Visual Object Recognition

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333

Distinctive Image Features from Scale-Invariant Keypoints

Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589

Histograms of Oriented Gradients for Human Detection

Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584

Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography

Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893

A decision-theoretic generalization of on-line learning and an application to boosting

Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466

The pascal visual object classes (VOC) challenge

Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599

Rapid object detection using a boosted cascade of simple features

Viola, P., & Jones, M. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990517 · Cited by 18,300

Normalized cuts and image segmentation

Shi, J., & Malik, J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868688 · Cited by 15,935

Robust Real-Time Face Detection

Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097

Speeded-Up Robust Features (SURF)

Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.014 · Cited by 13,557

Deep Learning for Generic Object Detection: A Survey

Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01247-4 · Cited by 2,965

Multimodal Distributional Semantics

Bruni, E., Tran, N. K., & Baroni, M. (2014) · Journal of Artificial Intelligence Research · doi:10.1613/jair.4135 · Cited by 929

R2D2: Repeatable and Reliable Detector and DescriptorPreprint

Revaud, J., Weinzaepfel, P., Souza, C. D., Pion, N., Csurka, G., Cabon, Y., & Humenberger, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.06195 · Cited by 379

A comprehensive and systematic look up into deep learning based object detection techniques: A review

Sharma, V., & Mir, R. N. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100301 · Cited by 230

Hubness and Pollution: Delving into Cross-Space Mapping for Zero-Shot Learning

Lazaridou, A., Dinu, G., & Baroni, M. (2015) · doi:10.3115/v1/p15-1027 · Cited by 218

Exploring Deep Learning-Based Architecture, Strategies, Applications and Current Trends in Generic Object Detection: A Comprehensive Review

Aziz, L., Salam, M. S. B. H., Sheikh, U. U., & Ayub, S. (2020) · IEEE Access · doi:10.1109/access.2020.3021508 · Cited by 224

Visual descriptors for content-based retrieval of remote-sensing images

Napoletano, P. (2017) · International Journal of Remote Sensing · doi:10.1080/01431161.2017.1399472 · Cited by 119

Survey and Performance Analysis of Deep Learning Based Object Detection in Challenging Environments

Ahmed, M. S., Hashmi, K. A., Pagani, A., Liwicki, M., Stricker, D., & Afzal, M. Z. (2021) · Sensors · doi:10.3390/s21155116 · Cited by 122

Object recognition using local invariant features for robotic applications: A survey

Loncomilla, P., Ruíz-del-Solar, J., & Martínez, L. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.05.021 · Cited by 112

Object class detection

Zhang, X., Yang, Y., Han, Z., Wang, H., & Gao, C. (2013) · ACM Computing Surveys · doi:10.1145/2522968.2522978 · Cited by 117

Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques

Xu, R., Razavi, S., & Zheng, R. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3323091 · Cited by 115

In the eye of the beholder

Yanulevskaya, V., Uijlings, J., Bruni, E., Sartori, A., Zamboni, E., Bacci, F., Melcher, D., & Sebe, N. (2012) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2393347.2393399 · Cited by 103

Grounding Distributional Semantics in the Visual World

Baroni, M. (2015) · Language and Linguistics Compass · doi:10.1111/lnc3.12170 · Cited by 104

Towards Real-Time Object Detection on Embedded Systems

Mao, H., Yao, S., Tang, T., Li, B., Yao, J., & Wang, Y. (2018) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2016.2593643 · Cited by 94

A computer vision system to monitor the infestation level of Varroa destructor in a honeybee colony

Bjerge, K., Frigaard, C. E., Mikkelsen, P. H., Nielsen, T. H., Misbih, M., & Kryger, P. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.104898 · Cited by 89

Reading visually embodied meaning from the brain: Visually grounded computational models decode visual-object mental imagery induced by written text

Anderson, A. J., Bruni, E., Lopopolo, A., Poesio, M., & Baroni, M. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.06.093 · Cited by 77

Content based image retrieval with sparse representations and local feature descriptors : A comparative study

Celik, C., & Bılge, H. Ş. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.03.006 · Cited by 72

Image dataset development for measuring construction equipment recognition performance

Tajeen, H., & Zhu, Z. (2014) · Automation in Construction · doi:10.1016/j.autcon.2014.07.006 · Cited by 69

Computer Vision and Natural Language Processing

Wiriyathammabhum, P., Summers-Stay, D. A., Fermuller, C. M., & Aloimonos, Y. (2016) · ACM Computing Surveys · doi:10.1145/3009906 · Cited by 69

