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A shape transformation-based dataset augmentation framework for pedestrian detection / Zhe Chen in International journal of computer vision, vol 129 n° 4 (April 2021)  

Public [article]  inInternational journal of computer vision > vol 129 n° 4 (April 2021) . - pp 1121 - 1138 Titre : A shape transformation-based dataset augmentation framework for pedestrian detection Type de document : Article/Communication Auteurs : Zhe Chen, Auteur ; Wanli Ouyang, Auteur ; Tongliang Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1121 - 1138 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage par transformation [Termes IGN] apprentissage profond [Termes IGN] déformation d'objet [Termes IGN] détection de piéton [Termes IGN] jeu de données [Termes IGN] synthèse d'image [Termes IGN] vision par ordinateur Résumé : (auteur) Deep learning-based computer vision is usually data-hungry. Many researchers attempt to augment datasets with synthesized data to improve model robustness. However, the augmentation of popular pedestrian datasets, such as Caltech and Citypersons, can be extremely challenging because real pedestrians are commonly in low quality. Due to the factors like occlusions, blurs, and low-resolution, it is significantly difficult for existing augmentation approaches, which generally synthesize data using 3D engines or generative adversarial networks (GANs), to generate realistic-looking pedestrians. Alternatively, to access much more natural-looking pedestrians, we propose to augment pedestrian detection datasets by transforming real pedestrians from the same dataset into different shapes. Accordingly, we propose the Shape Transformation-based Dataset Augmentation (STDA) framework. The proposed framework is composed of two subsequent modules, i.e. the shape-guided deformation and the environment adaptation. In the first module, we introduce a shape-guided warping field to help deform the shape of a real pedestrian into a different shape. Then, in the second stage, we propose an environment-aware blending map to better adapt the deformed pedestrians into surrounding environments, obtaining more realistic-llooking pedestrians and more beneficial augmentation results for pedestrian detection. Extensive empirical studies on different pedestrian detection benchmarks show that the proposed STDA framework consistently produces much better augmentation results than other pedestrian synthesis approaches using low-quality pedestrians. By augmenting the original datasets, our proposed framework also improves the baseline pedestrian detector by up to 38% on the evaluated benchmarks, achieving state-of-the-art performance. Numéro de notice : A2021-354 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s11263-020-01412-0 Date de publication en ligne : 09/01/2021 En ligne : https://doi.org/10.1007/s11263-020-01412-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97606 [article]

Joint sparse representation and multitask learning for hyperspectral target detection / Yuxiang Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 2 (February 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 2 (February 2017) . - pp 894 - 906 Titre : Joint sparse representation and multitask learning for hyperspectral target detection Type de document : Article/Communication Auteurs : Yuxiang Zhang, Auteur ; Bo Du, Auteur ; Liangpei Zhang, Auteur ; Tongliang Liu, Auteur Année de publication : 2017 Article en page(s) : pp 894 - 906 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] détection de cible [Termes IGN] image hyperspectrale [Termes IGN] représentation parcimonieuse Résumé : (Auteur) With the high spectral resolution, hyperspectral images (HSIs) provide great potential for target detection, which is playing an increasingly important role in HSI processing. Many target detection methods uniformly utilize all the spectral information or employ reduced spectral information to distinguish the targets and background. Simultaneously reducing spectral redundancy and preserving the discriminative information is a challenging problem in hyperspectral target detection. The multitask learning (MTL) technique may have the potential to solve the above problem, since it can explore the redundancy knowledge to construct multiple sub-HSIs and integrate them without any information loss. This paper proposes the joint sparse representation and MTL (JSR-MTL) method for hyperspectral target detection. This approach: 1) explores the HSIs similarity by a band cross-grouping strategy to construct multiple sub-HSIs; 2) takes full advantage of the MTL technique to integrate the sparse representation models for the multiple related sub-HSIs; and 3) applies the total reconstruction error difference accumulated over all the tasks to detect the targets. Extensive experiments were carried out on three HSIs, and it was founded that JSR-MTL generally shows a better detection performance than the other target detection methods. Numéro de notice : A2017-144 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2616649 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2616649 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84632 [article]