امین صداقت

Reconstruction of 3D models and their use in photogrammetry and remote sensing has been considered as the most important and challenging topics in recent years. With the development of laser scanner technology and obtaining spatial data of the environment and objects, the use of this technology has increased nowadays. This technology extracts points from the external surfaces of the environment or objects in high volume, in a short time, which is called point cloud.
Due to laser scanners’ easy placement, point clouds are usually taken from different angles, so they define in the different coordinate systems, which must be unified to give a complete 3d view of the object. The process is considered as “registration”.
For this purpose, first, the corresponding pairs of points in each point cloud must be determined and then they must be matched correctly.after all a three-dimensional model is created.
Finding the best pair of corresponding points in the Point clouds as well as estimating the optimal error metric and the displacement between pairs of corresponding points is one of the most important and challenging steps of three-dimensional reconstruction.
Three-dimensional descriptors are one of the most suitable tools for determining the corresponding pairs of points in Point cloud. These descriptors create a set of information for every single point to determine the corresponding points in each Point cloud. Defining a three-dimensional descriptor whose computation complexity is low but its descriptive is high, can help to find the correct pair of points for 3d registration and modeling.

The main purpose of the present study is to define a strong three-dimensional descriptor to find the best corresponding pair of points to reconstruct the three-dimensional model.
The descriptor proposed in this study consists of two single local three-dimensional descriptors based on the spatial and geometric properties of the Point cloud, which combine to form a strong descriptor to determine corresponding points in the Point cloud.
Laser scanners extract a large volume of points from surfaces in a short period of time, which due to the reflection of laser beams, Point cloud may contain noise and mistakes. In the process of analyzing and using the data, these mistakes cause problems and should be removed in the pre-processing phase. To define the desired descriptor, in the pre-processing phase the Point cloud gets ready to extract the required properties.
The Statistical removal filter method is used to remove the noise and the voxel grid filter method is used to improve the speed of future preprocessing.
Each point in the neighborhood of Query Point provides a lot of that can be used to create the desired descriptor.
In the present study, by determining the appropriate neighborhood radius and Nearest Neighbor Search (NNS) method, using the k-dimensional tree, correct and efficient neighborhoods are determined for each point.
In the first step, a spatial descriptor is formed for each point. This descriptor is defined in the form of a histogram based on two distances for the point in its neighborhood. In the second step, the angles of the normal vectors of the Point cloud in different states are used to create a descriptor based on geometric information. In this research, two features called and have been used, which for each descriptor is formed in the form of a histogram. Then the spatial descriptor is combined with each of the descriptors based on the geometric feature and forms two desired descriptors.
To ensure the accuracy of the matching process based on the proposed descriptor, by assigning a suitable threshold for the basis of the distance between the Query point and its neighborhood, with the corresponding point of the Query point and its neighborhood in the second Point Cloud, incorrect correspondences are detected and removed. Next, the remained correct corresponding pairs of points are used to reconstruct the three-dimensional model.

In this research, two sets of Point cloud have been used to evaluate the proposed process. These two data sets are obtained in such a way that in the first data set the perspective and angle of view and in the second data set the position and arrangement of objects are changed.
By forming descriptors based on spatial and geometric features in different neighborhood radii and then forming a proposed combination descriptor based on what has been mentioned, it can be considered that combining the geometric descriptors with spatial descriptors, in cases where The two datasets have less relative overlap or more relative rotation than each other, in contrast to the position shift, leading to improved descriptor performance and increased matching accuracy.
Considering the results obtained from the comparison of the proposed descriptors, it can be said that because of the existence of two different radii in each part of descriptors based on spatial and geometric relations in the proposed descriptors, it turns out that the required descriptor is high quality.
On the other hand, the properties used in these descriptors are also resistant to changing the position of objects and have high efficiency in mentioned category. Also, the process of identifying and eliminating incorrect correspondences improves the matching process and increases the matching percentage of similar points up to 25% in the study data set.

The results of comparing the set of Point Cloud studied using the proposed descriptor indicate that this descriptor is more efficient in cases where two data sets rotate relative to each other, compared to cases where the location of the data pair has changed relative to each other. And the accuracy of the comparison obtained from the proposed method, in this case, increases compared to other data pair placement modes.

