4,471 research outputs found
Evaluation of distances between color image segmentations
Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), 2005, Estoril (Portugal)We illustrate the problem of comparing images by means of their color segmentations. A group of seven distances are proposed within the frame of the Integrated Region Matching distance and the employ of Multivariate Gaussian Distributions (MGD) for the color description of image regions. The performance of these distances is examined in tasks such as image retrieval and object recognition using the two segmentation algorithms in [1] and [2]. The best overall results are obtained for both tasks using the graphâpartition approach along with the FrĂ©chet distance, outperforming other distances in comparing MGDs.Peer Reviewe
3D Shape Segmentation with Projective Convolutional Networks
This paper introduces a deep architecture for segmenting 3D objects into
their labeled semantic parts. Our architecture combines image-based Fully
Convolutional Networks (FCNs) and surface-based Conditional Random Fields
(CRFs) to yield coherent segmentations of 3D shapes. The image-based FCNs are
used for efficient view-based reasoning about 3D object parts. Through a
special projection layer, FCN outputs are effectively aggregated across
multiple views and scales, then are projected onto the 3D object surfaces.
Finally, a surface-based CRF combines the projected outputs with geometric
consistency cues to yield coherent segmentations. The whole architecture
(multi-view FCNs and CRF) is trained end-to-end. Our approach significantly
outperforms the existing state-of-the-art methods in the currently largest
segmentation benchmark (ShapeNet). Finally, we demonstrate promising
segmentation results on noisy 3D shapes acquired from consumer-grade depth
cameras.Comment: This is an updated version of our CVPR 2017 paper. We incorporated
new experiments that demonstrate ShapePFCN performance under the case of
consistent *upright* orientation and an additional input channel in our
rendered images for encoding height from the ground plane (upright axis
coordinate values). Performance is improved in this settin
Automated Segmentation of Cells with IHC Membrane Staining
This study presents a fully automated membrane segmentation technique for immunohistochemical tissue images with membrane staining, which is a critical task in computerized immunohistochemistry (IHC). Membrane segmentation is particularly tricky in immunohistochemical tissue images because the cellular membranes are visible only in the stained tracts of the cell, while the unstained tracts are not visible. Our automated method provides accurate segmentation of the cellular membranes in the stained tracts and reconstructs the approximate location of the unstained tracts using nuclear membranes as a spatial reference. Accurate cell-by-cell membrane segmentation allows per cell morphological analysis and quantification of the target membrane proteins that is fundamental in several medical applications such as cancer characterization and classification, personalized therapy design, and for any other applications requiring cell morphology characterization. Experimental results on real datasets from different anatomical locations demonstrate the wide applicability and high accuracy of our approach in the context of IHC analysi
Template-Cut: A Pattern-Based Segmentation Paradigm
We present a scale-invariant, template-based segmentation paradigm that sets
up a graph and performs a graph cut to separate an object from the background.
Typically graph-based schemes distribute the nodes of the graph uniformly and
equidistantly on the image, and use a regularizer to bias the cut towards a
particular shape. The strategy of uniform and equidistant nodes does not allow
the cut to prefer more complex structures, especially when areas of the object
are indistinguishable from the background. We propose a solution by introducing
the concept of a "template shape" of the target object in which the nodes are
sampled non-uniformly and non-equidistantly on the image. We evaluate it on
2D-images where the object's textures and backgrounds are similar, and large
areas of the object have the same gray level appearance as the background. We
also evaluate it in 3D on 60 brain tumor datasets for neurosurgical planning
purposes.Comment: 8 pages, 6 figures, 3 tables, 6 equations, 51 reference
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