Flood Fill Algorithm Research Articles

A New Particle Swarm Optimization-Based Method for Phase Unwrapping of MRI Data

A new method based on discrete particle swarm optimization (dPSO) algorithm is proposed to solve the branch-cut phase unwrapping problem of MRI data. In this method, the optimal order of matching the positive residues with the negative residues is first identified by the dPSO algorithm, then the branch cuts are placed to join each pair of the opposite polarity residues, and in the last step phases are unwrapped by flood-fill algorithm. The performance of the proposed algorithm was tested on both simulated phase image and MRI wrapped phase data sets. The results demonstrated that, compared with conventionally used branch-cut phase unwrapping algorithms, the dPSO algorithm is rather robust and effective.

A fast, accurate phase unwrapping method for wavelet-transform profilometry

A novel phase unwrapping method used in wavelet-transform profilometry is presented to improve the accuracy and speed of the unwrapping process. The wrapped phase of the fringe image is extracted by using wavelet transform. How to establish quality map using scale factor at wavelet ridge is discussed firstly. The proposed method can reflect the reliability of the pixels in fringe image effectively. As the consuming time of traditional flood-fill phase unwrapping algorithm is too long, an improved quality-guided method is used in phase unwrapping process. We divide the wrapped phase map into two levels following the established quality map, and process these two levels using different algorithms. Compared with the traditional flood-fill algorithm, we can get the accurate result while the consuming time of the phase unwrapping process is much less by using the proposed method. Simulation and experiment results verify that the 3D information of the measured object can be obtained rapidly and accurately by using the proposed method. Also the dynamic object can be measured too.

Capturing Raster Data From Scanned Thematic Maps Using Desktop Graphics Software

Data capture from paper maps remain a slow and costly part of many Geographic Information System projects. In this paper a simple method is described which makes use of the facilities of desktop graphics software to capture raster data from scanned thematic maps. The first stage is to use the flood fill tool to identify the regions of the map that represent the data values and set them to a separate colour for each thematic category. This edited image is then imported into a GIS, where a specially written majority filter is used to classify any remaining pixels to produce a raster GIS layer. The technique is tested on samples from three maps, giving accuracy levels of over 90% in all cases when compared with digitising the same maps by hand. The technique could be useful for situations where a rapid and simple means of data capture is needed and could be developed further by drawing on techniques for contextual classification rather than relying on a general purpose flood fill algorithm.

A cell-based point-in-polygon algorithm suitable for large sets of points

The paper describes a new algorithm for solving the point-in-polygon problem. It is especially suitable when it is necessary to check whether many points are placed inside or outside a polygon. The algorithm works in two steps. First, a grid of cells equal in size is generated, and the polygon is laid on that grid. A heuristic approach is proposed for cell dimensioning. The cells of the grid are marked as being inside, outside, or on the polygon border. A modified flood-fill algorithm is applied for cell classification. In the second step, points are tested individually. If the tested point falls into an inner or an outer cell, the result is returned without any additional calculations. If the cell contains the polygon border, it is possible to determine the local point position. The analysis of time complexity shows that the initialization is finished in O(n n ) time, while the expected time complexity for checking an individual point is O( n ) , where n represents the number of polygon edges. The algorithm works with O( n) space complexity. The paper also gives practical results using artificial and real polygons from a GIS environment.

Fill 'er up! [Graphics filling algorithms

One of the most important and useful ideas in 2D computer graphics is the fill. Simple in concept but powerful in practice, filling is so basic a technique that there's a fill tool in just about every 2D drawing system sold today. Probably one of the first important uses of fills was to color in 2D cartoon characters, but that's just the tip of the iceberg. It's hard to know who wrote the first fill routine, but it may have been Dick Shoup, who implemented a fill technique for his Superpaint system in the mid-1970s. The next big step was Alvy Ray Smith's 24-bit flood-fill algorithm, which also included tint fill. Change came fast after that point, culminating in the sophisticated filling tools we have today. In this article, I discuss some fill techniques that I've cooked up over time to solve different jobs. I focus on techniques for identifying fill pixels.

Displacement field estimation and image segmentation using block matching enhanced by a neural network.

The block-matching method plays an important role in displacement field estimation due to its simplicity, achievement of long-range motion, and robustness to noise. In this paper, a single-layer feedback neural network model is proposed that enhances block matching, estimates the displacement field, and simultaneously performs image segmentation from consecutive images. In this paper, image segmentation is defined as partitioning each image into a set of moving objects and the background. For any two consecutive images, a neural network is created that learns the connection relationship of the pixels in an object from the displacement field and stores the relationship in the network. A modified block matching is used to compute a more accurate displacement field by utilizing the segmentation information embedded in the neural network. The displacement vector at the edge of an object or occluding boundary is hard to estimate, but the proposed model performs satisfactorily because it learns and uses the connection information. Furthermore, a flood-fill algorithm is used to compute the dense displacement field more efficiently and correctly than the exhaustive search does. The most important aspect of this paper is that image segmentation is performed simultaneously with the displacement-field estimation by the neural-network model. The novel idea of the work is to embed the segmentation information (connection relations) in the neural network and to perform the displacement-field estimation and image segmentation simultaneously. Two methods for retrieving segmentation information from the neural network with any two consecutive images are also presented.

An efficient flood-filling algorithm

Flood-filling of a region on raster devices is one of the most widely used techniques in the interactive graphical systems. Existing filling algorithms, in general, require significant amount of memory and/or are limited to the specific types of regions or fill styles. We discuss some of the up-to-date filling techniques and propose a general framework for generation of flood-filling algorithms. An “optimal” representative of this class of algorithms is presented. It is proved, in particular, that necessary size of work memory for this particular algorithm is proportional to the horizontal dimension of the box containing region to be filled. The algorithm may be used to fill 4-step and 8-step connected regions both border- and interior-defined.

An imaging routine for assessing the efficacy of instruments used for scaling and root planing.

Deposits of dental plaque or calculus are typically quantified using planimetric techniques. However, error is introduced into a quantitative analysis of plaque deposits using these methods, since they require a significant amount of human intervention. The purpose of this study is to describe and validate a computerized imaging routine which has the potential to objectively identify material on dental roots and measure the area covered by these deposits. Dentiform teeth with simulated plaque were videorecorded. A computer routine was developed based on a flood-fill algorithm which analyzed images of the dentiform teeth and determined the amount of simulated plaque on their root surfaces. Results showed that the dentiform teeth and their simulated plaque patterns are duplicated by the imaging routine in a rapid and reliable fashion. The system shows a high degree of accuracy with an average error factor of only 0.58%. As well, the system enables precise reproducibility with an average error factor of only 0.71%.

An algorithm for region filling using two-dimensional grammars

Interior-defined regions, flood-fill algorithms, and a simple 4-connected region filling algorithm are presented together with their properties. an algorithm for region filling using two-dimensional grammars is also presented together with illustrative examples. the results obtained in this article may have useful application in intelligent systems, computer graphics, artificial intelligence, expert systems, knowledge engineering, pattern recognition, pictorial databases, and related areas.