Image registration is the technique of aligning two or more images with a reference image all acquired at the same geographical area with different acquisition dates, viewpoints, and imaging sensors. This technique is a fundamental process in photogrammetry and remote sensing tasks, and change detection is known as one of its important applications. Urban change detection with an arbitrary time difference between the two images faces limited access to the required datasets. The Google Earth software is known as an appropriate resource to achieve such datasets. Nevertheless, a significant limitation of this software is the co-registration error of the images acquired at different times. In this work, a combined image registration approach has been proposed to cope with this problem. The proposed method is made up of three major steps. In the first step, the KAZE algorithm is applied to extract and describe image features with high stability against illumination distortions. In the second step, the image matching and mismatch elimination procedures are conducted using the VFC (Vector Field Consensus) technique. Finally, in the third step, the geometric relation between the two images is established using the TPS (Thin-Plate Spline) adaptive transformation model. The results derived from the KAZE feature extraction and matching algorithm are compared with those of the SIFT and SURF algorithms in four satellite image pairs related to various urban areas. For datasets 1 to 4, the highest correct correspondence rates of 0.63, 0.81, 0.6, and 0.76 were obtained using the KAZE algorithm compared to the other algorithms. Moreover, the TPS transformation model established the most accurate and reliable geometric relation between the two images with RMSE values of 2.1, 1.8, 2.1, and 1.7 pixels for datasets 1 to 4, respectively. Accordingly, the results indicate the efficacy of the proposed integrated method for robust feature extraction and image registration of the Google Earth imagery with significant brightness and landscape differences.

Wide-baseline image matching with significant viewpoint differences plays a fundamental role in many computer vision and photogrammetry applications, such as 3D reconstruction and image registration. One of the main problems of matching these images is the existence of a relatively large number of mismatches. Generally, a geometric consistency check process based on various geometrical constraints and robust estimator methods such as the epipolar line and RANSAC algorithm is used for mismatch elimination. However, conventional geometry filtering methods in wide-baseline images will fail if the number of outliers is very high. In addition, these methods have high computational complexity. In this paper, a novel mismatch elimination approach in wide-baseline images with significant viewpoint differences is presented. First, initial elliptical features are extracted using improved MSER (maximally stable extremal regions) detector in both images. Then, a distinctive DAISY descriptor is generated for each extracted feature. In the next step, the initial feature correspondence process is established using Euclidean distance between feature descriptors. Then, a novel mismatch elimination approach based on features shape matrix, named MESM (mismatch elimination based on shape matrix), is applied. Finally, the few remained blunders are removed by using a geometric constraint. The proposed image matching and mismatch elimination algorithms were successfully applied to match eight close-range image pairs with significant viewpoint differences, and the results demonstrate its capability to improve matching performance.

Image matching is a critical process in various photogrammetry, computer vision and remote sensing applications such as image registration, 3D model reconstruction, change detection, image fusion, pattern recognition, autonomous navigation, and digital elevation model (DEM) generation and orientation. The primary goal of the image matching process is to establish the correspondence between two images of the same scene (i.e., the reference and input images). Image matching methods are generally classified as feature-based matching and template matching.
Feature-based methods extract image features (points, lines, regions) and attempt to establish the correspondence between these features. Template matching methods, also known as area-based methods, are generally defined as the process of finding a template in an image, based on a similarity measure such as cross-correlation and mutual information. Identical image windows of predefined size are applied for the computation of correspondence. Similarity measures play an essential role in the quality of template matching in photogrammetry, remote sensing, and computer vision. Various similarity measures have been proposed in the literature. Each similarity measure has its strengths and weaknesses.
In this paper, the capability of some well-known similarity measures for matching of various close range and satellite images with diverse geometric and radiometric differences are evaluated. Also, to increase the template matching stability against geometric and radiometric variations, a novel weighting approach for computing of similarity measures has been introduced. The proposed approach is based on three weight factor that are computed using gradient and Gaussian functions. By applying this weighting approach for cross correlation similarity measure, a novel measure named Weighted Cross-Correlation (WCC) has been presented.
Ten algorithms, including SSD (Sum of Squared Differences), LSSSD (Locally Scaled Sum of Squared Differences), NSSD (Normalized Sum of Squared Differences), JF (Jeffrey Divergence), Tanimoto, ISD (Incremental Sign Distance), IRV (Intensity-Ratio Variance), CC (Cross-Correlation), MI (Mutual Information) and WCC are considered for evaluation.
To evaluate the capability of various similarity measures, a number of template-matching experiments were applied. Several synthetic and real images for different geometric and radiometric variations including, scale, rotation, viewpoint, blur, and illumination changes are used as data set. The similarity measures are evaluated using three evaluation criteria, including success rate, positional accuracy, and computation time. The experimental results indicate that the proposed WCC method outperforms the other similarity measures for all images and all types of transformations. Based on the evaluation results, the WCC method can be applied to the reliable template matching for a variety of photogrammetric and remote sensing applications. Generally, after the WCC, better results, on average, were obtained by the NSSD, LSSSD, and CC measures in most cases. For illumination variations, MI and ISD methods provide the best results. The fastest method is the IRV and the slowest method is MI. Evaluation of the performance of the various similarity measures for other applications such as dense matching process is suggested as future work.

