Image matting refers to the task of estimating the foreground of images, which is an important problem in image processing. Recently, trimap generation has attracted considerable attention because designing a trimap for every image is labor-intensive...
Smoke and fire detection is vital for timely fire alarms, but traditional sensor-based methods are often unresponsive and costly. While deep learning-based methods offer promise using aerial images and surveillance images, the scarcity and limited di...
Image matting methods based on deep learning have made tremendous success. However, the success of previous image matting methods typically relies on a massive amount of pixel-level labeled data, which are time-consuming and costly to obtain. This pa...
Multi-focus image fusion is a very essential method of obtaining an all focus image from multiple source images. The fused image eliminates the out of focus regions, and the resultant image contains sharp and focused regions. A novel multiscale image...
Image matting is a fundamental technique used to extract a fine foreground image from a given image by estimating the opacity values of each pixel. It is one of the key techniques in image processing and has a wide range of applications in practical...
A flame’s structural feature is a crucial parameter required to comprehensively understand the interaction between turbulence and flames. The generation and evolution processes of the structure feature have rarely been investigated in lean blow...
Extracting complete portrait foregrounds from natural images is widely used in image editing and high-definition map generation. When making high-definition maps, it is often necessary to matte passers-by to guarantee their privacy. Current matting m...
Dimensionality reduction techniques are often used by researchers in order to make high dimensional data easier to interpret visually, as data visualization is only possible in low dimensional spaces. Recent research in nonlinear dimensionality reduc...