Image representation is a crucial problem in image processing where there exist many low-level representations of image, i.e., SIFT, HOG and so on. But there is a missing link across low-level and high-level semantic representations. In fact, traditional machine learning approaches, e.g., non-negative matrix factorization, sparse representation and principle component analysis are employed to d...

while performance-based language assessment has led to an increased authenticity and content validity in the practice of writing assessment, the reliability of ratings has become a major issue. research findings have shown different reactions by native english speaker (nes) and non-native english speaker (nns) teachers to students’ writings. the focus of this study is on investigating whether i...

in this thesis, a structured hierarchical methodology based on petri nets is used to introduce a task model for a soccer goalkeeper robot. in real or robot soccer, goalkeeper is an important element which has a key role and challenging features in the game. goalkeeper aims at defending goal from scoring goals by opponent team, actually to prevent the goal from the opponent player’s attacks. thi...

In this paper, a new method for image denoising based on incoherent dictionary learning and domain transfer technique is proposed. The idea of using sparse representation concept is one of the most interesting areas for researchers. The goal of sparse coding is to approximately model the input data as a weighted linear combination of a small number of basis vectors. Two characteristics should b...

Due to the differences between the visible and thermal infrared images, combination of these two types of images is essential for better understanding the characteristics of targets and the environment. Thermal infrared images have most importance to distinguish targets from the background based on the radiation differences, which work well in all-weather and day/night conditions also in land s...