Disparity map filter based on Weighted Least Squares filter (in form of Fast Global Smoother that is a lot faster than traditional Weighted Least Squares filter implementations) and optional use of left-right-consistency-based confidence to refine the results in half-occlusions and uniform areas.
Interface for realizations of Domain Transform filter.
Class implementing EdgeBoxes algorithm from C. Lawrence Zitnick and Piotr Dollár. Edge boxes: Locating object proposals from edges. In ECCV, 2014.
Class implementing the FLD (Fast Line Detector) algorithm
Graph Based Segmentation Algorithm. The class implements the algorithm described in Pedro F Felzenszwalb and Daniel P Huttenlocher. Efficient graph-based image segmentation. volume 59, pages 167 - 181. Springer, 2004.
Helper class for training part of [P. Dollar and C. L. Zitnick. Structured Forests for Fast Edge Detection, 2013].
Applies Ridge Detection Filter to an input image. Implements Ridge detection similar to the one in [Mathematica] (http://reference.wolfram.com/language/ref/RidgeFilter.html) using the eigen values from the Hessian Matrix of the input image using Sobel Derivatives. Additional refinement can be done using Skeletonization and Binarization.
The matcher for computing the right-view disparity map that is required in case of filtering with confidence.
Selective search segmentation algorithm The class implements the algorithm described in: Jasper RR Uijlings, Koen EA van de Sande, Theo Gevers, and Arnold WM Smeulders. Selective search for object recognition. International journal of computer vision, 104(2):154–171, 2013.
Class implementing edge detection algorithm from Piotr Dollar and C Lawrence Zitnick. Structured forests for fast edge detection. In Computer Vision (ICCV), 2013 IEEE International Conference on, pages 1841-1848. IEEE, 2013.
Class implementing the LSC (Linear Spectral Clustering) superpixels algorithm described in "Zhengqin Li and Jiansheng Chen. Superpixel segmentation using linear spectral clustering. June 2015."
Class implementing the SEEDS (Superpixels Extracted via Energy-Driven Sampling) superpixels algorithm described in Michael Van den Bergh, Xavier Boix, Gemma Roig, Benjamin de Capitani, and Luc Van Gool. Seeds: Superpixels extracted via energy-driven sampling. In Computer Vision-ECCV 2012, pages 13-26. Springer, 2012.
Class implementing the SLIC (Simple Linear Iterative Clustering) superpixels algorithm described in Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Susstrunk. Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell., 34(11):2274-2282, nov 2012.
Library to invoke XImgproc functions
Main interface for all disparity map filters.