{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:UQX5HXDVCEG4D2PFNA7EMCQO4K","short_pith_number":"pith:UQX5HXDV","schema_version":"1.0","canonical_sha256":"a42fd3dc75110dc1e9e5683e460a0ee2ac8de63e593136f781f1f529a8b83944","source":{"kind":"arxiv","id":"1902.06131","version":1},"attestation_state":"computed","paper":{"title":"LISA: a MATLAB package for Longitudinal Image Sequence Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","eess.IV"],"primary_cat":"stat.CO","authors_text":"Jang Ik Cho, Jiayang Sun, Xiaofeng Wang, Yifan Xu","submitted_at":"2019-02-16T17:50:19Z","abstract_excerpt":"Large sequences of images (or movies) can now be obtained on an unprecedented scale, which poses fundamental challenges to the existing image analysis techniques. The challenges include heterogeneity, (automatic) alignment, multiple comparisons, potential artifacts, and hidden noises. This paper introduces our MATLAB package, Longitudinal Image Sequence Analysis (LISA), as a one-stop ensemble of image processing and analysis tool for comparing a general class of images from either different times, sessions, or subjects. Given two contrasting sequences of images, the image processing in LISA st"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1902.06131","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-02-16T17:50:19Z","cross_cats_sorted":["cs.CV","eess.IV"],"title_canon_sha256":"5ac3e49378709beff42495fc55143727b3526aaf9a9dbdd314cc3ca7cb80c3d7","abstract_canon_sha256":"c88c8404ce0c536c0b6bf02e3e274c3313fe764699e799459b986e2f72d8ea94"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:53:46.499019Z","signature_b64":"Ru1f0mjulaMEFC8FfHq8TN6vFgQ+K2mR47MfonarbM96+nXn3UWganDVn96o769d4tvMwtYEGwmRHBVRtG+5Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a42fd3dc75110dc1e9e5683e460a0ee2ac8de63e593136f781f1f529a8b83944","last_reissued_at":"2026-05-17T23:53:46.498465Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:53:46.498465Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LISA: a MATLAB package for Longitudinal Image Sequence Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","eess.IV"],"primary_cat":"stat.CO","authors_text":"Jang Ik Cho, Jiayang Sun, Xiaofeng Wang, Yifan Xu","submitted_at":"2019-02-16T17:50:19Z","abstract_excerpt":"Large sequences of images (or movies) can now be obtained on an unprecedented scale, which poses fundamental challenges to the existing image analysis techniques. The challenges include heterogeneity, (automatic) alignment, multiple comparisons, potential artifacts, and hidden noises. This paper introduces our MATLAB package, Longitudinal Image Sequence Analysis (LISA), as a one-stop ensemble of image processing and analysis tool for comparing a general class of images from either different times, sessions, or subjects. Given two contrasting sequences of images, the image processing in LISA st"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.06131","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1902.06131","created_at":"2026-05-17T23:53:46.498553+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.06131v1","created_at":"2026-05-17T23:53:46.498553+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.06131","created_at":"2026-05-17T23:53:46.498553+00:00"},{"alias_kind":"pith_short_12","alias_value":"UQX5HXDVCEG4","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_16","alias_value":"UQX5HXDVCEG4D2PF","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_8","alias_value":"UQX5HXDV","created_at":"2026-05-18T12:33:30.264802+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UQX5HXDVCEG4D2PFNA7EMCQO4K","json":"https://pith.science/pith/UQX5HXDVCEG4D2PFNA7EMCQO4K.json","graph_json":"https://pith.science/api/pith-number/UQX5HXDVCEG4D2PFNA7EMCQO4K/graph.json","events_json":"https://pith.science/api/pith-number/UQX5HXDVCEG4D2PFNA7EMCQO4K/events.json","paper":"https://pith.science/paper/UQX5HXDV"},"agent_actions":{"view_html":"https://pith.science/pith/UQX5HXDVCEG4D2PFNA7EMCQO4K","download_json":"https://pith.science/pith/UQX5HXDVCEG4D2PFNA7EMCQO4K.json","view_paper":"https://pith.science/paper/UQX5HXDV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.06131&json=true","fetch_graph":"https://pith.science/api/pith-number/UQX5HXDVCEG4D2PFNA7EMCQO4K/graph.json","fetch_events":"https://pith.science/api/pith-number/UQX5HXDVCEG4D2PFNA7EMCQO4K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UQX5HXDVCEG4D2PFNA7EMCQO4K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UQX5HXDVCEG4D2PFNA7EMCQO4K/action/storage_attestation","attest_author":"https://pith.science/pith/UQX5HXDVCEG4D2PFNA7EMCQO4K/action/author_attestation","sign_citation":"https://pith.science/pith/UQX5HXDVCEG4D2PFNA7EMCQO4K/action/citation_signature","submit_replication":"https://pith.science/pith/UQX5HXDVCEG4D2PFNA7EMCQO4K/action/replication_record"}},"created_at":"2026-05-17T23:53:46.498553+00:00","updated_at":"2026-05-17T23:53:46.498553+00:00"}