Journal Article10.1109/34.49050
Integrating region growing and edge detection
Theodosios Pavlidis,Y.-T. Liow +1 more
589
TL;DR: A method that combines region growing and edge detection for image segmentation is presented and is thought that the success in the tool images is because the objects shown occupy areas of many pixels, making it is easy to select parameters to separate signal information from noise.
read more
Abstract: A method that combines region growing and edge detection for image segmentation is presented. The authors start with a split-and merge algorithm wherein the parameters have been set up so that an over-segmented image results. Region boundaries are then eliminated or modified on the basis of criteria that integrate contrast with boundary smoothness, variation of the image gradient along the boundary, and a criterion that penalizes for the presence of artifacts reflecting the data structure used during segmentation (quadtree in this case). The algorithms were implemented in the C language on a Sun 3/160 workstation running under the Unix operating system. Simple tool images and aerial photographs were used to test the algorithms. The impression of human observers is that the method is very successful on the tool images and less so on the aerial photograph images. It is thought that the success in the tool images is because the objects shown occupy areas of many pixels, making it is easy to select parameters to separate signal information from noise. >
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Segmentation of color images based on relation matrix
Recep Demirci,Ferzan Katırcıoğlu +1 more
- 11 Jun 2007
TL;DR: In this study, segmentation is done by using the similarity feature between neighbour pixells using region growing algorithm to answer the effect of similarity measure functions and how threshold and normality value effect the segmentation.
3
Cuckoo Search Based Color Image Segmentation Using Seeded Region Growing
M. Mary Synthuja Jain Preetha,L. Padma Suresh,M. John Bosco +2 more
- 01 Jan 2015
TL;DR: A new meta-heuristic algorithm is proposed in this paper for color image segmentation that is optimized using cuckoo-search optimization algorithm and obtained texture information and the region growth map of the fully grown regions are considered for merging procedure to merge regions with similar characteristics.
3
Data and knowledge engineering for medical image and sensor data
Franz Graf
- 31 Jan 2012
TL;DR: A new technique is presented that allows a query-by-example search in CT volume scans that requires only a minimal set of input data to obtain a very accurate result and can be used as an initialization for several other techniques that are yet only semi-automatic as they often need a manual initialization.
3
Automatic Vector Seeded Region Growing for Parenchyma Classification in Brain MRI
Chuin-Mu Wang,Ruey-Maw Chen +1 more
- 01 Feb 2012
TL;DR: Through the UVSRG processing, the data dimensionality of MRI can be decreased and the desired target of interest can be classified which the brain tissue and brain tumor segmentation.
Background Extraction in Electron Microscope Images of Artificial Membranes
Argyro Karathanou,Jean-Luc Buessler,H. Kihl,Jean-Philippe Urban +3 more
- 23 Apr 2009
TL;DR: In the proposed solution, the elimination of false contours is based on the statistical examination of the perpendicular gradient component along the contour, and the background extraction can be easily effectuated since this resulting region appears bright and large.
References
Snakes : Active Contour Models
TL;DR: This work uses snakes for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest, and uses scale-space continuation to enlarge the capture region surrounding a feature.
Smoothing by spline functions. II
TL;DR: In this paper, the authors generalize the results of [4] and modify the algorithm presented there to obtain a better rate of convergence, which is the same as in this paper.
2.4K
Integrating region growing and edge detection
Theodosios Pavlidis,Y.-T. Liow +1 more
TL;DR: A method that combines region growing and edge detection for image segmentation is presented and is thought that the success in the tool images is because the objects shown occupy areas of many pixels, making it is easy to select parameters to separate signal information from noise.
590
On the optimal detection of curves in noisy pictures
TL;DR: A technique for recognizing systems of lines is presented, in which the heuristic of the problem is not embedded in the recognition algorithm but is expressed in a figure of merit, which allows for greater flexibility and adequacy in the particular problem.
339