Log in

Relevant bibliographies by topics / Classification de scène / Journal articles

To see the other types of publications on this topic, follow the link: Classification de scène.

Journal articles on the topic 'Classification de scène'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Classification de scène.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Benmostefa, Soumia, and Hadria Fizazi. "Classification automatique des images satellitaires optimisée par l'algorithme des chauves-souris." Revue Française de Photogrammétrie et de Télédétection, no. 203 (April 8, 2014): 11–17. http://dx.doi.org/10.52638/rfpt.2013.25.

Full text

Abstract:

Cet article propose une nouvelle approche de classification automatique non supervisée des images. La classification est l'une des opérations les plus importantes dans plusieurs domaines d'analyse d'images telles que la médecine et la télédétection. Elle consiste à rechercher les différents thèmes constituant une scène représentée. Cependant, en raison de sa complexité plusieurs méthodes ont été proposées, spécifiquement des méthodes d'optimisation. Nous nous intéressons à la technique des chauves-souris, une métaheuristique d'optimisation biologique très récente, visant à modéliser le comport

APA, Harvard, Vancouver, ISO, and other styles

2

Zhu Shuxin, 朱淑鑫, 周子俊 Zhou Zijun, 顾兴健 Gu Xingjian, 任守纲 Ren Shougang та 徐焕良 Xu Huanliang. "基于RCF网络的遥感图像场景分类研究". Laser & Optoelectronics Progress 58, № 14 (2021): 1401001. http://dx.doi.org/10.3788/lop202158.1401001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

GUO Dongen, 郭东恩, 夏英 XIA Ying, 罗小波 LUO Xiaobo та 丰江帆 FENG Jiangfan. "基于有监督对比学习的遥感图像场景分类". ACTA PHOTONICA SINICA 50, № 7 (2021): 79. http://dx.doi.org/10.3788/gzxb20215007.0710002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Sanesi, R. "Procureur/psychiatre : quelles collaborations ? Quelles attentes ?" European Psychiatry 29, S3 (2014): 627. http://dx.doi.org/10.1016/j.eurpsy.2014.09.122.

Full text

Abstract:

Le magistrat recherche de plus en plus l’éclairage du psychiatre au cours d’un procès. De manière générale, pour les faits les plus graves dans la classification des infractions l’expertise est de droit. Le législateur a agrandi le champ d’intervention du psychiatre dans la scène judiciaire, notamment pour les infractions de violence sexuelle. Le magistrat du parquet a besoin d’une articulation parfaite entre la matérialité des faits et la personnalité de l’auteur. Le rôle du parquet n’est pas simplement de réunir les éléments matériels du crime ou du délit mais de procéder aussi par une orien

APA, Harvard, Vancouver, ISO, and other styles

5

Kyawt Htay, Kyawt, and Nyein Aye. "Semantic Concepts Classification on Outdoor Scene Images Based on Region-Based Approach." International Journal of Future Computer and Communication 3, no. 6 (2014): 427–31. http://dx.doi.org/10.7763/ijfcc.2014.v3.341.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Daniel, Sylvie. "Revue des descripteurs tridimensionnels (3D) pour la catégorisation des nuages de points acquis avec un système LiDAR de télémétrie mobile." Geomatica 72, no. 1 (2018): 1–15. http://dx.doi.org/10.1139/geomat-2018-0001.

Full text

Abstract:

La compréhension de nuage de points LiDAR consiste à reconnaitre les objets qui sont présents dans la scène et à associer des interprétations aux nuages d’objets qui le composent. Les données LiDAR acquises en milieu urbain dans des environnements à grande échelle avec des systèmes terrestres de télémétrie mobile présentent plusieurs difficultés propres à ce contexte : chevauchement entre les nuages de points, occlusions entre les objets qui ne sont vus que partiellement, variations de la densité des points. Compte tenu de ces difficultés, beaucoup de descripteurs tridimensionnels (3D) proposé

APA, Harvard, Vancouver, ISO, and other styles

7

Zheng, Xiangtao, Yuan Yuan, and Xiaoqiang Lu. "A Deep Scene Representation for Aerial Scene Classification." IEEE Transactions on Geoscience and Remote Sensing 57, no. 7 (2019): 4799–809. http://dx.doi.org/10.1109/tgrs.2019.2893115.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Hussain, Md Arafat, and Emon Kumar Dey. "Remote Sensing Image Scene Classification." International Journal of Engineering and Manufacturing 8, no. 4 (2018): 13–20. http://dx.doi.org/10.5815/ijem.2018.04.02.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Boutell, Matthew R., Jiebo Luo, Xipeng Shen, and Christopher M. Brown. "Learning multi-label scene classification." Pattern Recognition 37, no. 9 (2004): 1757–71. http://dx.doi.org/10.1016/j.patcog.2004.03.009.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Liu, Yishu, and Chao Huang. "Scene Classification via Triplet Networks." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, no. 1 (2018): 220–37. http://dx.doi.org/10.1109/jstars.2017.2761800.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Liu, Shaopeng, and Guohui Tian. "An Indoor Scene Classification Method for Service Robot Based on CNN Feature." Journal of Robotics 2019 (April 24, 2019): 1–12. http://dx.doi.org/10.1155/2019/8591035.

