Application research of view high speed detection algorithm of small field based on sparse features Article Swipe
Aiming at the problem that the features of small field of view detection in the high-speed pipeline are difficult to mine and the millisecond cycle is fast, this paper takes the cigarette filter bead streamline detection as an example, and proposes a small field of view high-speed detection algorithm based on sparse features. Firstly, by adjusting the light source, a 'light spot' feature with strong robustness is designed. Secondly, the sparse representation and dictionary learning are used to obtain the projection histogram features of the light spot. To overcome the interference of unstructured backgrounds, the algorithm is combined with Markov-Bayesian reasoning to reduce the spot detection rate, and finally realize the high-speed accurate recognition of the bead in low contrast. The view high speed detection algorithm of small field based on sparse features was verified on the simulation and experimental platform. The conclusions show that the extracted light spot features can overcome the interference of the color, size and low contrast of the bead, and maintain the stability of the feature. The fused Markov-Bayesian sparse representation algorithm can improve the recognition accuracy of the spot. The method can achieve 3 000 tests per minute, and the detection accuracy can reach 99.5%.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- OA Status
- green
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3022117707
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3022117707Canonical identifier for this work in OpenAlex
- Title
-
Application research of view high speed detection algorithm of small field based on sparse featuresWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-12-01Full publication date if available
- Authors
-
Kezhan Zhang, Zhili Wang, Lei Hua, Manman Fei, Hui-lin ZhouList of authors in order
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- Concepts
-
Computer science, Artificial intelligence, Sparse approximation, Robustness (evolution), Pattern recognition (psychology), Feature (linguistics), Histogram, Field (mathematics), Computer vision, Algorithm, Mathematics, Image (mathematics), Gene, Philosophy, Linguistics, Biochemistry, Pure mathematics, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
20Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3022117707 |
|---|---|
| doi | |
| ids.mag | 3022117707 |
| ids.openalex | https://openalex.org/W3022117707 |
| fwci | 0.0 |
| type | article |
| title | Application research of view high speed detection algorithm of small field based on sparse features |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T13497 |
| topics[0].field.id | https://openalex.org/fields/12 |
| topics[0].field.display_name | Arts and Humanities |
| topics[0].score | 0.9879000186920166 |
| topics[0].domain.id | https://openalex.org/domains/2 |
| topics[0].domain.display_name | Social Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1211 |
| topics[0].subfield.display_name | Philosophy |
| topics[0].display_name | Hermeneutics and Narrative Identity |
| topics[1].id | https://openalex.org/T13695 |
| topics[1].field.id | https://openalex.org/fields/36 |
| topics[1].field.display_name | Health Professions |
| topics[1].score | 0.9749000072479248 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/3600 |
| topics[1].subfield.display_name | General Health Professions |
| topics[1].display_name | Aging, Elder Care, and Social Issues |
| topics[2].id | https://openalex.org/T13099 |
| topics[2].field.id | https://openalex.org/fields/36 |
| topics[2].field.display_name | Health Professions |
| topics[2].score | 0.95660001039505 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/3600 |
| topics[2].subfield.display_name | General Health Professions |
| topics[2].display_name | Health, Medicine and Society |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7137399315834045 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C154945302 |
| concepts[1].level | 1 |
| concepts[1].score | 0.6312100887298584 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[1].display_name | Artificial intelligence |
| concepts[2].id | https://openalex.org/C124066611 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6251207590103149 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q28684319 |
| concepts[2].display_name | Sparse approximation |
| concepts[3].id | https://openalex.org/C63479239 |
| concepts[3].level | 3 |
| concepts[3].score | 0.6176220774650574 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q7353546 |
