
Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers
Scene parsing, or semantic segmentation, consists in labeling each pixel in an image with the category of the object it belongs to. It is a challenging task that involves the simultaneous detection, segmentation and recognition of all the objects in the image. The scene parsing method proposed here starts by computing a tree of segments from a graph of pixel dissimilarities. Simultaneously, a set of dense feature vectors is computed which encodes regions of multiple sizes centered on each pixel. The feature extractor is a multiscale convolutional network trained from raw pixels. The feature vectors associated with the segments covered by each node in the tree are aggregated and fed to a classifier which produces an estimate of the distribution of object categories contained in the segment. A subset of tree nodes that cover the image are then selected so as to maximize the average "purity" of the class distributions, hence maximizing the overall likelihood that each segment will contain a single object. The convolutional network feature extractor is trained endtoend from raw pixels, alleviating the need for engineered features. After training, the system is parameter free. The system yields record accuracies on the Stanford Background Dataset (8 classes), the Sift Flow Dataset (33 classes) and the Barcelona Dataset (170 classes) while being an order of magnitude faster than competing approaches, producing a 320 × 240 image labeling in less than 1 second.
02/10/2012 ∙ by Clément Farabet, et al. ∙ 0 ∙ shareread it

VOIDD: automatic vessel of intervention dynamic detection in PCI procedures
In this article, we present the work towards improving the overall workflow of the Percutaneous Coronary Interventions (PCI) procedures by capacitating the imaging instruments to precisely monitor the steps of the procedure. In the long term, such capabilities can be used to optimize the image acquisition to reduce the amount of dose or contrast media employed during the procedure. We present the automatic VOIDD algorithm to detect the vessel of intervention which is going to be treated during the procedure by combining information from the vessel image with contrast agent injection and images acquired during guidewire tip navigation. Due to the robust guidewire tip segmentation method, this algorithm is also able to automatically detect the sequence corresponding to guidewire navigation. We present an evaluation methodology which characterizes the correctness of the guide wire tip detection and correct identification of the vessel navigated during the procedure. On a dataset of 2213 images from 8 sequences of 4 patients, VOIDD identifies vesselofintervention with accuracy in the range of 88 of tip with accuracy in range of 98
10/12/2017 ∙ by Ketan Bacchuwar, et al. ∙ 0 ∙ shareread it

Hierarchical image simplification and segmentation based on MumfordShahsalient level line selection
Hierarchies, such as the tree of shapes, are popular representations for image simplification and segmentation thanks to their multiscale structures. Selecting meaningful level lines (boundaries of shapes) yields to simplify image while preserving intact salient structures. Many image simplification and segmentation methods are driven by the optimization of an energy functional, for instance the celebrated MumfordShah functional. In this paper, we propose an efficient approach to hierarchical image simplification and segmentation based on the minimization of the piecewiseconstant MumfordShah functional. This method conforms to the current trend that consists in producing hierarchical results rather than a unique partition. Contrary to classical approaches which compute optimal hierarchical segmentations from an input hierarchy of segmentations, we rely on the tree of shapes, a unique and welldefined representation equivalent to the image. Simply put, we compute for each level line of the image an attribute function that characterizes its persistence under the energy minimization. Then we stack the level lines from meaningless ones to salient ones through a saliency map based on extinction values defined on the treebased shape space. Qualitative illustrations and quantitative evaluation on Weizmann segmentation evaluation database demonstrate the stateoftheart performance of our method.
03/15/2016 ∙ by Yongchao Xu, et al. ∙ 0 ∙ shareread it

New characterizations of minimum spanning trees and of saliency maps based on quasiflat zones
We study three representations of hierarchies of partitions: dendrograms (direct representations), saliency maps, and minimum spanning trees. We provide a new bijection between saliency maps and hierarchies based on quasiflat zones as used in image processing and characterize saliency maps and minimum spanning trees as solutions to constrained minimization problems where the constraint is quasiflat zones preservation. In practice, these results form a toolkit for new hierarchical methods where one can choose the most convenient representation. They also invite us to process nonimage data with morphological hierarchies.
05/27/2015 ∙ by Jean Cousty, et al. ∙ 0 ∙ shareread it

