FGUP “State Research Institute
of Aviation Systems” (GosNIIAS)
Development of Applied Computer
Vision Systems Using Projective
Morphologies and Evidence-Based
Image Analysis
Yury V. Vizilter
viz@gosniias.ru
FGUP “State Research Institute of
Aviation Systems” (GosNIIAS)
•Leading Russian organization in the field of avionics for flight vehicles of civil and military aviation founded in 1946
•26 Doctors of Sciences, 232 Candidates of Sciences
•Educational faculties of MFTI, MAI, MIREA
“Technical Vision” laboratories: |
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Computer and Machine Vision Laboratory |
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Laboratory for Close-range Digital Photogrammetry |
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Laboratory for Long-range Digital Photogrammetry |
www.gosniias.ru |
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Laboratory for Optic and Electronic Systems |
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About 60 developers (engineers and software engineers)
4 Doctors of Science, 8 Candidates of Sciences
More than 300 scientific publications in related area
JSC “Institute of Information Technologies”: |
MHTML-документ |
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More information about commercial products and projects: |
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www.iitvision.ru |
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Visual Data Representation and Processing
Levels |
Models |
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Problems |
Frameworks |
Low |
Image = |
1. |
Image Filtering |
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combination of |
2. |
Image Matching |
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pixels |
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1. |
Image |
Projective |
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Image = |
Segmentation |
Morphologies |
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Mid |
combination of |
(Feature Extraction, |
(Morphological |
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primitives (regions, |
Image Modeling) |
Systems) |
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features, figures, |
2. |
Inage Matching |
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etc.) |
(Image-to-Image |
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Model Matching) |
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1. |
Scene |
Photogrammetry |
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Scene = |
Segmentation |
Evidence-Based |
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combination of |
2. |
Object detection |
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High |
objects (2D, 2.5D, |
(Image-to-Object |
Image Analysis |
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3D, etc.) |
Model Matching) |
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3. |
Object |
Machine Learning |
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recognition |
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Visual Data Representation and Processing
Levels |
Models |
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Problems |
Low |
Image = |
1. |
Image Filtering |
combination of |
2. |
Image Matching |
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pixels |
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1. |
Image |
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Image = |
Segmentation |
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Mid |
combination of |
(Feature Extraction, |
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primitives (regions, |
Image Modeling) |
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features, figures, |
2. |
Inage Matching |
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etc.) |
(Image-to-Image |
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Model Matching) |
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1. |
Scene |
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Scene = |
Segmentation |
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High |
combination of |
2. |
Object detection |
objects (2D, 2.5D, |
(Image-to-Object |
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3D, etc.) |
Model Matching) |
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3. |
Object |
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recognition |
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Framework
Morphological
Evidence
Analysis
(engineer-oriented technique for design of
CV applications)
Two Vision Frameworks Presented
Vision Framework is a regular scheme for design of vision algorithms. It’s a special way for thinking about images and tasks.
Projective Morphology scheme is developed based on Serra’s Mathematical Morphology, Pavel’s Shape Theory and Pytiev’s Morphological Analysis.
This morphological framework utilizes the structural image modeling with regularization constrains and decides some image segmentation and image comparison problems.
Evidence-Based Image Analysis scheme evolves the voting techniques proposed by Hough, Ballard and Davies.
Ii is a voting scheme with the use of simple low-level image events, high- or mid-level parameterized object hypotheses and reasonably sophisticated analysis of voting results.
It provides the creation of robust and computationally effective model-based object detection procedures.
PROJECTIVE MORPHOLOGIES
MM 1. Математическая морфология Серра |
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Обработка с учетом формы, выделение деталей |
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A |
MIN |
MAX |
эрозия |
дилатация |
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реконструкция A |
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MAX |
MIN |
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эрозия |
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дилатация |
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Serra J. Image Analysis and
Mathematical Morphology.
Academic Press. London, 1982.
MM 1. Математическая морфология Серра
Структурирующий элемент ИсходныйТрансляциобраз
я
TBT
B
Базовые операции
ММ Сжатие
A B
A B
A
Расширение A B
A
Морфологические фильтры как комбинация базовых
Открытие: |
операторов |
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X○B = (X B) B |
Закрытие: X●B = (X B) B |
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MM 1. Математическая морфология Серра
Морфологические фильтры как комбинация
структурирующих элементов
Structuring Elements (“Struxel”) Исходное изображение
T BT B 
Трансляция Форма = Комбинация “Struxels”
XoB = {BT | BT X}
Открытие Opening |
Закрытие Closing |
MM 1. Математическая морфология Серра
ММ-фильтры = Проекция на Форму
Учет формы путем выбора структурирующих элементов:
ММ-операторы: ММ-проекторы:
Эрозия (сжатие) |
ММ-открытие |
Дилатация (расширение) ММ-закрытие
Морфологические фильтры как комбинация сегментации и