There are many methods for edge detection, but most of them can be grouped into two categories, search-based and zero-crossing based. The search-based methods detect edges by first computing a measure of edge strength, usually a first-order derivative expression such as the gradient magnitude, and then searching for local directional maxima of the gradient magnitude using a computed estimate of the local orientation of the edge, usually the gradient direction. The zero-crossing based methods search for zero crossings in a second-order derivative expression computed from the image in order to find edges, usually the zero-crossings of the Laplacian or the zero-crossings of a non-linear differential expression. As a pre-processing step to edge detection, a smoothing stage, typically Gaussian smoothing, is almost always applied.
The edge detection methods that have been published mainly differ in the types of smoothing filters that are applied and the way the measures of edge strength are computed. As many edge detection methods rely on the computation of image gradients, they also differ in the types of filters used for computing gradient estimates in the x– and y-directions.
Edge detection is a process of locating an edge of an image. Detection of edges in an image is a very important step towards understanding image features. Edges consist of meaningful features and contained significant information. It’s reduce significantly the amount of the image size and filters out information that may be regarded as less relevant, preserving the important structural properties of an image (Yuval, 1996). Most images contain some amount of redundancies that can sometimes be removed when edges are detected and replaced, when it is reconstructed (Osuna et al., 1997). Eliminating the redundancy could be done through edge detection. When image edges are detected, every kind of redundancy present in the image is removed (Sparr, 2000).
The purpose of detecting sharp changes in image brightness is to capture important events. Applying an edge detector to an image may significantly reduce the amount of data to be processed and may therefore filter out information that may be regarded as less relevant, while preserving the important structural properties of an image. The image quality reflects significant information in the output edge and the size of the image is reduced. This in turn explains further that edge detection is one of the ways of solving the problem of high volume of space images occupy in the computer memory. The problems of storage, transmission over the Internet and bandwidth could be solved when edges are detected (Vincent, 2007). Since edges often occur at image locations representing object boundaries, edge detection is extensively used in image segmentation when images are divided into areas corresponding to different objects.
- More Salt and Pepper noise
- More power consumption
- Edge Detection is not Better
Edge detection includes a variety of mathematical methods that aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. The points at which image brightness changes sharply are typically organized into a set of curved line segments termed edges. The same problem of finding discontinuities in 1D signals is known as step detection and the problem of finding signal discontinuities over time is known as change detection. Edge detection is a fundamental tool in image processing, machine vision and computer vision, particularly in the areas of feature detection and feature extraction
The edges extracted from a two-dimensional image of a three-dimensional scene can be classified as either viewpoint dependent or viewpoint independent. A viewpoint independent edge typically reflects inherent properties of the three-dimensional objects, such as surface markings and surface shape. A viewpoint dependent edge may change as the viewpoint changes, and typically reflects the geometry of the scene, such as objects occluding one another.
A typical edge might for instance be the border between a block of red color and a block of yellow. In contrast a line (as can be extracted by a ridge detector) can be a small number of pixels of a different color on an otherwise unchanging background. For a line, there may therefore usually be one edge on each side of the line.
Sobel edge detection:
The sobel operator is very similar to Prewitt operator. It is also a derivate mask and is used for edge detection. Like Prewitt operator sobel operator is also used to detect two kinds of edges in an image:
- Vertical direction
- Horizontal direction
When we apply this mask on the image it prominent vertical edges. It simply works like as first order derivate and calculates the difference of pixel intensities in a edge region.
As the center column is of zero so it does not include the original values of an image but rather it calculates the difference of right and left pixel values around that edge. Also the center values of both the first and third column is 2 and -2 respectively.
This give more weight age to the pixel values around the edge region. This increase the edge intensity and it become enhanced comparatively to the original image.
The Sobel edge detection uses two masks, one for detecting image derivatives in horizontal direction and the other for detecting image derivatives in vertical direction. One mask is simply the other rotated by 90°. The Sobel kernels can also be thought of as 3 × 3 approximations to first-derivative-of-Gaussian kernels.
The implementation of the Edge detector consists of RGB to grayscale color space conversion and line buffers to synchronize H-sync, V-sync and Data-enable signals. The filter and buffer block consists of line buffer to hold the corresponding rows and edge detector block. The edge detector block consists of two convolution block which performs the operation specified by the kernel. Individual rows of each kernel are multiplied by the delayed elements which are stored in the line buffers to yield vertical and horizontal edges. These are added and gradient magnitude is found out. The gradient magnitude is thresholded using a manual threshold to obtain proper edges.
- Less Salt and Pepper noise
- Less power consumption
- Edge Detection is Better
- Reduced Area