Full text

Abstract:

Indoor scene classification plays a vital part in environment cognition of service robot. With the development of deep learning, fine-tuning CNN (Convolutional Neural Network) on target datasets has become a popular way to solve classification problems. However, this method cannot obtain satisfying indoor scene classification results because of overfitting when scene training datasets are insufficient. To solve this problem, an indoor scene classification method is proposed in this paper, which utilizes CNN feature of scene images to generate scene category features to classify scenes by a nov

APA, Harvard, Vancouver, ISO, and other styles

12

Jiang, Yue, Runsheng Wang, and Cheng Wang. "Scene Classification with Context Pyramid Features." Journal of Computer-Aided Design & Computer Graphics 22, no. 8 (2010): 1366–73. http://dx.doi.org/10.3724/sp.j.1089.2010.10919.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Wei, Pengxu, Fei Qin, Fang Wan, Yi Zhu, Jianbin Jiao, and Qixiang Ye. "Correlated Topic Vector for Scene Classification." IEEE Transactions on Image Processing 26, no. 7 (2017): 3221–34. http://dx.doi.org/10.1109/tip.2017.2694320.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Cheng, H. D., and Rutvik Desai. "Scene Classification by Fuzzy Local Moments." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 07 (1998): 921–38. http://dx.doi.org/10.1142/s0218001498000506.

Full text

Abstract:

The identification of images irrespective of their location, size and orientation is one of the important tasks in pattern analysis. The use of global moment features has been one of the most popular techniques for this purpose. We present a simple and effective method for gray-level image representation and identification which utilizes fuzzy radial moments of image segments (local moments) as features as opposed to global features. A multilayer perceptron neural network is employed for classification. Fuzzy entropy measure is applied to optimize the parameters of the membership function. The

APA, Harvard, Vancouver, ISO, and other styles

15

Rangel, José Carlos, Miguel Cazorla, Ismael García-Varea, Jesus Martínez-Gómez, Élisa Fromont, and Marc Sebban. "Scene classification based on semantic labeling." Advanced Robotics 30, no. 11-12 (2016): 758–69. http://dx.doi.org/10.1080/01691864.2016.1164621.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Luo, Jiebo, and Matthew Boutell. "Natural scene classification using overcomplete ICA." Pattern Recognition 38, no. 10 (2005): 1507–19. http://dx.doi.org/10.1016/j.patcog.2005.02.015.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Cervantes, Salvador, Adriana Mexicano, Jose-Antonio Cervantes, Ricardo Rodriguez, and Jorge Fuentes-Pacheco. "Binary Pattern Descriptors for Scene Classification." IEEE Latin America Transactions 18, no. 01 (2020): 83–91. http://dx.doi.org/10.1109/tla.2020.9049465.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Liu, Yishu, Ching Y. Suen, Yingbin Liu, and Liwang Ding. "Scene Classification Using Hierarchical Wasserstein CNN." IEEE Transactions on Geoscience and Remote Sensing 57, no. 5 (2019): 2494–509. http://dx.doi.org/10.1109/tgrs.2018.2873966.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Tominaga, Shoji, Satoru Ebisui, and Brian A. Wandell. "Scene illuminant classification: brighter is better." Journal of the Optical Society of America A 18, no. 1 (2001): 55. http://dx.doi.org/10.1364/josaa.18.000055.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Jung, Jee-Weon, Hee-Soo Heo, Hye-Jin Shim, and Ha-Jin Yu. "Knowledge Distillation in Acoustic Scene Classification." IEEE Access 8 (2020): 166870–79. http://dx.doi.org/10.1109/access.2020.3021711.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Nandhakumar, N., and S. Malik. "Multisensor integration for underwater scene classification." Applied Intelligence 5, no. 3 (1995): 207–16. http://dx.doi.org/10.1007/bf00872222.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Lee, Yerin, Soyoung Lim, and Il-Youp Kwak. "CNN-Based Acoustic Scene Classification System." Electronics 10, no. 4 (2021): 371. http://dx.doi.org/10.3390/electronics10040371.

