Structuring Element

Mathematical morphology has been of a great significance to several scientific fields. Dilation, as one of the fundamental operations, has been very much reliant on the common methods based on the set theory and on using specific shaped structuring elements to morph binary blobs. We hypothesised that by performing morphological dilation while exploiting geometry relationship between dot patterns, one can gain some advantages. The Delaunay triangulation was our choice to examine the feasibility of such hypothesis due to its favourable geometric properties. We compared our proposed algorithm to existing methods and it becomes apparent that Delaunay based dilation has the potential to emerge as a powerful tool in preserving objects structure and elucidating the influence of noise. Additionally, defining a structuring element is no longer needed in the proposed method and the dilation is adaptive to the topology of the dot patterns. We assessed the property of object structure preservation by using common measurement metrics. We also demonstrated such property through handwritten digit classification using HOG descriptors extracted from dilated images of different approaches and trained using Support Vector Machines. The confusion matrix shows that our algorithm has the best accuracy estimate in 80% of the cases. In both experiments, our approach shows a consistent improved performance over other methods which advocates for the suitability of the proposed method.

Object segmentation plays an important role in human visual perception, medical image processing and
content based image retrieval. It
provides information for recognition and interpretation. This
paper uses
mathematical
morphology for image segmentation. Object segmentation is difficult because one usually
does not know a priori what type of object exists in an image, how many different
shapes are there and
what regions the image has. To carryout discrimination and segmentation several innovative segmentation
methods, based on morphology are proposed. The present study proposes segmentation method based on
multiscale morphological reconst
ructions. Various sizes of structuring elements have been used to segment
simple and complex shapes. It enhances local boundaries that may lead to improve segmentation accuracy.
The method is tested on various datasets and results shows that it can be use
d for both interactive and
automatic segmentation
.

— Intelligent System for speed estimation of vehicle in digital image sequences is one of the key technologies of Traffic Surveillance System with problems of enlarging urban scale and increasing number of vehicles. This research paper intends to develop the intelligent system for speed estimation of vehicle using image processing technique. Overall works were the software development of an intelligent system that required a video scene and user designed algorithm in MATLAB software to implement it. The Algorithm for vehicle speed detection from a video frame system consists of six major components namely, Image Acquisition, Foreground detection, Morphological operations, Vehicle detection, Speed Estimation and Result Analysis. Each algorithm consists of MATLAB codes to execute each components. The designed system is flexible and can be extended for other applications. The maximum possible error of the system was determined to be within ±3 km/h and the experiment was performed on different type of vehicles and different range of speed.