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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Örnek Bölütlemesi ile Nesne ve Renk Sınıflandırması
Ahmet Özcan,Ömer Çetin +1 more
TL;DR: Görüntü üzerinde nesne tespit ve sınıflandırma uygulamaları görüNTü işleme alanında ele alınan temel konulardandır. as discussed by the authors .
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k-means Mask Transformer
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Combining Deep Learning and Mathematical Morphology for Historical Map Segmentation
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Hierarchical Aggregation for 3D Instance Segmentation
TL;DR: Huang et al. as discussed by the authors proposed a hierarchical clustering-based framework named HAIS, which makes full use of spatial relation of points and point sets to progressively generate instance proposals.
Dissertation
Advances in Image Segmentation: Variational Approaches and Deep Learning
TL;DR: This paper surveys the advances in the field of image segmentation, an essential task for understanding images and focuses on the variational methods, including models from the edge-based and the region-based approaches.
References
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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.