Full text

Abstract:

Acoustic scene classification (ASC) categorizes an audio file based on the environment in which it has been recorded. This has long been studied in the detection and classification of acoustic scenes and events (DCASE). This presents the solution to Task 1 of the DCASE 2020 challenge submitted by the Chung-Ang University team. Task 1 addressed two challenges that ASC faces in real-world applications. One is that the audio recorded using different recording devices should be classified in general, and the other is that the model used should have low-complexity. We proposed two models to overcom

APA, Harvard, Vancouver, ISO, and other styles

23

Balamurugan, R., R. Arunkumar, and S. Mohan. "RetinaNet Based Environment Classification." Asian Journal of Computer Science and Technology 7, S1 (2018): 112–14. http://dx.doi.org/10.51983/ajcst-2018.7.s1.1792.

Full text

Abstract:

Environmental classification is very useful for visually impaired persons and Robotic applications. The main objective of this work is to detect and recognize the objects present in a scene and identify the environment based on the occurrence probability of the objects in the scene. Objects from the real-time images are detected and recognized by means of RetinaNet. Occurrence probabilities of the recognized objects are used to identify the environment.

APA, Harvard, Vancouver, ISO, and other styles

24

Yeo, Woon-Ha, Young-Jin Heo, Young-Ju Choi, and Byung-Gyu Kim. "Place Classification Algorithm Based on Semantic Segmented Objects." Applied Sciences 10, no. 24 (2020): 9069. http://dx.doi.org/10.3390/app10249069.

Full text

Abstract:

Scene or place classification is one of the important problems in image and video search and recommendation systems. Humans can understand the scene they are located, but it is difficult for machines to do it. Considering a scene image which has several objects, humans recognize the scene based on these objects, especially background objects. According to this observation, we propose an efficient scene classification algorithm for three different classes by detecting objects in the scene. We use pre-trained semantic segmentation model to extract objects from an image. After that, we construct

APA, Harvard, Vancouver, ISO, and other styles

25

Soudy, Mohamed, Yasmine Afify, and Nagwa Badr. "Insights into few shot learning approaches for image scene classification." PeerJ Computer Science 7 (September 20, 2021): e666. http://dx.doi.org/10.7717/peerj-cs.666.

Full text

Abstract:

Image understanding and scene classification are keystone tasks in computer vision. The development of technologies and profusion of existing datasets open a wide room for improvement in the image classification and recognition research area. Notwithstanding the optimal performance of exiting machine learning models in image understanding and scene classification, there are still obstacles to overcome. All models are data-dependent that can only classify samples close to the training set. Moreover, these models require large data for training and learning. The first problem is solved by few-sh

APA, Harvard, Vancouver, ISO, and other styles

26

Wu, Xuan, Zhijie Zhang, Wanchang Zhang, Yaning Yi, Chuanrong Zhang, and Qiang Xu. "A Convolutional Neural Network Based on Grouping Structure for Scene Classification." Remote Sensing 13, no. 13 (2021): 2457. http://dx.doi.org/10.3390/rs13132457.

Full text

Abstract:

Convolutional neural network (CNN) is capable of automatically extracting image features and has been widely used in remote sensing image classifications. Feature extraction is an important and difficult problem in current research. In this paper, data augmentation for avoiding over fitting was attempted to enrich features of samples to improve the performance of a newly proposed convolutional neural network with UC-Merced and RSI-CB datasets for remotely sensed scene classifications. A multiple grouped convolutional neural network (MGCNN) for self-learning that is capable of promoting the eff

APA, Harvard, Vancouver, ISO, and other styles

27

Zhao, Zhicheng, Ze Luo, Jian Li, Can Chen, and Yingchao Piao. "When Self-Supervised Learning Meets Scene Classification: Remote Sensing Scene Classification Based on a Multitask Learning Framework." Remote Sensing 12, no. 20 (2020): 3276. http://dx.doi.org/10.3390/rs12203276.

Full text

Abstract:

In recent years, the development of convolutional neural networks (CNNs) has promoted continuous progress in scene classification of remote sensing images. Compared with natural image datasets, however, the acquisition of remote sensing scene images is more difficult, and consequently the scale of remote sensing image datasets is generally small. In addition, many problems related to small objects and complex backgrounds arise in remote sensing image scenes, presenting great challenges for CNN-based recognition methods. In this article, to improve the feature extraction ability and generalizat

APA, Harvard, Vancouver, ISO, and other styles

28

Jiang, Qiang Rong, Jian Chang Song, and Zhe Wu. "Natural Scene Recognition Based on Graph Edit Distance." Applied Mechanics and Materials 513-517 (February 2014): 4411–16. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.4411.