| concepts[3].display_name | Robustness (evolution) |
| concepts[4].id | https://openalex.org/C153180895 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5658955574035645 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[4].display_name | Pattern recognition (psychology) |
| concepts[5].id | https://openalex.org/C2776401178 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5471413135528564 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[5].display_name | Feature (linguistics) |
| concepts[6].id | https://openalex.org/C53533937 |
| concepts[6].level | 3 |
| concepts[6].score | 0.4888879358768463 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q185020 |
| concepts[6].display_name | Histogram |
| concepts[7].id | https://openalex.org/C9652623 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4398098587989807 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q190109 |
| concepts[7].display_name | Field (mathematics) |
| concepts[8].id | https://openalex.org/C31972630 |
| concepts[8].level | 1 |
| concepts[8].score | 0.43589261174201965 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[8].display_name | Computer vision |
| concepts[9].id | https://openalex.org/C11413529 |
| concepts[9].level | 1 |
| concepts[9].score | 0.3957034647464752 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[9].display_name | Algorithm |
| concepts[10].id | https://openalex.org/C33923547 |
| concepts[10].level | 0 |
| concepts[10].score | 0.15543478727340698 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[10].display_name | Mathematics |
| concepts[11].id | https://openalex.org/C115961682 |
| concepts[11].level | 2 |
| concepts[11].score | 0.11669960618019104 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[11].display_name | Image (mathematics) |
| concepts[12].id | https://openalex.org/C104317684 |
| concepts[12].level | 2 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q7187 |
| concepts[12].display_name | Gene |
| concepts[13].id | https://openalex.org/C138885662 |
| concepts[13].level | 0 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[13].display_name | Philosophy |
| concepts[14].id | https://openalex.org/C41895202 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[14].display_name | Linguistics |
| concepts[15].id | https://openalex.org/C55493867 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q7094 |
| concepts[15].display_name | Biochemistry |
| concepts[16].id | https://openalex.org/C202444582 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q837863 |
| concepts[16].display_name | Pure mathematics |
| concepts[17].id | https://openalex.org/C185592680 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[17].display_name | Chemistry |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7137399315834045 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[1].score | 0.6312100887298584 |
| keywords[1].display_name | Artificial intelligence |
| keywords[2].id | https://openalex.org/keywords/sparse-approximation |
| keywords[2].score | 0.6251207590103149 |
| keywords[2].display_name | Sparse approximation |
| keywords[3].id | https://openalex.org/keywords/robustness |
| keywords[3].score | 0.6176220774650574 |
| keywords[3].display_name | Robustness (evolution) |
| keywords[4].id | https://openalex.org/keywords/pattern-recognition |
| keywords[4].score | 0.5658955574035645 |
| keywords[4].display_name | Pattern recognition (psychology) |
| keywords[5].id | https://openalex.org/keywords/feature |
| keywords[5].score | 0.5471413135528564 |
| keywords[5].display_name | Feature (linguistics) |
| keywords[6].id | https://openalex.org/keywords/histogram |
| keywords[6].score | 0.4888879358768463 |
| keywords[6].display_name | Histogram |
| keywords[7].id | https://openalex.org/keywords/field |
| keywords[7].score | 0.4398098587989807 |
| keywords[7].display_name | Field (mathematics) |
| keywords[8].id | https://openalex.org/keywords/computer-vision |
| keywords[8].score | 0.43589261174201965 |
| keywords[8].display_name | Computer vision |
| keywords[9].id | https://openalex.org/keywords/algorithm |
| keywords[9].score | 0.3957034647464752 |
| keywords[9].display_name | Algorithm |
| keywords[10].id | https://openalex.org/keywords/mathematics |
| keywords[10].score | 0.15543478727340698 |
| keywords[10].display_name | Mathematics |
| keywords[11].id | https://openalex.org/keywords/image |
| keywords[11].score | 0.11669960618019104 |
| keywords[11].display_name | Image (mathematics) |
| language | en |
| locations[0].id | pmh:oai:figshare.com:article/11439993 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400572 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | OPAL (Open@LaTrobe) (La Trobe University) |
