Papers by Seyed Mostafa Mousavi Kahaki
PLOS ONE, 2016
An invariant feature matching method is proposed as a spatially invariant feature matching approa... more An invariant feature matching method is proposed as a spatially invariant feature matching approach. Deformation effects, such as affine and homography, change the local information within the image and can result in ambiguous local information pertaining to image points. New method based on dissimilarity values, which measures the dissimilarity of the features through the path based on Eigenvector properties, is proposed. Evidence shows that existing matching techniques using similarity metrics-such as normalized cross-correlation, squared sum of intensity differences and correlation coefficient-are insufficient for achieving adequate results under different image deformations. Thus, new descriptor's similarity metrics based on normalized Eigenvector correlation and signal directional differences, which are robust under local variation of the image information, are proposed to establish an efficient feature matching technique. The method proposed in this study measures the dissimilarity in the signal frequency along the path between two features. Moreover, these dissimilarity values are accumulated in a 2D dissimilarity space, allowing accurate corresponding features to be extracted based on the cumulative space using a voting strategy. This method can be used in image registration applications, as it overcomes the limitations of the existing approaches. The output results demonstrate that the proposed technique outperforms the other methods when evaluated using a standard dataset, in terms of precision-recall and corner correspondence.
This paper proposes enhancement technique to introduce a robust descriptor for matching the verti... more This paper proposes enhancement technique to introduce a robust descriptor for matching the vertical lines between the two moving images in the context of omnidirectional images. The first step is to propose a new descriptor by using signal entropy to determine the number of circular areas for extracted lines. Then, to enhance the orientation histogram, standard deviation over the entropy value and number of circles is used to determine the number of bins in each area. Evaluation results demonstrates the robustness of the proposed descriptor in dynamic line matching (DLM) in omnidirectional images.
In this paper, a new deformation invariant image matching method, known as spatial orientation fe... more In this paper, a new deformation invariant image matching method, known as spatial orientation feature matching (SOFM), is presented. A new similarity value, which measures the similarity of the signal through the path based on triple-wise signal eigenvector correlation, is proposed. The proposed method extracts similarity feature values by relying on the distinct path between two specific interest points and following the alternation of the signal while traversing the path. Because these similarity values of the path are deformation invariant, the proposed method supports various types of transformation in the original image, such as scale, translation, rotation, intensity noises and occlusion. Moreover, the triple-wise similarity scores are accumulated in a 2-D similarity space; thus, robust matched correspondence points are obtained using cumulative similarity space. SOFM was compared to the most recent related methods using corner correspondence (CC) and precision-recall evaluation metrics. The findings confirmed that SOFM provides higher correspondence ratios, and the results indicate that it outperforms currently utilized methods in terms of accuracy and generalization.
Network security is a serious global concern. Usefulness Intrusion Detection Systems (IDS) are in... more Network security is a serious global concern. Usefulness Intrusion Detection Systems (IDS) are increasing incredibly in Information Security research using Soft computing techniques. In the previous researches having irrelevant and redundant features are recognized causes of increasing the processing speed of evaluating the known intrusive patterns. In addition, an efficient feature selection method eliminates dimension of data and reduce redundancy and ambiguity caused by none important attributes. Therefore, feature selection methods are well-known methods oovercome this problem. There are various approaches being utilized in intrusion detections, they are able to perform their method and relatively they are achieved with some improvements. This work is based on the enhancement of the highest Detection Rate (DR) algorithm which is Linear Genetic Programming (LGP) reducing the False Alarm Rate (FAR) incorporates with Bees Algorithm. Finally, Support Vector Machine (SVM) is one of the best candidate solutions to settle IDSs problems. In this study four sample dataset containing 4000 random records are excluded randomly from this dataset for training and testing purposes. Experimental results show that the LGP_BA method improves the accuracy and efficiency compared with the previous related research and the feature subcategory offered by LGP_BA gives a superior representation of data.
This paper proposes a fast algorithm for rotating images while preserving their quality. The new ... more This paper proposes a fast algorithm for rotating images while preserving their quality. The new approach rotates images based on vertical or horizontal lines in the original image and their rotated equation in the target image. The proposed method is a one-pass method that determines a based-line equation in the target image and extracts all corresponding pixels on the base-line. Floating-point multiplications are performed to calculate the base-line in the target image, and other line coordinates are calculated using integer addition or subtraction and logical justifications from the base-line pixel coordinates in the target image. To avoid a heterogeneous distance between rotated pixels in the target image, each line rotates to two adjacent lines. The proposed method yields good performance in terms of speed and quality according to the results of an analysis of the computation speed and accuracy.
