scholarly journals Object detection for graphical user interface: old fashioned or deep learning or a combination?

2020 ◽  
Author(s):  
Jieshan Chen ◽  
Mulong Xie ◽  
Zhenchang Xing ◽  
Chunyang Chen ◽  
Xiwei Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
User Interface ◽  
Object Detection ◽  
Graphical User Interface

scholarly journals Visual Recognition Of Graphical User Interface Components Using Deep Learning Technique

2020 ◽  
Vol 13 (1) ◽  
pp. 35
Author(s):  
Agyl Ardi Rahmadi ◽  
Aris Sudaryanto
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
User Interface ◽  
Graphical User Interface ◽  
Visual Recognition ◽  
Usability Testing ◽  
Learning Technique ◽  
Different Types ◽  
Vision Technique ◽  
Computer Vision Technique

Graphical User Interface (GUI) building in software development is a process which ideally need to go through several steps. Those steps in the process start from idea or rough sketch of the GUI, then refined into visual design, implemented in coding or prototype, and finally evaluated for its function and usability to discover design problem and to get feedback from users. Those steps repeated until the GUI considered satisfactory or acceptable by the user. Computer vision technique has been researched and developed to make the process faster and easier; for example generating code for implementation, or automatic GUI testing using component images. But among those techniques, there are still few for usability testing purpose. This preliminary research attempted to make the foundation for usability testing using computer vision technique by built minimalist dataset which has images of various GUI components and used the dataset in deep learning experiment for GUI components visual recognition. The experiment results showed deep learning technique suitable for the intended task, with accuracy of 95% for recognition of two different types of components, and accuracy of 72% for six different types of component.

scholarly journals A Deep Learning Graphical User Interface Application on MATLAB

2020 ◽  
Author(s):  
Aditya Akundi ◽  
Tzu-Liang Tseng ◽  
Zejing Cao ◽  
Hoejin Kim
Keyword(s):  
Deep Learning ◽  
User Interface ◽  
Graphical User Interface

Saliency detection in deep learning era: trends of development

2019 ◽  
pp. 10-36 ◽  
Author(s):  
M. N. Favorskaya ◽  
L. C. Jain
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Event Detection ◽  
Visual Analysis ◽  
Saliency Detection ◽  
Salient Object Detection ◽  
Public Image ◽  
Detection Methods ◽  
Salient Object ◽  
Salient Event

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

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