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      An Active contour model with shape regulation scheme

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      https://www.riss.kr/link?id=E806246

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      다국어 초록 (Multilingual Abstract)

      This paper presents an active method for locating target objects in images, which is aimed at improving the performance of detecting object boundaries by enhancing the behavioral characteristics of an active contour. The proposed active contour model simulates a mechanical system consisting of two main parts: the first is a rigid fixture, called the core, specifying the expected shape of target boundaries, while the second is an elastic rod attached to the rigid fixture. The rigid elastic rod deforms or moves relative to the rigid core according to the classical laws of the mechanical system. When the initial contour is applied to an image data, it is attracted near the dominant image features, but tries to keep its home shape and simultaneously make the deformation smooth if a deformation is more natural for force equilibrium. This mechanism significantly improves the performance of detecting object boundaries in the presence of some disturbing image features. The active contour is scale invariant, thereby significantly relieving the difficulty in selecting proper values for the model parameters. The values for the model parameters can be selected to make the contour have the desired behaviors around the equilibrium position through the analysis of the vibration mode of the mechanical system. The performance of the proposed method is validated through a series of experiments, which include detection of heavily degraded objects, tracking of objects under non-rigid motion and comparisons with the original snake models.
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      This paper presents an active method for locating target objects in images, which is aimed at improving the performance of detecting object boundaries by enhancing the behavioral characteristics of an active contour. The proposed active contour model ...

      This paper presents an active method for locating target objects in images, which is aimed at improving the performance of detecting object boundaries by enhancing the behavioral characteristics of an active contour. The proposed active contour model simulates a mechanical system consisting of two main parts: the first is a rigid fixture, called the core, specifying the expected shape of target boundaries, while the second is an elastic rod attached to the rigid fixture. The rigid elastic rod deforms or moves relative to the rigid core according to the classical laws of the mechanical system. When the initial contour is applied to an image data, it is attracted near the dominant image features, but tries to keep its home shape and simultaneously make the deformation smooth if a deformation is more natural for force equilibrium. This mechanism significantly improves the performance of detecting object boundaries in the presence of some disturbing image features. The active contour is scale invariant, thereby significantly relieving the difficulty in selecting proper values for the model parameters. The values for the model parameters can be selected to make the contour have the desired behaviors around the equilibrium position through the analysis of the vibration mode of the mechanical system. The performance of the proposed method is validated through a series of experiments, which include detection of heavily degraded objects, tracking of objects under non-rigid motion and comparisons with the original snake models.

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      목차 (Table of Contents)

      • 1. INTRODUCTION
      • 2. PROPERTIES OF THE SNAKE MODELS
      • 3. ACTIVE CONTROL MODEL
      • 3.1 Shape representation
      • 3.2 Active contour models
      • 1. INTRODUCTION
      • 2. PROPERTIES OF THE SNAKE MODELS
      • 3. ACTIVE CONTROL MODEL
      • 3.1 Shape representation
      • 3.2 Active contour models
      • 4. PROPERTY ANALYSIS
      • 4.1 Scale invariance
      • 4.2 Parameter analysis
      • 5. EXPERIMENTS AND DISCUSSIONS
      • 5.1 Implementation
      • 5.2 Experimental results
      • 6. CONCLUDING REMARKS
      • REFERENCES
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