Image corner detection is a fundamental task in computer vision. Many applications require reliab... more Image corner detection is a fundamental task in computer vision. Many applications require reliable detectors to accurately detect corner points, commonly achieved by using image contour information. The curvature definition is sensitive to local variation and edge aliasing, and available smoothing methods are not sufficient to address these problems properly. Hence, we propose Mean Projection Transform (MPT) as a corner classifier and parabolic fit approximation to form a robust detector. The first step is to extract corner candidates using MPT based on the integral properties of the local contours in both the horizontal and vertical directions. Then, an approximation of the parabolic fit is calculated to localize the candidate corner points. The proposed method presents fewer false-positive (FP) and false-negative (FN) points compared with recent standard corner detection techniques, especially in comparison with curvature scale space (CSS) methods. Moreover, a new evaluation metric, called accuracy of repeatability (AR), is introduced. AR combines repeatability and the localization error ( ) for finding the probability of correct detection in the target image. The output results exhibit better repeatability, localization, and AR for the detected points compared with the criteria in original and transformed images.
Traffic incident detection is one of the interesting fields of intelligent transportation system ... more Traffic incident detection is one of the interesting fields of intelligent transportation system (ITS) which recently rapidly increasing interest in their used. In this paper, we proposed an incident detection system based on incident features and reporting traffic incident in a special intersection using machine vision algorithms. The first step in this algorithm after image sequences acquisition from the video image of CCD camera is vehicle detection. Then the incident features such as direction of the moving vehicles, traffic flow and the rate of changing speed will extract in order to achieve the detection results. Machine vision based algorithm has been used in order to develop the system for incident detection goal. This process gives the best result by total 97.8% of correct rate, 1.02 of false alarm rate and 30 (S) is the meantime to detect. The result shows that this algorithm has a good detection rate.
One of the most important methods to solve traffic congestion is to detect the incident state of ... more One of the most important methods to solve traffic congestion is to detect the incident state of a roadway. This paper describes the development of a method for road traffic monitoring aimed at the acquisition and analysis of remote sensing imagery. We propose a strategy for road extraction, vehicle detection and incident detection from remote sensing imagery using techniques based on neural networks, Radon transform for angle detection and traffic-flow measurements. Traffic-bottleneck detection is another method that is proposed for recognizing incidents in both offline and real-time mode. Traffic flows and incidents are extracted from aerial images of bottleneck zones. The results show that the proposed approach has a reasonable detection performance compared to other methods. The best performance of the learning system was a detection rate of 87% and a false alarm rate of less than 18% on 45 aerial images of roadways. The performance of the traffic-bottleneck detection method had a detection rate of 87.5%.
Problem statement: As vehicle population increases, Intelligent Transportation Systems (ITS) beco... more Problem statement: As vehicle population increases, Intelligent Transportation Systems (ITS) become more significant and mandatory in today's overpopulated world. Vital problems in transportation such as mobility and safety of transportation are considered more, especially in metropolitans and highways. The main road traffic monitoring aims are: the acquisition and analysis of traffic figures, such as number of vehicles, incident detection and automatic driver warning systems are developed mainly for localization and safety purposes. Approach: The objective of this investigation was to propose a strategy for road extraction and incident detection using aerial images. Real time extraction and localization of roadways in an satellite image is an emerging research field which can applied to vision-based traffic controlling and unmanned air vehicles navigation. Results:
One of the most important methods to solve the traffic congestion is to detect the incident state... more One of the most important methods to solve the traffic congestion is to detect the incident state in a roadway. This paper describes the development of segmentation methods for road traffic monitoring aims at the acquisition and analysis remote sensing imagery of traffic figures, such as presence and number of vehicles, incident detection and automatic driver warning systems. We propose a strategy for road extraction, vehicle detection and incident detection from remote sensing imagery based on radon transform method. Real time extraction and localization of incident in aerial images is an emerging research area that can be applied to vision-based traffic controlling. The intensity imagery is used to extract the incident from satellite images. Techniques based on neural network, radon transform for angle detection and traffic flow measurements are used for road extraction, vehicle detection and incident detection. The results show that the proposed approach has a good detection performance. The maximum angle of vehicles applied for incident detection is 45˚ and the best performance of the learning system achieved by 87% for detection rate (DR) and a false alarm rate (FAR) under 18% on 45 aerial images of roadways.
3rd International Workshop on Intelligent Vehicle Controls and Intelligent Transportation Systems - IVC and ITS 2009 In Conjunction with ICINCO 2009; Milan; Italy, Jul 1, 2009
As vehicle population increases, ITS (Intelligent Transportation Systems) becomes more significan... more As vehicle population increases, ITS (Intelligent Transportation Systems) becomes more significant and mandatory in today's overpopulated world. Vital problems in transportation such as mobility and safety of transportation are considered more, especially in metropolitans and road ways. Road traffic monitoring aims at the acquisition and analysis of traffic figures, such as presence and numbers of vehicles, and automatic driver warning systems are developed mainly for localization and safety purposes. In this paper we propose a strategy for road following from aerial images. Real time extraction and localization of a road from an aerial image is an emerging research area that can be applied to vision-based traffic controlling and navigation of unmanned air vehicles. In order to deal with the high complexity of this type of images, we integrate detailed knowledge about roads using explicitly formulated scale dependent models. The intensity images are used for the extraction of road from aerial images. Threshold techniques, Hough transform and learning algorithm are used for the road extraction and car detection. The results show that the proposed approach has a good detection performance.
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Papers by Seyed Mostafa Mousavi Kahaki