{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:VID2ZNCUYP5IEROK7YSWISW6WX","short_pith_number":"pith:VID2ZNCU","schema_version":"1.0","canonical_sha256":"aa07acb454c3fa8245cafe25644adeb5c943bfc0df043a9c452c980ff6e8c83f","source":{"kind":"arxiv","id":"1511.07611","version":1},"attestation_state":"computed","paper":{"title":"Mouse Pose Estimation From Depth Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adam Claridge-Chang, Ashwin Nanjappa, Chi Xu, Li Cheng, Wei Gao, Zoe Bichler","submitted_at":"2015-11-24T08:42:01Z","abstract_excerpt":"We focus on the challenging problem of efficient mouse 3D pose estimation based on static images, and especially single depth images. We introduce an approach to discriminatively train the split nodes of trees in random forest to improve their performance on estimation of 3D joint positions of mouse. Our algorithm is capable of working with different types of rodents and with different types of depth cameras and imaging setups. In particular, it is demonstrated in this paper that when a top-mounted depth camera is combined with a bottom-mounted color camera, the final system is capable of deli"},"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":"1511.07611","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-24T08:42:01Z","cross_cats_sorted":[],"title_canon_sha256":"433cdccd2f35e7d12b5ff4bf561db44fde0fc870cd39032c0a4bb1fc3efc87a4","abstract_canon_sha256":"b1538d3889894dd7270f20d9816f74b629c8100dd8f03fda0ede618cc4d15146"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:26:04.221452Z","signature_b64":"gvWmUnPt5hpr78C0JFpnG5NL0zcO+7G7SMx4TZhgEkfPOKtyywzqtNQcj4Cor38S8Sf35IH5IF081y5eBLP/Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aa07acb454c3fa8245cafe25644adeb5c943bfc0df043a9c452c980ff6e8c83f","last_reissued_at":"2026-05-18T01:26:04.220635Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:26:04.220635Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mouse Pose Estimation From Depth Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adam Claridge-Chang, Ashwin Nanjappa, Chi Xu, Li Cheng, Wei Gao, Zoe Bichler","submitted_at":"2015-11-24T08:42:01Z","abstract_excerpt":"We focus on the challenging problem of efficient mouse 3D pose estimation based on static images, and especially single depth images. We introduce an approach to discriminatively train the split nodes of trees in random forest to improve their performance on estimation of 3D joint positions of mouse. Our algorithm is capable of working with different types of rodents and with different types of depth cameras and imaging setups. In particular, it is demonstrated in this paper that when a top-mounted depth camera is combined with a bottom-mounted color camera, the final system is capable of deli"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.07611","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":"1511.07611","created_at":"2026-05-18T01:26:04.220764+00:00"},{"alias_kind":"arxiv_version","alias_value":"1511.07611v1","created_at":"2026-05-18T01:26:04.220764+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.07611","created_at":"2026-05-18T01:26:04.220764+00:00"},{"alias_kind":"pith_short_12","alias_value":"VID2ZNCUYP5I","created_at":"2026-05-18T12:29:44.643036+00:00"},{"alias_kind":"pith_short_16","alias_value":"VID2ZNCUYP5IEROK","created_at":"2026-05-18T12:29:44.643036+00:00"},{"alias_kind":"pith_short_8","alias_value":"VID2ZNCU","created_at":"2026-05-18T12:29:44.643036+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/VID2ZNCUYP5IEROK7YSWISW6WX","json":"https://pith.science/pith/VID2ZNCUYP5IEROK7YSWISW6WX.json","graph_json":"https://pith.science/api/pith-number/VID2ZNCUYP5IEROK7YSWISW6WX/graph.json","events_json":"https://pith.science/api/pith-number/VID2ZNCUYP5IEROK7YSWISW6WX/events.json","paper":"https://pith.science/paper/VID2ZNCU"},"agent_actions":{"view_html":"https://pith.science/pith/VID2ZNCUYP5IEROK7YSWISW6WX","download_json":"https://pith.science/pith/VID2ZNCUYP5IEROK7YSWISW6WX.json","view_paper":"https://pith.science/paper/VID2ZNCU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1511.07611&json=true","fetch_graph":"https://pith.science/api/pith-number/VID2ZNCUYP5IEROK7YSWISW6WX/graph.json","fetch_events":"https://pith.science/api/pith-number/VID2ZNCUYP5IEROK7YSWISW6WX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VID2ZNCUYP5IEROK7YSWISW6WX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VID2ZNCUYP5IEROK7YSWISW6WX/action/storage_attestation","attest_author":"https://pith.science/pith/VID2ZNCUYP5IEROK7YSWISW6WX/action/author_attestation","sign_citation":"https://pith.science/pith/VID2ZNCUYP5IEROK7YSWISW6WX/action/citation_signature","submit_replication":"https://pith.science/pith/VID2ZNCUYP5IEROK7YSWISW6WX/action/replication_record"}},"created_at":"2026-05-18T01:26:04.220764+00:00","updated_at":"2026-05-18T01:26:04.220764+00:00"}