Morphological object picking based on the color tree of shapes

Carlinet, Edwin; Géraud, Thierry

doi:10.1109/ipta.2015.7367111

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…In addition to the references given throughout the paper, related either to the context of our work or to its theoretical background, several other works shall be mentioned. Actually the notion of path on tree nodes also appear in [7] to assign to each node a label and then perform some scribble-based segmentation, in [8] to count level lines and build a multi-variate tree of shapes, and in [12] to compute some curvilinear variation and separate an object from its background. The notion of barrier used on the tree of shapes is also close to the one of shape saliency used in [24].…”

Section: Illustration On Document Image Segmentationmentioning

confidence: 99%

Introducing the Dahu Pseudo-Distance

Géraud

Carlinet

et al. 2017

Lecture Notes in Computer Science

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The minimum barrier (MB) distance is defined as the minimal interval of gray-level values in an image along a path between two points, where the image is considered as a vertex-valued graph. Yet this definition does not fit with the interpretation of an image as an elevation map, i.e. a somehow continuous landscape. In this paper, based on the discrete set-valued continuity setting, we present a new discrete definition for this distance, which is compatible with this interpretation, while being free from digital topology issues. Amazingly, we show that the proposed distance is related to the morphological tree of shapes, which in addition allows for a fast and exact computation of this distance. That contrasts with the classical definition of the MB distance, where its fast computation is only an approximation.

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Section: Illustration On Document Image Segmentationmentioning

confidence: 99%

Introducing the Dahu Pseudo-Distance

Géraud

Carlinet

et al. 2017

Lecture Notes in Computer Science

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“…The multi-class classification problem is addressed with an extension of the interactive segmentation algorithm proposed in [14]. In this phase of preliminary experiments, the classification of the nodes is performed on the ToS (i.e., channel-wise) instead of using the Multivariate Tree of Shapes [15].…”

Section: Supervised Classification Of the Tosmentioning

confidence: 99%

“…Thus, performing the classification directly on the ToS (i.e., classification of the nodes) should provide comparable results of the SDAPs since they carry information which is present in the ToS. The adopted scheme is the interactive segmentation algorithm proposed in [14] for the Multivariate Tree of Shapes [15,16]. The algorithm is extended to a multi-class classification problem, with multispectral data sets acquired by QuickBird and IKONOS sensors over urban areas.…”

Section: Introductionmentioning

confidence: 99%

Region-based classification of remote sensing images with the morphological tree of shapes

Cavallaro

Mura

Carlinet

et al. 2016

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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International audienceSatellite image classification is a key task used in remote sensing for the automatic interpretation of a large amount of information. Today there exist many types of classification algorithms using advanced image processing methods enhancing the classification accuracy rate. One of the best state-of-the-art methods which improves significantly the classification of complex scenes relies on Self-Dual Attribute Profiles (SDAPs). In this approach, the underlying representation of an image is the Tree of Shapes, which encodes the inclusion of connected components of the image. The SDAP computes for each pixel a vector of attributes providing a local multi-scale representation of the information and hence leading to a fine description of the local structures of the image. Instead of performing a pixel-wise classification on features extracted from the Tree of Shapes, it is proposed to directly classify its nodes. Extending a specific interactive segmentation algorithm enables it to deal with the multi-class classification problem. The method does not involve any statistical learning and it is based entirely on morphological information related to the tree. Consequently, a very simple and effective region-based classifier relying on basic attributes is presented

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“…This issue has been addressed in the framework of the Image Foresting Transform (IFT) [11] with the Differential Image Foresting Transform (DIFT) [10] a WS-based and fuzzy-connected segmentation method, whose response time for interactive segmentation is proportional to size of the modified regions of the scene. Interactivity has also been addressed in the context of object segmentation with hierarchical representations, especially for trees based on threshold decomposition i.e., component trees [20] or tree of shapes [5,19].…”

Section: Introductionmentioning

confidence: 99%

Interactive Segmentation with Incremental Watershed Cuts

Lebon,

Lefèvre,

Cousty

et al. 2023

Lecture Notes in Computer Science

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In this article, we propose an incremental method for computing seeded watershed cuts for interactive image segmentation. We propose an algorithm based on the hierarchical image representation called the binary partition tree to compute a seeded watershed cut. We show that this algorithm fits perfectly in an interactive segmentation process by handling user interactions, seed addition or removal, in time linear with respect to the number of affected pixels. Run time comparisons with several state-of-the-art interactive and non-interactive watershed methods show that the proposed method can handle user interactions much faster than previous methods achieving significant speedup from 15 to 90, thus improving the user experience on large images.

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Morphological object picking based on the color tree of shapes

Cited by 5 publications

References 27 publications

Introducing the Dahu Pseudo-Distance

Introducing the Dahu Pseudo-Distance

Region-based classification of remote sensing images with the morphological tree of shapes

Interactive Segmentation with Incremental Watershed Cuts

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