Artificial intelligence for fish behavior recognition may unlock fishing gear selectivity

Abangan, A. S., Kopp, D., & Faillettaz, R. (2023) · Frontiers in Marine Science · doi:10.3389/fmars.2023.1010761 · Cited by 61

Hand-Crafted vs Learned Descriptors for Color Texture Classification

Napoletano, P. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-56010-6_22 · Cited by 53

Learning Based Industrial Bin-Picking Trained with Approximate Physics Simulator

Matsumura, R., Harada, K., Domae, Y., & Wan, W. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-01370-7_61 · Cited by 50

Finding region of interest in the infrared image of electrical installation

Jadin, M. S., Taib, S., & Ghazali, K. H. (2015) · Infrared Physics & Technology · doi:10.1016/j.infrared.2015.05.010 · Cited by 46

Of Words, Eyes and Brains: Correlating Image-Based Distributional Semantic Models with Neural Representations of Concepts

Anderson, A. J., Bruni, E., Bordignon, U., Poesio, M., & Baroni, M. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1202 · Cited by 42

Haptic Zero-Shot Learning: Recognition of objects never touched before

Abderrahmane, Z., Ganesh, G., Crosnier, A., & Cherubini, A. (2018) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2018.03.002 · Cited by 38

One Shot Detection withLaplacian Objectand Fast Matrix Cosine Similarity

Biswas, S. K., & Milanfar, P. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2453950 · Cited by 38

Identification of Nutritional Deficiencies in Crops Using Machine Learning and Image Processing Techniques

Krishna, V., Raju, Y. D. S., Raghavendran, C. V., Naresh, P., & Rajesh, A. (2022) · International Conference on Intelligent Engineering and Management (ICIEM) · doi:10.1109/iciem54221.2022.9853072 · Cited by 33

A computer vision-based perception system for visually impaired

Țapu, R., Mocanu, B., & Zaharia, T. B. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3617-6 · Cited by 32

Robust Semantic Template Matching Using a Superpixel Region Binary Descriptor

Yang, H. S., Huang, C., Wang, F., Song, K., & Yin, Z. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2893743 · Cited by 32

Image Classification Using Bag of Visual Words (BoVW)

Karim, A. A. A., & Sameer, R. A. (2018) · Journal of Al-Nahrain University-Science · doi:10.22401/anjs.21.4.11 · Cited by 32

A Unified Probabilistic Monitoring Framework for Multimode Processes Based on Probabilistic Linear Discriminant Analysis

Liu, Y., Zeng, J., Bao, J., & Xie, L. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2966707 · Cited by 32

Fingerprint authentication using geometric features

M., S. K. K., Rajeev, S., Panetta, K. A., & Agaian, S. С. (2017) · IEEE Conference on Technologies for Homeland Security (HST) · doi:10.1109/ths.2017.7943449 · Cited by 30

Inferring Visual Persuasion via Body Language, Setting, and Deep Features

Huang, X., & Kovashka, A. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.102 · Cited by 30

Hotels-50K: A Global Hotel Recognition Dataset

Stylianou, A., Xuan, H., Shende, M., Brandt, J., Souvenir, R., & Pless, R. B. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301726 · Cited by 30

Tracking of Ball and Players in Beach Volleyball Videos

Gomez, G., López, P. H., Link, D., & Eskofier, B. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0111730 · Cited by 31

Large-scale and rotation-invariant template matching using adaptive radial ring code histograms

Yang, H. S., Huang, C., Wang, F., Song, K., Zheng, S., & Yin, Z. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.03.003 · Cited by 28

Visual pattern discovery in image and video data: a brief survey

Wang, H., Zhao, G., & Yuan, J. (2013) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1110 · Cited by 23

Do you like my outfit?

