75 research outputs found
Streaming Algorithm for Euler Characteristic Curves of Multidimensional Images
We present an efficient algorithm to compute Euler characteristic curves of
gray scale images of arbitrary dimension. In various applications the Euler
characteristic curve is used as a descriptor of an image.
Our algorithm is the first streaming algorithm for Euler characteristic
curves. The usage of streaming removes the necessity to store the entire image
in RAM. Experiments show that our implementation handles terabyte scale images
on commodity hardware. Due to lock-free parallelism, it scales well with the
number of processor cores. Our software---CHUNKYEuler---is available as open
source on Bitbucket.
Additionally, we put the concept of the Euler characteristic curve in the
wider context of computational topology. In particular, we explain the
connection with persistence diagrams
Edge Guided Reconstruction for Compressive Imaging
We propose EdgeCS—an edge guided compressive sensing reconstruction approach—to recover images
of higher quality from fewer measurements than the current methods. Edges are important
image features that are used in various ways in image recovery, analysis, and understanding. In
compressive sensing, the sparsity of image edges has been successfully utilized to recover images.
However, edge detectors have not been used on compressive sensing measurements to improve the
edge recovery and subsequently the image recovery. This motivates us to propose EdgeCS, which
alternatively performs edge detection and image reconstruction in a mutually beneficial way. The
edge detector of EdgeCS is designed to faithfully return partial edges from intermediate image reconstructions
even though these reconstructions may still have noise and artifacts. For complex-valued
images, it incorporates joint sparsity between the real and imaginary components. EdgeCS has
been implemented with both isotropic and anisotropic discretizations of total variation and tested
on incomplete k-space (spectral Fourier) samples. It applies to other types of measurements as well.
Experimental results on large-scale real/complex-valued phantom and magnetic resonance (MR)
images show that EdgeCS is fast and returns high-quality images. For example, it exactly recovers
the 256×256 Shepp–Logan phantom from merely 7 radial lines (3.03% k-space), which is impossible
for most existing algorithms. It is able to accurately reconstruct a 512 × 512 MR image with 0.05
white noise from 20.87% radial samples. On complex-valued MR images, it obtains recoveries with
faithful phases, which are important in many medical applications. Each of these tests took around
30 seconds on a standard PC. Finally, the algorithm is GPU friendly
Leaf segmentation in plant phenotyping: a collation study
Image-based plant phenotyping is a growing application area of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants, Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variability in leaf shape and pose, as well as imaging conditions, render this problem challenging. The aim of this paper is to compare several leaf segmentation solutions on a unique and first-of-its-kind dataset containing images from typical phenotyping experiments. In particular, we report and discuss methods and findings of a collection of submissions for the first Leaf Segmentation Challenge of the Computer Vision Problems in Plant Phenotyping workshop in 2014. Four methods are presented: three segment leaves by processing the distance transform in an unsupervised fashion, and the other via optimal template selection and Chamfer matching. Overall, we find that although separating plant from background can be accomplished with satisfactory accuracy (>>90 % Dice score), individual leaf segmentation and counting remain challenging when leaves overlap. Additionally, accuracy is lower for younger leaves. We find also that variability in datasets does affect outcomes. Our findings motivate further investigations and development of specialized algorithms for this particular application, and that challenges of this form are ideally suited for advancing the state of the art. Data are publicly available (online at http://​www.​plant-phenotyping.​org/​datasets) to support future challenges beyond segmentation within this application domain
Actes du deuxième colloque africain sur la recherche en informatique = Proceedings of the second African Conference on research in computer science
This paper presents an optimal multithreshold selection algorithm for segmentation of grey level images when objects can be distinguished by their grey level values. We define performance measures and we compare our algorithm to others. (Résumé d'auteur
Fusion des contours dans un espace échelle
Dans cet article, nous présentons un ensemble de règles de comportement du contour dans un espace échelle et dérivons ensuite à partir de ces règles un algorithme pour la fusion des contours obtenus à différentes échelles par un détecteur laplacien. Enfin, nous montrons que deux échelles sont suffisantes pour une description correcte de l'image en terme de contours
Elimination des faux contours par séparation et propagation de seuil
Un opérateur Laplacien d'une Gaussienne engendre une classe de faux points de contour qui ne peut-être supprimée par les méthode classiques de seuillage. Nous proposons un algorithme d'élimination des faux contours en tenant compte de leurs différentes origines. Par ailleurs, en étudiant le comportement du contour dans un espace échelle, nous déduisons un algorithme d'élimination automatique des faux contours obtenus à différentes échelles
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