{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:T6GKQSFN4YGDUHIHWYXU3IQF3N","short_pith_number":"pith:T6GKQSFN","canonical_record":{"source":{"id":"1706.03686","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2017-06-12T15:29:48Z","cross_cats_sorted":[],"title_canon_sha256":"3b57a91171e146df0a1424286060400e442bdf3faafc7c96a48f3289131069c9","abstract_canon_sha256":"d6b445b26ecd8c532bbe2acb5e3a0580805278b63dd4676038634b32040b7c42"},"schema_version":"1.0"},"canonical_sha256":"9f8ca848ade60c3a1d07b62f4da205db6211cab4366b40bc6789bcbed5b5b331","source":{"kind":"arxiv","id":"1706.03686","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.03686","created_at":"2026-05-18T00:32:42Z"},{"alias_kind":"arxiv_version","alias_value":"1706.03686v3","created_at":"2026-05-18T00:32:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.03686","created_at":"2026-05-18T00:32:42Z"},{"alias_kind":"pith_short_12","alias_value":"T6GKQSFN4YGD","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"T6GKQSFN4YGDUHIH","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"T6GKQSFN","created_at":"2026-05-18T12:31:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:T6GKQSFN4YGDUHIHWYXU3IQF3N","target":"record","payload":{"canonical_record":{"source":{"id":"1706.03686","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2017-06-12T15:29:48Z","cross_cats_sorted":[],"title_canon_sha256":"3b57a91171e146df0a1424286060400e442bdf3faafc7c96a48f3289131069c9","abstract_canon_sha256":"d6b445b26ecd8c532bbe2acb5e3a0580805278b63dd4676038634b32040b7c42"},"schema_version":"1.0"},"canonical_sha256":"9f8ca848ade60c3a1d07b62f4da205db6211cab4366b40bc6789bcbed5b5b331","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:32:42.787329Z","signature_b64":"2KI5/i1/VI68el7MoV4IXehFVv2fZxrbyjtHtAmt36Wilcp+Rgkf4N44cpXY+Teoh4Ds58c3zcd82ULtl2HlDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9f8ca848ade60c3a1d07b62f4da205db6211cab4366b40bc6789bcbed5b5b331","last_reissued_at":"2026-05-18T00:32:42.786591Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:32:42.786591Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.03686","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:32:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dE+t/cdfnuOOhhesdbzR6F7KyrHGdYmCQxU2cuYqyI+Tf77ZM9CdJhhRNpJUbTPhrH2xcLyIuzlEqdvXPZEuAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T04:46:03.727158Z"},"content_sha256":"a7b775084f89f623be80aa8998c193c5ebe2691c97a8502f7e3aa5ba352d43a4","schema_version":"1.0","event_id":"sha256:a7b775084f89f623be80aa8998c193c5ebe2691c97a8502f7e3aa5ba352d43a4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:T6GKQSFN4YGDUHIHWYXU3IQF3N","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Image Crowd Counting Using Convolutional Neural Network and Markov Random Field","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haiyan Yao, Kang Han, Li Hou, Wanggen Wan","submitted_at":"2017-06-12T15:29:48Z","abstract_excerpt":"In this paper, we propose a method called Convolutional Neural Network-Markov Random Field (CNN-MRF) to estimate the crowd count in a still image. We first divide the dense crowd visible image into overlapping patches and then use a deep convolutional neural network to extract features from each patch image, followed by a fully connected neural network to regress the local patch crowd count. Since the local patches have overlapping portions, the crowd count of the adjacent patches has a high correlation. We use this correlation and the Markov random field to smooth the counting results of the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.03686","kind":"arxiv","version":3},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:32:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Qz054AxwCzdYhmvzv2VQB40bd0H4RvKxZjrO1yRfT7TsE/QtM1/+HmQpCP6Ndq/4riVPoPn9wHzd8dYfpuIuAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T04:46:03.727528Z"},"content_sha256":"3cfc7e8658d69ff7c08ffca5c7310cc6f858c76d9931e84075678ea6dbdbbf2e","schema_version":"1.0","event_id":"sha256:3cfc7e8658d69ff7c08ffca5c7310cc6f858c76d9931e84075678ea6dbdbbf2e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/T6GKQSFN4YGDUHIHWYXU3IQF3N/bundle.json","state_url":"https://pith.