Sharif Digital Repository

When a crack is subjected to a compression field, it will close and its edges will get into contact with each other. Depending on the direction and magnitude of the loads and also the coefficient of friction, ‘stick’ or ‘slip’ situationsbetween the edges will occur. This type of crack is known as ‘frictional crack.’ In this project, first these cracks are studied analytically and the order of singularity is derived using asymptotic analysis and also the analytical fields are determined for both ‘isotropic’ and ‘orthotropic’ materials. Then, numerical simulations are carried out using extended finite element method which is considered as the most powerful means for analyzing the problems... 

Digital watermarking is an efficacious technique to protect the copyright and ownership of digital information. Traditional image watermarking algorithms embed a logo in the image that could reduce its visual quality. A new approach in watermarking called zero watermarking doesn’t need to embed a logo in the image. In this algorithm we find a feature from the main image and combine it with a logo to obtain a key. This key is securely kept by a trusted authority. In this thesis we show that we can increase the robustness of digital zero watermarking by a new counter detection method in comparison to Canny Edge detection and morphological dilatation that is mostly used by related works.... 

In the extensive horizon of artificial intelligence technology, one of the grand challenges in computer vision has been semantic segmentation. This task which aimed to predict label for each pixel of image, describes the scene, due to the need of low level information, is more complicated in comparison with other computer vision tasks. However, as part of concept of scene understanding and a crucial step in many real world applications such as autonomous driving, human-computer interaction and robot navigation, many researchers have been sought to resolve it. What makes this task more challenging rather than other computer vision tasks is that information beyond a pixel, its neighbors and... 

Despite widespread applications and high performance of deep neural networks in the fields of computer vision, they have been shown to be vulnerable to adversarial examples. An adversarial example is a perturbated image that the magnitude of its difference with its corresponding natural image is small and yet given such example, the network produces incorrect output. In recent years, many approaches have been proposed to increase the robustness of DNNs against adversarial examples with adversarial training being proposed as the most effective defense measure. Approaches based on adversarial training try to increase the robustness of the network by training on the adversarial examples. One of... 

For radiotherapy and removal of cancerous tissues, it is necessary to determine the location of the tumor and the vulnerable structures around the tumor before treating and irradiating the high-energy beam. To do this, the images received from the patient need to be segmented. This is usually done manually, which is not only time consuming but also very expensive.Various methods for segmenting these images are presented automatically and semi-automatically, among which methods based on machine learning and deep learning have shown much higher accuracy than other methods. Despite this superiority, these methods have problems such as high computational costs, inability to learn the shape and... 

Image segmentation in medical imaging, as one of the most important branches of medical image analysis, often faces the challenge of limited labeled data for application in deep learning methods. The high cost of data collection and the need for expertise in image segmentation, particularly in three-dimensional images such as MRI and CT or sequence images like CMR, have all contributed to this problem, even for popular networks like U-Net, which struggle to achieve high accuracy. As a result, research efforts have focused on semi-supervised learning approaches, weakly supervised learning, as well as multi-instance learning in medical image segmentation. Unfortunately, each of these methods... 

Geographical data, agricultural field boundaries and their segmentation are essential for many agricultural applications. For example, monitoring of field parcel for resource management. Since manual delineation of land parcels with the help of a real person requires a lot of time and special tools, the need for repeatable automation of this work is felt. Traditional approaches of image segmentation do not have enough generalizability and can be used only for specific areas; so we turned to deep learning, which has proven to be successful in computer vision tasks. Instance segmentation is the most advanced deep learning-based method in object recognition and has numerous applications in...