Image registration aims to establish precise geometrically alignment between two images of the same scene taken at different times, different viewpoints, or different sensors. It is an essential task in diverse remote sensing and photogrammetry processes such as change detection, 3D modeling, information fusion, geometric correction, and bundle block adjustment. Nowadays, feature based approaches are generally used for satellite image registration due to their resistant against geometric and radiometric variations. A feature based image registration method comprises three main steps: (1) control point (tie-point) extraction, (2) transformation model computation, and (3) image resampling. In the first step, some distinctive conjugate features are automatically extracted using various image matching approaches. In the second step, a suitable transformation model between two images is computed using the extracted control points from the previous step. In the third step, the input image is rectified to the geometry of the base image using the computed transformation model.
Transformation models generate the spatial relation between the images and play a critical role in the positional accuracy of the image registration process. Various transformation models have been proposed for remote sensing image registration. The transformation models are generally divided into two types: (a) global models and (b) adaptive models. The global models have a constant number of parameters and describe the global spatial relations between two images. In contrast, the number of parameters in adaptive models are not constant and varies with the severity of the geometric variations and the number of control points. Each transformation model has its strengths and drawbacks. In this paper the capability of some popular transformation models, including similarity, projective, polynomials of degrees 1 to 4, piecewise linear (PL), Multiquaderic (MQ) and Pointwise (PW) are evaluated for high resolution stereo satellite imagery.
To extract high accurate and well distributed control points, an integrated image matching process based on FAST (Features from Accelerated Segment Test) detector, SIFT (Scale Invariant Feature Transform) descriptor and the least square matching algorithm has been proposed. In this method, the initial point features are efficiently extracted using FAST algorithm in a gridding strategy. Then, the well-known SIFT descriptors are computed for extracted features. The control points are determined using feature descriptor comparison in two images, and their positional accuracy are improved using least square matching method.
Two evaluation criteria, including computation speed and positional accuracy are used to investigate the capability of the transformation models. To investigate the effect of the feature density in quality of the transformation models, the extracted control points are divided into four classes with different number and distribution of the control points. The experimental results using two high resolution remote sensing image pairs from ZY3 and IKONOS sensors, show that the adaptive MQ model provides the best results, followed by PW and PL models. In contrast, the similarity, projective and global polynomial models do not provide acceptable results for accurate registration in high resolution remote sensing images. However, the commutation time in adaptive models especially in MQ is very high. The registration accuracy of the proposed approach for the MQ model is 2 and 1.9 pixel, for the ZY3 and IKONOS images respectively

Reliable image matching is a vital step in many photogrammetric processes. Most image matching methods are based on the local feature algorithms because of their robustness to significant geometric and radiometric differences. A local feature is generally defined as a distinct structure with properties differing from its immediate neighbourhood. Generally, local-feature-based image matching methods consist of three main steps, including feature detection, feature description and feature correspondence. In the feature detection step, distinctive structures are extracted from images. In the feature description step, extracted features are represented with descriptors to characterize them. Finally in the correspondence step, the extracted features from two images are matched using particular similarity measures.
n this paper, an automatic image matching approach based on the affine invariant features is proposed for wide-baseline images with significant viewpoint differences. The proposed approach consists of three main steps. In the first step, well-known Hessian-affine feature detector is used to extract local affine invariant features in the image pair. In Hessian-affine detector a multi-scale representation and an iterative affine shape adaption are used to deal with significant viewpoint differences including large scale changes. To improve the Hessian-affine detector capability, an advanced strategy based on the well-known UR-SIFT (uniform robust scale invariant feature transform) algorithm is applied to extract effective, robust, reliable, and uniformly distributed elliptical local features. For this purpose, a selection strategy based on the stability and distinctiveness constraints is used in the full distribution of the location and the scale.
In the second step, a distinctive descriptor based on MROGH (Multisupport Region Order-Based Gradient Histogram) method, which is robust to significant geometrical distortions, is generated for each extracted feature. The main idea of the MROGH method is to pool rotation invariant local features based on intensity orders. Instead of assigning a main orientation to each feature, a locally rotation invariant schema is used. For this purpose a rotation invariant coordinate system is used to compute the pixels gradient. To compute descriptor, the pixels in the feature region are partitioned into several groups based on their intensity orders. Then, a specific histogram based on the pixels gradient magnitude and orientation is calculated for each group. Finally, the MROGH descriptor is generated by combining the values of all the gradient histogram from all groups into a single feature vector.
Finally, feature correspondence and blunder detection process is performed using epipolar geometry based on fundamental matrix. The initial matched features that are not consistent with the estimated fundamental matrix are identified as false matches and eliminated. A distance threshold TE = 1 pixel between each feature point and its epipolar line is considered as elimination condition. The experimental results using six close-range images show that the proposed method improves the matching performance compared with several state-of-the-art methods, including the MSER-SIFT, UR-SIFT and A-SIFT, in terms of the number of correct matched features, recall and positional accuracy. Based on the matching results, the proposed integrated method can be easily applied to a variety of photogrammetric and computer vision applications such as relative orientation, bundle adjustment, structure from motion and simultaneous localization and mapping (SLAM).