Full text

Abstract:

Natural scene classification is a challenging pattern classification problem nowadays. The description of image plays a crucial role in the process of recognition. Many different approaches and feature extraction methodologies concerning scene classification have been proposed and applied in the last few years. This paper proposed a novel method of natural scene recognition based on graph edit distance (GED) in which scene images are represented by attributed graph. The vertex label is the features of regions and edge label is the features of public area of adjacent regions. This method used l

APA, Harvard, Vancouver, ISO, and other styles

29

Feng, Jiangfan, Yuanyuan Liu, and Lin Wu. "Bag of Visual Words Model with Deep Spatial Features for Geographical Scene Classification." Computational Intelligence and Neuroscience 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/5169675.

Full text

Abstract:

With the popular use of geotagging images, more and more research efforts have been placed on geographical scene classification. In geographical scene classification, valid spatial feature selection can significantly boost the final performance. Bag of visual words (BoVW) can do well in selecting feature in geographical scene classification; nevertheless, it works effectively only if the provided feature extractor is well-matched. In this paper, we use convolutional neural networks (CNNs) for optimizing proposed feature extractor, so that it can learn more suitable visual vocabularies from the

APA, Harvard, Vancouver, ISO, and other styles

30

Anwer, Rao Muhammad, Fahad Shahbaz Khan, and Jorma Laaksonen. "Compact Deep Color Features for Remote Sensing Scene Classification." Neural Processing Letters 53, no. 2 (2021): 1523–44. http://dx.doi.org/10.1007/s11063-021-10463-4.

Full text

Abstract:

AbstractAerial scene classification is a challenging problem in understanding high-resolution remote sensing images. Most recent aerial scene classification approaches are based on Convolutional Neural Networks (CNNs). These CNN models are trained on a large amount of labeled data and the de facto practice is to use RGB patches as input to the networks. However, the importance of color within the deep learning framework is yet to be investigated for aerial scene classification. In this work, we investigate the fusion of several deep color models, trained using color representations, for aerial

APA, Harvard, Vancouver, ISO, and other styles

31

Gayathri, V., Eric Clapten, S. Mahalakshmi, and S. Rajes Kannan. "Color and Texture Feature Based Scene Classification." Journal of Computational and Theoretical Nanoscience 17, no. 11 (2020): 4897–901. http://dx.doi.org/10.1166/jctn.2020.9254.

Full text

Abstract:

Right now, overall trademark based multiscale multiresolution multistructure (M3LBP) neighborhood parallel example and nearby characteristic based totally min blend feature extraction is proposed for scene category. To extract international functions, characterize the leading spatial features in a couple of scale, a couple of choice, more than one structure way. The micro/macro shape facts and rotation invariance are guaranteed inside the worldwide function extraction approach. Neighborhood function extraction, coloration histogram characteristic (CHF) can thoroughly explain the spatial colora

APA, Harvard, Vancouver, ISO, and other styles

32

TANG, Yingjun, De XU, Guanghua GU, and Shuoyan LIU. "Category Constrained Learning Model for Scene Classification." IEICE Transactions on Information and Systems E92-D, no. 2 (2009): 357–60. http://dx.doi.org/10.1587/transinf.e92.d.357.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

LIU, Shuoyan, De XU, and Songhe FENG. "Discriminating Semantic Visual Words for Scene Classification." IEICE Transactions on Information and Systems E93-D, no. 6 (2010): 1580–88. http://dx.doi.org/10.1587/transinf.e93.d.1580.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Vosselman, G. "Point cloud segmentation for urban scene classification." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W2 (October 29, 2013): 257–62. http://dx.doi.org/10.5194/isprsarchives-xl-7-w2-257-2013.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Hui Wang, Hongwei Zhao, and Hongchang Ke. "Scene Classification Method Based on Markov Chain." International Journal of Digital Content Technology and its Applications 7, no. 5 (2013): 153–60. http://dx.doi.org/10.4156/jdcta.vol7.issue5.19.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Choi, Sun-Wook, and Chong Ho Lee. "Hypergraph model based Scene Image Classification Method." Journal of Korean Institute of Intelligent Systems 24, no. 2 (2014): 166–72. http://dx.doi.org/10.5391/jkiis.2014.24.2.166.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

