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Plan zemelʻnogo otvoda uchastka N 50-51 poselka Nº 1 Krivorogskogo okruga, Shirokovskogo raĭona, ploshchadiʻ︠u︡ 592,2279 gektarov

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Plan zemelʻnogo otvoda s.-kh. arteli "Krasnyĭ Pakharʻ" Krivorogskogo okruga, Sofievskogo raĭona ploshchadiʻ︠u︡ 1380 desi︠a︡tin

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Obshchestvennye usadʻby na uchastke 165/15

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Plan zemelʻnogo otvoda uchastka N 165/15 Krivorogskogo okruga, Mikhaĭlovskogo raĭona ploshchadiʻ︠u︡ 329 gektarov

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Skhematicheskiĭ chertezh razbivki zemli po poli︠a︡m shestipolʻnogo sevooborota na uchastke "Ėrdarbeĭter" Dnepropetrovskogo okruga Sinelnikovskogo raĭona

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Vykopirovka s proektnogo plana Kurmanskoĭ D.R. Dzhankoĭskogo raĭona

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Sundukovskiĭ fond KOMZETa

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Vykopirovka s plana Baraszhanskoĭ dachi razverstanii︠a︡ Karasubazarskogo raĭona na zemli trudovogo polzovanii︠a︡ "KOMZETa" i zapadnoĭ chasti Mushashskoĭ dachi

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Plan uchastka kolonizat︠s︡ionnogo fonda pod Nº 102/7, Krivorogskogo okruga Bozhedarovskogo raĭona ploshchadiʻ︠u︡ 1907 gektarov

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Uchastok Komzeta - Sarabuzskai︠a︡ dacha

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Uchastok N 56 Krivorogskogo okruga i raĭona

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Learning Nonlinear Visual Processing from Natural Images

The paradigm of computational vision hypothesizes that any visual function -- such as the recognition of your grandparent -- can be replicated by computational processing of the visual input. What are these computations that the brain performs? What should or could they be? Working on the latter question, this dissertation takes the statistical approach, where the suitable computations are attempted to be learned from the natural visual data itself. In particular, we empirically study the computational processing that emerges from the statistical properties of the visual world and the constraints and objectives specified for the learning process. This thesis consists of an introduction and 7 peer-reviewed publications, where the purpose of the introduction is to illustrate the area of study to a reader who is not familiar with computational vision research. In the scope of the introduction, we will briefly overview the primary challenges to visual processing, as well as recall some of the current opinions on visual processing in the early visual systems of animals. Next, we describe the methodology we have used in our research, and discuss the presented results. We have included some additional remarks, speculations and conclusions to this discussion that were not featured in the original publications. We present the following results in the publications of this thesis. First, we empirically demonstrate that luminance and contrast are strongly dependent in natural images, contradicting previous theories suggesting that luminance and contrast were processed separately in natural systems due to their independence in the visual data. Second, we show that simple cell -like receptive fields of the primary visual cortex can be learned in the nonlinear contrast domain by maximization of independence. Further, we provide first-time reports of the emergence of conjunctive (corner-detecting) and subtractive (opponent orientation) processing due to nonlinear projection pursuit with simple objective functions related to sparseness and response energy optimization. Then, we show that attempting to extract independent components of nonlinear histogram statistics of a biologically plausible representation leads to projection directions that appear to differentiate between visual contexts. Such processing might be applicable for priming, \ie the selection and tuning of later visual processing. We continue by showing that a different kind of thresholded low-frequency priming can be learned and used to make object detection faster with little loss in accuracy. Finally, we show that in a computational object detection setting, nonlinearly gain-controlled visual features of medium complexity can be acquired sequentially as images are encountered and discarded. We present two online algorithms to perform this feature selection, and propose the idea that for artificial systems, some processing mechanisms could be selectable from the environment without optimizing the mechanisms themselves. In summary, this thesis explores learning visual processing on several levels. The learning can be understood as interplay of input data, model structures, learning objectives, and estimation algorithms. The presented work adds to the growing body of evidence showing that statistical methods can be used to acquire intuitively meaningful visual processing mechanisms. The work also presents some predictions and ideas regarding biological visual processing.

Karta Kalinindorfskogo raĭona Odesskoĭ oblasti

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