The graph-cut-segmentation from sri-sai-charan

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As applied in the field of computer vision, graph cut optimization can be employed to efficiently solve a wide variety of low-level computer vision problems, such as image smoothing, the stereo correspondence problem, image segmentation, object co-segmentation, cv problems that can be formulated in terms of energy minimization.

License: MIT License

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graph-cut-segmentation's Introduction

class SriSaiCharan:

    def __init__(self):
        self.username = 'Sri-Sai-Charan'
        self.name = 'SriSaiCharan Velisetti'
        self.code = {
            'machine_learning': ['Keras', 'TensorFlow', 'Pytorch', 'Anaconda', 'Python','Mujoco','Isaac Sim'],
            'computer_vision':  ['OpenCV', 'Python', 'Matlab','Pygame'],
            'path_planning':  ['AStar', 'Dijkstra', 'RRT*','BFS'],
            'ros':              ['C++', 'Python', 'ROS 1', 'ROS 2', 'Gazebo',
                                    'RViz','SolidWorks','Catkin'],
            'programing_misc':  ['LabView','Simulink','Google Colab', 
                                    'Firebase', 'Ansys', 'Doxygen']
        }
        self.education = {
            'undergraduate':    ['University' : 'SRM IST',
                                 'Degree'     : 'Mechatronics'],
            'graduate':         ['University' : 'UMD College Park',
                                 'Degree'     : 'Robotics']
        }
        

    def __str__(self):
        return self.name

if __name__ == '__main__':
    me = SriSaiCharan()