Memoire Online - Implementation of edge detection for a digital image

The basic requirement of this MATLAB program is to implement edge detection. The Sobel and Prewitt edge detector can also be applied to range the image (sampled picture) called Sobel figure and Prewitt figure. The corresponding edge image has been detected and can nice separated from the background.

The Sobel edge detection based on Sobel operator has the advantages of emphasizing the central part of the edge. It places emphasis on pixels that are closer to the center of the mask. Each direction of Sobel operator is applied to the image. One image shows the vertical response and other show the horizontal response. Purpose is to determine the existence and location of edge in picture.

When the object containing sharp edges and its surface is rather smooth, therefore could easily detect the boundary of the object without getting any erroneous pixels.

For the object containing many fine depth variations on its surface, applying the Sobel operator we can see that the intensity of many pixels on surface is high as long as the actual edges. Once reason is that the output of many edge pixels is greater than the maximum pixels values therefore there is `cutoff' at 255.

To avoid this over flow we scale the range image by a factor 0.25 prior to edge detection and then normalize the output although the result has improved significantly.

The Sobel operator is based on convolution in very small neighborhood and work well for specific image only.

The many disadvantages of this edge detection is its dependence on the size of object and sensitivity to noise.

The Sobel operator is very quick to computer and rather simple to implement. It yields the best result. The Sobel operator is one of the most commonly used edge detectors..

The Prewitt edge detector based on Prewitt operator approximates the first derivatives. This operator does not place any pixels that are closer to the center of the mask.

The Prewitt edge detector masks are one of the oldest and best understood methods of detecting edges in image. Basically there two mask one for detecting image derivatives in X and other one for detecting image derivatives in Y. The results of Prewitt edge detection are thresholded in order to produce a discrete set of edges. The direction of gradient mask is given by the mask giving maximal response.

The sobel and Prewitt operators use the first derivative. They have very simple calculation to detect edges and their orientation but they have inaccurate detection sensitivity in case of noise because the subjective evaluation of edge detection result images show that under noisy condition Prewitt and Sobel operator have poor quality.

As we have seeing in the precession of digital images (in chap.3); Prewitt and Sobel mark difference between them. The difference is that Sobel edge detector marks a lot of number of pixels while the Prewitt edge detector marks a few number of pixels.

The processed picture has been saved in different file format. The chosen file format can impact the clarity of picture. The following file formats have been chosen in this work such as JPEG, BMP, PNG, and GIF. The digital camera used when we shooted pictures offered JPEG setting, in order to achieve uor objective we tried to put the taken pictures into different file format using software that is able to convert them into the wanted format(e.g: Microsoft office picture manager).

The processed done using both sobel and Prewitt operators. A picture which is in JPEG file format when it is converted into other file format the number of pixels are variable according to the used operator(sobel or Prewitt).

When the picture is converted into GIF file format the sobel operators shows high number of pixels than JPEG, but Prewitt operator shows few number of pixels than the JPEG.

When the picture is converted into PNG file format, its procession with sobel shows high number of pixels than JPEG, but the Prewitt shows some number of number of pixels as JPEG. When a picture is converted into BMP file format, its procession with sobel and Prewitt operators show the same number of pixels as JPEG file format.

Original format picture (reference)	Format after conversion	Number of pixels
		Sobel operator	Prewitt operator
JPEG	JPEG	same	Same
JPEG	PNG	High	Same
JPEG	GIF	High	Low
JPEG	BMP	same	Same

Table 4.1: shows comparison of number of pixels between PNG, GIF, BMP file formats and JPEG file format.

As we know that : the term contrast refer to the amount of color or gray scale differentiation that exist between various images features in both analog and digital image while brightness is the relative lightness or darkness of particular color from black to white. When a brightness of processed picture is increased at high level and contrast in the normal contrast of image's capturing, they were no pixels at the segmented image in both operator s(sobel and Prewitt).

When brightness is reduced at low level and contrast remains in the contrast of image's capturing, the unwanted pixels at segmented image occurred because of the background and the object were changed when the brightness was changed.

When contrast is increased at high level and brightness remains in normal condition, the Prewitt operator shows the unwanted pixels because there were no edges at segmented image and sobel operator shows us only the background.

When contrast was reduced at low level and brightness remains in its normal condition, pixels are available as the background remains unchangeable, there is no noise in both the sobel and Prewitt operators.

When contrast and brightness are increased there are no pixels on the face because the face tends to be as the background. But when both are reduced at low level there are the unwanted pixels on the segmented image, because pixels are located any where brightness is cleared.

Reducing contrast level and increasing brightness level no pixels are shown on segmented image in both sobel and Prewitt operators while reducing brightness level and increasing contrast level more pixels are shown on brig area only.

Contrast level	Brightness level	Observation on the output
Normal position of image's capturing	high	No pixels
Normal position of image's capturing	Low	Noise(unwanted pixels on segmented image)
High	Normal position of capturing of image	Background
Low	Normal position of capturing of image.	Pixels available
High	High	No pixels
Low	Low	Noise(unwanted pixels on segmented image)
High	Low	Pixels available on bright area
Low	High	No pixels
Normal position of image's capturing	Normal position of image's capturing	Pixels available

Implementation of edge detection for a digital image

4.1. RESULTS.