On the public citation graph

Interval Sorting Optimisation Method for Fast Supervoxel Segmentation Based on Manhattan Distance Metric

https://doi.org/10.2139/ssrn.4767281
0 signed · 32 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-031-26212-8_4openalex-extracted
Introduction to Algorithms
inferredcites →https://doi.org/10.1007/978-3-642-33786-4_2openalex-extracted
SEEDS: Superpixels Extracted via Energy-Driven Sampling
inferredcites →https://doi.org/10.1007/s10462-022-10176-7openalex-extracted
Deep learning for video object segmentation: a review
inferredcites →https://doi.org/10.1007/s10489-019-01550-0openalex-extracted
Multi-channel biomimetic visual transformation for object feature extraction and recognition of complex scenes
inferredcites →https://doi.org/10.1016/j.cagd.2022.102080openalex-extracted
GPU-based supervoxel segmentation for 3D point clouds
inferredcites →https://doi.org/10.1016/j.isprsjprs.2018.05.004openalex-extracted
Toward better boundary preserved supervoxel segmentation for 3D point clouds
inferredcites →https://doi.org/10.1049/el.2014.3249openalex-extracted
Boundary‐enhanced supervoxel segmentation for sparse outdoor LiDAR data
inferredcites →https://doi.org/10.1093/mnras/stac3185openalex-extracted
Magnetorotational supernovae: a nucleosynthetic analysis of sophisticated 3D models
inferredcites →https://doi.org/10.1109/access.2022.3206387openalex-extracted
A Supervoxel Segmentation Method With Adaptive Centroid Initialization for Point Clouds
inferredcites →https://doi.org/10.1109/cvpr.2012.6247802openalex-extracted
Evaluation of super-voxel methods for early video processing
inferredcites →https://doi.org/10.1109/cvpr.2013.264openalex-extracted
Voxel Cloud Connectivity Segmentation - Supervoxels for Point Clouds
inferredcites →https://doi.org/10.1109/cvpr.2018.00479openalex-extracted
Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs
inferredcites →https://doi.org/10.1109/cvpr.2019.00762openalex-extracted
Point Cloud Oversegmentation With Graph-Structured Deep Metric Learning
inferredcites →https://doi.org/10.1109/cvpr46437.2021.00577openalex-extracted
Towards Open World Object Detection
inferredcites →https://doi.org/10.1109/iccv.2019.00387openalex-extracted
Fast Computation of Content-Sensitive Superpixels and Supervoxels Using Q-Distances
inferredcites →https://doi.org/10.1109/iccv48922.2021.00546openalex-extracted
Superpoint Network for Point Cloud Oversegmentation
inferredcites →https://doi.org/10.1109/icip.2013.6738558openalex-extracted
Depth-adaptive supervoxels for RGB-D video segmentation
inferredcites →https://doi.org/10.1109/iwssip48289.2020.9145130openalex-extracted
A Survey on Performance Metrics for Object-Detection Algorithms
inferredcites →https://doi.org/10.1109/jproc.2023.3238524openalex-extracted
Object Detection in 20 Years: A Survey
inferredcites →https://doi.org/10.1109/sibgrapi59091.2023.10347178openalex-extracted
Superpixel Segmentation: From Theory to Applications*
inferredcites →https://doi.org/10.1109/tgrs.2023.3313734openalex-extracted
A Normalized Spatial–Spectral Supervoxel Segmentation Method for Multispectral Point Cloud Data
inferredcites →https://doi.org/10.1109/tip.2015.2449552openalex-extracted
Multiscale Superpixels and Supervoxels Based on Hierarchical Edge-Weighted Centroidal Voronoi Tessellation
inferredcites →https://doi.org/10.1109/tmm.2019.2907047openalex-extracted
Superpixel Segmentation Based on Square-Wise Asymmetric Partition and Structural Approximation
inferredcites →https://doi.org/10.1109/tpami.2012.120openalex-extracted
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
inferredcites →https://doi.org/10.1109/tpami.2020.2979714openalex-extracted
Feature-Aware Uniform Tessellations on Video Manifold for Content-Sensitive Supervoxels
inferredcites →https://doi.org/10.1109/tvcg.2018.2856744openalex-extracted
FeatureLego: Volume Exploration Using Exhaustive Clustering of Super-Voxels
inferredcites →https://doi.org/10.1109/wacv56688.2023.00587openalex-extracted
Unsupervised Video Object Segmentation via Prototype Memory Network
inferredcites →https://doi.org/10.1117/1.jei.26.6.061610openalex-extracted
Supervised and unsupervised segmentation using superpixels, model estimation, and graph cut
inferredcites →https://doi.org/10.1137/17m1113436openalex-extracted
Cut Pursuit: Fast Algorithms to Learn Piecewise Constant Functions on General Weighted Graphs
inferredcites →https://doi.org/10.48550/arxiv.2206.11914openalex-extracted
Magnetorotational supernovae: A nucleosynthetic analysis of sophisticated 3D models
inferredcites →https://doi.org/10.5194/isprs-archives-xlii-1-w1-151-2017openalex-extracted
WEAKLY SUPERVISED SEGMENTATION-AIDED CLASSIFICATION OF URBAN SCENES FROM 3D LIDAR POINT CLOUDS
inferredcites →https://doi.org/10.5951/mt.92.8.0692openalex-extracted
Discovering an Optimal Property of the Median
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.