{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/segmentation/papers/131","list_of":"/task/segmentation","task":"Segmentation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":131,"pages_in_order":131,"rows_per_page":100,"rows":[13001,13072],"of":13072,"counts":{"archive_papers_tagged":13072,"with_a_code_link":5255,"where_syntology_ran_a_sample":976,"not_listed_spam_title":0,"listed":13072,"listed_where_code_ran":976,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":838,"every_run_a_failure_of_syntologys_instrument":138,"listed_with_a_run_with_no_instrument_failure":838,"listed_every_run_a_failure_of_syntologys_instrument":138,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/segmentation","prev":"/task/segmentation/papers/130","next":null,"papers":[{"url":null,"slug":"geof-geodesic-forests-for-learning-coupled","title":"GeoF: Geodesic Forests for Learning Coupled Predictors","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"globally-consistent-multi-label-assignment-on","title":"Globally Consistent Multi-label Assignment on the Ray Space of 4D Light Fields","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-based-optimization-with-tubularity","title":"Graph-Based Optimization with Tubularity Markov Tree for 3D Vessel Segmentation","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"image-segmentation-by-cascaded-region","title":"Image Segmentation by Cascaded Region Agglomeration","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-3d-scene-reconstruction-and-class","title":"Joint 3D Scene Reconstruction and Class Segmentation","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"large-displacement-optical-flow-from-nearest","title":"Large Displacement Optical Flow from Nearest Neighbor Fields","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"measures-and-meta-measures-for-the-supervised","title":"Measures and Meta-Measures for the Supervised Evaluation of Image Segmentation","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-class-video-co-segmentation-with-a","title":"Multi-class Video Co-segmentation with a Generative Multi-video Model","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"perceptual-organization-and-recognition-of","title":"Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pose-from-flow-and-flow-from-pose","title":"Pose from Flow and Flow from Pose","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-graphlet-cut-exploiting-spatial","title":"Probabilistic Graphlet Cut: Exploiting Spatial Structure Cue for Weakly Supervised Image Segmentation","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"prostate-segmentation-in-ct-images-via","title":"Prostate Segmentation in CT Images via Spatial-Constrained Transductive Lasso","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"scalpel-segmentation-cascades-with-localized","title":"SCALPEL: Segmentation Cascades with Localized Priors and Efficient Learning","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-strategies-for-streaming-speech","title":"Segmentation Strategies for Streaming Speech Translation","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-inference-machines","title":"Spatial Inference Machines","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-based-high-order-semantic-relation","title":"Tensor-Based High-Order Semantic Relation Transfer for Semantic Scene Segmentation","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"top-down-segmentation-of-non-rigid-visual","title":"Top-Down Segmentation of Non-rigid Visual Objects Using Derivative-Based Search on Sparse Manifolds","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-fast-and-accurate-segmentation","title":"Towards Fast and Accurate Segmentation","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/unsupervised-joint-object-discovery-and","slug":"unsupervised-joint-object-discovery-and","title":"Unsupervised Joint Object Discovery and Segmentation in Internet Images","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"voxel-cloud-connectivity-segmentation","title":"Voxel Cloud Connectivity Segmentation - Supervoxels for Point Clouds","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-dual-clustering-for-image","title":"Weakly-Supervised Dual Clustering for Image Semantic Segmentation","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"winding-number-for-region-boundary-consistent","title":"Winding Number for Region-Boundary Consistent Salient Contour Extraction","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"higher-order-segmentation-via-multicuts","title":"Higher-order Segmentation via Multicuts","date":"2013-05-28","arxiv_id":"1305.6387","repositories_listed":0,"syntology":null},{"url":null,"slug":"reduce-meaningless-words-for-joint-chinese","title":"Reduce Meaningless Words for Joint Chinese Word Segmentation and Part-of-speech Tagging","date":"2013-05-25","arxiv_id":"1305.5918","repositories_listed":0,"syntology":null},{"url":null,"slug":"binary-tree-based-chinese-word-segmentation","title":"Binary Tree based Chinese Word Segmentation","date":"2013-05-17","arxiv_id":"1305.3981","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bag-of-words-approach-for-semantic","title":"A Bag of Words Approach for Semantic Segmentation of Monitored Scenes","date":"2013-05-14","arxiv_id":"1305.3189","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-contrario-selection-of-optimal-partitions","title":"A Contrario Selection of Optimal Partitions for Image Segmentation","date":"2013-05-06","arxiv_id":"1305.1206","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybridization-of-otsu-method-and-median","title":"Hybridization of Otsu Method and Median Filter for Color Image Segmentation","date":"2013-05-05","arxiv_id":"1305.1052","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-segmentation-via-diffusion-bases","title":"Video Segmentation via Diffusion Bases","date":"2013-05-01","arxiv_id":"1305.0218","repositories_listed":0,"syntology":null},{"url":null,"slug":"pulmonary-vascular-tree-segmentation-from","title":"Pulmonary Vascular Tree Segmentation from Contrast-Enhanced CT Images","date":"2013-04-26","arxiv_id":"1304.7140","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-low-rank-subspace-segmentation","title":"Distributed Low-rank Subspace Segmentation","date":"2013-04-20","arxiv_id":"1304.5583","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-automatic-reconstruction-of","title":"Large-Scale Automatic Reconstruction of Neuronal Processes from Electron Microscopy Images","date":"2013-03-28","arxiv_id":"1303.7186","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multiscale-graph-cut-approach-to-bright","title":"A multiscale graph cut approach to bright-field multiple cell image segmentation using a Bhattacharyya measure","date":"2013-03-13","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multiclass-data-segmentation-using-diffuse","title":"Multiclass Data Segmentation using Diffuse Interface Methods on Graphs","date":"2013-02-15","arxiv_id":"1302.3913","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-mrf-energy-propagation-for-video","title":"Efficient MRF Energy Propagation