ZENG, Pu. "Scene classification based on block latent semantic." Journal of Computer Applications 28, no. 6 (2008): 1537–39. http://dx.doi.org/10.3724/sp.j.1087.2008.01537.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Li, Zhao, Wei Lu, Zhanquan Sun, and Weiwei Xing. "Improving multi-label classification using scene cues." Multimedia Tools and Applications 77, no. 5 (2017): 6079–94. http://dx.doi.org/10.1007/s11042-017-4517-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Ali, Mayada M., Magda B. Fayek, and Elsayed E. Hemayed. "Human-inspired features for natural scene classification." Pattern Recognition Letters 34, no. 13 (2013): 1525–30. http://dx.doi.org/10.1016/j.patrec.2013.06.012.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Rakotomamonjy, Alain. "Supervised Representation Learning for Audio Scene Classification." IEEE/ACM Transactions on Audio, Speech, and Language Processing 25, no. 6 (2017): 1253–65. http://dx.doi.org/10.1109/taslp.2017.2690561.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Othman, Esam, Yakoub Bazi, Farid Melgani, Haikel Alhichri, Naif Alajlan, and Mansour Zuair. "Domain Adaptation Network for Cross-Scene Classification." IEEE Transactions on Geoscience and Remote Sensing 55, no. 8 (2017): 4441–56. http://dx.doi.org/10.1109/tgrs.2017.2692281.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Hu, Junlin, Liang Wang, Fuqing Duan, and Ping Guo. "Adaptive Multilevel Kernel Machine for Scene Classification." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/324945.

Full text

Abstract:

Scene classification is a challenging problem in computer vision applications and can be used to model and analyze a special complex system, the internet community. The spatial PACT (Principal component Analysis of Census Transform histograms) is a promising representation for recognizing instances and categories of scenes. However, since the original spatial PACT only simply concatenates compact census transform histograms at all levels together, all levels have the same contribution, which ignores the difference among various levels. In order to ameliorate this point, we propose an adaptive

APA, Harvard, Vancouver, ISO, and other styles

43

Horng-Horng Lin, Tyng-Luh Liu, and Jen-Hui Chuang. "Learning a Scene Background Model via Classification." IEEE Transactions on Signal Processing 57, no. 5 (2009): 1641–54. http://dx.doi.org/10.1109/tsp.2009.2014810.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Farinella, G. M., and S. Battiato. "Scene classification in compressed and constrained domain." IET Computer Vision 5, no. 5 (2011): 320. http://dx.doi.org/10.1049/iet-cvi.2010.0056.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Gauch, John M., Susan Gauch, Sylvain Bouix, and Xiaolan Zhu. "Real time video scene detection and classification." Information Processing & Management 35, no. 3 (1999): 381–400. http://dx.doi.org/10.1016/s0306-4573(98)00067-3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

FENG, Wen-Gang. "Data Driven Hierarchical Serial Scene Classification Framework." Acta Automatica Sinica 40, no. 4 (2014): 763–70. http://dx.doi.org/10.1016/s1874-1029(14)60008-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Sikiric, Ivan, Karla Brkic, Petra Bevandic, Ivan Kreso, Josip Krapac, and Sinisa Segvic. "Traffic Scene Classification on a Representation Budget." IEEE Transactions on Intelligent Transportation Systems 21, no. 1 (2020): 336–45. http://dx.doi.org/10.1109/tits.2019.2891995.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Tang, Yingjun, Li Xianhong, Zhu Wenqiang, Huang Shuying, and Zhang Yong. "Scene Classification Based on Spatial Semantic Topic." Journal of Computational and Theoretical Nanoscience 14, no. 1 (2017): 299–305. http://dx.doi.org/10.1166/jctn.2017.6320.

Full text

Abstract:

We proposed a novel approach to provide BoV (Bag of Visterms) with spatial context in a low computational expense way, which includes three steps. At first, a pyramid was built to preserve the spatial context for features by fusing local features and global features, and adopted K-means was proposed to get codebook. Then common of general topics and uniqueness of category topics were fully considered on the middle layer to generate semantic topic representation for each image scene. At last, SVM (Support Vector Machine) was applied to do scene classification. We investigated our approach with

APA, Harvard, Vancouver, ISO, and other styles

49

Wang Kaixuan, 王凯旋, 李卓容 Li Zhuorong, 王晓宾 Wang Xiaobin, 严圣东 Yan Shengdong, and 唐云祁 Tang Yunqi. "Automated Classification Method for Crime Scene Sketches." Laser & Optoelectronics Progress 57, no. 4 (2020): 041009. http://dx.doi.org/10.3788/lop57.041009.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Park, Sangwook, Woohyun Choi, and Hanseok Ko. "Acoustic scene classification using recurrence quantification analysis." Journal of the Acoustical Society of Korea 35, no. 1 (2016): 42–48. http://dx.doi.org/10.7776/ask.2016.35.1.042.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!