Segmenting images corrupted by correlated noise

Lee, T.C.M.

doi:10.1109/icip.1997.647751

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…The traditional algorithms of denoising, such as Gaussian filter [8], [20], reduce the noise, but they do not maintain the edge information. When noise is removed, it is required to not only smooth all of the homogenous regions that contain noise, but also to keep the position of boundaries, i.e., not to lose the edge information that defines the structure of objects.…”

Section: Weibull Noise Indexmentioning

confidence: 99%

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Volumetric segmentation using weibull E-SD fields

Razdan

Nielson

et al. 2003

IEEE Trans. Visual. Comput. Graphics

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Abstract-This paper presents a coarse-grain approach for segmentation of objects with gray levels appearing in volume data. The input data is on a 3D structured grid of vertices vði; j; kÞ, each associated with a scalar value. In this paper, we consider a voxel as a Â Â cube and each voxel is assigned two values: expectancy and standard deviation (E-SD). We use the Weibull noise index to estimate the noise in a voxel and to obtain more precise E-SD values for each voxel. We plot the frequency of voxels which have the same E-SD, then 3D segmentation based on the Weibull E-SD field is presented. Our test bed includes synthetic data as well as real volume data from a confocal laser scanning microscope (CLSM). Analysis of these data all show distinct and defining regions in their E-SD fields. Under the guide of the E-SD field, we can efficiently segment the objects embedded in real and simulated 3D data.Index Terms-3D segmentation, Weibull E-SD field, noise index, confocal laser scanning microscope, CLSM.

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Section: Weibull Noise Indexmentioning

confidence: 99%

“…It is assumed that the value of a voxel is characterized by the Weibull distribution. If we use (3) and (4) to calculate the E-SD value, the results are not reliable due to noise, especially for a standard deviation [18], [20]. Therefore, we must find a way to distinguish whether or not the data distribution in a -voxel is uniform.…”

Section: Weibull Noise Indexmentioning

confidence: 99%

Volumetric segmentation using weibull E-SD fields

Razdan

Nielson

et al. 2003

IEEE Trans. Visual. Comput. Graphics

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“…In order to derive scene depth information in the absence of hard constraints, we use the principle of minimum description length [4,11]. We discretize the scene depth into a few ( ½¼) layers and assume that every blob ¬ Ø belongs to such a depth layer ´Øµ ¾ ½ ¾…”

Section: Depth Computation Using Minimum Description Lengthmentioning

confidence: 99%

Depth layers from occlusions

Schödl

Essa

Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001

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Noise estimation and adaptive filtering during visual tracking

Ndiour

Vela

2009

2009 16th IEEE International Conference on Image Processing (ICIP)

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This paper proposes a procedure to characterize segmentation-based visual tracking performance with respect to imaging noise. It identifies how imaging noise affects the target segmentation as measured through local shape metrics (Sobolev and Laplace metrics). Such a procedure would be an important calibration step prior to implementing a visual tracking filter for a given need. We utilize the Bhattacharyya coefficient between the target and background intensity distributions to estimate the segmentation error. An empirical study is conducted to establish a correspondence between the Bhattacharyya coefficient and the segmentation error. The correspondence is used to adaptively filter temporally correlated segmentations. Preliminary results show improved performance when compared to fixed gains.

show abstract

Segmenting images corrupted by correlated noise

Cited by 3 publications

References 20 publications

Volumetric segmentation using weibull E-SD fields

Volumetric segmentation using weibull E-SD fields

Depth layers from occlusions

Noise estimation and adaptive filtering during visual tracking

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