scispace - formally typeset
Topic

Scale-space segmentation

About: Scale-space segmentation is a(n) research topic. Over the lifetime, 26741 publication(s) have been published within this topic receiving 599613 citation(s).

...read more

Papers
  More

Open accessProceedings ArticleDOI: 10.1109/CVPR.2015.7298965
07 Jun 2015-
Abstract: Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. Our key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet [20], the VGG net [31], and GoogLeNet [32]) into fully convolutional networks and transfer their learned representations by fine-tuning [3] to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes less than one fifth of a second for a typical image.

...read more

Topics: Scale-space segmentation (55%)

18,335 Citations


Open accessJournal ArticleDOI: 10.1109/TPAMI.2017.2699184
Abstract: In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First , we highlight convolution with upsampled filters, or ‘atrous convolution’, as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second , we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third , we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed “DeepLab” system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online.

...read more

Topics: Scale-space segmentation (59%), Convolutional neural network (56%), Deep learning (55%) ...read more

8,005 Citations


Open accessJournal ArticleDOI: 10.1023/B:VISI.0000022288.19776.77
Abstract: This paper addresses the problem of segmenting an image into regions. We define a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. We then develop an efficient segmentation algorithm based on this predicate, and show that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties. We apply the algorithm to image segmentation using two different kinds of local neighborhoods in constructing the graph, and illustrate the results with both real and synthetic images. The algorithm runs in time nearly linear in the number of graph edges and is also fast in practice. An important characteristic of the method is its ability to preserve detail in low-variability image regions while ignoring detail in high-variability regions.

...read more

5,470 Citations


Open accessJournal ArticleDOI: 10.1016/J.NEUROIMAGE.2006.01.015
01 Jul 2006-NeuroImage
Abstract: Active contour segmentation and its robust implementation using level set methods are well-established theoretical approaches that have been studied thoroughly in the image analysis literature. Despite the existence of these powerful segmentation methods, the needs of clinical research continue to be fulfilled, to a large extent, using slice-by-slice manual tracing. To bridge the gap between methodological advances and clinical routine, we developed an open source application called ITK-SNAP, which is intended to make level set segmentation easily accessible to a wide range of users, including those with little or no mathematical expertise. This paper describes the methods and software engineering philosophy behind this new tool and provides the results of validation experiments performed in the context of an ongoing child autism neuroimaging study. The validation establishes SNAP intrarater and interrater reliability and overlap error statistics for the caudate nucleus and finds that SNAP is a highly reliable and efficient alternative to manual tracing. Analogous results for lateral ventricle segmentation are provided.

...read more

  • Table 5: Ventricle volumes (in mm3) from the SNAP reliability experiment. Five test cases, replicated three times (column one) have been segmented by two raters (A, B), who were blinded to the cases.
    Table 5: Ventricle volumes (in mm3) from the SNAP reliability experiment. Five test cases, replicated three times (column one) have been segmented by two raters (A, B), who were blinded to the cases.
  • Fig. 8. User interface for specifying contour evolution parameters, including the relative weights of the forces acting on the contour. The intuitive interface is shown on the left and the mathematical interface on the right. The parameter specification window also shows how the forces interrelate in a two-dimensional example.
    Fig. 8. User interface for specifying contour evolution parameters, including the relative weights of the forces acting on the contour. The intuitive interface is shown on the left and the mathematical interface on the right. The parameter specification window also shows how the forces interrelate in a two-dimensional example.
  • Fig. 7. a. User interface for feature image specification: the user is setting the values of the smooth threshold parameters, and the feature image is displayed in the orthogonal slice views using a color map.b. User interface for active contour initialization: the user has placed two spherical bubbles in the caudate nucleus.
    Fig. 7. a. User interface for feature image specification: the user is setting the values of the smooth threshold parameters, and the feature image is displayed in the orthogonal slice views using a color map.b. User interface for active contour initialization: the user has placed two spherical bubbles in the caudate nucleus.
  • Table 6: Intrarater and interrater reliability of lateral ventricle segmentation in SNAP. Reliability was measured based on 3 replications of 5 test datasets by two raters.
    Table 6: Intrarater and interrater reliability of lateral ventricle segmentation in SNAP. Reliability was measured based on 3 replications of 5 test datasets by two raters.
  • Table 2: Intrarater and interrater reliability of caudate segmentation. Reliability was measured based on 3 replications of 5 test datasets by two raters. Reliability values for (1) manual segmentation by two experts; (2) manual vs. SNAP segmentation by the same expert; and (3) SNAP segmentation by two experts show the excellent reliability of both methods and the excellent agreement between manual expert’s segmentation and SNAP. SNAP reduced segmentation time from 1.5 hours to 30 minutes, while the training period to establish reliability was several months for the manual method and significantly shorter for SNAP.
    Table 2: Intrarater and interrater reliability of caudate segmentation. Reliability was measured based on 3 replications of 5 test datasets by two raters. Reliability values for (1) manual segmentation by two experts; (2) manual vs. SNAP segmentation by the same expert; and (3) SNAP segmentation by two experts show the excellent reliability of both methods and the excellent agreement between manual expert’s segmentation and SNAP. SNAP reduced segmentation time from 1.5 hours to 30 minutes, while the training period to establish reliability was several months for the manual method and significantly shorter for SNAP.
  • + 14

Topics: Scale-space segmentation (63%), Image segmentation (59%), Active contour model (54%) ...read more

5,189 Citations


Open accessJournal ArticleDOI: 10.1109/TPAMI.2010.161
Abstract: This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local cues into a globalization framework based on spectral clustering. Our segmentation algorithm consists of generic machinery for transforming the output of any contour detector into a hierarchical region tree. In this manner, we reduce the problem of image segmentation to that of contour detection. Extensive experimental evaluation demonstrates that both our contour detection and segmentation methods significantly outperform competing algorithms. The automatically generated hierarchical segmentations can be interactively refined by user-specified annotations. Computation at multiple image resolutions provides a means of coupling our system to recognition applications.

