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Detection of Dynamic Gabor Patches in 1/F Noise MASTER THESIS Martin Ser´yˇ Detection of dynamic gabor patches in 1/f noise Department of Software and Computer Science Education Supervisor of the master thesis: Mgr. Dˇechtˇerenko Filip, Ph.D. Study programme: Computer Science Study branch: Artificial Intelligence Prague 2021 I declare that I carried out this master thesis independently, and only with the cited sources, literature and other professional sources. It has not been used to obtain another or the same degree. I understand that my work relates to the rights and obligations under the Act No. 121/2000 Sb., the Copyright Act, as amended, in particular the fact that the Charles University has the right to conclude a license agreement on the use of this work as a school work pursuant to Section 60 subsection 1 of the Copyright Act. In ............. date ............. ..................................... Author’s signature i I would like to thank my supervisor Mgr. Dˇechtˇerenko Filip, Ph.D. for the idea and his help with this work. Next I thank my girlfriend for grammar check and patience she has had with me during my work on this thesis. ii Title: Detection of dynamic gabor patches in 1/f noise Author: Martin Ser´yˇ Department: Department of Software and Computer Science Education Supervisor: Mgr. Dˇechtˇerenko Filip, Ph.D., Department of Software and Com- puter Science Education Abstract: Research focusing on static scenes with static objects is omitting the time factor from real life examples we are trying to study. Can we say that a lifeguard looking for a drowning man is using the same brain processes that were observed in the laboratory for static scenes? We can conclude that a static scene is a big simplification of the task itself. The aim of this thesis is to prepare a tool which would allow researching dynamic scenes and thus broadening the possibilities of visual detection tasks at hand. Along the tool we also present a couple of simplified examples with which we would like to demonstrate the utilization of the tool. All concluding with a final experiment in which we will try to detect masked patterns in a noisy environment. Keywords: detection modeling 1/f noise Gabor patch iii Contents Introduction 3 1 Theoretical background 5 1.1 Visual sensory system . .5 1.2 Object detection . .6 1.3 Camouflage . .6 1.3.1 Background blending . .7 1.3.2 Disruptive coloration . .7 1.3.3 Countershading . .8 1.4 Motion detection . .8 1.4.1 Human motion detection . .8 1.4.2 Computer motion detection . .9 1.4.3 Motion analysis . .9 1.4.4 Motion dazzle . 10 2 Technical background 11 2.1 Image properties . 11 2.1.1 Luminance . 11 2.1.2 Contrast . 12 2.2 Pink noise . 12 2.3 Gabor patch . 14 2.3.1 Sinusoidal grating . 15 2.3.2 Gaussian window . 15 2.4 Structural similarity index measure (SSIM) . 17 2.4.1 Luminance comparison . 18 2.4.2 Contrast comparison . 18 2.4.3 Structure comparison . 18 2.4.4 Combining function . 19 2.4.5 Complex wavelet SSIM - CW-SSIM . 19 3 Methods 20 3.1 Background description . 20 3.1.1 Linear interpolation . 20 3.1.2 3D pink noise . 22 3.1.3 Shifting pink noise . 22 3.2 Stimuli description . 24 3.3 Scene description . 25 3.4 Detection approach . 25 4 Preliminary experiments 27 4.1 Simple POC . 28 4.2 SSIM precision . 30 4.3 Dynamic stimuli . 31 4.4 Moving stimuli . 33 1 5 Main experiment 34 5.1 Methods . 34 5.1.1 Sliding window . 34 5.1.2 SSIM . 35 5.1.3 Frame difference . 35 5.1.4 Improved frame difference . 35 5.1.5 Heat maps . 35 5.1.6 Heat map evaluation . 36 5.2 Results . 36 5.2.1 SSIM . 36 5.2.2 Frame difference . 39 5.2.3 Recapitulation . 43 Conclusion 44 Further research 45 Bibliography 46 List of Figures 54 Glossary 55 A Attachments 57 A.1 Source code . 57 A.1.1 Experiments . 57 A.1.2 Final experiment . 57 A.2 Results . 58 2 Introduction Object detection is an everyday task for many animals and humans alike. Preda- tors need to detect prey before they are even able to attack. Prey on the other hand needs to detect the predator before they can attack. For humans those tasks are often not the question of survival, but important nevertheless. Have you ever tried finding your car keys on a table? Was the table ever messy? The less order there is on the table and the more things there are the harder the task. The human brain has to process a lot of information that is not relevant. This irrelevant information is called noise. In nature it is no different. Rarely is the prey being presented to the predator on a simple background with no noise. Natural scenes are never completely random. If they were, our