SIMULATION OF EDGE DETECTION SYSTEMS

INTRODUCTION:

Edge detection is a fundamental tool used in most image processing applications to obtain information from the frames before feature extraction and object segmentation. This process detects outlines of an object and boundaries between objects and the background in the image. Beyond that, Edge Detection refers to the process of identifying and locating sharp discontinuities in intensities in an image. The discontinuities are abrupt changes in pixels intensity which characterize boundaries of objects in a scene structure. This process significantly reduces the amount of date in the image, while preserving the most important structural feature of that image. Edge detection is considered to be the ideal algorithm for images that are corrupted with white noise. The Edge is characterized by its height, slope angle,and horizontal coordinate of the slope midpoint. An ideal Edge Detector should  produce an edge indication localized to a single pixel located at the midpoint of the slope.There are many ways to perform Edge detection. However, the majority of different methods may be grouped into two categories, gradient and Laplacian. The basic Edge detection operator is a matrix area gradient operation that determines the level of variance between different pixels. The edge detection operator is calculated by forming a matrix centered on a pixel chosen as the centre of the matrix area. If the value of the matrix area is above a given threshold, then the middle pixel is classified as an edge. Examples of gradient based edge detectors are Roberts, Prewitt and Sobel operators. All the gradient –based algorithms have Kernel operators that calculate the strength of the slope in directions that are orthogonal to each other, generally horizontal and vertical.

The requirements that the algorithms must meet are:

a) Show the effectiveness and the noise resistance for remote sensing image.

b) Satisfying real time-constraints, and minimizing hardware resources in order to meet embedding requirements.

c) Significantly reducing the amount of date and filters out useless information.

Classically, Edge detection algorithms are implemented on software. With advances in the VLSI technology hardware implementation has become an attractive alternative. Assigning complex computation tasks to hardware and exploiting the parallelism and pipelining in algorithm yield significant speedup in running times. Implementation image processing on reconfigurable hardware minimizes the time-to-market cost, enables rapid prototyping of complex algorithm and simplifies debugging and verification.

APPLICATIONS:

a) Brillouin frequency shift distribution in fibre sensors based on double-technique.

b) Progressive Edge Detection on multi-bit images using polynomial-based binarization.

c) Application of an edge detection method to satellite images for distinguishing sea surface temperature fronts near janpanese coast.

d) An algorithm of sub-pixel edge detection based on ZOM and application in calibration for robot vision.

e) It has some more wide applications such as 3D reconstruction, recognition, image enhancement, image restoration and compression.

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