...read more

  • Fig. 5. Filters for creating textons. We use 8 oriented even- and odd-symmetric Gaussian derivative filters and a center-surround (difference of Gaussians) filter.
    Fig. 5. Filters for creating textons. We use 8 oriented even- and odd-symmetric Gaussian derivative filters and a center-surround (difference of Gaussians) filter.
  • Fig. 6. Multiscale Pb. Left Column, Top to Bottom: The brightness and color a and b channels of Lab color space, and the texton channel computed using image-specific textons, followed by the input image. Rows: Next to each channel, we display the oriented gradient of histograms (as outlined in Figure 4) for θ = 0 and θ = π2 (horizontal and vertical), and the maximum response over eight orientations in [0, π) (right column). Beside the original image, we display the combination of oriented gradients across all four channels and across three scales. The lower right panel (outlined in red) shows mPb, the final output of the multiscale contour detector.
    Fig. 6. Multiscale Pb. Left Column, Top to Bottom: The brightness and color a and b channels of Lab color space, and the texton channel computed using image-specific textons, followed by the input image. Rows: Next to each channel, we display the oriented gradient of histograms (as outlined in Figure 4) for θ = 0 and θ = π2 (horizontal and vertical), and the maximum response over eight orientations in [0, π) (right column). Beside the original image, we display the combination of oriented gradients across all four channels and across three scales. The lower right panel (outlined in red) shows mPb, the final output of the multiscale contour detector.
  • Fig. 14. Hierarchical segmentation from contours. Far Left: Image. Left: Maximal response of contour detector gPb over orientations. Middle Left: Weighted contours resulting from the Oriented Watershed Transform - Ultrametric Contour Map (OWT-UCM) algorithm using gPb as input. This single weighted image encodes the entire hierarchical segmentation. By construction, applying any threshold to it is guaranteed to yield a set of closed contours (the ones with weights above the threshold), which in turn define a segmentation. Moreover, the segmentations are nested. Increasing the threshold is equivalent to removing contours and merging the regions they separated. Middle Right: The initial oversegmentation corresponding to the finest level of the UCM, with regions represented by their mean color. Right and Far Right: Contours and corresponding segmentation obtained by thresholding the UCM at level 0.5.
    Fig. 14. Hierarchical segmentation from contours. Far Left: Image. Left: Maximal response of contour detector gPb over orientations. Middle Left: Weighted contours resulting from the Oriented Watershed Transform - Ultrametric Contour Map (OWT-UCM) algorithm using gPb as input. This single weighted image encodes the entire hierarchical segmentation. By construction, applying any threshold to it is guaranteed to yield a set of closed contours (the ones with weights above the threshold), which in turn define a segmentation. Moreover, the segmentations are nested. Increasing the threshold is equivalent to removing contours and merging the regions they separated. Middle Right: The initial oversegmentation corresponding to the finest level of the UCM, with regions represented by their mean color. Right and Far Right: Contours and corresponding segmentation obtained by thresholding the UCM at level 0.5.
  • Fig. 19. Evaluating regions on the BSDS300. Contour detector influence on segmentation quality is evident when benchmarking the regions of the resulting hierarchical segmentation. Left: Probabilistic Rand Index. Right: Variation of Information.
    Fig. 19. Evaluating regions on the BSDS300. Contour detector influence on segmentation quality is evident when benchmarking the regions of the resulting hierarchical segmentation. Left: Probabilistic Rand Index. Right: Variation of Information.
  • Fig. 17. Boundary benchmark on the BSDS500. Comparing boundaries to human ground-truth allows us to evaluate contour detectors [3], [22] (dotted lines) and segmentation algorithms [4], [32], [33], [34] (solid lines) in the same framework. Performance is consistent when going from the BSDS300 (Figures 1 and 2) to the BSDS500 (above). Furthermore, the OWT-UCM algorithm preserves contour detector quality. For both gPb and Canny, comparing the resulting segment boundaries to the original contours shows that our OWT-UCM algorithm constructs hierarchical segmentations from contours without losing performance on the boundary benchmark.
    Fig. 17. Boundary benchmark on the BSDS500. Comparing boundaries to human ground-truth allows us to evaluate contour detectors [3], [22] (dotted lines) and segmentation algorithms [4], [32], [33], [34] (solid lines) in the same framework. Performance is consistent when going from the BSDS300 (Figures 1 and 2) to the BSDS500 (above). Furthermore, the OWT-UCM algorithm preserves contour detector quality. For both gPb and Canny, comparing the resulting segment boundaries to the original contours shows that our OWT-UCM algorithm constructs hierarchical segmentations from contours without losing performance on the boundary benchmark.
  • + 17

4,329 Citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20202
201914
2018152
20171,292
20161,904
20152,125

Top Attributes

Show by:

Topic's top 5 most impactful authors

Dinggang Shen

26 papers, 1.4K citations

Milan Sonka

24 papers, 1.7K citations

Licheng Jiao

21 papers, 399 citations

B.S. Manjunath

17 papers, 2.2K citations

Aly A. Farag

16 papers, 168 citations

Network Information
Related Topics (5)
Image segmentation

79.6K papers, 1.8M citations

96% related
Segmentation-based object categorization

17.9K papers, 386.6K citations

95% related
Feature extraction

111.8K papers, 2.1M citations

94% related
Edge detection

25.5K papers, 486.4K citations

94% related
Image texture

29.1K papers, 736.4K citations

94% related