# Convolutional Oriented Boundaries

arXiv: https://arxiv.org/abs/1608.02755 · HTML: https://mlsanity.com/paper/1608.02755/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2016.

## Built on by (most cited first, 25 of 25)

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- [Large-Scale Multi-Hypotheses Cell Tracking Using Ultrametric Contours Maps](https://mlsanity.com/paper/2308.04526/) (2023)
- [Internal-External Boundary Attention Fusion for Glass Surface Segmentation](https://mlsanity.com/paper/2307.00212/) (2023)
- [Complete Instances Mining for Weakly Supervised Instance Segmentation](https://mlsanity.com/paper/2402.07633/) (2024)
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- [BoundarySqueeze: Image Segmentation as Boundary Squeezing](https://mlsanity.com/paper/2105.11668/) (2021)
- [Measuring uncertainty in human visual segmentation](https://mlsanity.com/paper/2301.07807/) (2023)
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- [Triplet-Watershed for Hyperspectral Image Classification](https://mlsanity.com/paper/2103.09384/) (2021)
- [Clicking Matters:Towards Interactive Human Parsing](https://mlsanity.com/paper/2111.06162/) (2021)
- [Zero Pixel Directional Boundary by Vector Transform](https://mlsanity.com/paper/2203.08795/) (2022)
- [RusTitW: Russian Language Text Dataset for Visual Text in-the-Wild Recognition](https://mlsanity.com/paper/2303.16531/) (2023)
- [Hierarchical Graph Pattern Understanding for Zero-Shot VOS](https://mlsanity.com/paper/2312.09525/) (2023)
- [EDCSSM: Edge Detection with Convolutional State Space Model](https://mlsanity.com/paper/2409.01609/) (2024)
- [Fusing RGBD Tracking and Segmentation Tree Sampling for Multi-Hypothesis Volumetric Segmentation](https://mlsanity.com/paper/2104.00205/) (2021)
- [Assessing hierarchies by their consistent segmentations](https://mlsanity.com/paper/2204.04969/) (2022)
- [Msmsfnet: a multi-stream and multi-scale fusion net for edge detection](https://mlsanity.com/paper/2404.04856/) (2024)
- [PEdger++: Practical Edge Detection via Assembling Cross Information](https://mlsanity.com/paper/2508.11961/) (2025)
- [Amped: Adaptive Multi-stage Non-edge Pruning for Edge Detection](https://mlsanity.com/paper/2603.27661/) (2026)

## Abstract (arXiv)

We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs). COB is computationally efficient, because it requires a single CNN forward pass for contour detection and it uses a novel sparse boundary representation for hierarchical segmentation; it gives a significant leap in performance over the state-of-the-art, and it generalizes very well to unseen categories and datasets. Particularly, we show that learning to estimate not only contour strength but also orientation provides more accurate results. We perform extensive experiments on BSDS, PASCAL Context, PASCAL Segmentation, and MS-COCO, showing that COB provides state-of-the-art contours, region hierarchies, and object proposals in all datasets.