Published on March 9, 2014
Guided by ANOOP CHANDRAN.P ASST.PROF. CAARMEL ENG. COLLEGE Presented by ABHIRAM.S ROLL NO:01 MTECH COMM ENGG 1
Introduction Digitalization of topographic maps is an important data source of constructing GIS Maps consist of linear features(roads and condor lines) and backgrounds(green fields and water bodies) Linear features are fundamental to GIS ;so its separation is important Manual separation is time consuming Automated separation is based on the colours 2
Contd.. When linear feature colour and background colour are similar then it is difficult to separate them This paper present a method based on energy density and shear transform Shear transform preserves lines directional info during one directional separation method Horizontal and vertical templates are used to separate lines from background 3
Contd.. Remaining grid background can be removed by grid template matching Isolated patches of one pixel and less than ten pixels are also removed Union operation on these sheared images give the final result 4
Existing systems In 1994 N.Ebi developed a system by converting RGB colour to another colour space In 1994 H.Yan proposed a system based on fuzzy theory ;which combines fuzzy clustering and neural n/w’s In 1996 C.Feng developed a system based on colour clustering In 2003 L.Zheng developed a system of fuzzy clustering based on 2D histogram 5
Contd.. In 2008 Aria Pezeshek introduced a semi automated method; in this method contour lines are removed by an algorithm based intensity quantization followed by contrast limited adaptive histogram equalization. In 2010 S.Leyk introduced a segmentation method which uses information from local image plane, frequency domain and colour space All methods described above work where the colour difference b/w line and background is seperable 6
Characteristic Analysis of Linear Features and Background Colour based separation is difficult in some case 7
Figures show histogram of image in lab colour space The are number of peaks in the histogram of first image But in second image; colour of pixels are close to each other ; so there is only one peak in the histogram. It is very hard to separate the line from background of second image. 8
1 This figure shows a binary image with complicated background Some portion of the image is ideal and other is complicated 9
Ideal portion of background can be removed by using the Grid templates shown If the centre pixel and adjacent 8 pixels satisfy the fig 4(a) and 4(a1) then the pixel is treated as background and replaced by 1/white If the centre pixel and adjacent 8 pixels satisfy the fig 4(b) and 4(b1) then the pixel is treated as line info and replaced by 0/black In the fig 3(c) it is a portion of image with complicated background; it cannot be operated with our grid template matching 10
Energy characteristics Energy of an image is given by i=1,2,3...M j=1,2,3...N M and N are the height and width of image f(i,j) is the gray value of pixel Energy of one pixel f(i,j) i-k<m<i+k j-k<n<j+k size of window w=2k+1 11
Line in gray s/m is dark; but HVS is more sensitive to brightness so we take negative of gray image The figure shows that, the energy of negative image is concentrated on lines Here the distribution of line and background in ideal case is shown here 12
The figure shows the distribution of line and background in the case of actual image Here fig c. Represents the background and fig d. Represents the line 13
The histogram of line in fig b. is shown in fig d. It has only few pixels but all of its energy concentrated on the lines Energy ranging from 2.5*104 -3* 104 ;extreme case it is 6* 104 Energy of background is also in the same range; but energy conc. is higher for lines 14
Horizontal and vertical templates are used to separate lines from background h2 corresponds to line, it is selected adaptively by experience; generally 2*2 h1 and h3 corresponds to background pixels generally of size 4*2 and 2*4 15
Energy density of the template is Edk = 2 /m*n m*n-area of template k=1,2,3 Edk =energy density of each area of template 16
Proposed method Traditional colour based system fails when the colour of background and the colour of the lines are similar This method is based on energy density Energy density of a negative image is defined as the average energy in an area 2 Ed = /M*N M*N-size of area Ed =energy density 17
Rules for line separation Rule 1: energy of line is distributed in small area so energy density is high energy of background is distributed in large area so energy density is low Ed2>Ed1 Ed2>Ed3 ie: energy density of line >energy density of background 18
Rule 2: if line and background cannot be separated by rule 1 , it is necessary to control the energy difference of line and background to a certain range Ed’=Ed1+Ed2+Ed3/3 T=Ed2-Ed’+α α is acquired by experience; α =3000-5000 Ed2-Ed1>T Ed2-Ed3>T h2 is treated as line if and only if Ed2 satisfies rule 1 and rule 2 19
Background pixel h1 and h3 and isolated patches of one pixel or less than ten pixels are removed Finally union operation is performed on the two images 20
Shear transform Shear transform is a linear transform that displaces point in a fixed direction Introduced to avoid the separation difficulties while operating lines with many direction Ws,k is the shear operation s=0,1 k€[-2ndir ,2ndir] f’s,k(x,y)=f(x,y)*Ws,k Total number of sheared image is given by 2ndir+1+1 21
Shear transform is performed by sampling pixel according to the shear matrix S=0 operation is performed in horizontal direction S=1 operation is performed in vertical direction (x’,y’)=(x,y)S1=(x,y) 22
This is the result of shear transform of s=0, ndir=2 so a total of 9 images; union of these images gives a perfect map 23
Steps of proposed method STEP 1: colour image is converted into gray image Gray=0.233R+0.587G+0.114B negative of the gray image is taken I=e*255-gray ‘e’ is a matrix with same size of gray matrix with all elements equal to one STEP 2: Apply shear transform to the negative image 24
Contd.. STEP 3: Establishment of template: horizontal and vertical STEP 4: Linear feature separation from background: i.e. : energy of each area in template is calculated, line is separated from background by rule 1 and rule 2 with α =4000 25
Contd... STEP 5: Removal of miscellaneous point: i.e. : remaining grid background can be removed by grid template matching and isolated points can also be removed STEP 6: Inverse shear transform and union operation 26
Experiments and Discussions This is a 342*198 size 7 colour topographical map image Colour of linear feature and background are similar here so it is very difficult to separate lines from background 28
Here size of h2 is 2*2 h1&h3 is 4*2 if vertical template is used h1&h3 is 2*4 if horizontal template is used α=4000 Fig(b) is the gray image Fig(c) is the negative image 29
The first set of figures shows the sheared images with k=-1, k=0, k=1 Second set shows energy density based extraction by templates 30
Fig (a) shows the union operation of a2, b2, c2, Fig (b) shows lines with colour info extracted from colour image Fig (c) shows the remaining background 31
Comparison of different methods 32
Conclusion This paper proposes a method to linear separation from background Here shear transform is used to overcome the limitation of directions for lines Energy density concept is introduced to separate lines from background The new method can easily be applied to maps for efficient separation of lines Adaptive size fixing of template is a draw back of this method 33
Reference R. Samet and E. Hancer, “A new approach to the reconstruction of contour lines extracted from topographic maps,” J. Visual Commun. E. Hancer and R. Samet, “Advanced contour reconnection in scanned topographic maps,” H. Chen, X.-A. Tang, C.-H. Wang, and Z. Gan, “Object oriented segmentation of scanned topographical maps,” S. Leyk, “Segmentation of colour layers in historical maps based on hierarchical colour sampling,” in Graphics Recognition. Achievements, Challenges, and Evolution (Lecture Notes in Computer Science), 34
Thank you 35
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