science/pith/T6GKQSFN4YGDUHIHWYXU3IQF3N/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/T6GKQSFN4YGDUHIHWYXU3IQF3N/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-30T04:46:03Z","links":{"resolver":"https://pith.science/pith/T6GKQSFN4YGDUHIHWYXU3IQF3N","bundle":"https://pith.science/pith/T6GKQSFN4YGDUHIHWYXU3IQF3N/bundle.json","state":"https://pith.science/pith/T6GKQSFN4YGDUHIHWYXU3IQF3N/state.json","well_known_bundle":"https://pith.science/.well-known/pith/T6GKQSFN4YGDUHIHWYXU3IQF3N/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:T6GKQSFN4YGDUHIHWYXU3IQF3N","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d6b445b26ecd8c532bbe2acb5e3a0580805278b63dd4676038634b32040b7c42","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2017-06-12T15:29:48Z","title_canon_sha256":"3b57a91171e146df0a1424286060400e442bdf3faafc7c96a48f3289131069c9"},"schema_version":"1.0","source":{"id":"1706.03686","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.03686","created_at":"2026-05-18T00:32:42Z"},{"alias_kind":"arxiv_version","alias_value":"1706.03686v3","created_at":"2026-05-18T00:32:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.03686","created_at":"2026-05-18T00:32:42Z"},{"alias_kind":"pith_short_12","alias_value":"T6GKQSFN4YGD","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"T6GKQSFN4YGDUHIH","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"T6GKQSFN","created_at":"2026-05-18T12:31:43Z"}],"graph_snapshots":[{"event_id":"sha256:3cfc7e8658d69ff7c08ffca5c7310cc6f858c76d9931e84075678ea6dbdbbf2e","target":"graph","created_at":"2026-05-18T00:32:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"In this paper, we propose a method called Convolutional Neural Network-Markov Random Field (CNN-MRF) to estimate the crowd count in a still image. We first divide the dense crowd visible image into overlapping patches and then use a deep convolutional neural network to extract features from each patch image, followed by a fully connected neural network to regress the local patch crowd count. Since the local patches have overlapping portions, the crowd count of the adjacent patches has a high correlation. We use this correlation and the Markov random field to smooth the counting results of the ","authors_text":"Haiyan Yao, Kang Han, Li Hou, Wanggen Wan","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2017-06-12T15:29:48Z","title":"Image Crowd Counting Using Convolutional Neural Network and Markov Random Field"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.03686","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a7b775084f89f623be80aa8998c193c5ebe2691c97a8502f7e3aa5ba352d43a4","target":"record","created_at":"2026-05-18T00:32:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d6b445b26ecd8c532bbe2acb5e3a0580805278b63dd4676038634b32040b7c42","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2017-06-12T15:29:48Z","title_canon_sha256":"3b57a91171e146df0a1424286060400e442bdf3faafc7c96a48f3289131069c9"},"schema_version":"1.0","source":{"id":"1706.03686","kind":"arxiv","version":3}},"canonical_sha256":"9f8ca848ade60c3a1d07b62f4da205db6211cab4366b40bc6789bcbed5b5b331","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9f8ca848ade60c3a1d07b62f4da205db6211cab4366b40bc6789bcbed5b5b331","first_computed_at":"2026-05-18T00:32:42.786591Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:32:42.786591Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2KI5/i1/VI68el7MoV4IXehFVv2fZxrbyjtHtAmt36Wilcp+Rgkf4N44cpXY+Teoh4Ds58c3zcd82ULtl2HlDg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:32:42.787329Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.03686","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a7b775084f89f623be80aa8998c193c5ebe2691c97a8502f7e3aa5ba352d43a4","sha256:3cfc7e8658d69ff7c08ffca5c7310cc6f858c76d9931e84075678ea6dbdbbf2e"],"state_sha256":"0f71f065450873d26980d57fe3e1c983722f57b42096babb820ee6a2829e92c7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZXz8MtiVVVhnoz4dSNhv/kTYtkkTk6YXFAvmFMUjzKkwh1Jv02naWWqJJ/wM6r4Vhe/K1scuPCwtSPbO3Hi2Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-30T04:46:03.729497Z","bundle_sha256":"e98179b3d556832c89477f2498b43ba7aa003889cc7d90ba217d368ad8ab56ef"}}