A graphbased mathematical morphology reader
This survey paper aims at providing a "literary" anthology of mathematical morphology on graphs. It describes in the English language many ideas stemming from a large number of different papers, hence providing a unified view of an active and diverse field of research.
04/30/2014 ∙ by Laurent Najman, et al. ∙ 0 ∙ shareread it

Indoor Semantic Segmentation using depth information
This work addresses multiclass segmentation of indoor scenes with RGBD inputs. While this area of research has gained much attention recently, most works still rely on handcrafted features. In contrast, we apply a multiscale convolutional network to learn features directly from the images and the depth information. We obtain stateoftheart on the NYUv2 depth dataset with an accuracy of 64.5 sequences that could be processed in realtime using appropriate hardware such as an FPGA.
01/16/2013 ∙ by Camille Couprie, et al. ∙ 0 ∙ shareread it

Writing Reusable Digital Geometry Algorithms in a Generic Image Processing Framework
Digital Geometry software should reflect the generality of the underlying mathe matics: mapping the latter to the former requires genericity. By designing generic solutions, one can effectively reuse digital geometry data structures and algorithms. We propose an image processing framework focused on the Generic Programming paradigm in which an algorithm on the paper can be turned into a single code, written once and usable with various input types. This approach enables users to design and implement new methods at a lower cost, try crossdomain experiments and help generalize results
09/18/2012 ∙ by Roland Levillain, et al. ∙ 0 ∙ shareread it

An efficient hierarchical graph based image segmentation
Hierarchical image segmentation provides regionoriented scalespace, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Most image segmentation algorithms, such as region merging algorithms, rely on a criterion for merging that does not lead to a hierarchy, and for which the tuning of the parameters can be difficult. In this work, we propose a hierarchical graph based image segmentation relying on a criterion popularized by Felzenzwalb and Huttenlocher. We illustrate with both real and synthetic images, showing efficiency, ease of use, and robustness of our method.
06/13/2012 ∙ by Silvio Jamil F. Guimarães, et al. ∙ 0 ∙ shareread it

Morphological Filtering in Shape Spaces: Applications using TreeBased Image Representations
Connected operators are filtering tools that act by merging elementary regions of an image. A popular strategy is based on treebased image representations: for example, one can compute an attribute on each node of the tree and keep only the nodes for which the attribute is sufficiently strong. This operation can be seen as a thresholding of the tree, seen as a graph whose nodes are weighted by the attribute. Rather than being satisfied with a mere thresholding, we propose to expand on this idea, and to apply connected filters on this latest graph. Consequently, the filtering is done not in the space of the image, but on the space of shapes build from the image. Such a processing is a generalization of the existing treebased connected operators. Indeed, the framework includes classical existing connected operators by attributes. It also allows us to propose a class of novel connected operators from the leveling family, based on shape attributes. Finally, we also propose a novel class of selfdual connected operators that we call morphological shapings.
04/20/2012 ∙ by Yongchao Xu, et al. ∙ 0 ∙ shareread it

Combinatorial Continuous Maximal Flows
Maximum flow (and minimum cut) algorithms have had a strong impact on computer vision. In particular, graph cuts algorithms provide a mechanism for the discrete optimization of an energy functional which has been used in a variety of applications such as image segmentation, stereo, image stitching and texture synthesis. Algorithms based on the classical formulation of maxflow defined on a graph are known to exhibit metrication artefacts in the solution. Therefore, a recent trend has been to instead employ a spatially continuous maximum flow (or the dual mincut problem) in these same applications to produce solutions with no metrication errors. However, known fast continuous maxflow algorithms have no stopping criteria or have not been proved to converge. In this work, we revisit the continuous maxflow problem and show that the analogous discrete formulation is different from the classical maxflow problem. We then apply an appropriate combinatorial optimization technique to this combinatorial continuous maxflow CCMF problem to find a nulldivergence solution that exhibits no metrication artefacts and may be solved exactly by a fast, efficient algorithm with provable convergence. Finally, by exhibiting the dual problem of our CCMF formulation, we clarify the fact, already proved by Nozawa in the continuous setting, that the maxflow and the total variation problems are not always equivalent.
10/13/2010 ∙ by Camille Couprie, et al. ∙ 0 ∙ shareread it