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Affinity Derivation and Graph Merge for Instance Segmentation [article]

Yiding Liu, Siyu Yang, Bin Li, Wengang Zhou, Jizheng Xu, Houqiang Li, Yan Lu
2018 arXiv   pre-print
Regarding pixels as the vertexes and affinities as edges, we then propose a simple yet effective graph merge algorithm to cluster pixels into instances.  ...  One is to predict pixel level semantic score and the other is designed to derive pixel affinities.  ...  Conclusions In this paper, we introduce a proposal-free instance segmentation scheme via affinity derivation and graph merge.  ... 
arXiv:1811.10870v1 fatcat:6uz4wesdrfavdk4nubxmyxzb4a

Learning Metric Graphs for Neuron Segmentation In Electron Microscopy Images [article]

Kyle Luther, H. Sebastian Seung
2019 arXiv   pre-print
In this case, segmentations from a "metric graph" turn out to be competitive or even superior to segmentations from a directly predicted affinity graph.  ...  To segment a new image, the feature vectors are computed and clustered.  ...  In the final section we provide an possible reason for the improved performance of the vector-derived segmentations over the affinity-derived segmentations.  ... 
arXiv:1902.00100v1 fatcat:35mbkttjxja7jaqabykc3tlkxu

Deep Affinity Net: Instance Segmentation via Affinity [article]

Xingqian Xu, Mang Tik Chiu, Thomas S. Huang, Honghui Shi
2020 arXiv   pre-print
Despite the maturity of these two paradigms, we would like to report an alternative affinity-based paradigm where instances are segmented based on densely predicted affinities and graph partitioning algorithms  ...  Most of the modern instance segmentation approaches fall into two categories: region-based approaches in which object bounding boxes are detected first and later used in cropping and segmenting instances  ...  Liu, Y., Yang, S., Li, B., Zhou, W., Xu, J., Li, H., Lu, Y.: Affinity derivation and graph merge for instance segmentation. In: European Conference on Computer Vision (2018) 39.  ... 
arXiv:2003.06849v1 fatcat:7u2yidtuwbcdjidvkt7p2knjoq

iShape: A First Step Towards Irregular Shape Instance Segmentation [article]

Lei Yang, Yan Zi Wei, Yisheng HE, Wei Sun, Zhenhang Huang, Haibin Huang, Haoqiang Fan
2021 arXiv   pre-print
Hence, we propose an affinity-based instance segmentation algorithm, called ASIS, as a stronger baseline.  ...  In this paper, we introduce a brand new dataset to promote the study of instance segmentation for objects with irregular shapes.  ...  The recent affinity-based methods obtain instance segmentation via affinity derivation [27] and graph partition [31] .  ... 
arXiv:2109.15068v1 fatcat:y6x74hqjgjgvvh3dyatcs7bkum

Learning and Segmenting Dense Voxel Embeddings for 3D Neuron Reconstruction [article]

Kisuk Lee, Ran Lu, Kyle Luther, H. Sebastian Seung
2021 arXiv   pre-print
Partitioning the metric graph with long-range edges as repulsive constraints yields an initial segmentation with high precision, with substantial accuracy gain for very thin objects.  ...  A "metric graph" on a set of edges between voxels is constructed from the dense voxel embeddings generated by a convolutional network.  ...  Each affinity map is obtained by stitching and blending the patch-wise affinity maps derived from the patch-wise dense voxel embeddings.Full merge-split plot on the test set.  ... 
arXiv:1909.09872v2 fatcat:mmk2qd2krfgkpjwvu7jdk2w7x4

The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation [article]

Steffen Wolf, Yuyan Li, Constantin Pape, Alberto Bailoni, Anna Kreshuk, Fred A. Hamprecht
2019 arXiv   pre-print
We propose a greedy algorithm for joint graph partitioning and labeling derived from the efficient Mutex Watershed partitioning algorithm.  ...  Semantic instance segmentation is the task of simultaneously partitioning an image into distinct segments while associating each pixel with a class label.  ...  We predict affinities with two separate 3D U-Nets [9] to derive graph edge weights and semantic class probabilities respectively.  ... 
arXiv:1912.12717v1 fatcat:bw4cz3srfvekzbbigoi4nqxf24

Maximin affinity learning of image segmentation [article]

Srinivas C. Turaga, Kevin L. Briggman, Moritz Helmstaedter, Winfried Denk, H. Sebastian Seung
2009 arXiv   pre-print
Images can be segmented by first using a classifier to predict an affinity graph that reflects the degree to which image pixels must be grouped together and then partitioning the graph to yield a segmentation  ...  We present the first machine learning algorithm for training a classifier to produce affinity graphs that are good in the sense of producing segmentations that directly minimize the Rand index, a well  ...  Acknowledgements SCT and HSS were supported in part by the Howard Hughes Medical Institute and the Gatsby Charitable Foundation.  ... 
arXiv:0911.5372v1 fatcat:lxe3kibvufcf7otggihrbksh6i

Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks [article]

Alberto Bailoni, Constantin Pape, Steffen Wolf, Anna Kreshuk, Fred A. Hamprecht
2020 arXiv   pre-print
This work introduces a new proposal-free instance segmentation method that builds on single-instance segmentation masks predicted across the entire image in a sliding window style.  ...  In contrast to related approaches, our method concurrently predicts all masks, one for each pixel, and thus resolves any conflict jointly across the entire image.  ...  This is not an ideal setup for the MWS, which is a greedy algorithm merging and constraining clusters according to the most attractive and repulsive weights in the graph.  ... 
arXiv:2009.04998v1 fatcat:bm2lc3najrcojoz2cpb5yulxpm

