Image Matting
94 papers with code • 8 benchmarks • 8 datasets
Image Matting is the process of accurately estimating the foreground object in images and videos. It is a very important technique in image and video editing applications, particularly in film production for creating visual effects. In case of image segmentation, we segment the image into foreground and background by labeling the pixels. Image segmentation generates a binary image, in which a pixel either belongs to foreground or background. However, Image Matting is different from the image segmentation, wherein some pixels may belong to foreground as well as background, such pixels are called partial or mixed pixels. In order to fully separate the foreground from the background in an image, accurate estimation of the alpha values for partial or mixed pixels is necessary.
Source: Automatic Trimap Generation for Image Matting
Image Source: Real-Time High-Resolution Background Matting
Libraries
Use these libraries to find Image Matting models and implementationsDatasets
Most implemented papers
Non-Causal Tracking by Deblatting
Tracking by Deblatting stands for solving an inverse problem of deblurring and image matting for tracking motion-blurred objects.
Sub-frame Appearance and 6D Pose Estimation of Fast Moving Objects
We propose a novel method that tracks fast moving objects, mainly non-uniform spherical, in full 6 degrees of freedom, estimating simultaneously their 3D motion trajectory, 3D pose and object appearance changes with a time step that is a fraction of the video frame exposure time.
$F$, $B$, Alpha Matting
Cutting out an object and estimating its opacity mask, known as image matting, is a key task in many image editing applications.
Camouflaged Object Detection
We present a comprehensive study on a new task named camouflaged object detection (COD), which aims to identify objects that are "seamlessly" embedded in their surroundings.
Real-Time High-Resolution Background Matting
We introduce a real-time, high-resolution background replacement technique which operates at 30fps in 4K resolution, and 60fps for HD on a modern GPU.
Deep PCB To COCO Convertor
It has 1500 image pairs.
SAPA: Similarity-Aware Point Affiliation for Feature Upsampling
We introduce point affiliation into feature upsampling, a notion that describes the affiliation of each upsampled point to a semantic cluster formed by local decoder feature points with semantic similarity.
On Point Affiliation in Feature Upsampling
We introduce the notion of point affiliation into feature upsampling.
Information-Flow Matting
Our resulting novel linear system formulation can be solved in closed-form and is robust against several fundamental challenges of natural matting such as holes and remote intricate structures.
Fast Deep Matting for Portrait Animation on Mobile Phone
Finally, an automatic portrait animation system based on fast deep matting is built on mobile devices, which does not need any interaction and can realize real-time matting with 15 fps.