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+/*********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright (c) 2014, 2015
+ * Zhengqin Li <li-zq12 at mails dot tsinghua dot edu dot cn>
+ * Jiansheng Chen <jschenthu at mail dot tsinghua dot edu dot cn>
+ * Tsinghua University
+ *
+ *  Redistribution and use in source and binary forms, with or without
+ *  modification, are permitted provided that the following conditions
+ *  are met:
+ *
+ *   * Redistributions of source code must retain the above copyright
+ *     notice, this list of conditions and the following disclaimer.
+ *   * Redistributions in binary form must reproduce the above
+ *     copyright notice, this list of conditions and the following
+ *     disclaimer in the documentation and/or other materials provided
+ *     with the distribution.
+ *   * Neither the name of the copyright holders nor the names of its
+ *     contributors may be used to endorse or promote products derived
+ *     from this software without specific prior written permission.
+ *
+ *  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ *  "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ *  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+ *  FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+ *  COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+ *  INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+ *  BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ *  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+ *  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+ *  LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+ *  ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+ *  POSSIBILITY OF SUCH DAMAGE.
+ *********************************************************************/
+
+/*
+
+ "Superpixel Segmentation using Linear Spectral Clustering"
+ Zhengqin Li, Jiansheng Chen, IEEE Conference on Computer Vision and Pattern
+ Recognition (CVPR), Jun. 2015
+
+ OpenCV port by: Cristian Balint <cristian dot balint at gmail dot com>
+ */
+
+#ifndef __OPENCV_LSC_HPP__
+#define __OPENCV_LSC_HPP__
+#ifdef __cplusplus
+
+#include <opencv2/core.hpp>
+
+namespace cv
+{
+namespace ximgproc
+{
+
+//! @addtogroup ximgproc_superpixel
+//! @{
+
+/** @brief Class implementing the LSC (Linear Spectral Clustering) superpixels
+algorithm described in @cite LiCVPR2015LSC.
+
+LSC (Linear Spectral Clustering) produces compact and uniform superpixels with low
+computational costs. Basically, a normalized cuts formulation of the superpixel
+segmentation is adopted based on a similarity metric that measures the color
+similarity and space proximity between image pixels. LSC is of linear computational
+complexity and high memory efficiency and is able to preserve global properties of images
+
+ */
+
+class CV_EXPORTS_W SuperpixelLSC : public Algorithm
+{
+public:
+
+    /** @brief Calculates the actual amount of superpixels on a given segmentation computed
+    and stored in SuperpixelLSC object.
+     */
+    CV_WRAP virtual int getNumberOfSuperpixels() const = 0;
+
+    /** @brief Calculates the superpixel segmentation on a given image with the initialized
+    parameters in the SuperpixelLSC object.
+
+    This function can be called again without the need of initializing the algorithm with
+    createSuperpixelLSC(). This save the computational cost of allocating memory for all the
+    structures of the algorithm.
+
+    @param num_iterations Number of iterations. Higher number improves the result.
+
+    The function computes the superpixels segmentation of an image with the parameters initialized
+    with the function createSuperpixelLSC(). The algorithms starts from a grid of superpixels and
+    then refines the boundaries by proposing updates of edges boundaries.
+
+     */
+    CV_WRAP virtual void iterate( int num_iterations = 10 ) = 0;
+
+    /** @brief Returns the segmentation labeling of the image.
+
+    Each label represents a superpixel, and each pixel is assigned to one superpixel label.
+
+    @param labels_out Return: A CV_32SC1 integer array containing the labels of the superpixel
+    segmentation. The labels are in the range [0, getNumberOfSuperpixels()].
+
+    The function returns an image with the labels of the superpixel segmentation. The labels are in
+    the range [0, getNumberOfSuperpixels()].
+     */
+    CV_WRAP virtual void getLabels( OutputArray labels_out ) const = 0;
+
+    /** @brief Returns the mask of the superpixel segmentation stored in SuperpixelLSC object.
+
+    @param image Return: CV_8U1 image mask where -1 indicates that the pixel is a superpixel border,
+    and 0 otherwise.
+
+    @param thick_line If false, the border is only one pixel wide, otherwise all pixels at the border
+    are masked.
+
+    The function return the boundaries of the superpixel segmentation.
+     */
+    CV_WRAP virtual void getLabelContourMask( OutputArray image, bool thick_line = true ) const = 0;
+
+    /** @brief Enforce label connectivity.
+
+    @param min_element_size The minimum element size in percents that should be absorbed into a bigger
+    superpixel. Given resulted average superpixel size valid value should be in 0-100 range, 25 means
+    that less then a quarter sized superpixel should be absorbed, this is default.
+
+    The function merge component that is too small, assigning the previously found adjacent label
+    to this component. Calling this function may change the final number of superpixels.
+     */
+    CV_WRAP virtual void enforceLabelConnectivity( int min_element_size = 20 ) = 0;
+
+
+};
+
+/** @brief Class implementing the LSC (Linear Spectral Clustering) superpixels
+
+@param image Image to segment
+@param region_size Chooses an average superpixel size measured in pixels
+@param ratio Chooses the enforcement of superpixel compactness factor of superpixel
+
+The function initializes a SuperpixelLSC object for the input image. It sets the parameters of
+superpixel algorithm, which are: region_size and ruler. It preallocate some buffers for future
+computing iterations over the given image. An example of LSC is ilustrated in the following picture.
+For enanched results it is recommended for color images to preprocess image with little gaussian blur
+with a small 3 x 3 kernel and additional conversion into CieLAB color space.
+
+![image](pics/superpixels_lsc.png)
+
+ */
+
+    CV_EXPORTS_W Ptr<SuperpixelLSC> createSuperpixelLSC( InputArray image, int region_size = 10, float ratio = 0.075f );
+
+//! @}
+
+}
+}
+#endif
+#endif