Large-scale image segmentation based on distributed clustering algorithms [article]

Ran Lu, Aleksandar Zlateski, H. Sebastian Seung
2021 arXiv   pre-print
We demonstrate the algorithm by clustering an affinity graph with over 1.5 trillion edges between 135 billion supervoxels derived from a 3D electron microscopic brain image.  ...  The trick is to delay merge decisions for regions that touch chunk boundaries, and only complete them in a later round after the regions are fully contained within a chunk.  ...  We are grateful for assistance from Google, Amazon, and Intel.  ... 
arXiv:2106.10795v1 fatcat:y65omnrijfcovifdlrkfyulvo4

Robust Realtime Motion-Split-And-Merge for Motion Segmentation [chapter]

Ralf Dragon, Jörn Ostermann, Luc Van Gool
2013 Lecture Notes in Computer Science  
In this paper, we analyze and modify the Motion-Split-and-Merge (MSAM) algorithm [3] for the motion segmentation of correspondences between two frames.  ...  We compare our (robust realtime) RMSAM with J-Linkage [16] and Graph-Based Segmentation [5] and show that it is superior to both.  ...  Acknowledgment This work was partially funded by ERC project VarCity, SNF project AerialCrowd and BMBF project ASEV.  ... 
doi:10.1007/978-3-642-40602-7_45 fatcat:44p3kzd3urgflltrl22g537224

Learning Superpixels with Segmentation-Aware Affinity Loss

Wei-Chih Tu, Ming-Yu Liu, Varun Jampani, Deqing Sun, Shao-Yi Chien, Ming-Hsuan Yang, Jan Kautz
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
Instead, we propose a segmentation-aware affinity learning approach for superpixel segmentation.  ...  Specifically, we propose a new loss function that takes the segmentation error into account for affinity learning. We also develop the Pixel Affinity Net for affinity prediction.  ...  Yang is supported in part by NSF CAREER (No. 1149783) and gifts from Adobe, Toyota, Panasonic, Samsung, NEC, Verisk, and NVidia.  ... 
doi:10.1109/cvpr.2018.00066 dblp:conf/cvpr/Tu0JSC0K18 fatcat:ubikm7kbsfdilkfg5fxcv4iltu

A graph-cut approach to image segmentation using an affinity graph based on ℓ0-sparse representation of features

Xiaofang Wang, Huibin Li, Charles-Edmond Bichot, Simon Masnou, Liming Chen
2013 2013 IEEE International Conference on Image Processing  
This provides a 0 affinity graph that has interesting properties of long range and sparsity, and a suitable graph cut yields a segmentation.  ...  We propose a graph-cut based image segmentation method by constructing an affinity graph using 0 sparse representation.  ...  The final 0 affinity graph is obtained by merging multiple 0 graphs built over different features and different superpixel scales.  ... 
doi:10.1109/icip.2013.6738828 dblp:conf/icip/WangLBMC13 fatcat:g4bdkimwmne6razrg3lmyxvtfy

Leveraging Domain Knowledge to Improve Microscopy Image Segmentation With Lifted Multicuts

Constantin Pape, Alex Matskevych, Adrian Wolny, Julian Hennies, Giulia Mizzon, Marion Louveaux, Jacob Musser, Alexis Maizel, Detlev Arendt, Anna Kreshuk
2019 Frontiers in Computer Science  
Using this formulation, we demonstrate significant improvement in segmentation accuracy for four challenging boundary-based segmentation problems from neuroscience and developmental biology.  ...  The amount of data acquired in such studies makes manual instance segmentation, a fundamental step in many analysis pipelines, impossible.  ...  Schippers and Nicole L. Schieber in the Electron Microscopy Facility of EMBL.  ... 
doi:10.3389/fcomp.2019.00006 fatcat:scgszaejabbsre3svg6y7pymgu

Multi-sensor clustering using Layered Affinity Propagation

Lionel Ott, Fabio Ramos
2013 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems  
In this paper, we propose a novel clustering method, coined Layered Affinity Propagation, for automatic clustering of observations that only requires the definition of features on individual data sources  ...  A second experiment shows how this novel method handles the task of clustering segmented colour and depth data obtained from a Velodyne and camera in an urban environment.  ...  The term layered refers to the fact that we represent the similarity values derived from each data source and the associated features by a separate affinity propagation instance or layer.  ... 
doi:10.1109/iros.2013.6696755 dblp:conf/iros/OttR13 fatcat:3sjmdo7dvjcg3ke5vq34xp2li4

Two--level MRF Models for Image Restoration and Segmentation

M. Rivera, J.C. Gee
2004 Procedings of the British Machine Vision Conference 2004  
We present a new general Bayesian formulation for simultaneously restoring and segmenting piecewise smooth images.  ...  This implies estimation of the associated parameters of the classes within an image, the class label for each image pixel and the number of classes.  ...  We derived a set of methods suitable for tasks in image processing and low-level computer vision.  ... 
doi:10.5244/c.18.83 dblp:conf/bmvc/RiveraG04 fatcat:wqylbesfebdq5i2rnb5q6dfvvy
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