Image Processing. Cosimo Distante. Lecture: Texture

Transcription

1 Image Processing Cosimo Distante Lecture: Texture

2 Today: Texture What defines a texture?

3 Includes: more regular pa>erns

4 Includes: more random pa>erns

5 Scale: objects vs. texture OEen the same thing in the world can occur as texture or an object, depending on the scale we are considering.

6 Why analyze texture? Importance to percepion: OEen indicaive of a material s properies Can be important appearance cue, especially if shape is similar across objects Aim to disinguish between shape, boundaries, and texture Technically: RepresentaIon-wise, we want a feature one step above building blocks of filters, edges.

7 Texture-related tasks Shape from texture EsImate surface orientaion or shape from image texture

8 Shape from texture Use deformaion of texture from point to point to esimate surface shape Pics from A. Loh: h>p://

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10 Texture-related tasks Shape from texture EsImate surface orientaion or shape from image texture Segmenta0on/classifica0on from texture cues Analyze, represent texture Group image regions with consistent texture Synthesis Generate new texture patches/images given some examples

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12 h>p://animals.naionalgeographic.com/

13 Color vs. texture Recall: These looked very similar in terms of their color distribuions (when our features were R-G-B) But how would their texture distribuions compare?

14 Psychophysics of texture Some textures disinguishable with prea(en*ve percepion without scruiny, eye movements [Julesz 1975] Same or different?

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18 Julesz Textons: analyze the texture in terms of staisical relaionships between fundamental texture elements, called textons. It generally required a human to look at the texture in order to decide what those fundamental units were...

19 Texture representaion Textures are made up of repeated local pa>erns, so: Find the pa>erns Use filters that look like pa>erns (spots, bars, raw patches ) Consider magnitude of response Describe their staisics within each local window Mean, standard deviaion Histogram Histogram of prototypical feature occurrences

20 Texture representaion: example mean d/ dx value mean d/ dy value Win. # original image deriva0ve filter responses, squared sta0s0cs to summarize pa=erns in small windows

21 Texture representaion: example mean d/ dx value mean d/ dy value Win. # Win.# original image deriva0ve filter responses, squared sta0s0cs to summarize pa=erns in small windows

22 Texture representaion: example mean d/ dx value mean d/ dy value Win. # Win.# original image deriva0ve filter responses, squared sta0s0cs to summarize pa=erns in small windows

23 Texture representaion: example mean d/ dx value mean d/ dy value Win. # Win.# Win.# original image deriva0ve filter responses, squared sta0s0cs to summarize pa=erns in small windows

24 Texture representaion: example Dimension 2 (mean d/dy value) Dimension 1 (mean d/dx value) mean d/ dx value mean d/ dy value Win. # Win.# Win.# sta0s0cs to summarize pa=erns in small windows

25 Texture representaion: example Windows with primarily horizontal edges Both Dimension 2 (mean d/dy value) Dimension 1 (mean d/dx value) mean d/ dx value mean d/ dy value Win. # Win.# Win.# Windows with small gradient in both direcions Windows with primarily verical edges sta0s0cs to summarize pa=erns in small windows

26 Texture representaion: example original image visualiza0on of the assignment to texture types deriva0ve filter responses, squared

27 Texture representaion: example Dimension 2 (mean d/dy value) Dimension 1 (mean d/dx value) mean d/ dx value mean d/ dy value Far: dissimilar textures Win. # Win.# Close: similar textures Win.# sta0s0cs to summarize pa=erns in small windows

28 Texture representaion: window scale We re assuming we know the relevant window size for which we collect these staisics. Possible to perform scale selecion by looking for window scale where texture descripion not changing.

29 Filter banks Our previous example used two filters, and resulted in a 2-dimensional feature vector to describe texture in a window. x and y derivaives revealed something about local structure. We can generalize to apply a collecion of muliple (d) filters: a filter bank Then our feature vectors will be d-dimensional. sill can think of nearness, farness in feature space

30 d-dimensional features... 2d 3d

31 Filter banks orientaions scales What filters to put in the bank? Typically we want a combinaion of scales and orientaions, different types of pa>erns. Matlab code available for these examples: h>p://