. Shape From Silhouette is a technique of such which does not require texture for matching, thus is adopted in this study. The problem with SFS is that it requires many images to form a complete detailed model. This in turn needs several camera positions, the availability of which is both expensive and difficult. To overcome this problem, in this paper it is suggested to use videos as an alternative source. As using video leads to difficulty in orientation of images, coded targets are used to simplify and automate the process. Experiments carried out to test the proposed system, suggest that not only the accuracy of measurements is achieved but also the process is simple and practical enough to be used in practical projects.

Terrestrial Laser Scanner (TLS) acquired 3D information¡ Intensity image and color image around settlement point simultaneously. The output of this device can be categorized intwo groups: 2D images and 3D point cloud. Physical and geometric properties of discontinuities and effects¡ as well as explain the position of the laser scanner and camera effects cause changes in the amount of reflected energy¡ lighting and explain the depth of the point cloud and the image is recorded. Such as machine vision¡ properties of 2D images and 3D point cloud are complementary in the field of surveying¡ can be combined and used these to understanding and objects detection. In this paper¡ we combined the image processing techniques in 2D and 3D data¡ for occlusion extraction. The point cloud and images recorded in K.N.T University was used for this purpose. Canny algorithm for edge detection in combination with Range Border Detection was used. This method has a high ability to find hidden areas¡ as one of the main problems is point cloud data. In the data sample that used¡ obtained 65 region¡ with a total area of 4719 square meters in the 8000 square meters scan area.

Producing a complete large scale environment model which enables access to geometric as well as visual information is required by most organizations. In this regard، the use of stereo panoramas is an image based solution which has attracted the developers and users of city maps. This is due to the fact that stereo panoramas offering visual and complete view of surrounding simply. There exist a number of techniques which are used to develop stereo panoramas and each structure has its own advantages and disadvantages. This paper investigates the possibility of using off-the-shelf stereo cameras in production of stereo panoramic models from environment. In this regard، the possibility of using stereo cameras in developing a stereo panorama system is studied and different issues to produce an appropriate 3D model are investigated. Finally، the accuracy of using these types of cameras in geometric measurement through generated stereo panorama model is evaluated.

Surveying has great improvements in data collection techniques in last decade. one of these techniques is laser scanner. With that method we can collect 3D data automatically. Investigating of the error sources in TLS measurements is rather complicated due to a large number of influencing factors that are quite interrelated. Thus calibration is an important issue in these devices. Several models have been proposed to improve the accuracy of the laser scanners data until now. Each of these models includes some physically parameters and some empirically parameters which have been produced by observation of residuals diagram، in this paper a parametric model based on the internal structure of laser scanner is presented for calibrating these devices. This model compared with another models shows that due to having just physical parameters and not empirical parameters it can be used for a variety of TLS instruments.

There exist a number of factors that affect the quality laser scanner. In other words, the accuracy of a terrestrial scanner is limited extensively by systematic errors and thus must be calibrated. Indeed, calibration is a prerequisite for obtaining 3D precise and reliable data from point clouds. Until now, several models have been proposed to improve the accuracy of laser scanner data, most of which include both physical empirical parameters which are produced by observing point residuals, As a result, these models are just usable solely for those observations. The authors of have previously developed a new general parametric model based on the internal structure of laser scanner which can be used for a variety of TLS instruments. Due to of the importance of stability of parameters in a model, stability of them and the correlation between them needs to be investigated precisely, a task which is addressed thoroughly in this paper through a number of practical experiments. The results show that this model with a relative stability can improve the accuracy of TLS data.

Today, a complete large scale city map which enables access to geometric as well as visual information is required by most organizations. There exist a number of problems such as moving objects and occlusions which affect the development of such maps. In this regard, the use of stereo panoramas is an image based solution which has attracted the developers and users of city maps. This is due to the fact that stereo panoramas besides offering a visual and complete view of surrounding static and moving are simple to create. There exist a number of techniques which are used to develop stereo panoramas. This paper, reviews these techniques from the projection as well as the geometrical stand points. The review includes systems offered for image acquisition, stereo panorama generation, geometric measurement and representation, and their related problems and offers some issues for future researches in this area.

Background and The use of intraoral direct digital radiography has been possible only the past two decades. Controlled laboratory and clinical studies are needed to determine whether the new digital system changes the diagnosis، treatment and prognosis than the conventional methods. Purpose of this study is compare the diagnostic accuracy for the detection of approximal caries of three CCD-based digital systems. seventy eight surfaces in thirty nine extract unrestored posterior teeth were radiographed under standard condition using three CCD systems، Planmecadixi 2 image receptor with Dimaxis 2. 4. 1 (Helsinki، Finland) software، Progeny dental image receptor with Cygnus media 3. 0 (USA) software and RVGai Trophy image receptor with Trophy windows access (Eastman Kodak، France). The images were assessed by three observers. True caries existence and depth determined by histological examination. Diagnostic accuracy of the systems was validated by using Kappa agreement coefficient. for approximal carries، no significant differences were found in diagnostic accuracy between three CCD systems the diagnostic accuracy of digital CCD systems is comparable with each other. The existence and depth of the lesion had no significant effect on the performance of the imaging modality. Planmeca performed consistently better than others and Progeny performed consistently better than Trophy، but the differences were not significant.