Otolith shape classification for fish stock discrimination
| Autor: | Jose Antonio Soria, Abdesslam Benzinou, Kamal Nasreddine, Vicenç Parisi-Baradad, Lluis Ferrer-Arnau |
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| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Universitat Politècnica de Catalunya. Centre de Desenvolupament Tecnològic de Sistemes d'Adquisició Remota i Tractament de la Informació (SARTI), Universitat Politècnica de Catalunya. TIEG - Terrassa Industrial Electronics Group, Nasreddine, Kamal, Universitat Politècnica de Catalunya. SARTI - Centre de Desenvolupament Tecnològic de Sistemes d'Adquisició Remota i Tractament de la Informació, Universitat Politècnica de Catalunya. (TIEG) - Terrassa Industrial Electronics Group, UPC, Universitat Politècnica de Catalunya [Barcelona] (UPC), Lab-STICC_ENIB_CID_TOMS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS) |
| Předmět: |
[SDE] Environmental Sciences
0106 biological sciences Red mullet [SPI] Engineering Sciences [physics] Feature extraction Aquaculture Fish stock 010603 evolutionary biology 01 natural sciences Fons marins -- Investigació [SPI]Engineering Sciences [physics] Image processing medicine Enginyeria agroalimentària::Ciències de la terra i de la vida::Zoologia [Àrees temàtiques de la UPC] 14. Life underwater Fourier Anàlisi de North sea Robustness Biology Accuracy Otolith biology business.industry 010604 marine biology & hydrobiology Enginyeria de la telecomunicació::Processament del senyal::Reconeixement de formes [Àrees temàtiques de la UPC] Wavelet transform Shape Pattern recognition Pattern recognition systems biology.organism_classification Form perception Otoliths Europe Geography medicine.anatomical_structure Otòlits [SDE]Environmental Sciences Principal component analysis Artificial intelligence Imatges -- Processament -- Aplicacions business Cartography Shape analysis (digital geometry) |
| Zdroj: | Recercat. Dipósit de la Recerca de Catalunya Universitat Jaume I UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) IEEE International Image Processing, Applications and Systems Conference (IPAS'14) IEEE International Image Processing, Applications and Systems Conference (IPAS'14), Nov 2014, Hammamet, Tunisia HAL |
| Popis: | The shape analysis of otoliths, which are calcified structures in the inner ear of teleostean fishes, is known to be particularly relevant to address species identification and stock discrimination. Generally, scientists use classical methodologies of statistical analysis and shape recognition such as Fourier shape descriptors and Principal Component Analysis (PCA). These methods are subject to several limitations mainly to their incapacity to locate irregularities because they are based on global characterization of shape. Recently, more advanced techniques are proposed in this context in order to improve classification accuracies. The first recent method exploits the potential of shape geodesics which rely on local shape features for classification issues. The second one addresses the Best-Basis paradigm which combines the Wavelet Transform, and the potential of statistical analysis in order to fully automate the selection process of efficient features for classification. These methods have been shown to significantly outperform the standard approaches but they are not compared together yet. This study compare these two methods on a real dataset. The comparison is performed on 600 striped red mullet calcified structures collected for the NESPMAN European project. For each method, performances are reported for the classification of samples coming from three geographical zones in the Northwest European seas: the Bay of Biscay, a mixing zone composed of the Celtic Sea and the Western English Channel and a northern zone composed of the Eastern English Channel and the North Sea. Comparison shows that both methods lead to same conclusions. |
| Databáze: | OpenAIRE |
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