Unsupervised Multiresolution Segmentation for Images with Low Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video object segmentation and tracking using region-based statistics
Image Communication
IEEE Transactions on Image Processing
Vision-guided Robot System for Picking Objects by Casting Shadows
International Journal of Robotics Research
Detecting and segmenting un-occluded items by actively casting shadows
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
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This paper describes a novel system for computing a three-dimensional (3-D) range segmentation of an arbitrary visible scene using focus information. The process of range segmentation is divided into three steps: an initial range classification, a surface merging process, and a 3-D multiresolution range segmentation. First, range classification is performed to obtain quantized range estimates. The range classification is performed by analyzing focus cues within a Bayesian estimation framework. A combined energy functional measures the degree of focus and the Gibbs distribution of the class field. The range classification provides an initial range segmentation. Second, a statistical merging process is performed to merge the initial surface segments. This gives a range segmentation at a coarse resolution. Third, 3-D multiresolution range segmentation (3-D MRS) is performed to refine the range segmentation into finer resolutions. The proposed range segmentation method does not require initial depth estimates, it allows the analysis of scenes containing multiple objects, and it provides a rich description of the 3-D structure of a scene