Convolutional Oriented Boundaries

Maninis, Kevis-Kokitsi; Pont-Tuset, Jordi; Arbeláez, Pablo; Gool, Luc J. Van

doi:10.1007/978-3-319-46448-0_35

Cited by 105 publications

(80 citation statements)

References 43 publications

(140 reference statements)

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“…Another challenge will be to take account for richer multi-scale and oriented gradient information provided by deep learning methods that enabled a large performance boost in COB [58].…”

Section: Resultsmentioning

confidence: 99%

“…As reference state-of-the-art results, we include Multiscale Combinatorial Grouping (MCG) hierarchies from [10], Convolutional Object Boundaries (COB) hierarchies from [55], [58], and Least Effort Segmentation (LEP) from [59] in our assessments. MCG also uses SED as the main cue for contour detection, but then merges several hierarchies (referred to as OWT-UCM in the literature [7]) computed at different scales.…”

Section: Discussionmentioning

confidence: 99%

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Evaluation of Hierarchical Watersheds

Perret¹,

Cousty²,

Guimarães³

et al. 2018

IEEE Trans. on Image Process.

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Abstract-This article aims to understand the practical features of hierarchies of morphological segmentations, namely the quasi-flat zones hierarchy and watershed hierarchies, and to evaluate their potential in the context of natural image analysis. We propose a novel evaluation framework for hierarchies of partitions designed to capture various aspects of those representations: precision of their regions and contours, possibility to extract high quality horizontal cuts and optimal non-horizontal cuts for image segmentation, and ease of finding a set of regions representing a semantic object. This framework is used to assess and to optimize hierarchies with respect to the possible pre-and post-processing steps. We show that, used in conjunction with a state-of-the-art contour detector, watershed hierarchies are competitive with complex state-of-the-art methods for hierarchy construction. In particular, the proposed framework allows us to identify a watershed hierarchy based on a novel extinction value, the number of parent nodes, that outperforms the other hierarchies of morphological segmentations. This coupled with the fact that watershed hierarchies satisfy clear global optimality properties and can be efficiently computed on large data, make them valuable candidates for various computer vision tasks.Index Terms-mathematical morphology, hierarchy of partitions, watershed segmentation, image analysis.

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“…Another challenge will be to take account for richer multi-scale and oriented gradient information provided by deep learning methods that enabled a large performance boost in COB [58].…”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Evaluation of Hierarchical Watersheds

Perret¹,

Cousty²,

Guimarães³

et al. 2018

IEEE Trans. on Image Process.

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show abstract

“…It then inspires researchers to upgrade the existing edge-based line segment detectors to deep-edge based line segment detectors. Convolutional Oriented Boundaries (COB) [23], [42] detector was proposed to get multi-scale oriented contours and region hierarchies from a single ConvNet. Since the oriented contours are adaptive to the input format (i.e., edge pixels and orientations) of fast LSD [2], they can be used to address the issue of incomplete detection in LSD effectively.…”

Section: Deep Edge and Line Segment Detectionmentioning

confidence: 99%

Learning Regional Attraction for Line Segment Detection

Xue

Bai

Wang

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

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This paper presents regional attraction of line segment maps, and hereby poses the problem of line segment detection (LSD) as a problem of region coloring. Given a line segment map, the proposed regional attraction first establishes the relationship between line segments and regions in the image lattice. Based on this, the line segment map is equivalently transformed to an attraction field map (AFM), which can be remapped to a set of line segments without loss of information. Accordingly, we develop an end-to-end framework to learn attraction field maps for raw input images, followed by a squeeze module to detect line segments. Apart from existing works, the proposed detector properly handles the local ambiguity and does not rely on the accurate identification of edge pixels. Comprehensive experiments on the Wireframe dataset and the YorkUrban dataset demonstrate the superiority of our method. In particular, we achieve an F-measure of 0.831 on the Wireframe dataset, advancing the state-of-the-art performance by 10.3 percent.

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“…Segmentation algorithms can also be treated as boundary detection technique. convolutional features are also very useful from that perspective [139]. While earlier layers can provide fine details, later layers focus more on the coarser boundaries.…”

Section: Fully Convolutional Layersmentioning

confidence: 99%

Understanding Deep Learning Techniques for Image Segmentation

et al. 2019

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The machine learning community has been overwhelmed by a plethora of deep learning based approaches. Many challenging computer vision tasks such as detection, localization, recognition and segmentation of objects in unconstrained environment are being efficiently addressed by various types of deep neural networks like convolutional neural networks, recurrent networks, adversarial networks, autoencoders and so on. While there have been plenty of analytical studies regarding the object detection or recognition domain, many new deep learning techniques have surfaced with respect to image segmentation techniques. This paper approaches these various deep learning techniques of image segmentation from an analytical perspective. The main goal of this work is to provide an intuitive understanding of the major techniques that has made significant contribution to the image segmentation domain. Starting from some of the traditional image segmentation approaches, the paper progresses describing the effect deep learning had on the image segmentation domain. Thereafter, most of the major segmentation algorithms have been logically categorized with paragraphs dedicated to their unique contribution. With an ample amount of intuitive explanations, the reader is expected to have an improved ability to visualize the internal dynamics of these processes.

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Convolutional Oriented Boundaries

Cited by 105 publications

References 43 publications

Evaluation of Hierarchical Watersheds

Evaluation of Hierarchical Watersheds

Learning Regional Attraction for Line Segment Detection

Understanding Deep Learning Techniques for Image Segmentation

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