Powered by OpenAIRE graph
Found an issue? Give us feedback

Метод оценки диспаритета стереопар

Метод оценки диспаритета стереопар

Abstract

Рассматривается метод оценки границ диспаритета стереопар. Оценка производится как для всего изображения в целом, так и для отдельных его фрагментов. Это позволяет оптимизировать поиск пиксельных соответствий путем сужения диапазона возможных значений диспаритета в пределах фрагмента. Также, предварительное разделение изображения на фрагменты, с последующим вычислением статистических характеристик на них, позволяет оценивать степень устойчивости корреляционных алгоритмов в пределах фрагментов. Таким образом, метод оценки может выделять и обрабатывать особым образом участки изображения с ровным фоном, на которых корреляционные алгоритмы недостаточно эффективны. Результаты экспериментов свидетельствуют об эффективности предложенного метода, в частности среднее значение границы диспаритета в пределах фрагментов в четыре раза меньше границ диспаритета в пределах всей стереопары. При этом точность оценки верхней и нижней границы диспаритета для некоторых стереопар достигает 97 %.

This paper presents stereopair disparity estimate method. Estimate is provided for the whole image as well as for separate image fragments. This allows optimizing pixel correspondence search within a fragment by narrowing the range of possible disparity values. Moreover, splitting the initial image on fragments and calculating mathematical statistics functions on them allows estimating correlation-like approaches robustness within fragments. So, the suggested method allows to mark and handle in some special way textureless image parts on which correlation-like approaches are usually weak. Experiments demonstrate the effectiveness of the suggested method. In particular the expected value of disparity range within fragments is four times less than within the whole stereopair. For some stereopairs disparity lower and upper bounds estimate accuracy reaches 97 %.

Keywords

СТЕРЕОПАРА, ПИКСЕЛЬНОЕ СООТВЕТСТВИЕ, КОРРЕЛЯЦИЯ, ДИСПАРИТЕТ, СТАТИСТИЧЕСКАЯ ХАРАКТЕРИСТИКА

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average