fence removal from images

8/9/2019 Fence Removal From Images

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What is De-Fencing?

Fences: near-regular foreground patterns that are often unwanted, but unavoidabimagery.


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What is De-Fencing?De-fencing: Process of automatically removing ‘fences’ from an image.


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ApplicationsAnimal photography in zoo.

Sports photography

Photography of buildings behind fences


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Present Techniques1. "Image De-fencing“, Yanxi Liu, Tamara Belkina, James H. Hays, and Roberto Lub

Computer Vision and Pattern Recognition Conference (CVPR) 2008.

2. “Image De-fencing Revisited ”, Minwoo Park, Kyle Brocklehurst, Robert T. CollinsLiu , Asian Conference on Computer Vision (ACCV) 2010.


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Steps in De-fencing1. Finding a lattice.

2. Classifying pixels as foreground or background.

3. Filling the background holes with texture inpainting.


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Finding the Lattice1. Assign neighbor relationships among a set of interest points.

2. Use the strongest cluster of repeated elements to propose new, visually similar points.

3. Higher-order constrains are used to promote geometric consistency between paassignments.

Online learning using a support vector machine can be performed to improve the

of lattice points and for foreground segmentation.


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Foreground/background Separation

a) Align the texels

b) Compute the standard deviation o

this stack of texels.

c) The cluster which has the lowest v

taken as foreground and the rest b

d) Create a mask containing the foreg


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Texture InpaintingΩ= Target Region Φ= Source Region δΩ= Cont

1. The square template Ψp ϵ Ω centred at the point p is to be

2. The best-match sample from the source region comes fromΨqϵΦ which is most similar to those parts that are already

3. To propagate the isophote inwards, transfer the pattern fromatch source patch to Ψp.

Additional techniques: Multi-view inpainting

Symmetry augmented inpainting


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A Novel Spatial Domain Approach foFence Detection.

1. Find the edges of the input image.

2. Obtain Hough Transform of the edges.

3. Detect the peaks in the Hough transform.

4. Find the dominant angles in the edge map from the peaks in the Hough transfor

5. Separate the fences from the image making use of the information about the doangles of the fence.


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Input Image


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Edges


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Hough Transform of Edges


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Strength of edges in different directi


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Work Done

Image Inpainting

Exemplar based inpainting

Fence Detection

Mean Shift belief propagation

Morphological operations Frequency domain technique

Hough Transform


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Further Work

Refine the developed spatial domain technique to make the detection more accu

Develop other techniques for fence detection.


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Reference1. "Image De-fencing“, Yanxi Liu, Tamara Belkina, James H. Hays, and Roberto Lub

Computer Vision and Pattern Recognition Conference (CVPR) 2008.

2. “Image De-fencing Revisited ”, Minwoo Park, Kyle Brocklehurst, Robert T. CollinsLiu , Asian Conference on Computer Vision (ACCV) 2010.

3. “Discovering texture regularity as a higher-order correspondence problem” , J. Leordeanu, A. Efros, and Y. Liu, European Conference on Computer Vision (ECCV’

4. ‘Object Removal by Exemplar -based Inpainting’, Antonio Criminisi, Patrick PereKentaro Toyama, IEEE Computer Vision and Pattern Recognition (CVPR), June 20


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fence removal from images

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