A marked point process model with strong prior shape information for extraction of multiple, arbitrarily-shaped objects.

We define a method for incorporating strong prior shape information into a recently extended Markov point process model for the extraction of arbitrarily-shaped objects from images. To estimate the optimal configuration of objects, the process is sampled using a Markov chain based on a stochastic birth-and-death process defined in a space of multiple objects. The single objects considered are defined by both the image data and the prior information in a way that controls the computational complexity of the estimation problem. The method is tested via experiments on a very high resolution aerial image of a scene composed of tree crowns

A marked point process model with strong prior shape information for extraction of multiple, arbitrarily-shaped objects

We define a method for incorporating strong prior shape information into a recently extended Markov point process model for the extraction of arbitrarily-shaped objects from images. To estimate the optimal configuration of objects, the process is sampled using a Markov chain based on a stochastic birth-and-death process defined in a space of multiple objects. The single objects considered are defined by both the image data and the prior information in a way that controls the computational complexity of the estimation problem. The method is tested via experiments on a very high resolution aerial image of a scene composed of tree crowns

Extraction of arbitrarily shaped objects using stochastic multiple birth-and-death dynamics and active contours.

We extend the marked point process models that have been used for object extraction from images to arbitrarily shaped objects, without greatly increasing the computational complexity of sampling and estimation. The approach can be viewed as an extension of the active contour methodology to an a priori unknown number of objects. Sampling and estimation are based on a stochastic birth-and-death process defined in a space of multiple, arbitrarily shaped objects, where the objects are defined by the image data and prior information. The performance of the approach is demonstrated via experimental results on synthetic and real data

Extraction of arbitrarily shaped objects using stochastic multiple birth-and-death dynamics and active contours

We extend the marked point process models that have been used for object extraction from images to arbitrarily shaped objects, without greatly increasing the computational complexity of sampling and estimation. The approach can be viewed as an extension of the active contour methodology to an a priori unknown number of objects. Sampling and estimation are based on a stochastic birth-and-death process defined in a space of multiple, arbitrarily shaped objects, where the objects are defined by the image data and prior information. The performance of the approach is demonstrated via experimental results on synthetic and real data

Characterization of Posidonia Oceanica Seagrass Aerenchyma through Whole Slide Imaging: A Pilot Study

Characterizing the tissue morphology and anatomy of seagrasses is essential to predicting their acoustic behavior. In this pilot study, we use histology techniques and whole slide imaging (WSI) to describe the composition and topology of the aerenchyma of an entire leaf blade in an automatic way combining the advantages of X-ray microtomography and optical microscopy. Paraffin blocks are prepared in such a way that microtome slices contain an arbitrarily large number of cross sections distributed along the full length of a blade. The sample organization in the paraffin block coupled with whole slide image analysis allows high throughput data extraction and an exhaustive characterization along the whole blade length. The core of the work are image processing algorithms that can identify cells and air lacunae (or void) from fiber strand, epidermis, mesophyll and vascular system. A set of specific features is developed to adequately describe the convexity of cells and voids where standard descriptors fail. The features scrutinize the local curvature of the object borders to allow an accurate discrimination between void and cell through machine learning. The algorithm allows to reconstruct the cells and cell membrane features that are relevant to tissue density, compressibility and rigidity. Size distribution of the different cell types and gas spaces, total biomass and total void volume fraction are then extracted from the high resolution slices to provide a complete characterization of the tissue along the leave from its base to the apex

Moving Target Analysis in ISAR Image Sequences with a Multiframe Marked Point Process Model

In this paper we propose a Multiframe Marked Point Process model of line segments and point groups for automatic target structure extraction and tracking in Inverse Synthetic Aperture Radar (ISAR) image sequences. For the purpose of dealing with scatterer scintillations and high speckle noise in the ISAR frames, we obtain the resulting target sequence by an iterative optimization process, which simultaneously considers the observed image data and various prior geometric interaction constraints between the target appearances in the consecutive frames. A detailed quantitative evaluation is performed on 8 real ISAR image sequences of different carrier ship and airplane targets, using a test database containing 545 manually annotated frames

COMBINING MARKOV RANDOM FIELD AND MARKED POINT PROCESS FOR MICROSCOPY IMAGE MODELING

In many microscopy image analysis applications, it is of critical importance to addres