8 May 2012 A manifold learning based identification of latent variations in root cross sections of plants
Author Affiliations +
Abstract
Currently a lot of plant biology research focuses on understanding the genetic, physiological, and ecology of plants. Root is an important organ for plant to uptake nutrient and water from the surrounding soil. The capability of plant to obtain nutrient and water is closely related to root physiology. Quantitative measurement and analysis of plant root architecture parameters are very important for understanding and study growth of plant. A fundamental aim of developmental plant root biology is to understand how the three-dimensional morphology of plant roots arises through cellular mechanisms. However, traditional anatomical studies of plant development have mainly relied on two-dimensional images. Though this may be sufficient for some aspects of plant biology, deeper understanding of plant growth and function increasingly requires at least some amount of three dimensional measures and use chemical staining as a technique to bring pseudo structure and segmentation to the cross section image data. Thus parameters like uniformity of illumination and thickness of the specimen then becomes critical. Unfortunately these are also the causes of major variations. The variation of thickness of specimen can be interpreted as an effect which increases the latent dimensionality of the data. Addressing the variability due to specimen thickness can then be viewed in a manifold learning framework, wherein it is assumed that the data of interest lies on an embedded manifold within the higher-dimensional space and can be visualized in low dimensional space, using manifold learning constraints.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sumit Chakravarty and Madhushri Banerjee "A manifold learning based identification of latent variations in root cross sections of plants", Proc. SPIE 8406, Mobile Multimedia/Image Processing, Security, and Applications 2012, 84060S (8 May 2012); doi: 10.1117/12.921608; https://doi.org/10.1117/12.921608
PROCEEDINGS
11 PAGES


SHARE
Advertisement
Advertisement
RELATED CONTENT

Autotract: automatic cleaning and tracking of fibers
Proceedings of SPIE (March 20 2016)
Three-dimensional active net for volume extraction
Proceedings of SPIE (May 13 1998)
Texture compilation for example-based synthesis
Proceedings of SPIE (September 30 2011)
Toward the automation of road networks extraction processes
Proceedings of SPIE (December 30 1996)
MRI feature extraction using a linear transformation
Proceedings of SPIE (September 13 1993)
Multistage hierarchy for fast image analysis
Proceedings of SPIE (December 16 1996)

Back to Top