Globally Measuring the Similarity of Superpixels by Binary Edge Maps for Superpixel Clustering

Let G be a simple graph with vertex set V(G) and edge set E(G), and let Z2 = {0,1}. For a given binary edge labeling f :E(G)→Z2 , the edge labeling f induces a partial vertex labeling f*:V(G)→Z2 such that f*(v) =1(0) iff the number of 1-edges (0-edges) is strictly greater than the number of 0-edges (1-edges) incident to v , otherwise f*(v) is idefined. For i∈Z2 , let v(i)=card{v∈V(G): f*(v) =i} and e(i) = card{e∈E(G) : f (e)=i}. The edge-balance index sets of a graph G,EBI(G), is defined as {|v(0) −v(1) |: the edge labeling f satisfies |e(0)−e(1) |≤1}.In this paper, we completely determine the edge-balance CnxP 6(n=3,4,5mod6).

Download Full-text

Automic Edge Detection in Noisy Fringe Patterns: A Practical Approach

Jurnal Teknologi ◽

10.11113/jt.v37.541 ◽

2012 ◽

Author(s):

Mani Maran Ratnam

Keyword(s):

Edge Detection ◽

Threshold Value ◽

Practical Approach ◽

Fringe Projection ◽

Edge Detector ◽

Noise Levels ◽

Detection Techniques ◽

Real Objects ◽

Fringe Patterns ◽

Binary Edge

Corak-corak pinggir biasanya dikaji dengan menggunakan tingkap yang ditakrifkan awal kerana kesukaran mencari sempadannya. Hal ini berlaku kerana pinggir-pinggir tersebut biasanya mempunyai keamatan minimum yang serupa dengan keamatan latar belakang dan adalah sukar untuk menentukan di mana sesuatu pinggir bermula dan berakhir. Kehadiran pinggir-pinggir boleh menyebabkan ralat dalam pengesanan sempadan dengan menggunakan kaedah-kaedah pengesanan sempadan yang sedia ada. Kertas kerja ini membentangkan suatu pendekatan praktik ke arah pengesanan automatik sempadan corak pinggir berhingar dengan paras hingar yang berbeza. Teknik tersebut menggunakan operasi-operasi morfologi pengembangan dan penghakisan serta pengesan sempadan binari kejiranan mudah. Nilai ambang yang diperlukan dalam operasi binarisasi ditentukan daripada hubungkait linear antara nilai ambang dengan sisihan piawai hingar Gaussian daripada kajian simulasi. Teknik tersebut kemudian dilaksanakan dengan jayanya pada corak-corak pinggir pada beberapa objek sebenar yang didapati melalui kaedah pengunjuran pinggir. Beberapa kelemahan kaedah tersebut dibincangkan. Kata kunci: Pengesanan sempadan, corak pinggir Fringe patterns are usually analyzed using a predefined window due to the difficulty associated with finding their edges. This is because, the fringes usually have minimum intensities similar to that of the background and it is difficult to determine where a fringe starts or ends. The presence of the fringes can therefore lead to errors in detecting the edges of the object when using existing edge detection techniques. This paper presents a practical approach towards the automatic detection of the edges of a noisy fringe pattern having different noise levels. The technique uses the dilation and erosion morphological operators combined with a simple neighborhood binary edge detector. The threshold required for the binarization operation is determined from a linear relationship between the threshold value and the standard deviation of Gaussian noise from a simulation study. The proposed technique has been successfully applied to fringe patterns on real objects obtained using the fringe projection method. Some of the limitations of the technique are discussed. Key words: Edge detection; fringe patterns

Download Full-text

Encoding the vertices of a graph with binary edge labels

Sequences ◽

10.1007/978-1-4612-3352-7_23 ◽

1990 ◽

pp. 287-299 ◽

Cited By ~ 1

Author(s):

Zsolt Tuza

Keyword(s):

Binary Edge

Download Full-text

Fast Content Based Color Image Retrieval System Based on Texture Analysis of Edge Map

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.341-342.168 ◽

2011 ◽

Vol 341-342 ◽

pp. 168-172

Author(s):

Hesamoddin Salehian ◽

Fatemeh Zamani ◽

Mansour Jamzad

Keyword(s):

Time Complexity ◽

Retrieval System ◽

Color Image ◽

Color Spaces ◽

Edge Detector ◽

Complementary Information ◽

Edge Image ◽

Image Retrieval System ◽

Different Color ◽

Binary Edge

In this paper we propose a method for CBIR based on the combination of texture, edge map and color. As texture of edges yields important information about the images, we utilized an adaptive edge detector that produces a binary edge image. Also, using the statistics of color in two different color spaces provides complementary information to retrieve images. Our method is time efficient since we have applied texture calculations on the binary edge image. Our experimental results showed both the higher accuracy and lower time complexity of our method with similar related works using SIMPLIcity database.

Download Full-text