Selection of an Automated Morphological Gradient Threshold for Image Segmentation. Application to Vision-Based Path Planning

The assessment of forensic photographs often requires the calibration of the resolution of the image so that accurate measurements can be taken of crime-scene exhibits or latent marks. In the case of latent marks, such as fingerprints, image calibration to a given dots-per-inch is a necessary step for image segmentation, preprocessing, extraction of feature minutiae and subsequent fingerprint matching. To enable scaling, such photographs are taken with forensic rulers in the frame so that image pixel distances can be converted to standard measurement units (metric or imperial). In forensic bureaus, this is commonly achieved by manual selection of two or more points on the ruler within the image, and entering the units of the measure distance. The process can be laborious and inaccurate, especially when the ruler graduations are indistinct because of poor contrast, noise or insufficient resolution. Here the authors present a fully automated method for detecting and estimating the direction and graduation spacing of rulers in forensic photographs. The method detects the location of the ruler in the image and then uses spectral analysis to estimate the direction and wavelength of the ruler graduations. The authors detail the steps of the algorithm and demonstrate the accuracy of the estimation on both a calibrated set of test images and a wide collection of good and poor quality crime-scene images. The method is shown to be fast and accurate and has wider application in other imaging disciplines, such as radiography, archaeology and surveying.

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Automatic Seeded Region Growing Image Segmentation for Medical Image Segmentation: A Brief Review

International Journal of Image and Graphics ◽

10.1142/s0219467820500187 ◽

2020 ◽

Vol 20 (03) ◽

pp. 2050018

Author(s):

Neeraj Shrivastava ◽

Jyoti Bharti

Keyword(s):

Image Segmentation ◽

Region Growing ◽

Magnetic Resonance Images ◽

Medical Image Segmentation ◽

Seed Selection ◽

Seeded Region Growing ◽

Processing Strategies ◽

Edge Based ◽

Seed Points ◽

Selection Of

In the domain of computer technology, image processing strategies have become a part of various applications. A few broadly used image segmentation methods have been characterized as seeded region growing (SRG), edge-based image segmentation, fuzzy [Formula: see text]-means image segmentation, etc. SRG is a quick, strongly formed and impressive image segmentation algorithm. In this paper, we delve into different applications of SRG and their analysis. SRG delivers better results in analysis of magnetic resonance images, brain image, breast images, etc. On the other hand, it has some limitations as well. For example, the seed points have to be selected manually and this manual selection of seed points at the time of segmentation brings about wrong selection of regions. So, a review of some automatic seed selection methods with their advantages, disadvantages and applications in different fields has been presented.

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Path Planning and Image Segmentation using the FDTD Method

IEEE Antennas and Propagation Magazine ◽

10.1109/map.2011.5949376 ◽

2011 ◽

Vol 53 (2) ◽

pp. 230-245 ◽

Cited By ~ 3

Author(s):

Mustafa Cakir ◽

Levent Sevgi

Keyword(s):

Image Segmentation ◽

Path Planning ◽

Fdtd Method

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Mobile robot path planning using an improved ant colony optimization

International Journal of Advanced Robotic Systems ◽

10.1177/1729881418774673 ◽

2018 ◽

Vol 15 (3) ◽

pp. 172988141877467 ◽

Cited By ~ 17

Author(s):

Khaled Akka ◽

Farid Khaber

Keyword(s):

Path Planning ◽

Mobile Robot ◽

Ant Colony Algorithm ◽

Search Space ◽

Step Length ◽

Ant Colony ◽

Dynamic Adjustment ◽

Feedback Information ◽

Intelligent Optimization Algorithm ◽

Selection Of

Ant colony algorithm is an intelligent optimization algorithm that is widely used in path planning for mobile robot due to its advantages, such as good feedback information, strong robustness and better distributed computing. However, it has some problems such as the slow convergence and the prematurity. This article introduces an improved ant colony algorithm that uses a stimulating probability to help the ant in its selection of the next grid and employs new heuristic information based on the principle of unlimited step length to expand the vision field and to increase the visibility accuracy; and also the improved algorithm adopts new pheromone updating rule and dynamic adjustment of the evaporation rate to accelerate the convergence speed and to enlarge the search space. Simulation results prove that the proposed algorithm overcomes the shortcomings of the conventional algorithms.

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