EE Seminar: Co-occurrence Based Texture Synthesis

06 במרץ 2019, 15:30 
חדר 011, בניין כיתות-חשמל 

 

Speaker: Anna Darzi

M.Sc. student under the supervision of Prof. Shai Avidan

 

Wednesday, March 6th, 2019 at 15:30

Room 011, Kitot Bldg., Faculty of Engineering

 

Co-occurrence Based Texture Synthesis

 

Abstract

 

We model local texture patterns using the co-occurrence statistics of pixel values. We then train a conditional generative adversarial network (cGAN) to synthesize new textures from the co-occurrence statistics and a random seed noise. Co-occurrences have long been used to measure similarity between textures. That is, two textures are considered similar if their corresponding co-occurrence matrices are similar. By the same token, we show that multiple textures generated from the same co-occurrence matrix are similar to each other. This gives rise to a new texture synthesis algorithm.

We use co-occurrence based texture synthesis in various settings. For example, we generate variations on the input texture by using the same co-occurrence statistics with different seed noise, or we merge two co-occurrence matrices to smoothly
interpolate between different textures.

In another case, we synthesize a dynamic texture sequence by interpolating between two co-occurrence matrices. Yet another option is to create a sequence that
summarizes the various local texture patterns in a given texture image. And because co-occurrence statistics have clear and intuitive meaning we develop a tool that lets users modify them directly and hence influence the local characteristics of the synthesized texture image.

אוניברסיטת תל אביב עושה כל מאמץ לכבד זכויות יוצרים. אם בבעלותך זכויות יוצרים בתכנים שנמצאים פה ו/או השימוש שנעשה בתכנים אלה לדעתך מפר זכויות
שנעשה בתכנים אלה לדעתך מפר זכויות נא לפנות בהקדם לכתובת שכאן >>