Deep Watershed Transform for Instance Segmentation
Min Bai,Raquel Urtasun +1 more
- pp 2858-2866
TLDR
This paper presents a simple yet powerful end-to-end convolutional neural network that achieves more than double the performance over the state-of-the-art on the challenging Cityscapes Instance Level Segmentation task.Abstract:
Most contemporary approaches to instance segmentation use complex pipelines involving conditional random fields, recurrent neural networks, object proposals, or template matching schemes. In this paper, we present a simple yet powerful end-to-end convolutional neural network to tackle this task. Our approach combines intuitions from the classical watershed transform and modern deep learning to produce an energy map of the image where object instances are unambiguously represented as energy basins. We then perform a cut at a single energy level to directly yield connected components corresponding to object instances. Our model achieves more than double the performance over the state-of-the-art on the challenging Cityscapes Instance Level Segmentation task.read more
Citations
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Journal ArticleDOI
Trainable Active Contour Model for Histological Image Segmentation
TL;DR: A hybrid method of glands object segmentation in histological images, based on the trainable active contour model, which combines the use of both modern convolutional neural networks and classical methods of mathematical image processing is presented.
Posted Content
PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model
TL;DR: In this article, a CNN is used to detect individual keypoints and predict their relative displacements, allowing them to group keypoints into person pose instances and then associate semantic person pixels with their corresponding person instance, delivering instance-level person segmentations.
Book ChapterDOI
Towards Bounding-Box Free Panoptic Segmentation
TL;DR: BBFNet as mentioned in this paper predicts watershed levels and uses them to detect large instance candidates where boundaries are well defined, and predicts instance centers by means of Hough voting followed by mean-shift to reliably detect small objects.
Journal ArticleDOI
Deep Learning in Cell Image Analysis
Junde Xu,Donghao Zhou,Danruo Deng,Jingpeng Li,Cheng Chen,Xiangyun Liao,Guangyong Chen,Pheng-Ann Heng +7 more
TL;DR: This study presents an in-depth survey of the three most critical tasks in cell image analysis: segmentation, tracking, and classification and reviews more advanced machine learning technologies, aiming to make deep learning-based methods more useful and eventually promote the application of deep-learning algorithms.
Journal ArticleDOI
A Unified Neural Network for Panoptic Segmentation
TL;DR: A unified neural network is proposed for panoptic segmentation, a task aiming to achieve more fine‐grained segmentation and achieves a competitive Panoptic Quality (PQ) metric score with the state of the art.
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Proceedings ArticleDOI
The Cityscapes Dataset for Semantic Urban Scene Understanding
Marius Cordts,Mohamed Omran,Sebastian Ramos,Timo Rehfeld,Markus Enzweiler,Rodrigo Benenson,Uwe Franke,Stefan Roth,Bernt Schiele +8 more
TL;DR: This work introduces Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling, and exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity.