Defect detection plays a vital role in the manufacturing process of integrated circuits (ICs).
The developed network captures both model and noise uncertainty which is found to be useful tools in assessing performance.
The regression model learns the inter-dependency between the stages and outputs a score corresponding to the severity level of DR generating a higher score for a higher severity level.
Furthermore, 2D and 3D human pose estimation datasets and evaluation metrics are included.
Human action recognition is used in many applications such as video surveillance, human computer interaction, assistive living, and gaming.
Existing prescriptive compression strategies used in hearing aid fitting are designed based on gain averages from a group of users which are not necessarily optimal for a specific user.
In this paper, a method for automatically selecting the exposure settings of such images is introduced based on the camera characteristic function.
Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among many others.
From the variety of available deep learning tools, the most suited ones are used in this paper to enable real-time deployment of deep learning inference networks on smartphones.
This paper presents a smartphone app that performs real-time voice activity detection based on convolutional neural network.