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Thresholding[ edit ] The simplest method of image segmentation is called the thresholding method. This method is based on a clip-level or a threshold value to turn a gray-scale image into a binary image. There is also a balanced histogram thresholding. The key of this method is to select the threshold value or values when multiple-levels are selected.
Recently, methods have been developed for thresholding computed tomography CT images. Data clustering Source image. Note that a common technique to improve performance Bone tissue engineering thesis large images is to downsample the image, compute the clusters, and then reassign the values to the larger image if necessary.
The K-means algorithm is an iterative technique that is used to partition an image into K clusters. The difference is typically based on pixel colorintensitytextureand location, or a weighted combination of these factors.
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K can be selected manually, randomlyor by a heuristic. This algorithm is guaranteed to converge, but it may not return the optimal solution. The quality of the solution depends on the initial set of clusters and the value of K.
The idea is simple: Assuming the object of interest is moving, the difference will be exactly that object. Improving on this idea, Kenney et al.
They use a robot to poke objects in order to generate the motion signal necessary for motion-based segmentation. Interactive segmentation follows the interactive perception framework proposed by Dov Katz  and Oliver Brock .
Compression-based methods[ edit ] Compression based methods postulate that the optimal segmentation is the one that minimizes, over all possible segmentations, the coding length of the data.
The method describes each segment by its texture and boundary shape. Each of these components is modeled by a probability distribution function and its coding length is computed as follows: The boundary encoding leverages the fact that regions in natural images tend to have a smooth contour.
This prior is used by Huffman coding to encode the difference chain code of the contours in an image. Thus, the smoother a boundary is, the shorter coding length it attains. Texture is encoded by lossy compression in a way similar to minimum description length MDL principle, but here the length of the data given the model is approximated by the number of samples times the entropy of the model.
The texture in each region is modeled by a multivariate normal distribution whose entropy has a closed form expression. An interesting property of this model is that the estimated entropy bounds the true entropy of the data from above.
This is because among all distributions with a given mean and covariance, normal distribution has the largest entropy.
Thus, the true coding length cannot be more than what the algorithm tries to minimize. For any given segmentation of an image, this scheme yields the number of bits required to encode that image based on the given segmentation.
Thus, among all possible segmentations of an image, the goal is to find the segmentation which produces the shortest coding length.
This can be achieved by a simple agglomerative clustering method. The distortion in the lossy compression determines the coarseness of the segmentation and its optimal value may differ for each image.
This parameter can be estimated heuristically from the contrast of textures in an image. For example, when the textures in an image are similar, such as in camouflage images, stronger sensitivity and thus lower quantization is required.
Histogram-based methods[ edit ] Histogram -based methods are very efficient compared to other image segmentation methods because they typically require only one pass through the pixels.
In this technique, a histogram is computed from all of the pixels in the image, and the peaks and valleys in the histogram are used to locate the clusters in the image.
A refinement of this technique is to recursively apply the histogram-seeking method to clusters in the image in order to divide them into smaller clusters. This operation is repeated with smaller and smaller clusters until no more clusters are formed.
Histogram-based approaches can also be quickly adapted to apply to multiple frames, while maintaining their single pass efficiency. The histogram can be done in multiple fashions when multiple frames are considered. The same approach that is taken with one frame can be applied to multiple, and after the results are merged, peaks and valleys that were previously difficult to identify are more likely to be distinguishable.Requirements for the Bachelor’s Degree.
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