Fast image-based object localization in natural scenes

In many robot applications, autonomous robots must be capable of localizing the objects they are to manipulate. In this paper we address the object localization problem by fitting a parametric curve model to the object contour in the image. The initial prior of the object pose is iteratively refined to the posterior distribution by optimizing the separation of the object and background. The local separation criteria are based on local statistics which are iteratively computed from the object and background region. No prior knowledge on color distributions is needed. Experiments show that the method is capable of localizing objects in a cluttered and textured scene even under strong variations of illumination. The method is able to localize a soccer ball within frame rate.

Authors

• Robert Hanek
• Thorsten Schmitt
• Sebastian Buck 0001
• Michael Beetz

Keywords

• Layout
• Robots
• Image edge detection
• Lighting
• Shape
• Deformable models
• Parametric statistics
• Statistical distributions
• Object recognition
• Active contours
• Natural Scenes
• Model Parameters
• Autonomic System
• Robotic Applications
• Color Distribution
• Soccer Ball
• Contour Images
• Imaging Data
• Distribution Curve
• Imaging Device
• Separate Regions
• Particle Filter
• Mobile Robot
• Statistical Dependence
• Image Gradient
• RGB Values
• Partial Occlusion
• Distribution Of The Model Parameters
• Color Categories
• Side Of Curve
• Strong Texture
• Areas Of Convergence
• Ball Position
• Region-based Methods