Image forgery, the manipulation of an image to hide some meaningful or helpful information, is widely used to manage the large amount of information being exchanged in the form of images. There are different forms of image forgery, and copy--move forgery is the most common form of it. The copy-move forgery is easy to perform yet challenging to detect due to the similarity between the original part of the image and the copied part. In this paper, we employ a Keypoint descriptor inspired by the human visual system, namely the FREAK (Fast Retina Keypoint) descriptor, for robust copy-move forgery Detection. This method uses the advantages of FREAK descriptor such as fast computing and low memory load compared to SIFT, SURF, and BRISK. Finally, geometric transformation parameters are extracted and discussed. Results confirm promising results in the case of image post-processing operations such as adding noise, illumination change, and geometric transformations such as rotation and scaling.

Purpose: The process of Magnetic Resonance Imaging (MRI) image registration is one of the important branches in MRI image analysis, which is a necessary pre-processing to use the information in these images. The purpose of this paper is to present a new approach for MRI image registration that can maintain the total number of initial matches and have the highest precision. Materials and Methods: The Clustered Redundant Keypoint Elimination Method-Scale Invariant Feature Transform (CRKEM-SIFT) algorithm has recently been introduced to eliminate redundancies and upgrade the correspondence precision. The disadvantages of this algorithm include the high execution time and the number of incorrect correspondences. In this paper, to increase the accuracy and speed of MRI image registration, the CRKEM method is first used over the Speeded Up Robust Features (SURF) algorithm. Then, Spatial Relations Correspondence (SRC) and Alpha-Trimmed Spatial Relations Correspondence (ATSRC) methods are suggested to improve correspondences. These suggested methods, unlike conventional methods such as Random Sample Consensus (RANSAC(, which only eliminates incorrect correspondences, detect incorrect correspondences based on spatial relationships and turn them into correct correspondences. Converting incorrect correspondences to correct ones can increase the number of correct correspondences and ultimately increase the precision of correspondences. Results: The simulation results show that the suggested CRKEMSURF-ATSRC approach improves the mean by 28. 92% in terms of precision and 37. 58% in SITMMC compared to those of the SIFT-ARANSAC method. Conclusion: The suggested SRC and ATSRC methods use the spatial relations of the initial correspondences to convert the incorrect correspondences into correct ones. The number of initial correspondences is maintained in these suggested approaches. These methods are better than other methods of improving correspondences such as RANSAC, and Graph Transformation Matching (GTM). These suggested methods can be used as a new and efficient approach to improve the correspondence of medical images.

Image mosaicing refers to stitching two or more images which have overlapping regions to a larger and more comprehensive image. Scale Invariant Feature transform (SIFT) is one of the most commonly used detectors previously used in image mosaicing. The defects of SIFT algorithm are the large number of redundant Keypoints and high execution time due to the high dimensions of classical SIFT descriptor, that reduces the efficiency of this algorithm. In this paper, to solve these problems a new four-step approach for image mosaicing is proposed. At first, the Keypoints of both reference and sensed images are extracted based on Redundant Keypoint Elimination-SIFT (RKEM-SIFT) algorithm to improve the mosaicing process. Then, to increase the speed of the algorithm, the 64-D SIFT descriptor for Keypoints description is used. Afterwards, the proposed RANdom SAmple Consensus (RANSAC) algorithm is used for removing mismatches. Finally, a new method for image blending is proposed. The details of the proposed steps are as follows. RKEM-SIFT algorithm has been proposed in [1] to eliminate redundant points based on redundancy index. In this paper, RKEM algorithm is used to extract Keypoints to improve the accuracy of image mosaicing. In the second stage, for each Keypoint of the image, 64-D SIFT descriptor is computed. In this descriptor, unlike the 128-D SIFT descriptor, a smaller window is used which improves the accuracy of matching and reduces the running time. In the third stage, the proposed adaptive RANSAC algorithm is suggested to determine the adaptive threshold in the RANSAC algorithm to remove the mismatches and to improve the image mosaicing. Determining the appropriate threshold value in RANSAC is so important, because if an appropriate value is not chosen for this algorithm, the mismatches are not removed, and eventually there will be a serious impact on the outcome of the image mosaicing process. In this method, the threshold value is based on the median value of distances between matching points and their transformed model. Image blending in the mosaicing process is the final step which blends the pixels intensity in the overlapped region to avoid seams. The proposed method of blending is to combine the images based on the Gaussian weighting function, which the mean of this function is considered as the average of the data in the overlapped region of two images. The proposed blending method reduces artifacts in the image for better performance of the mosaicing process. Another advantage of this proposed method is the possibility to combine more than two images that are suitable for creating panoramic images. The simulation results of the proposed image mosaicing technique, which includes the RKEM-SIFT algorithm as feature detector, 64-D SIFT descriptor, proposed adaptive RANSAC algorithm and proposed image blending algorithm on different image databases show the superiority of the proposed method according to RMSE criteria, precision and running time.

Automatic topic Detection seems unavoidable in social media analysis due to big text data which their users generate. Clustering-based methods are one of the most important and up-to-date categories in topic Detection. The goal of this research is to have a wide study on this category. Therefore, this paper aims to study the main components of clustering-based-topic-Detection, which are embedding methods, distance metrics, and clustering algorithms. Transfer learning and consequently pretrained language models and word embeddings have been considered in recent years. Regarding the importance of embedding methods, the efficiency of five new embedding methods, from earlier to recent ones, are compared in this paper. To conduct our study, two commonly used distance metrics, in addition to five important clustering algorithms in the field of topic Detection, are implemented by the authors. As COVID-19 has turned into a hot trending topic on social networks in recent years, a dataset including one-month tweets collected with COVID-19-related hashtags is used for this study. More than 7500 experiments are performed to determine tunable parameters. Then all combinations of embedding methods, distance metrics and clustering algorithms (50 combinations) are evaluated using Silhouette metric. Results show that T5 strongly outperforms other embedding methods, cosine distance is weakly better than other distance metrics, and DBSCAN is superior to other clustering algorithms.

Farsi font Detection is considered as the first stage in the Farsi optical character recognition (FOCR) of scanned printed texts. To this aim, this paper proposes an improved version of the speeded-up robust features (SURF) algorithm, as the feature detector in the font recognition process. The SURF algorithm suffers from creation of several redundant features during the Detection phase. Thus, the presented version employs the redundant Keypoint elimination method (RKEM) to enhance the matching performance of the SURF by reducing unnecessary Keypoints. Although the performance of the RKEM is acceptable in this task, it exploits a fixed experimental threshold value which has a detrimental impact on the results. In this paper, an Adaptive RKEM is proposed for the SURF algorithm which considers image type and distortion, when adjusting the threshold value. Then, this improved version is applied to recognize Farsi fonts in texts. To do this, the proposed Adaptive RKEM-SURF detects the Keypoints and then SURF is used as the descriptor for the features. Finally, the matching process is done using the nearest neighbor distance ratio. The proposed approach is compared with recently published algorithms for FOCR to confirm its superiority. This method has the capability to be generalized to other languages such as Arabic and English.

INTRODUCTIONExtraction and processing of various features with the help of aerial imagery reduces the time and financial costs associated with the use of ground mapping and the resulting human error. Advances in the field of aerial sensors in terms of spatial and spectral resolution with precise place and performance picking up altitude from the ground have led to the use of each part of information about terrestrial phenomena such as spectral and spatial characteristics Brought. Today, complementary data used to detect complications are Lidar data, the sensor of which is sent and received, and the electromagnetic spectrum in the near-infrared spectrum (in its aerial form) and joined the spectrum. Pays close infrared and green band (in space type). DATA AND METHODSLidar data and spectral images were analyzed using different types of algorithms effective in landfill extraction to assess density. New layers of images were obtained in the form of raster from the study area, which was analyzed by performing slope extraction steps on flat and sloping surfaces. Buildings that were definitely not buildings were removed. The size and spectral characteristics of the missing structures were identified and the parcels were redistributed to extract the impermeable surfaces. Which led to the achievement of two levels of parcels and impenetrable points. The data set is related to the northern part of Bandar Anzali, which includes a vertical aerial photograph, irregular cloud points of the region with dense one to two points per square meter with an average point space of 0.69 square meters, and vertical aerial photograph with spatial resolution. It is 8 cm square. RESULTS AND DISCUSSIONIn this study, a different method for extracting buildings using airborne Lidar data and ultracam images was presented. The proposed system used geometric and spatial information of Lidar data and ultracam images, which included three general steps, in the first step; Lidar data were filtered and extracted using spectral clustering of buildings. In the second step; The obtained model was compared with the two-dimensional boundaries of buildings by the height threshold method. In the third step; After extraction, the first building boundaries were merged with the structures extracted by the checker algorithm. In the stage of separating terrestrial from non-terrestrial points, all points related to land were classified and extracted. The remaining points were classified as roof points, which were dealt with in the fault section of the buildings. All the functions used enabled the system to successfully extract the structures from the Lidar data. CONCLUSIONThe data for the first return points were subtracted from the data for the last return points and a fixed value was obtained which depended on the altitude accuracy of the difference between the two returns. In addition to the mentioned method, the clustering method was used during the research that each cluster belonged to a roof section so that the characteristics of each surface model could be easily determined.Then, to complete the shape of the roof, the footprint of the building that was extracted was used. In fact, the borderlines and inner vertices extracted only part of the shape of the border. Other sections, such as vertical edges, were not detected due to intersection. This is due to the lack of front sampling. Finally, the items extracted through spectral clustering in eCoginition software and two-dimensional boundaries extracted from ENVI Lidar software, to increase the accuracy of land surface extraction (buildings) and make the area of ​​buildings studied in this data Were merged. As mentioned; After extraction, the primary building boundaries were merged with the structures extracted by the checker algorithm. In the section of buildings diagnostics, buildings with errors were discussed and the evaluation of the results showed that the system used has relatively reached the set goals and the methods used include the threshold method. Elevation, clustering, spectral method, and integration method were evaluated for each of the four blocks with error rates of 28%, 15%, and 0%, respectively, based on the area of ​​extracted tolls to the study area. The error of each building was first examined in general and then in detail, and it was found that aerial Lidar technology has an extraordinary ability to collect very right and dense samples of altitude measurements of cities and a new level of detail information can be Accurately extracted building density automatically and efficiently from aerial Lidar data. In 417 buildings that were surveyed and analyzed, the height of the buildings was achieved with high accuracy and all the buildings in the study area were extracted with a compact and organic density as well as scattered and planned.

The nuchal translucency (NT) Detection and thickness measurement is a milestone in the prediction of the abnormalities in addition to chromosomal disorders in a fetus in ultrasound imagery. Nuchal translucency is an accumulation of fluids just at bottom of the foetal neck which is closely associated with chromosome abnormalities with cardiac arrest within the pregnancy period of the first trimester. At the hospital, the sonographers manually estimate the thickness of the mid-sagittal plane of nuchal translucency, which is a significant marker for prenatal screening. Such a conventional process done by a technician is quite time-consuming and requires a skilled technician. Within this methodology, an automatic NT Detection method based on SIFT Keypoint and GRNN is proposed in the mid-sagittal plane. This Non-invasive approach is crucial not just for the assessment of NT, as well as for the Detection of extreme deformities and the identification of high-risk pregnancies. The proposed method is tested on a large image dataset which shows that the proposed technique has better accuracy than well-known state of the art methods. The proposed SIFT and GRNN based method have an error of 0.02 which is very less compared to the SVM, ANN, NB and KNN.