13 papers with code • 2 benchmarks • 0 datasets

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Most implemented papers

Object Contour Detection with a Fully Convolutional Encoder-Decoder Network

jimeiyang/objectContourDetector CVPR 2016

We develop a deep learning algorithm for contour detection with a fully convolutional encoder-decoder network.

Richer Convolutional Features for Edge Detection

yun-liu/rcf CVPR 2017

Using VGG16 network, we achieve \sArt results on several available datasets.

Photo-Sketching: Inferring Contour Drawings from Images

USTCzzl/Deecamp27 2 Jan 2019

Edges, boundaries and contours are important subjects of study in both computer graphics and computer vision.

Bi-Directional Cascade Network for Perceptual Edge Detection

pkuCactus/BDCN CVPR 2019

Exploiting multi-scale representations is critical to improve edge detection for objects at different scales.

Object Contour and Edge Detection with RefineContourNet

AndreKelm/RefineContourNet 30 Apr 2019

A ResNet-based multi-path refinement CNN is used for object contour detection.

Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation

jponttuset/mcg 3 Mar 2015

We propose a unified approach for bottom-up hierarchical image segmentation and object proposal generation for recognition, called Multiscale Combinatorial Grouping (MCG).

AMAT: Medial Axis Transform for Natural Images

tsogkas/amat ICCV 2017

We introduce Appearance-MAT (AMAT), a generalization of the medial axis transform for natural images, that is framed as a weighted geometric set cover problem.

Superpixel Segmentation via Convolutional Neural Networks with Regularized Information Maximization

DensoITLab/ss-with-RIM 17 Feb 2020

We propose an unsupervised superpixel segmentation method by optimizing a randomly-initialized convolutional neural network (CNN) in inference time.

Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation

JianqiangWan/Super-BPD CVPR 2020

In this paper, we propose a fast image segmentation method based on a novel super boundary-to-pixel direction (super-BPD) and a customized segmentation algorithm with super-BPD.

Unmixing Convolutional Features for Crisp Edge Detection

WHUHLX/CATS 19 Nov 2020

This paper presents a context-aware tracing strategy (CATS) for crisp edge detection with deep edge detectors, based on an observation that the localization ambiguity of deep edge detectors is mainly caused by the mixing phenomenon of convolutional neural networks: feature mixing in edge classification and side mixing during fusing side predictions.