| locations[0].source.host_organization | https://openalex.org/I196829312 |
| locations[0].source.host_organization_name | La Trobe University |
| locations[0].source.host_organization_lineage | https://openalex.org/I196829312 |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | submittedVersion |
| locations[0].raw_type | Text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | |
| authorships[0].author.id | https://openalex.org/A5010383174 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Kezhan Zhang |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | K Zhang |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5061756960 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-6737-2672 |
| authorships[1].author.display_name | Zhili Wang |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Z Wang |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5006115076 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-2696-3521 |
| authorships[2].author.display_name | Lei Hua |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | L Hua |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5020257116 |
| authorships[3].author.orcid | https://orcid.org/0009-0001-5616-2485 |
| authorships[3].author.display_name | Manman Fei |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | M Fei |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5009904345 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Hui-lin Zhou |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | H Zhou |
| authorships[4].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Application research of view high speed detection algorithm of small field based on sparse features |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T04:12:42.849631 |
| primary_topic.id | https://openalex.org/T13497 |
| primary_topic.field.id | https://openalex.org/fields/12 |
| primary_topic.field.display_name | Arts and Humanities |
| primary_topic.score | 0.9879000186920166 |
| primary_topic.domain.id | https://openalex.org/domains/2 |
| primary_topic.domain.display_name | Social Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1211 |
| primary_topic.subfield.display_name | Philosophy |
| primary_topic.display_name | Hermeneutics and Narrative Identity |
| related_works | https://openalex.org/W2354573197, https://openalex.org/W2371745854, https://openalex.org/W2775068283, https://openalex.org/W2896761279, https://openalex.org/W2368063274, https://openalex.org/W2524554322, https://openalex.org/W3123612308, https://openalex.org/W3005822240, https://openalex.org/W2161886654, https://openalex.org/W2253677904, https://openalex.org/W2089025617, https://openalex.org/W1996019456, https://openalex.org/W2366781454, https://openalex.org/W3086124023, https://openalex.org/W2962394572, https://openalex.org/W3090379965, https://openalex.org/W2383241728, https://openalex.org/W327728245, https://openalex.org/W2361617902, https://openalex.org/W2374342147 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | pmh:oai:figshare.com:article/11439993 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400572 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | OPAL (Open@LaTrobe) (La Trobe University) |
| best_oa_location.source.host_organization | https://openalex.org/I196829312 |
| best_oa_location.source.host_organization_name | La Trobe University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I196829312 |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | Text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | |
| primary_location.id | pmh:oai:figshare.com:article/11439993 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400572 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | OPAL (Open@LaTrobe) (La Trobe University) |
| primary_location.source.host_organization | https://openalex.org/I196829312 |
| primary_location.source.host_organization_name | La Trobe University |
| primary_location.source.host_organization_lineage | https://openalex.org/I196829312 |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | submittedVersion |
| primary_location.raw_type | Text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | |
| publication_date | 2018-12-01 |
| publication_year | 2018 |
| referenced_works_count | 0 |
| abstract_inverted_index.3 | 189 |
| abstract_inverted_index.a | 41, 59 |
| abstract_inverted_index.To | 87 |
| abstract_inverted_index.an | 37 |
| abstract_inverted_index.as | 36 |
| abstract_inverted_index.at | 1 |
| abstract_inverted_index.by | 54 |
| abstract_inverted_index.in | 13, 117 |
