lmlib.utils.beta.edge_detection

lmlib.utils.beta.edge_detection(y, ab, g)

Performs edge detection applying joint line models

Uses a two-line model to detect edges, according to example: sphx_glr_generated_examples_11-lssm-costs-detection_example-ex110.0-edge-detection.py

Parameters

• y (array_like of shape=(K,) of floats) – observation vector

• ab (tuple (a,b)) –

  • a (int, >0): left-sided window length (>0)

  • b (int, >0): right-sided window length (>0)

• g (tuple (g_a, g_b) of float) –

  Effective number of samples under the left-sided and right-sided window, see CostSegment.

  • g_a (float, >0): left-sided window length (>0)

  • g_b (float, >0): right-sided window length (>0)

• g_a (float) – Effective number of samples under the right-sided window, see CostSegment.

Returns

• lcr (ndarray of shape(K,) of floats) – log-likelihood ratio for a detected edge

• y0 (ndarray of shape(K,) of floats) – position estimate of edge on y-axis

• a0 (ndarray of shape(K,) of floats) – line slope of left-sided model

• a1 (ndarray of shape(K,) of floats) – line slope of right-sided model

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