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Hough transform
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The Hough transform in its simplest form is a method to detect straight lines.

http://en.wikipedia.org/wiki/Hough_transform

For a first example we construct an example of intersecting lines.

In [1]: import numpy as np

In [2]: from scikits.image.transform import hough, probabilistic_hough

In [3]: import matplotlib.pyplot as plt

In [4]: from matplotlib.lines import Line2D                        

In [5]: image = np.zeros((100, 100))

In [6]: for i in range(25, 75):
   ...:         image[100 - i, i] = 255
   ...:     image[i, i] = 255
   ...: 

In [7]: plt.imshow(image)

In [8]: plt.show()


The Hough transform converts the image into a parameter space that represents 
lines. A line can be represented by the distance r of its closest point to the 
origin and by the angle theta of this vector.
 
Every non-zero pixel of the image votes for potential line candidates, and the 
local maxima represents the parameters of probable lines.

In [9]: h, theta, d = hough(image)

In [10]: plt.figure()

In [10]: plt.title("hough transform")

In [10]: plt.xlabel("degrees")

In [10]: plt.ylabel("distance")

In [11]: plt.imshow(h)

In [12]: plt.show()

As can be seen, the maxima occur at 45 and 135 degrees, corresponding to the 
normal vector angles of each line.

Another method is to use probabilistic_hough, an implementation
based on the Progressive Probabilistic Hough Transform [1]. It states that a 
random subset of voting points give good enough results, and that lines can
be extracted during the voting process by walking along connected components.

The function has three parameters: a general threshold that is applied to 
the hough accumulator, a minimum line length and the line gap that influences
line merging.

In [13]: lines = probabilistic_hough(image, threshold=10, line_length=10, line_gap=1)

In [14]: plt.figure()

In [15]: for line in lines:
   ....:         p0, p1 = line
   ....:     plt.plot((p0[0], p1[0]), (p0[1], p1[1]))
   ....: 

In [16]: plt.show()

The Hough transform are often used on edge detected images.

In [17]: from scikits.image.io import imread

In [18]: from scikits.image import data_dir

In [19]: from scikits.image.filter import canny

In [20]: image = imread(data_dir + "/camera.png")

In [21]: edges = canny(image, 2, 1, 25)

In [22]: plt.imshow(edges)

In [23]: plt.show()

Apply the Probabilistic Hough Transform and find lines longer than 10 with a
gap less than 3 pixels.

In [24]: plt.figure()

In [25]: plt.imshow(np.zeros(edges.shape))

In [26]: lines = probabilistic_hough(edges, threshold=1, line_length=10, line_gap=3)

In [27]: for line in lines:
   ....:         p0, p1 = line
   ....:     plt.plot((p0[0], p1[0]), (p0[1], p1[1]))
   ....: 

In [28]: plt.show()


References
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.. [1]  C. Galamhos, J. Matas and J. Kittler,"Progressive probabilistic Hough 
        transform for line detection", in  IEEE Computer Society Conference on 
        Computer Vision and Pattern Recognition, 1999. 
.. [2]  Duda, R. O. and P. E. Hart, "Use of the Hough Transformation to Detect 
        Lines and Curves in Pictures," Comm. ACM, Vol. 15, pp. 11–15 (January, 
        1972)