| abstract_inverted_index.is | 25, 66, 96 |
| abstract_inverted_index.of | 7, 10, 44, 83, 91, 114, 126, 154, 161, 168, 182 |
| abstract_inverted_index.on | 50, 130, 135 |
| abstract_inverted_index.to | 19, 77, 101 |
| abstract_inverted_index.000 | 190 |
| abstract_inverted_index.The | 120, 141, 171, 185 |
| abstract_inverted_index.and | 21, 39, 72, 107, 138, 158, 164, 194 |
| abstract_inverted_index.are | 17, 75 |
| abstract_inverted_index.can | 150, 177, 187, 198 |
| abstract_inverted_index.low | 118, 159 |
| abstract_inverted_index.per | 192 |
| abstract_inverted_index.the | 2, 5, 14, 22, 30, 56, 69, 79, 84, 89, 94, 103, 110, 115, 136, 145, 152, 155, 162, 166, 169, 179, 183, 195 |
| abstract_inverted_index.was | 133 |
| abstract_inverted_index.bead | 33, 116 |
| abstract_inverted_index.high | 122 |
| abstract_inverted_index.mine | 20 |
| abstract_inverted_index.show | 143 |
| abstract_inverted_index.size | 157 |
| abstract_inverted_index.spot | 104, 148 |
| abstract_inverted_index.that | 4, 144 |
| abstract_inverted_index.this | 27 |
| abstract_inverted_index.used | 76 |
| abstract_inverted_index.view | 11, 45, 121 |
| abstract_inverted_index.with | 63, 98 |
| abstract_inverted_index.based | 49, 129 |
| abstract_inverted_index.bead, | 163 |
| abstract_inverted_index.cycle | 24 |
| abstract_inverted_index.fast, | 26 |
| abstract_inverted_index.field | 9, 43, 128 |
| abstract_inverted_index.fused | 172 |
| abstract_inverted_index.light | 57, 85, 147 |
| abstract_inverted_index.paper | 28 |
| abstract_inverted_index.rate, | 106 |
| abstract_inverted_index.reach | 199 |
| abstract_inverted_index.small | 8, 42, 127 |
| abstract_inverted_index.speed | 123 |
| abstract_inverted_index.spot' | 61 |
| abstract_inverted_index.spot. | 86, 184 |
| abstract_inverted_index.takes | 29 |
| abstract_inverted_index.tests | 191 |
| abstract_inverted_index.'light | 60 |
| abstract_inverted_index.99.5%. | 200 |
| abstract_inverted_index.Aiming | 0 |
| abstract_inverted_index.color, | 156 |
| abstract_inverted_index.filter | 32 |
| abstract_inverted_index.method | 186 |
| abstract_inverted_index.obtain | 78 |
| abstract_inverted_index.reduce | 102 |
| abstract_inverted_index.sparse | 51, 70, 131, 174 |
| abstract_inverted_index.strong | 64 |
| abstract_inverted_index.achieve | 188 |
| abstract_inverted_index.feature | 62 |
| abstract_inverted_index.finally | 108 |
| abstract_inverted_index.improve | 178 |
| abstract_inverted_index.minute, | 193 |
| abstract_inverted_index.problem | 3 |
| abstract_inverted_index.realize | 109 |
| abstract_inverted_index.source, | 58 |
| abstract_inverted_index.Firstly, | 53 |
| abstract_inverted_index.accuracy | 181, 197 |
| abstract_inverted_index.accurate | 112 |
| abstract_inverted_index.combined | 97 |
| abstract_inverted_index.contrast | 160 |
| abstract_inverted_index.example, | 38 |
| abstract_inverted_index.feature. | 170 |
| abstract_inverted_index.features | 6, 82, 132, 149 |
| abstract_inverted_index.learning | 74 |
| abstract_inverted_index.maintain | 165 |
| abstract_inverted_index.overcome | 88, 151 |
| abstract_inverted_index.pipeline | 16 |
| abstract_inverted_index.proposes | 40 |
| abstract_inverted_index.verified | 134 |
| abstract_inverted_index.Secondly, | 68 |
| abstract_inverted_index.adjusting | 55 |
| abstract_inverted_index.algorithm | 48, 95, 125, 176 |
| abstract_inverted_index.cigarette | 31 |
| abstract_inverted_index.contrast. | 119 |
| abstract_inverted_index.designed. | 67 |
| abstract_inverted_index.detection | 12, 35, 47, 105, 124, 196 |
| abstract_inverted_index.difficult | 18 |
| abstract_inverted_index.extracted | 146 |
| abstract_inverted_index.features. | 52 |
| abstract_inverted_index.histogram | 81 |
| abstract_inverted_index.platform. | 140 |
| abstract_inverted_index.reasoning | 100 |
| abstract_inverted_index.stability | 167 |
| abstract_inverted_index.dictionary | 73 |
| abstract_inverted_index.high-speed | 15, 46, 111 |
| abstract_inverted_index.projection | 80 |
| abstract_inverted_index.robustness | 65 |
| abstract_inverted_index.simulation | 137 |
| abstract_inverted_index.streamline | 34 |
| abstract_inverted_index.conclusions | 142 |
| abstract_inverted_index.millisecond | 23 |
| abstract_inverted_index.recognition | 113, 180 |
| abstract_inverted_index.backgrounds, | 93 |
| abstract_inverted_index.experimental | 139 |
| abstract_inverted_index.interference | 90, 153 |
| abstract_inverted_index.unstructured | 92 |
| abstract_inverted_index.representation | 71, 175 |
| abstract_inverted_index.Markov-Bayesian | 99, 173 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.58862243 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |