Image matching is known as a vital process in digital photogrammetry. Despite development of many image matching Algorithms, this process still has some difficulties in close range photogrammetry, due to geometric changes which are made by changes in viewpoint. In this paper, an effective and robust image matching approach is presented for wide-baseline image matching. In the proposed method, in order to perform matching operation, after extracting blob-like features in base and input image, using SIFT detector, an elliptical region is constructed for each feature. In the following, in order to control the created geometric changes resulted from changes in the imaging perspective, parameters of this ellipse are calculated by using second moment matrix. In addition, descriptor for each feature is constructed by normalization of the respective ellipse to a circular region with a constant radius. Finally, by applying nearest neighbor method, matching process is done and mismatches are eliminated by implementing epipolar constraint based on RANSAC method. Test results on different close range image datasets, beside the increased accuracy rate of 3 to 8 percent, represents significant function of the proposed method since the results are two times higher than the results finalized using the standard SIFT method.

Moving target tracking is a process in which an object is tracked and its location is determined in each frame. The goal of this process is facilitating the subsequent process in order to analyze the behavior or detect moving objects. In this paper, a new approach has been proposed for aerial moving targets detection and tracking based on feature matching Algorithms. By this way, we propose spectral density for target detection and ASIFT feature matching Algorithm for tracking. The challenge is selecting features that are robust against the changes of brightness, noise, rotation, scaling and viewing angle. To solve this problem, key points and their correspondence on the patterns extracted from consecutive frames, are calculated by the ASIFT Algorithm. Also, to reduce false matches in consecutive frames, the RANSAC Algorithm is used. In addition to strengthening the proposed Algorithm against the scale change of target, the object history scale in the 10 previous frames is used. The proposed Algorithm was performed on an AIRCRAFT TRACKING standard database. Experimental results demonstrate the robustness and accuracy of our proposed technique.

Introduction: 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. Materials and Methods: 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. Results and discussion: 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%. Conclusion: 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 Scale Invariant Feature Transform (SIFT) Algorithm is one of the most widely used Algorithms in the machine vision field on which researchers have extensively studied and improved. SIFT is one of the common local detectors used in image registration, image mosaicking, copy-move image forgery, and etc. In this review paper, along with introducing the SIFT Algorithm, the applications, pros and cons, modifications, categories and new research approaches in this Algorithm are discussed. In addition, via four experiments, different aspects of this Algorithm have been evaluated. This paper can help image processing researchers when utilizing the SIFT Algorithm. The aim of the authors was to explore all the aspects of this Algorithm.

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.

RFID system is a wireless technology that can transfer data between tags and readers via radio frequency. In an RFID network, readers are located close to each other to obtain optimal connectivity and sufficient coverage. In such an environment, which is called dense reader environment (DRE).Different types of collisions such as Reader-to-Reader and Reader-to-Tag ones, often lead to serious problems such as decreasing the performance. Accordingly, providing an appropriate method to resolve reader collision appeared to be one of the most important research topics in the field. To solve this problem, different methods have been introduced of which NFRA protocol has higher throughput. In this paper, we use multi-channel technique and SIFT distribution function on the NFRA protocol to improve RFID system throughput while avoiding increase in reader collision. In addition of supporting mobile reader, the proposed method provides higher throughput compared to other protocols in dense environments.

Detecting and tracking of moving objects is an important task in analyzing videos. In this paper, we propose a new method for tracking several concurrent moving objects of fixed camera. In the proposed method, at each stage, the location of moving objects in front of camera view is obtained information between two current and previous frames. In each step, SIFT’s edge points is obtained based on previous frame and to get the correspondence of these feature points by the use of KLT feature point correspondence Algorithm on the current frame. Then having correspondent feature points between two sequence frames, we would estimate the distance by eliminating partial or fixed moving feature points related to moving objects. The classification of labeled features as moving objects is done using DBSCAN clustering Algorithm into different clusters. By this method and on each moment, the situation of all existing moving objects in camera view which has got by one by one correspondence between these objects, is determined. The obtained results of the proposed method shows a high degree of accuracy and acceptable consuming time to track moving objects.

With the growing Internet and digital imaging tools, the size of the image database is increasing rapidly. Therefore, there is a strong need for tools and methods to search for images in a large database. Feature extraction is the most basic step in creating an image-retrieval systems. This paper presents a new method for image retrieval systems. After extracting the feature and computing descriptors for each category by the SIFT Algorithm, then the appropriate descriptors are identified by the TF-IDF Algorithm and used clustering to find candidate descriptors for each category. In the next step, the descriptor coefficients of each category were used with regard to the representatives from the previous stage by the local coding Algorithm as the attribute. Finally we used Extreme Learning Machine (ELM) for classification. Experimental results show that the accuracy achieved in proposed method on the Caltech-101 database is about 98.5% and in Flowers data set is about 97.9%.