Vitiello, G., Sebillo, M., Fornaro, L., Gregorio, M. D., Cirillo, S., Rosa, M. D., Fuccella, V., & Costagliola, G. (2018) · EAI International Conference on Smart Objects and Technologies for Social Good (GOODTECHS) · doi:10.1145/3284869.3284908 · Cited by 23

Ancient Coin Classification Using Reverse Motif Recognition: Image-based classification of Roman Republican coins

Anwar, H., Zambanini, S., Kampel, M., & Vondrovec, K. (2015) · IEEE Signal Processing Magazine · doi:10.1109/msp.2015.2409331 · Cited by 23

From passive to interactive object learning and recognition through self-identification on a humanoid robot

Lyubova, N., Ivaldi, S., & Filliat, D. (2015) · Autonomous Robots · doi:10.1007/s10514-015-9445-0 · Cited by 28

Classification of non-tumorous skin pigmentation disorders using voting based probabilistic linear discriminant analysis

Liang, Y., Сун, Л., Ser, W., Lin, F., Thng, S. T. G., Chen, Q., & Lin, Z. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.026 · Cited by 21

Bag–of–Colors for Biomedical Document Image Classification

de Herrera, A. G. S., Markonis, D., & Müller, H. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-36678-9_11 · Cited by 20

Exploring Semantic Representation in Brain Activity Using Word Embeddings

Ruan, Y.-P., Ling, Z.-H., & Hu, Y. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1064 · Cited by 21

Iterative Design and Prototyping of Computer Vision Mediated Remote Sighted Assistance

Xie, J., Reddie, M., Lee, S., Billah, S. M., Zhou, Z., Tsai, C.-H., & Carroll, J. M. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3501298 · Cited by 19

How much are we exposed to alcohol in electronic media? Development of the Alcoholic Beverage Identification Deep Learning Algorithm (ABIDLA)

Kuntsche, E. N., Bonela, A. A., Caluzzi, G., Miller, M., & He, Z. (2020) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2020.107841 · Cited by 19

Focal-Plane Sensing-Processing: A Power-Efficient Approach for the Implementation of Privacy-Aware Networked Visual Sensors

Fernández‐Berni, J., Carmona‐Galán, R., Río, R. D., Kleihorst, R. P., Philips, W. R., & Rodriguez-Vazquez, A. (2014) · Sensors · doi:10.3390/s140815203 · Cited by 18

Benchmarking the Grasping Capabilities of the iCub Hand With the YCB Object and Model Set

Jamone, L., Bernardino, A. J. M., & Santos-Victor, J. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2016.2517209 · Cited by 18

Automatic GCP extraction with high resolution COSMO-SkyMed products

Nitti, D. O., Morea, A., Nutricato, R., Chiaradia, M. T., Mantia, C. L., Agrimano, L., & Samarelli, S. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2241281 · Cited by 17

A Survey on Visual Perception for RoboCup MSL Soccer Robots

Li, X., Lu, H., Xiong, D., Zhang, H., & Zheng, Z. (2013) · International Journal of Advanced Robotic Systems · doi:10.5772/55718 · Cited by 16

A passive, camera-based head-tracking system for real-time, three-dimensional estimation of head position and orientation in rodents

Vanzella, W., Grion, N., Bertolini, D., Perissinotto, A., Gigante, M., & Zoccolan, D. (2019) · Journal of Neurophysiology · doi:10.1152/jn.00301.2019 · Cited by 16

Machine Learning to Classify Suicidal Thoughts and Behaviors: Implementation Within the Common Data Elements Used by the Military Suicide Research Consortium

Littlefield, A. K., Cooke, J. T., Bagge, C. L., Glenn, C. R., Kleiman, E. M., Jacobucci, R., Millner, A. J., & Steinley, D. (2021) · Clinical Psychological Science · doi:10.1177/2167702620961067 · Cited by 15

ADAMAS: Interweaving unicode and color to enhance CAPTCHA security

Roshanbin, N., & Miller, J. T. (2014) · Future Generation Computer Systems · doi:10.1016/j.future.2014.11.004 · Cited by 15

Modified Dendrite Morphological Neural Network Applied to 3D Object Recognition

Sossa, H., & Guevara‐Gutiérrez, E. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38989-4_32 · Cited by 13

Tamper detection for active surveillance systems

Tsesmelis, T., Christensen, L., Fihl, P., & Moeslund, T. B. (2013) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2013.6636616 · Cited by 13

Automatic quantification of multi-modal rigid registration accuracy using feature detectors

Hauler, F., Furtado, H., Jurisic, M., Polanec, S. H., Spick, C., Laprie, A., Nestle, U., Sabatini, U., & Birkfellner, W. (2016) · Physics in Medicine and Biology · doi:10.1088/0031-9155/61/14/5198 · Cited by 13

Machine Learning Methods for Social Signal Processing

Rudovic, O. O., Nicolaou, M. A., & Pavlović, V. (2017) · Cambridge University Press eBooks · doi:10.1017/9781316676202.018 · Cited by 13