Drugs and Psychotropic feasts to cause psychological damage among patients addicted been so many different aspects of their mental health is a serious risk. Religious psychotherapy approaches can improve public health and social vitality is effective.
This study aimed to increase the effectiveness of services psychotherapy for mental health patients taking the drug religious centers, addiction treatment and harm reduction is Bvshhrmy University of Medical Sciences.
For this purpose, between patients taking the drug addiction treatment centers affiliated to Bushehr University of Medical Sciences, 60 patients randomly assigned to experimental and control groups with an equal volume of psychotherapy interventions religious test on the experimental group for 8 sessions 60 experts led by psychologist minutes for the control group did not receive any services. In order to assess the subject's mental state before applying the necessary interventions ghq28 test and diagnostic interviews were conducted by a psychologist. The tools used in this research, mental health and a demographic questionnaire.
The analysis of data based on the analysis of covariance (MANCOVA) were analyzed. The results showed that mental health services increased religious therapy among patients taking the drug. It also increases the effectiveness of services psychotherapy for mental health and religion somatic, anxiety, social dysfunction and depression among patients.
The mental health therapist religious and spiritual approach can be effective in reducing psychological problems patients seeking drug treatment and mental health is improving.

A descriptor is computed on a local region around a feature point and is used to characterize and compare the features. Various descriptors have been proposed in the literature which have different properties and performance in different image data. Evaluation of the local feature descriptor is important to identify the strengths and weaknesses of each algorithm in different applications. In this paper a performance evaluation of the state of the art in local descriptors is performed on a set of satellite images under varying imaging conditions. Ten descriptors are included, which are spin image (SI), shape context (SC), SIFT, PIIFD, SURF, DAISY, LSS, LBP, LIOP and BRISK. 80 satellite image pairs in three groups including simulated images, multi-temporal, and multi sensor images are used as data set and descriptors are evaluated using four evaluation criteria including Recall, Precision, positional accuracy and speed. The evaluation results indicate that there does not exist one descriptor which outperforms the other descriptor for all scene types and all types of transformations, but in average DAISY and SIFT show the best performance.

Image matching is an essential task in digital photogrammetry. The fully aoutomatic matching of convergent close range image is a difficult task due to possible significant geometric distortions in these images. In this paper an efficient and fully aoutomatic matching method for convergent close range images is presented. The proposed method consists of three main steps. In first, the initial feature extraction and matching is performed using ASIFT (Affine-SIFT) algorithm. In the second step the epipolar geometry based on fundamental matrix is estimated using genetic algoritm to eliminate most of outliers in initial match features. Due to the defect of epipolar geometry to remove all outliers, in third step of proposed method a new approach to remove reminding outlier from previous step is proposed. In this method cluetering process is performed by k-Means algorithm and followed by a consistency check in the local affine transformation model for each cluster. The experiment results using both synthetic data and various sets of convergent close-range images show that the proposed algorithm provides reliabile and acuurate matching.

