Podcast
Questions and Answers
Segmentation is the partitioning of an image into the set of regions, which represent meaningful areas of the image. Separate the foreground regions (object) from the background regions which are ______.
Segmentation is the partitioning of an image into the set of regions, which represent meaningful areas of the image. Separate the foreground regions (object) from the background regions which are ______.
ignored
Segmentation have two main objectives: o Decompose the image into parts for further analysis. o Perform change of ______.
Segmentation have two main objectives: o Decompose the image into parts for further analysis. o Perform change of ______.
representation
Regions of image segmentation should be uniform and homogenous with respect to some characteristics such as gray level, color or ______.
Regions of image segmentation should be uniform and homogenous with respect to some characteristics such as gray level, color or ______.
texture
The regions that humans see as homogenous may not be homogenous in terms of low-level features available to the segmentation system, so higher-level knowledge may have to be ______.
The regions that humans see as homogenous may not be homogenous in terms of low-level features available to the segmentation system, so higher-level knowledge may have to be ______.
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Image segmentation can be viewed as a ______.
Image segmentation can be viewed as a ______.
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Depending on what we choose as the feature space, we can group pixels in different ______.
Depending on what we choose as the feature space, we can group pixels in different ______.
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Mean shift algorithm seeks modes or local maxima of density in the feature space. Mean shift is a procedure for locating maxima of a ______ function given discrete data samples from that function.
Mean shift algorithm seeks modes or local maxima of density in the feature space. Mean shift is a procedure for locating maxima of a ______ function given discrete data samples from that function.
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Mean shift finds ______ regions in the image.
Mean shift finds ______ regions in the image.
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Mean shift algorithm: Choose a search window size. Choose the initial location of the search window. Compute the mean location (centroid of the data) in the search window. Center the search window at the mean location computed in Step 3 (shift). Repeat Steps 3 and 4 until ______.
Mean shift algorithm: Choose a search window size. Choose the initial location of the search window. Compute the mean location (centroid of the data) in the search window. Center the search window at the mean location computed in Step 3 (shift). Repeat Steps 3 and 4 until ______.
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Mean shift algorithm: Find features (color, gradients, texture, etc.). Initialize windows at individual feature points. Perform mean shift algorithm for each window until ______.
Mean shift algorithm: Find features (color, gradients, texture, etc.). Initialize windows at individual feature points. Perform mean shift algorithm for each window until ______.
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Semantic Segmentation: Label each pixel in the image with a category label (pixel-level annotation). Don’t differentiate instances, only care about ______s.
Semantic Segmentation: Label each pixel in the image with a category label (pixel-level annotation). Don’t differentiate instances, only care about ______s.
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Feature space: Intensity + position Segmentation as clustering. Can combine color and ______…
Feature space: Intensity + position Segmentation as clustering. Can combine color and ______…
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Mean shift: is a procedure for locating maxima of a ______ function given discrete data samples from that function.
Mean shift: is a procedure for locating maxima of a ______ function given discrete data samples from that function.
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Mean shift Pros: Automatically finds basins of ______.
Mean shift Pros: Automatically finds basins of ______.
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Mean shift Cons: Output depend on window size. Computationally ______.
Mean shift Cons: Output depend on window size. Computationally ______.
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Semantic Segmentation Applications: A key part of Scene Understanding. Medical ______. Autonomou.
Semantic Segmentation Applications: A key part of Scene Understanding. Medical ______. Autonomou.
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