for Video Segmentation via Bilateral Filters","date":"2013-01-22","arxiv_id":"1301.5356","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-for-chinese-word-segmentation","title":"Active Learning for Chinese Word Segmentation","date":"2012-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"chinese-tweets-segmentation-based-on","title":"Chinese Tweets Segmentation based on Morphemes","date":"2012-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"incremental-learning-of-affix-segmentation","title":"Incremental Learning of Affix Segmentation","date":"2012-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"micro-blogs-oriented-word-segmentation-system","title":"Micro blogs Oriented Word Segmentation System","date":"2012-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nonparametric-model-for-inupiaq-word","title":"Nonparametric Model for Inupiaq Word Segmentation","date":"2012-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-segmentation-of-fluorescence","title":"Automatic Segmentation of Fluorescence Lifetime Microscopy Images of Cells Using Multi-Resolution Community Detection","date":"2012-08-23","arxiv_id":"1208.4662","repositories_listed":0,"syntology":null},{"url":null,"slug":"anatomical-structure-segmentation-in-liver","title":"Anatomical Structure Segmentation in Liver MRI Images","date":"2012-07-03","arxiv_id":"1207.0805","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-linear-approximation-to-the-chi2-kernel","title":"A Linear Approximation to the chi^2 Kernel with Geometric Convergence","date":"2012-06-18","arxiv_id":"1206.4074","repositories_listed":0,"syntology":null},{"url":null,"slug":"defi-dannotation-degels2012-la-segmentation","title":"D\\'efi d'annotation DEGELS2012 : la segmentation (DEGELS2012 annotation challenge: Segmentation) [in French]","date":"2012-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"getting-more-from-segmentation-evaluation","title":"Getting More from Segmentation Evaluation","date":"2012-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-non-supervisee-le-cas-du","title":"Segmentation non supervis\\'ee : le cas du mandarin (Unsupervized Word Segmentation) [in French]","date":"2012-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-similarity-and-agreement","title":"Segmentation Similarity and Agreement","date":"2012-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-graph-cut-from-a-filtering-point-of","title":"Spectral Graph Cut from a Filtering Point of View","date":"2012-05-20","arxiv_id":"1205.4450","repositories_listed":0,"syntology":null},{"url":null,"slug":"arabic-segmentation-combination-strategies","title":"Arabic-Segmentation Combination Strategies for Statistical Machine Translation","date":"2012-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-layer-discourse-annotation-of-a-dutch","title":"Multi-Layer Discourse Annotation of a Dutch Text Corpus","date":"2012-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-section-segmentation-in-free-text","title":"Statistical Section Segmentation in Free-Text Clinical Records","date":"2012-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-a-goodness-measurement-for-domain","title":"Using a Goodness Measurement for Domain Adaptation: A Case Study on Chinese Word Segmentation","date":"2012-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"higher-order-correlation-clustering-for-image","title":"Higher-Order Correlation Clustering for Image Segmentation","date":"2011-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-joint-image-segmentation-and","title":"Probabilistic Joint Image Segmentation and Labeling","date":"2011-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pylon-model-for-semantic-segmentation","title":"Pylon Model for Semantic Segmentation","date":"2011-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-distance-dependent-chinese-restaurant","title":"Spatial distance dependent Chinese restaurant processes for image segmentation","date":"2011-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"epitome-driven-3-d-diffusion-tensor-image","title":"Epitome driven 3-D Diffusion Tensor image segmentation: on extracting specific structures","date":"2010-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-as-maximum-weight-independent","title":"Segmentation as Maximum-Weight Independent Set","date":"2010-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"static-analysis-of-binary-executables-using","title":"Static Analysis of Binary Executables Using Structural SVMs","date":"2010-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"synergies-in-learning-words-and-their","title":"Synergies in learning words and their referents","date":"2010-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"maximin-affinity-learning-of-image","title":"Maximin affinity learning of image segmentation","date":"2009-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"region-based-segmentation-and-object","title":"Region-based Segmentation and Object Detection","date":"2009-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"segmenting-scenes-by-matching-image","title":"Segmenting Scenes by Matching Image Composites","date":"2009-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-ordered-residual-kernel-for-robust-motion","title":"The Ordered Residual Kernel for Robust Motion Subspace Clustering","date":"2009-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/evaluation-framework-for-algorithms","slug":"evaluation-framework-for-algorithms","title":"Evaluation framework for algorithms segmenting short axis cardiac MRI.","date":"2009-07-09","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/nuclei-segmentation-in-microscope-cell-images","slug":"nuclei-segmentation-in-microscope-cell-images","title":"Nuclei Segmentation In Microscope Cell Images: A Hand-Segmented Dataset And Comparison Of Algorithms","date":"2009-06-28","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-of-clustered-nuclei-with-shape","title":"Segmentation of Clustered Nuclei With Shape Markers and Marking Function","date":"2009-04-15","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"shared-segmentation-of-natural-scenes-using","title":"Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes","date":"2008-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"text-line-segmentation-of-historical","title":"Text line segmentation of historical documents: a survey","date":"2006-12-28","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"image-segmentation-based-on-watershed-and","title":"Image Segmentation Based on Watershed and Edge Detection Techniques","date":"2005-02-13","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-image-segmentation-using-a","title":"Unsupervised image segmentation using a simple MRF model with a new implementation scheme","date":"2004-04-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"shamann-shared-memory-augmented-neural","title":"SHAMANN: Shared Memory Augmented Neural Networks","date":null,"arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"eec8eaa8629db9b5b39d451d6cc9b0e6ac61c8b75f33d4aa93a777fc4ae5bfa1","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}