Multi-Layer Background Subtraction Based on Color and Texture

By Jian Yao, Jean-Marc Odobez. 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007

311 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Deep learning for smart fish farming: applications, opportunities and challenges (2020, background): “…many studies have been conducted to investigate the above issues, most emphasized the extraction of conventional low-level features, which usually involve small details in an image such as feature points, colors, textures, contours, and shapes of interest (White et al., 2006 ; Yao & Odobez, 2007).”
Size distribution and Sauter mean diameter of micro bubbles for a Venturi type bubble generator (2016, methodology): “The initial step recovers the foreground grayscale image obtained by ML-BGS for the candidate bubble cluster area, as shown in Fig.”
Topic models for scene analysis and abnormality detection (2009, methodology): “Motion: To identify the relevant parts of the scene, we first perform background subtraction using the algorithm proposed in [11] and detect the foreground pixels.”
Joint Customer Pose and Orientation Estimation Using Deep Neural Network from Surveillance Camera (2016, methodology): “The customer images are automatically cropped by the MultiLayer Background Subtraction algorithm [14].”
Camera-trap images segmentation using multi-layer robust principal component analysis (2017, background): “Color descriptors could overcome the texture descriptor limitation [16].”