In software development process,the last step is usually the Graphic User Interface(GUI) test,which is part of the final user experience(UE) test.Traditionally,there exist some GUI test tools in the market,such as Abbot Java GUI Test Framework and Pounder,in which testers pre-configure in the script all desired actions and instructions for the computer,nonetheless requiring too much of invariance of GUI environment;and they require reconfiguration in case of GUI changes,therefore still to be done mostly manually and hard for non-programmer testers to.Consequently,we proposed GUI tests by image recognition to automate the last process;we managed to innovate upon current algorithms such as SIFT and Random Fern,from which we develop the new algorithm scheme retrieving most efficient feature and dispelling inefficient part of each algorithm.Computers then apply the algorithm,to search for target patterns themselves and take subsequent actions such as manual mouse,keyboard and screen I/O automatically to test the GUI without any manual instructions.Test results showed that the proposed approach can accelerate GUI test largely compared to current benchmarks.
Image resizing is a key technique for displaying images on different devices, and has attracted much attention in the past few years. This paper reviews the image resizing methods proposed in recent years, gives a detailed comparison on their performance, and reveals the main challenges raised in several important issues such as preserving an important region, minimizing distortions, and improving efficiency. Furthermore, this paper discusses the research trends and points out the possible hotspots in this field. We believe this survey can give some guidance for researchers from relevant research areas, offering them an overall and novel view.
Anti-aliasing is a well-established technique in computer graphics that reduces the blocky or stair-wise appearance of pixels. This paper provides a comprehensive overview of the anti-aliasing techniques used in computer graphics, which can be classified into two categories: post-filtering based anti-aliasing and pre-filtering based anti-aliasing. We discuss post-filtering based anti-aliasing algorithms through classifying them into hardware anti-aliasing techniques and post-process techniques for deferred rendering. Comparisons are made among different methods to illustrate the strengths and weaknesses of every category. We also review the utilization of anti-aliasing techniques from the first category in different graphic processing units, i.e., different NVIDIA and AMD series. This review provides a guide that should allow researchers to position their work in this important research area, and new research problems are identified.
Xu-dong JIANGBin SHENGWei-yao LINWei LULi-zhuang MA
