Canny Edge Detector

Detailed Description

Provides a Canny edge detector kernel.

This function implements an edge detection algorithm similar to that described in [2]. The main components of the algorithm are:

• Gradient magnitude and orientation computation using a noise resistant operator (Sobel).
• Non-maximum suppression of the gradient magnitude, using the gradient orientation information.
• Tracing edges in the modified gradient image using hysteresis thresholding to produce a binary result.

The details of each of these steps are described below.

• Gradient Computation: Conceptually, the input image is convolved with vertical and horizontal Sobel kernels of the size indicated by the gradient_size parameter. The Sobel kernels used for the gradient computation shall be as shown below. The two resulting directional gradient images ( \(dx \) and \( dy \)) are then used to compute a gradient magnitude image and a gradient orientation image. The norm used to compute the gradient magnitude is indicated by the norm_type parameter, so the magnitude may be \( |dx| + |dy| \) for VX_NORM_L1 or \( \sqrt{dx^{2} + dy^{2}} \) for VX_NORM_L2. The gradient orientation image is quantized into 4 values: 0, 45, 90, and 135 degrees.
• For gradient size 3:

  \[ \mathbf{sobel}_{x}= \begin{vmatrix} -1 & 0 & 1\\ -2 & 0 & 2\\ -1 & 0 & 1 \end{vmatrix} \]

\[ \mathbf{sobel}_{y}=transpose({sobel}_{x})= \begin{vmatrix} -1 & -2 & -1\\ 0 & 0 & 0\\ 1 & 2 & 1 \end{vmatrix} \]

• For gradient size 5:

  \[ \mathbf{sobel}_{x}= \begin{vmatrix} -1 & -2 & 0 & 2 & 1\\ -4 & -8 & 0 & 8 & 4\\ -6 & -12 & 0 & 12 & 6\\ -4 & -8 & 0 & 8 & 4\\ -1 & -2 & 0 & 2 & 1\\ \end{vmatrix} \]

  \[ \mathbf{sobel}_{y}=transpose({sobel}_{x}) \]

• For gradient size 7:

  \[ \mathbf{sobel}_{x}= \begin{vmatrix} -1 & -4 & -5 & 0 & 5 & 4 & 1\\ -6 & -24 & -30 & 0 & 30 & 24 & 6\\ -15 & -60 & -75 & 0 & 75 & 60 & 15\\ -20 & -80 & -100 & 0 & 100 & 80 & 20\\ -15 & -60 & -75 & 0 & 75 & 60 & 15\\ -6 & -24 & -30 & 0 & 30 & 24 & 6\\ -1 & -4 & -5 & 0 & 5 & 4 & 1\\ \end{vmatrix} \]

  \[ \mathbf{sobel}_{y}=transpose({sobel}_{x}) \]

• Non-Maximum Suppression: This is then applied such that a pixel is retained as a potential edge pixel if and only if its magnitude is greater than or equal to the pixels in the direction perpendicular to its edge orientation. For example, if the pixel's orientation is 0 degrees, it is only retained if its gradient magnitude is larger than that of the pixels at 90 and 270 degrees to it. If a pixel is suppressed via this condition, it must not appear as an edge pixel in the final output, i.e., its value must be 0 in the final output.
• Edge Tracing: The final edge pixels in the output are identified via a double thresholded hysteresis procedure. All retained pixels with magnitude above the high threshold are marked as known edge pixels (valued 255) in the final output image. All pixels with magnitudes less than or equal to the low threshold must not be marked as edge pixels in the final output. For the pixels in between the thresholds, edges are traced and marked as edges (255) in the output. This can be done by starting at the known edge pixels and moving in all eight directions recursively until the gradient magnitude is less than or equal to the low threshold.
• Caveats: The intermediate results described above are conceptual only; so for example, the implementation may not actually construct the gradient images and non-maximum-suppressed images. Only the final binary (0 or 255 valued) output image must be computed so that it matches the result of a final image constructed as described above.

Enumerations
enum	vx_norm_type_e {   VX_NORM_L1 = ((( VX_ID_KHRONOS ) << 20) | ( VX_ENUM_NORM_TYPE << 12)) + 0x0,   VX_NORM_L2 = ((( VX_ID_KHRONOS ) << 20) | ( VX_ENUM_NORM_TYPE << 12)) + 0x1 }
	A normalization type. More...

Functions
vx_node	vxCannyEdgeDetectorNode (vx_graph graph, vx_image input, vx_threshold hyst, vx_int32 gradient_size, vx_enum norm_type, vx_image output)
	[Graph] Creates a Canny Edge Detection Node. More...
vx_status	vxuCannyEdgeDetector (vx_context context, vx_image input, vx_threshold hyst, vx_int32 gradient_size, vx_enum norm_type, vx_image output)
	[Immediate] Computes Canny Edges on the input image into the output image. More...

Enumeration Type Documentation

enum vx_norm_type_e

A normalization type.

See also

Canny Edge Detector

Enumerator
VX_NORM_L1	The L1 normalization.
VX_NORM_L2	The L2 normalization.

Definition at line 1100 of file vx_types.h.

Function Documentation

vx_node vxCannyEdgeDetectorNode	(	vx_graph	graph,
		vx_image	input,
		vx_threshold	hyst,
		vx_int32	gradient_size,
		vx_enum	norm_type,
		vx_image	output
	)

[Graph] Creates a Canny Edge Detection Node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input VX_DF_IMAGE_U8 image.
[in]	hyst	The double threshold for hysteresis.
[in]	gradient_size	The size of the Sobel filter window, must support at least 3, 5, and 7.
[in]	norm_type	A flag indicating the norm used to compute the gradient, VX_NORM_L1 or VX_NORM_L2.
[out]	output	The output image in VX_DF_IMAGE_U8 format with values either 0 or 255.

Returns

vx_node.

Return values

0	Node could not be created.
*	Node handle.

vx_status vxuCannyEdgeDetector	(	vx_context	context,
		vx_image	input,
		vx_threshold	hyst,
		vx_int32	gradient_size,
		vx_enum	norm_type,
		vx_image	output
	)

[Immediate] Computes Canny Edges on the input image into the output image.

Parameters

[in]	context	The reference to the overall context.
[in]	input	The input VX_DF_IMAGE_U8 image.
[in]	hyst	The double threshold for hysteresis.
[in]	gradient_size	The size of the Sobel filter window, must support at least 3, 5 and 7.
[in]	norm_type	A flag indicating the norm used to compute the gradient, VX_NORM_L1 or VX_NORM_L2.
[out]	output	The output image in VX_DF_IMAGE_U8 format.

Returns

A vx_status_e enumeration.

Return values

VX_SUCCESS	Success
*	An error occurred. See vx_status_e.