sensory systems would have a hard time processing all the visual information. Luckily natural im- ages are not random and their properties allow us to design simplified experiments that resemble the real time situations rather well. One such example is 1/f noise which shares many statistical properties with natural scenes. Using 1/f noise instead of real natural scenes is beneficial in laboratory con- ditions. We can remove unnecessary distractors that are present in natural scenes and observe the search task in a more controlled environment. Another discovery we are using is a gabor patch named after Hungarian sci- entist Dennis Gabor. The gabor patch has properties similar to the simple cells in the visual cortex of mammalian brains. However there is one feature that not even real images share with natural scenes. Real images are not dynamic whereas nothing is ever completely static in nature. We probably will not be able to take the very same photo of the sea waves twice, because the sea itself is dynamic and moves. The waves move around in the sea, the leaves quiver in the wind and the water flows in the river. To better illustrate the problematics we present a table with real life examples. Environemnt Dynamic Static Dynamic Hunting a prey Catching a mosquito Stimulus Static Searching for a body in water Locating keys Hunting a prey is an example of a fully dynamic environment. The prey moves around and there is a lot of distractors present especially if the prey is a part of a bigger group. Catching a mosquito in a room is an example of a dynamic target presented on a static background. The furniture in an apartment probably does not move around while we are trying to catch a mosquito that is flying around. In this case the mosquito is the dynamic stimulus and the furniture provides the static background. Water in a sea rolls around all the time so it can hardly be considered a static background. Whereas an unconscious body floating on the water surface is rather static. So searching for a body in sea is an example of a static stimuli in a dynamic environment. 3 Keys on our messy table are not moving around just like the rest of the noise around. Therefore this is an example of a static stimulus in a static environment. There are many studies exploring the functionality of the human brain in lab- oratory conditions without unwanted distractors. Najemnik and Geisler [2005]1 found that humans achieve nearly optimal performance while finding a static ga- bor patch target in 1/f noise. Work by Sebastian et al. [2017] also measured the detection of a static stimulus in a static scene. All those experiments resemble the task of finding keys on a messy table. But how will the results change, if we add motion to the scene and stimulus? Dorr et al. [2010] suggests that static images do not trigger the same eye movement as moving scene. At the same time there has been a research which used dynamic environment. Kimmig et al. [2008] used a simple dot as a stimulus on a black background to observe eye movement. Results of such experiment could be different if there were more natural like stimulus and scene used. KristjAnsson´ et al. [2009] studied search strategies used to detect a drifting gabor patch among distractors. Search strategy can be altered in presence of dynamic background as a distractor. We plan to explore the remaining three situations using pink noise and gabor patches. We decided against using videos to create dynamic scenes because of Dorr et al. [2010], who concluded that professionally cut material is not very representative of natural viewing behavior. We design a method to dynamize pink noise as well as a gabor patch. In this work we introduce the problematics of object detection. We explain the differences between real life and laboratory experiments. We present methods that are used in vision labs and the general idea behind them. Goal of the work The aim of this thesis is bringing the laboratory conditions closer to real life while preserving the control over the environment. To do so we introduce sev- eral methods to dynamize the 1/f noise and also gabor patches. New methods should provide more realistic dynamic environment which has not been studied before. Such environment could bring us closer to the understanding of the neural processes happening in the human brain. 1Later followed by Geisler and Najemnik [2005] 4 1. Theoretical background In the first chapter we introduce some background theory that ought to help us understand the motivation behind this work. We briefly introduce the visual sensory system.
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