{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:PQ7MNYQHYQM2XFUWZZPEJ77H7H","short_pith_number":"pith:PQ7MNYQH","schema_version":"1.0","canonical_sha256":"7c3ec6e207c419ab9696ce5e44ffe7f9fb73ff18044c6ec6f5e3af2dd5592cde","source":{"kind":"arxiv","id":"1907.03576","version":1},"attestation_state":"computed","paper":{"title":"Deep Learning-Based Semantic Segmentation of Microscale Objects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","stat.ML"],"primary_cat":"eess.IV","authors_text":"Ashis G. Banerjee, Ekta U. Samani, Wei Guo","submitted_at":"2019-07-03T23:07:01Z","abstract_excerpt":"Accurate estimation of the positions and shapes of microscale objects is crucial for automated imaging-guided manipulation using a non-contact technique such as optical tweezers. Perception methods that use traditional computer vision algorithms tend to fail when the manipulation environments are crowded. In this paper, we present a deep learning model for semantic segmentation of the images representing such environments. Our model successfully performs segmentation with a high mean Intersection Over Union score of 0.91."},"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":"1907.03576","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-07-03T23:07:01Z","cross_cats_sorted":["cs.CV","cs.LG","stat.ML"],"title_canon_sha256":"275f2a4a0170d4a757d8f9312ec0cc479736b7fb9daa672e46fed68f45d816fb","abstract_canon_sha256":"8704cfd5596064f6eb0968ea49daaa30e8e5c1652a026fac68779e6768f07ec2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:14.286829Z","signature_b64":"U9+noOvb6/OLqm3BsBy+DXRHH7yLSbKKikX4L/9qQ3XohrID1RHNQx/EUEH3qDgkEOmkD2IUSXUZtBP/rlVmAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7c3ec6e207c419ab9696ce5e44ffe7f9fb73ff18044c6ec6f5e3af2dd5592cde","last_reissued_at":"2026-05-17T23:41:14.286382Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:14.286382Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Learning-Based Semantic Segmentation of Microscale Objects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","stat.ML"],"primary_cat":"eess.IV","authors_text":"Ashis G. Banerjee, Ekta U. Samani, Wei Guo","submitted_at":"2019-07-03T23:07:01Z","abstract_excerpt":"Accurate estimation of the positions and shapes of microscale objects is crucial for automated imaging-guided manipulation using a non-contact technique such as optical tweezers. Perception methods that use traditional computer vision algorithms tend to fail when the manipulation environments are crowded. In this paper, we present a deep learning model for semantic segmentation of the images representing such environments. Our model successfully performs segmentation with a high mean Intersection Over Union score of 0.91."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.03576","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":"1907.03576","created_at":"2026-05-17T23:41:14.286452+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.03576v1","created_at":"2026-05-17T23:41:14.286452+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.03576","created_at":"2026-05-17T23:41:14.286452+00:00"},{"alias_kind":"pith_short_12","alias_value":"PQ7MNYQHYQM2","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"PQ7MNYQHYQM2XFUW","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"PQ7MNYQH","created_at":"2026-05-18T12:33:24.271573+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/PQ7MNYQHYQM2XFUWZZPEJ77H7H","json":"https://pith.science/pith/PQ7MNYQHYQM2XFUWZZPEJ77H7H.json","graph_json":"https://pith.science/api/pith-number/PQ7MNYQHYQM2XFUWZZPEJ77H7H/graph.json","events_json":"https://pith.science/api/pith-number/PQ7MNYQHYQM2XFUWZZPEJ77H7H/events.json","paper":"https://pith.science/paper/PQ7MNYQH"},"agent_actions":{"view_html":"https://pith.science/pith/PQ7MNYQHYQM2XFUWZZPEJ77H7H","download_json":"https://pith.science/pith/PQ7MNYQHYQM2XFUWZZPEJ77H7H.json","view_paper":"https://pith.science/paper/PQ7MNYQH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.03576&json=true","fetch_graph":"https://pith.science/api/pith-number/PQ7MNYQHYQM2XFUWZZPEJ77H7H/graph.json","fetch_events":"https://pith.science/api/pith-number/PQ7MNYQHYQM2XFUWZZPEJ77H7H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PQ7MNYQHYQM2XFUWZZPEJ77H7H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PQ7MNYQHYQM2XFUWZZPEJ77H7H/action/storage_attestation","attest_author":"https://pith.science/pith/PQ7MNYQHYQM2XFUWZZPEJ77H7H/action/author_attestation","sign_citation":"https://pith.science/pith/PQ7MNYQHYQM2XFUWZZPEJ77H7H/action/citation_signature","submit_replication":"https://pith.science/pith/PQ7MNYQHYQM2XFUWZZPEJ77H7H/action/replication_record"}},"created_at":"2026-05-17T23:41:14.286452+00:00","updated_at":"2026-05-17T23:41:14.286452+00:00"}