Persistent Homology Meets Object Unity: Object Recognition in Clutter

Samani, E. U., & Banerjee, A. G. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3343994 · Cited by 15

VSEM: An open library for visual semantics representation

Bruni, E., Bordignon, U., Liska, A., Uijlings, J., & Sergienya, I. (2013) · Cited by 11

Summarisation of short-term and long-term videos using texture and colour

Carvajal, J., McCool, C., & Sanderson, C. (2014) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2014.6836025 · Cited by 11

Recognizing Landmarks in Large-Scale Social Image Collections

Crandall, D., Li, Y., Lee, S., & Huttenlocher, D. P. (2016) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-25781-5_7 · Cited by 11

A computer vision method for respiratory monitoring in intensive care environment using RGB-D cameras

Rehouma, H., Noumeir, R., Jouvet, P. A., Bouachir, W., & Essouri, S. (2017) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2017.8310155 · Cited by 11

A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and DatasetsPreprint

Muzammul, M., & Li, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.07927 · Cited by 19

OrthoMaps: an efficient convolutional neural network with orthogonal feature maps for tiny image classification

Moradi, R., Berangi, R., & Bidgoli, B. M. (2019) · IET Image Processing · doi:10.1049/iet-ipr.2018.6620 · Cited by 10

Artificial intelligence for pathology

Xing, F., Zhang, X., & Cornish, T. C. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821259-2.00011-9 · Cited by 13

Embedding high-level information into low level vision: Efficient object search in clutter

Teo, C. L., Myers, A., Fermuller, C. M., & Aloimonos, Y. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6630566 · Cited by 10

A Structure-from-Motion Pipeline for Topographic Reconstructions Using Unmanned Aerial Vehicles and Open Source Software

Meza, J., Marrugo, A. G., Sierra, E., Guerrero, M., Meneses, J. E., & Romero, L. A. (2018) · Communications in computer and information science · doi:10.1007/978-3-319-98998-3_17 · Cited by 10

Evolving weighting schemes for the Bag of Visual Words

Escalante, H. J., Ponce-López, V., Escalera, S., Baró, X., Morales-Reyes, A., & Martínez-Carranza, J. (2016) · Neural Computing and Applications · doi:10.1007/s00521-016-2223-x · Cited by 10

An improved mixture robust probabilistic linear discriminant analyzer for fault classification

Liu, Y., Zeng, J., Xie, L., Lang, X., Luo, S., & Su, H. (2019) · ISA Transactions · doi:10.1016/j.isatra.2019.08.037 · Cited by 10

Poacher Detection in African Game Parks and Reserves with IoT: Machine Learning Approach

Edemacu, K., Kim, J. W., Jang, B., & Park, H. K. (2019) · International Conference on Green and Human Information Technology (ICGHIT) · doi:10.1109/icghit.2019.00011 · Cited by 9

Research on Convolutional Neural Network Image Recognition Algorithm Based on Computer Big Data

Chen, Y. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1744/2/022096 · Cited by 9

A comparative study of the performance of local feature-based pattern recognition algorithms

Roshanbin, N., & Miller, J. (2016) · Pattern Analysis and Applications · doi:10.1007/s10044-016-0554-y · Cited by 9

Combine color and shape in real-time detection of texture-less objects

Peng, X. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.02.010 · Cited by 8

Co-Training for Visual Object Recognition Based on Self-Supervised Models Using a Cross-Entropy Regularization

Díaz, G., Peralta, B., Caro, L., & Nicolis, O. (2021) · Entropy · doi:10.3390/e23040423 · Cited by 8

A New Shape Feature for Vehicle Classification in Thermal Video Sequences

Yang, D., & Park, H. (2015) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2015.2452780 · Cited by 8

Recognition and distance estimation of an irregular object in package sorting line based on monocular vision

Wang, X., Zhou, B., Ji, J., & Pu, B. (2019) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881419827215 · Cited by 7

DR-CapsNet with CAEMRA: Looking deep inside instance for boosting object detection effect

Lin, Z., Zheng, Z., Jia, J., Gao, W., & Huang, F. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106218 · Cited by 7

Adaptive Aggregating Multiresolution Feature Coding for Image Classification

Liao, H., Xiang, J., Sun, W., & Yu, S. (2014) · Mathematical Problems in Engineering · doi:10.1155/2014/847608 · Cited by 7