The automatic change detection in urban environment using multitemporal high resolution satellite images is one of the fundamental processes in photogrammetry and remote sensing. Conventional approaches to automated building change detection relied to multitemporal images comparison based on multispectral classification methods. These approaches require accurate alignment between the two images to detect changes. Accurate registration of high resolution satellite images is a difficult process due to local distortion in these images due to relife displatement. This paper introduces a multi temporal image processing approach towards an efficient and automated detection of urban changes. The proposed method is based on local features obtained from sequential images including corners, blobs and regions. In the first step, local features are extracted in each of the images using three well-known feature extractor operator incuding phase congruency, UR-SIFT method and MSER algorithm. Then, feature matching process in a gridding structure is performed between two feature sets using SIFT descriptor. This process followd by a new mismatch elimination method based on distance ratio. In the next step, a similarity image based on a new measure is estimated between multitemporal images. In final, using an automatic threshold changed regions are determined. The method has been evaluated by using QuickBird and World Wiew image for the area of the city of Sun francisco, USA. Experimental results prove the capabilities of the proposed change detection algorithm with appropriate accuracy in multitemporal high resolution satellite images.

In this paper an efficient method for camouflage assessment in remote sensing images is presented. The proposed method consists of two main parts including template matching algorithm and UR-SIFT (Uniform Robust Scale Invariant Feature Transform) feature extraction algorithm. In the first part, the distinctiveness between target and background based on template matching algorithm is computed and using a Gaussian function a quantitative measure is presented for description of target camouflage. In the second part, the detectability rate of the target is investigated based on UR-SIFT algorithm which is an improved version of well-known SIFT algorithm that has been developed for feature extraction in remote sensing images. If the target would not be extracted using UR-SIFT algorithm then, it can be concluded that target are concealed in the background. Four satellite images included a specific object in four camouflage status are considered to assessment of proposed algorithm. Experimental result indicates the good performance of proposed method in camouflage assessment in remote sensing images.

Image matching is the process of finding correspondences between two or more images of the same scene taken at different times, from different viewpoints, and/or by different sensors. Extracting welldistributed, reliable and precisely aligned point pairs for accurate image registration is a difficult task, especially for multi-source remote sensing images that have significant illumination, rotation and scene differences. SIFT approach, as a well-known feature based image matching algorithm, has been successfully applied in a number of automatic registration of remote sensing images. Regardless of its distinctiveness and robustness, SIFT algorithm suffers from several problems in the fields of quality, quantity and distribution of extracted features especially in multi-source remote sensing imageries. In this paper, an improved SIFT algorithm is introduced that is fully automated and applicable to various kinds of optical remote sensing images. The main key of the proposed approach is a selection strategy of SIFT features in the full distribution of location and scale where the feature qualities are quarantined based on the stability and distinctiveness constraints. Then the extracted features are introduced to an initial cross-matching process followed by a consistency check in the projective transformation model. Comprehensive evaluations of efficiency, distribution quality and positional accuracy of the extracted point pairs prove the capabilities of the proposed matching algorithm on a variety of optical remote sensing images.

The objective of this paper is to introduce a new method for coastlines delineation fromhigh-resolution satellite images automatically. The proposed approach integrates the colorhistogram analysis for estimating the number of clusters, fuzzy c-partition, and geneticalgorithms for optimizing fuzzy c-partition matrix. The imagery is classified into homogenous areas using the proposed fuzzy logic segmentation approach. Morphological filtering is then used to remove noises from the segmented image. The coastlines are finally extracted from the filtered image by a delineation algorithm. Thedeveloped approach is evaluated based on a 1 m resolution IKONOS pansharpened imagery of Konarak region in Chabahar, Iran. It is demonstrated that 95% of the extracted lines remain within the three-pixels buffer areas around the coastlines. This presents the effectiveness of the proposed algorithm.

Image registration is a fundamental technique in image processing which plays a main and critical role in photogrammetry and remote sensing. The SIFT (Scale Invariant Feature Transform) algorithm is among the most powerful matching approaches which has recently been applied successfully in fully automatic remote sensing images registration. But the feature detector in SIFT algorithm still have basic weaknesses. High computation time, and low controllability of SIFT detector are among the main challenges with this algorithm in remote sensing image registration. In this paper a new approach for remote sensing image registration is designed to reduce the computation time and increase the efficiency of standard SIFT algorithm. In this approach the SIFT detector is replaced withHarris corners in pyramidal structure. Using a specific selection strategy for the best corner features, based on stability and distinctiveness constraints, is the main characteristic of this approach which is performed in full distribution of location and scale. After feature extraction and description, initial matching process is done employing a dual-strategy using Euclidean distance in multilevel form. In the next step, based on projective transformation model and the least squares method, accuracy of initial conjugates are evaluated and then the removal of inconsistent conjugate pairs is performed.Registration approach is completed with a piecewise transformation between the reference and input images. Experimental results for four multisensor and multidate remote sensing images proved the high efficiency improvement in comparison to standard SIFT method.