image processing

Detection of dangerous objects in images obtained by X-ray scanners in security inspections has played an important role in protecting the public space from security threats such as terrorism and the occurrence of dangerous crimes. Perform diagnostic operations by an expert despite the remarkable features of the human sensory and visual systems; Due to being exhausting, non-stop, excessive dependence on human error, etc., it has low operational value. One suitable solution for similar situations is to use car vision systems. In this study, we intend to first examine the hazardous object in the x-ray images in the SIX-ray database in a training phase with hard segmentation, and by extracting the properties of these objects by the SURF algorithm, which is capable of extracting properties even in complex conditions. It is confusing to create a database of properties of objects in different dimensions and angles. Then, in the detection phase, the experimental image first goes through a soft segmentation step, and then the image properties are extracted by the SURF algorithm. The extracted properties are matched with the properties of the object in the training database, and then the probability of the object being present, which is the ratio of the number of matching properties of the object to the total number of properties in the object, is calculated for each case. be. After finding the most likely valid matches, the M-estimator sample consensus algorithm (MSAC) removes the incorrect matching properties that originated from the image background. Finally, a two-dimensional transfer (Affine transformation) is obtained between the pairs of matching points of each valid state with the input image, and with the help of this transfer and dimensionality, a square is drawn around the object and the location of the object is identified. The following is a complete description of the training and diagnosis phase and the results of SIX-ray data.

Unmanned aerial vehicles (UAVs) search dangerous or difficult places for humans and collect various environmental data. Human face recognition in drones is essential for various applications, such as surveillance, search, and security. Previous methods for face recognition are highly sensitive to limitations such as height, angle and distance from the face. Therefore, locating and identifying faces at high altitudes and long distances reduces the accuracy and efficiency of previous methods. In this paper, a new deep leanring-based face detection and recognition is presented. The proposed method is performed in three steps. In the first step, input images are segmented with the selective search algorithm. In the second step, a deep network is proposed for filtering bounding boxs to identify the target boxes with high accuracy and speed. In fact, a two-class classification problem is performed by deep learning forfacelocalization. In the third step, the localized faces are usedto perform face recognition using the proposed deep network. In the proposed architecture, the properties of widely used deep networks are used, and a quantitative comparison of the proposed method with new methods in terms of computational complexity shows that training the proposed model requires less runing time than other methods. In addition, the evaluation of the proposed method on the DroneFace dataset for different distance and height from the target shows thatthe proposed method has an average face detection rate of 75.9 and an average face recognition rate of 84.6. Therefore, the proposed method has higher accuracy and efficiency than state-of-the-art methods in this field and can be used for surveillance and security applications.

Visual tracking of microscopic objects is one of the most important studies of dynamic biological processes and requires automated segmentation and tracking methods. It is often limited to the morphology of objects or human study and lacks the automation and scalability to detect objects, track the path of any object, and examine their topology with the detection of related anomalies. This paper presents a fast scalable agent-oriented method for automatic detection, real-time video tracking, simultaneous tracking of microscopic objects, monitoring object behavior, and their topology based on graph theory applicable to the Internet of Things. It has no mentioned restrictions. Its segmentation method is a combination of temporal and spatial changes of the image to detect moving objects and predict their movement path, and the possibility of detecting individual anomalies of the object (death, moving a stop, collision of objects, a sudden departure from and a sudden entry into processing frame). Provides abrupt onset and onset of anomalies (network splitting, batch changes, batch decomposition, batch spacing, attenuation, and network collapse). The results of experimental experiments to track microscopic objects of sperm and birds in 2D images of 3D video film show that it has 99% sensitivity and 97% accuracy of instantaneous detection of objects with 99% detection accuracy. In monitoring and tracking, correlation and collision of sperm objects have an accuracy of 99.8% and in birds due to environmental noise and error detection in rapid topology changes, birds have an accuracy of 88%.

After the occurrence of natural disasters such as earthquakes, the engineers' first action is the initial safety assessment and determination of the performance grade of the structures. Existing methods, including eye examinations, are prone to many errors. These types of methods, depend on the level of knowledge, experience, and judgment of individuals. Therefore, attempts have been made to use methods to quantify fault detection. In each of these methods, it is necessary to use an indicator to measure failures. Another point to keep in mind is that new computer science methods have made it possible to use image processing tools to measure breakdown indices. In this study, after examining the types of shear wall failure, the crack width criterion has been introduced as an indicator to evaluate the failure. By examining image processing methods, a suitable method for evaluating shear wall failure has been presented. This study's results can be used to assess the failure of shear walls and determine their damage.

In the present study, the uranium qualitative detector package containing Arsenazo and Bromo-PADAP indicators was designed; and also, the metalic ion detector package using Dithizone Indicator was built for detection and determination of some ionic interference via Uranium colorimetric method. The obtained results indicated that the detection limits of Uranium ions were equal to 50 ppm and 20 ppm respectively using detectors containing Arsenazo and Bromo-PADAP indicators. In the acidic media (pH=5), the uranium detectors had good detection performance. The metalic ion detector detected ions such as cobalt (II), copper (II), nickel (II), zinc (II), cadmium and silver until the detection limit 50 ppm. Arsenazo detector was superior to Bromo-PADAP detector for detection of Uranium since it prevented any ionic interference even in very high concentrations which with absorbtion of a solution containing Uranium ion, Arsenazo and Bromo-PADAP detectors in the presence masking agents in buffer solution produced miry green and purple colors respectively. While the operation time for Uranium ion detection in the presence of Arsenazo and Bromo-PADAP detectors was3 minutes and 1 minute respectively. Also, the quantitative evaluations based on image processing methods using MATLAB software show that the experimental results are consistent with the evaluation results.

Sending and receiving information confidentially has always been very important. The cryptography Plays a major role in exchanging information securely. For this purpose, several encryption algorithms are designed and implemented. Among the designed algorithms, the old RSA and Diffie-Hellman algorithms have been superseded by the elliptic curve encryption algorithm, due to its unique features. The proposed method has higher speed for image encryption and decryption in comparison with the current method. For this purpose, the grouping of pixels by remainder Chinese theorem has been employed. The proposed method is applicable on other data such as text, sound and video.