Evaluating the Stability of Spatial Keypoints via Cluster Core Correspondence Index

Mukherjee, S., Lagache, T., & Olivo‐Marín, J. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3036759 · Cited by 7

Learning by focusing: A new framework for concept recognition and feature selection

Cao, L., Gong, L., Kender, J. R., Codella, N., & Smith, J. R. (2013) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2013.6607609 · Cited by 6

Quality monitoring for injection moulding process using a semi-supervised learning approach

Nam, D. N. C., Tung, T. V., & Yapp, E. K. Y. (2021) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon48115.2021.9589593 · Cited by 6

Reducing Dimensionality of Spectro-Temporal Data by Independent Component Analysis

You, S. D., & Hung, M.-J. (2020) · International Conference on Computer Communication and the Internet (ICCCI) · doi:10.1109/iccci49374.2020.9145984 · Cited by 6

Exploiting Concepts of Instance Segmentation to Boost Detection in Challenging Environments

Hashmi, K. A., Pagani, A., Liwicki, M., Stricker, D., & Afzal, M. Z. (2022) · Sensors · doi:10.3390/s22103703 · Cited by 6

A Training-Free Approach for Generic Object Detection

Baheti, B., Talbar, S. N., & Gajre, S. S. (2019) · IETE Journal of Research · doi:10.1080/03772063.2019.1611491 · Cited by 4

From visual words to a visual grammar: using language modelling for image classificationPreprint

Foncubierta–Rodríguez, A., Müller, H., & Depeursinge, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.05571 · Cited by 4

Improving bag of visual words representations with genetic programming

Escalante, H. J., Martínez-Carranza, J., Escalera, S., Ponce-López, V., & Baró, X. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280799 · Cited by 3

Application Based Cigarette Detection on Social Media Platforms Using Machine Learning Algorithms

Hashmi, M. U., Nguyen, N. D., Johnstone, M., Backholer, K., & Bhatti, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-91387-8_5 · Cited by 3

Avoiding to Face the Challenges of Visual Place Recognition

Mihankhah, E., & Wang, D. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-01054-6_52 · Cited by 3

Review of Local Descriptor in RGB-D Object Recognition

Rachmawati, E., Suwardi, I. S., & Khodra, M. L. (2014) · TELKOMNIKA (Telecommunication Computing Electronics and Control) · doi:10.12928/telkomnika.v12i4.388 · Cited by 4

A biologically inspired neurocomputing circuit for image representation

Wei, H., & Zuo, Q. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.01.078 · Cited by 3

Meaningful Bags of Words for Medical Image Classification and Retrieval

Foncubierta–Rodríguez, A., de Herrera, A. G. S., & Müller, H. (2015) · Springer eBooks · doi:10.1007/978-3-319-17963-6_5 · Cited by 3

Multi-sensory based novel household object categorization system by using interactive behaviours

Guan, H., & Zhang, J. (2016) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2016.7866570 · Cited by 3

Physics Simulation Games

Renz, J., & Ge, X. (2015) · Springer eBooks · doi:10.1007/978-981-4560-52-8_29-1 · Cited by 3

A quantitative evaluation of the conceptual consistency of visual words and visual vocabularies

Stommel, M., Herzog, O., & Xu, W. (2014) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2014.11.015 · Cited by 2

An Abstract Deep Network for Image Classification

Knittel, A., & Blair, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35101-3_14 · Cited by 2

Visual support and interaction for error prevention in aircraft maintenance

Arosio, G., Giordani, I., Arieni, L., & Archetti, F. (2014) · IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace) · doi:10.1109/metroaerospace.2014.6865952 · Cited by 2

OV2SLAM on EuRoC MAV Datasets: a Study of Corner Detector Performance

Burghoffer, A., Seyssaud, J., & Magnier, B. (2023) · IEEE International Conference on Imaging Systems and Techniques (IST) · doi:10.1109/ist59124.2023.10355706 · Cited by 2

Pitch Frames Classification in a Cricket Video Using Bag-of-Visual-Words

Ravinder, M., & Venugopal, T. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-81-322-2656-7_72 · Cited by 2

Taxonomy of Texture and Color-Texture Features for Developing Hand Detection System under Non-Ideal Conditions

Misra, S., & Laskar, R. H. (2017) · IEEE India Conference · doi:10.1109/indicon.2017.8487569 · Cited by 2

Multimode Process Monitoring Based on Modified Probabilistic Linear Discriminant Analysis

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