Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform

Chen, Liang-Chieh; Barron, Jonathan T.; Papandreou, George; Murphy, Kevin; Yuille, Alan

doi:10.1109/cvpr.2016.492

Cited by 309 publications

(184 citation statements)

References 46 publications

Supporting

Mentioning

184

Contrasting

Order By: Relevance

“…Chen et al [19] propose a framework which refines FCN output using the fully connected CRF for semantic segmentation. In [20], they replace the fully connected CRF into mordern domain transform. Likewise semantic segmentation, depth estimation methods based on FCN concentrate how to refine depth boundaries using CRF [21], or annotations of relative depth [22].…”

Section: Parametric Learning Methodsmentioning

confidence: 99%

Single Image Depth Estimation With Integration of Parametric Learning and Non-Parametric Sampling

Jung¹,

Sohn²

2016

Journal of Korea Multimedia Society

View full text Add to dashboard Cite

Understanding 3D structure of scenes is of a great interest in various vision-related tasks. In this paper, we present a unified approach for estimating depth from a single monocular image. The key idea of our approach is to take advantages both of parametric learning and non-parametric sampling method. Using a parametric convolutional network, our approach learns the relation of various monocular cues, which make a coarse global prediction. We also leverage the local prediction to refine the global prediction. It is practically estimated in a non-parametric framework. The integration of local and global predictions is accomplished by concatenating the feature maps of the global prediction with those from local ones. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods both qualitatively and quantitatively.

show abstract

Section: Parametric Learning Methodsmentioning

confidence: 99%

Single Image Depth Estimation With Integration of Parametric Learning and Non-Parametric Sampling

Jung¹,

Sohn²

2016

Journal of Korea Multimedia Society

View full text Add to dashboard Cite

show abstract

“…To the best of our knowledge, the most similar works to our ERN are from Chen et al [32] and Cheng et al [33], where they build edge-aware nets to further filter the semantic segmentation results using domain transfer technology and regularization method, respectively. ERN constructs multiple edge loss reinforced structures from the encoder and decoder separately (namely, encoder edge and decoder edge), while only one edge-aware net has been constructed in [32] (similar to our encoder edge) and [33] (constructed by concatenating hierarchical features cross encoder and decoder).…”

Section: General Analysismentioning

confidence: 99%

“…ERN constructs multiple edge loss reinforced structures from the encoder and decoder separately (namely, encoder edge and decoder edge), while only one edge-aware net has been constructed in [32] (similar to our encoder edge) and [33] (constructed by concatenating hierarchical features cross encoder and decoder). Multiple structures and corresponding weighted edge losses are introduced to strengthen the ability of preserving the boundary information rather than post-fine-tuning the semantic segmentation results.…”

Section: General Analysismentioning

confidence: 99%

“…Thanks to the holistically-nested edge detection (HED) network [29], deep net-style edges have shown the capacity to improve the performance of high-level semantic tasks [30,31]. Chen et al [32] proposed an edge-preserving filtering method using domain transform to enhance object localization accuracy in semantic segmentation. Cheng et al [33] fused semantic segmentation net and edge net with a regularization method to refine entire network.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ERN: Edge Loss Reinforced Semantic Segmentation Network for Remote Sensing Images

et al. 2018

View full text Add to dashboard Cite

Abstract:The semantic segmentation of remote sensing images faces two major challenges: high inter-class similarity and interference from ubiquitous shadows. In order to address these issues, we develop a novel edge loss reinforced semantic segmentation network (ERN) that leverages the spatial boundary context to reduce the semantic ambiguity. The main contributions of this paper are as follows: (1) we propose a novel end-to-end semantic segmentation network for remote sensing, which involves multiple weighted edge supervisions to retain spatial boundary information; (2) the main representations of the network are shared between the edge loss reinforced structures and semantic segmentation, which means that the ERN simultaneously achieves semantic segmentation and edge detection without significantly increasing the model complexity; and (3) we explore and discuss different ERN schemes to guide the design of future networks. Extensive experimental results on two remote sensing datasets demonstrate the effectiveness of our approach both in quantitative and qualitative evaluation. Specifically, the semantic segmentation performance in shadow-affected regions is significantly improved.

show abstract

“…It has become a basic issue of image processing and computer vision. For example, object detection [4,5], object recognition [6,7], knowledge inference [8,9], image understanding [10], and medical image processing [11,12] are all dependent on image segmentation, whose accuracy determines the quality of image analysis and interpretation.…”

Section: Introductionmentioning

confidence: 99%

A Fast Color Image Segmentation Approach Using GDF with Improved Region-Level Ncut

Liu¹,

Wang²,

Li³

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Color image segmentation is fundamental in image processing and computer vision. A novel approach, GDF-Ncut, is proposed to segment color images by integrating generalized data field (GDF) and improved normalized cuts (Ncut). To start with, the hierarchy-grid structure is constructed in the color feature space of an image in an attempt to reduce the time complexity but preserve the quality of image segmentation. Then a fast hierarchy-grid clustering is performed under GDF potential estimation and therefore image pixels are merged into disjoint oversegmented but meaningful initial regions. Finally, these regions are presented as a weighted undirected graph, upon which Ncut algorithm merges homogenous initial regions to achieve final image segmentation. The use of the fast clustering improves the effectiveness of Ncut because regions-based graph is constructed instead of pixel-based graph. Meanwhile, during the processes of Ncut matrix computation, oversegmented regions are grouped into homogeneous parts for greatly ameliorating the intermediate problems from GDF and accordingly decreasing the sensitivity to noise. Experimental results on a variety of color images demonstrate that the proposed method significantly reduces the time complexity while partitioning image into meaningful and physically connected regions. The method is potentially beneficial to serve object extraction and pattern recognition.

show abstract

Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform

Cited by 309 publications

References 46 publications

Single Image Depth Estimation With Integration of Parametric Learning and Non-Parametric Sampling

Single Image Depth Estimation With Integration of Parametric Learning and Non-Parametric Sampling

ERN: Edge Loss Reinforced Semantic Segmentation Network for Remote Sensing Images

A Fast Color Image Segmentation Approach Using GDF with Improved Region-Level Ncut

Contact Info

Product

Resources

About