Discontinuity detection plays an important role in image analysis applications like image registration, comparison, segmentation, time sequence analysis and object recognition. This paper presents a new approach for corner detection using first order difference chain-encoding. The proposed method is based on integer operations it is very simple and efficient. Preliminary results are presented and evaluation with respect to standard corner detectors like Harris and Yung is done as a benchmark.

/pdf/corner-detection-using-difference-chain-code-as-curvature-20k5jyvjfc.pdf

Corner Detection Using Difference Chain Code as Curvature

Edges form the outline of an object. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects also. So, by identifying edges in an image accurately, all the objects can be located and basic properties such as area, perimeter and shape can be measured. Therefore, edge detection is used to find the discontinuities in surface orientation, changes in material properties and variations in scene illuminations. In this paper, implementation of various morphological operators is done for medical images.

Detection of Edges Using Mathematical Morphological Operators

Micro-computed tomographic and SEM study of porous bioceramics using an adaptive method based on the mathematical morphological operations.

Today, digital image processing is an important area of research. Many medical applications apply image processing techniques to enhance and improve the quality of medical diagnosis. Effective edge detection of images is a fundamental problem in digital image processing. There are different approaches proposed to detect edges of the image such as classical gradients and second derivatives based operators. However, existing approaches have various tradeoffs in the presence of noise and suffer from poor de-noising which leads to an inaccurate edge detection. In this paper, an edge detection approach based on mathematical morphology is proposed to improve edge detection in noisy medical images of fractured bones, head CT, mammogram and colored eye’s fundus. Our evaluation results showed that the proposed detector outperforms classical detectors as well as existing mathematical morphology approaches in detecting edges of medical images under noise effect named “salt and pepper”. The proposed detector applies multiple structured elements to suppress noise and detect edges, thus produces thinner and better linked edges in noisy medical images.

A novel mathematical morphology based edge detection method for medical images

Medicines have helped to make our lives easy. Drug industry is developing industry in terms of production as well as consumption. Medication has become very important in everyone’s life as we are affected by so many diseases. But these medicines might be defected, tablets may be broken, there may be missing tablet in a strip and consumption of these drugs might be dangerous. This paper shows a method in digital image processing technique to find the defects in tablets. In this paper we use mathematical manipulation, to detect the defected tablet packet.

/pdf/edge-detection-in-digital-image-using-statistical-method-4lyswry09c.pdf

Edge Detection in Digital Image Using Statistical Method

Edges and corners are regions of interest where there is a sudden change in intensity. These features play an important role in object identification methods used in machine vision and image processing systems. This paper presents a novel method for edge and corner detection in images. The approach used here is extracting Edges of the input image using morphological operator and then sending it for Chain Encoding. We are proposing a new morphological edge detector which returns a one pixel thick m-connected binary boundary image. This is followed by our chain encoding method to detect corners on the extracted edges. The algorithm works on all types of images (i.e. binary, gray level and color images). Since the proposed methods are based on morphological operations, these are very simple, efficient and fast. Experimental results on a variety of images identified all the prominent edges and significant corners efficiently.

/pdf/morphological-edge-detection-and-corner-detection-algorithm-2pgar6p3a9.pdf

Morphological Edge Detection and Corner Detection Algorithm Using Chain Encoding.

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Morphological Edge Detection and Corner Detection Algorithm Using Chain Encoding.