Latent Marked Poisson Process with Applications to Object Segmentation

Ghanta, Sindhu; Dy, Jennifer G.; Niu, Donglin; Jordan, Michael I.

doi:10.1214/16-ba1035

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…In [5], multiple segmentation hypotheses are produced applying the spatially dependent Pitman-Yor process. Recent work applies a Poisson process with segment shape priors for segmentation [4].…”

Section: Related Workmentioning

confidence: 99%

Object Proposal Generation Applying the Distance Dependent Chinese Restaurant Process

Lauri

Frintrop

2017

Image Analysis

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In application domains such as robotics, it is useful to represent the uncertainty related to the robot's belief about the state of its environment. Algorithms that only yield a single "best guess" as a result are not sufficient. In this paper, we propose object proposal generation based on non-parametric Bayesian inference that allows quantification of the likelihood of the proposals. We apply Markov chain Monte Carlo to draw samples of image segmentations via the distance dependent Chinese restaurant process. Our method achieves state-of-the-art performance on an indoor object discovery data set, while additionally providing a likelihood term for each proposal. We show that the likelihood term can effectively be used to rank proposals according to their quality.

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Section: Related Workmentioning

confidence: 99%

Object Proposal Generation Applying the Distance Dependent Chinese Restaurant Process

Lauri

Frintrop

2017

Image Analysis

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Morphology Preserving Segmentation Method for Occluded Cell Nuclei from Medical Microscopy Image

Khan

Debnath

2021

Int. J. Image Grap.

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Nowadays, image segmentation techniques are being used in many medical applications such as tissue culture monitoring, cell counting, automatic measurement of organs, etc., for assisting doctors. However, high-level segmentation results cannot be obtained without manual annotation or prior knowledge for high variability, noise and other imaging artifacts in medical images. Furthermore, unstable and continuously changing characteristics of all human cells, tissues and organs manipulate training-based segmentation methods. Detecting appropriate contour of a region of interest and single cells from overlapping condition are extremely challenging. In this paper, we aim for a model that can detect biological structure (e.g. cell nuclei and lung contour) with their proper morphology even in overlapping or occluded condition without manual annotation or prior knowledge. We have introduced a new optimal approach for automatic medical image region segmentation. The method first clearly focuses the boundaries of all object regions in a microscopy image. Then it detects the areas by following their contours. Our model is capable of detecting and segmenting object regions from medial image using less computation effort. Our experimental results prove that our model provides better detection on several datasets of different types of medical data and ensures more than 98% segmentation rate in the case of densely connected regions.

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Latent Marked Poisson Process with Applications to Object Segmentation

Cited by 2 publications

References 37 publications

Object Proposal Generation Applying the Distance Dependent Chinese Restaurant Process

Object Proposal Generation Applying the Distance Dependent Chinese Restaurant Process

Morphology Preserving Segmentation Method for Occluded Cell Nuclei from Medical Microscopy Image

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