Detection of head motions using a vision model

Conference item

Gao, X., Shaposhnikov, D., Podladchikova, L., Batty, S. and Clark, J. 2007. Detection of head motions using a vision model. Bashshur, R. (ed.) 3rd IASTED International Conference on Telehealth. Montreal, QC, Canada 31 May - 01 Jun 2007 Anaheim, CA Acta Press. pp. 167-171
TitleDetection of head motions using a vision model
AuthorsGao, X., Shaposhnikov, D., Podladchikova, L., Batty, S. and Clark, J.
Abstract

Head movement during a brain scan is one of the major reasons causing the blurriness of images. In order to improve the quality of scanned brain images, the head movement parameters should be known and embedded into the image reconstruction algorithms. In this study, a system consisted of two cameras is studied to monitor the head movements under the scanning conditions. The camera is calibrated before the shooting. Images are acquired by the video camera and transferred to a colour monitor that has been calibrated. The range of face skin colours are then obtained based on these images captured under varying lighting conditions to perform face segmentation. The vision model, Foveal System for Face Images (FOSFI) is then applied to find the position of eyes and nose, arriving at the findings of parameters of rotation and translation of head motion. Preliminary results on 2D images with known moving parameters show that movement parameters can be obtained very accurately via the described methods.

KeywordsMotion detection; modelling; PET imaging
Research GroupArtificial Intelligence group
Conference3rd IASTED International Conference on Telehealth
Page range167-171
Proceedings TitleProceedings of the 3rd IASTED International Conference on Telehealth
EditorsBashshur, R.
ISBN
Hardcover9780889866676
PublisherActa Press
Place of publicationAnaheim, CA
Publication dates
Print2007
Publication process dates
Deposited01 Apr 2009
Output statusPublished
Additional information

Conference held on May 31 - June 1, 2007, Montreal, Quebec, Canada.

Web address (URL)http://www.actapress.com/Abstract.aspx?paperId=30798
Scopus EID2-s2.0-54949154864
LanguageEnglish
Permalink -

https://repository.mdx.ac.uk/item/816q3

Log in to edit
  • 57
    total views
  • 0
    total downloads
  • 1
    views this month
  • 0
    downloads this month

Export as

Related outputs

Development of an ensemble CNN model with explainable AI for the classification of gastrointestinal cancer
Auzine, M., Heenaye-Mamode Khan, M., Baichoo, S., Gooda Sahib, N., Bissoonauth-Daiboo, P., Gao, X. and Heetun, Z. 2024. Development of an ensemble CNN model with explainable AI for the classification of gastrointestinal cancer. PLoS ONE. 19 (6), p. e0305628. https://doi.org/10.1371/journal.pone.0305628
An evolutionary approach to automated class-specific data augmentation for image classification
Marc, S., Belavkin, R., Windridge, D. and Gao, X. 2024. An evolutionary approach to automated class-specific data augmentation for image classification. Moosaei, H., Hladík, M. and Pardalos, P. (ed.) 6th International Conference on the Dynamics of Information Systems. Prague, Czech Republic 03 - 06 Dec 2023 Springer. pp. 170–185 https://doi.org/10.1007/978-3-031-50320-7_12
MesoGraph: automatic profiling of mesothelioma subtypes from histological images
Eastwood, M., Sailem, H., Marc, S., Gao, X., Offman, J., Karteris, E., Fernandez, A., Jonigk, D., Cookson, W., Moffatt, M., Popat, S., Minhas, F. and Robertus, J. 2023. MesoGraph: automatic profiling of mesothelioma subtypes from histological images. Cell Reports Medicine. 4 (10). https://doi.org/10.1016/j.xcrm.2023.101226
Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data
Eastwood, M., Marc, S., Gao, X., Sailem, H., Offman, J., Karteris, E., Montero Fernandez, A., Jonigk, D., Cookson, W., Moffatt, M., Popat, S., Minhas, F. and Robertus, J. 2023. Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data. Artificial Intelligence in Medicine. 143. https://doi.org/10.1016/j.artmed.2023.102628
Fusion of colour contrasted images for early detection of oesophageal squamous cell dysplasia from endoscopic videos in real time
Gao, X., Taylor, S., Pang, W., Hui, R., Lu, X., Oxford GI Investigators and Braden, B. 2023. Fusion of colour contrasted images for early detection of oesophageal squamous cell dysplasia from endoscopic videos in real time. Information Fusion. 92, pp. 64-79. https://doi.org/10.1016/j.inffus.2022.11.023
Endoscopic image analysis using deep convolutional GAN and traditional data augmentation
Auzine, M., Khan, M., Baichoo, S., Gooda Sahib, N., Gao, X. and Bissoonauth-Daiboo, P. 2022. Endoscopic image analysis using deep convolutional GAN and traditional data augmentation. International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). Maldives 16 - 18 Nov 2022 IEEE. https://doi.org/10.1109/ICECCME55909.2022.9988503
COVID-VIT: classification of Covid-19 from 3D CT chest images based on vision transformer model
Gao, X., Khan, M., Hui, R., Tian, Z., Qian, Y., Gao, A. and Baichoo, S. 2022. COVID-VIT: classification of Covid-19 from 3D CT chest images based on vision transformer model. 3rd International Conference on Next Generation Computing Applications (NextComp). Flic-en-Flac, Mauritius 06 - 08 Oct 2022 IEEE. https://doi.org/10.1109/NextComp55567.2022.9932246
Malignant Mesothelioma subtyping of tissue images via sampling driven multiple instance prediction
Eastwood, M., Marc, S., Gao, X., Sailem, H., Offman, J., Karteris, E., Montero Fernandez, A., Jonigk, D., Cookson, W., Moffatt, M., Popat, S., Minhas, F. and Robertus, J. 2022. Malignant Mesothelioma subtyping of tissue images via sampling driven multiple instance prediction. Michalowski, M., Abidi, S. and Abidi, S. (ed.) 20th International Conference on Artificial Intelligence in Medicine. Halifax, Canada 14 - 17 Jun 2022 Springer. https://doi.org/10.1007/978-3-031-09342-5_25
Identification of human papillomavirus from super resolution microscopic images generated using deep learning architectures
Gao, X., Wen, S., Li, D., Liu, W., Xiong, J., Xu, B., Liu, J., Zhang, H. and Liu, X. 2022. Identification of human papillomavirus from super resolution microscopic images generated using deep learning architectures. in: Wani, M. and Palade, V. (ed.) Deep Learning Applications, Volume 4 Springer.
Detection of human papillomavirus (HPV) from super resolution microscopic images applying an explainable deep learning network
Gao, X., Wen, S., Li, D., Liu, W., Xiong, J., Xu, B., Liu, J., Zhang, H. and Liu, X. 2022. Detection of human papillomavirus (HPV) from super resolution microscopic images applying an explainable deep learning network. SPIE Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging. San Diego, USA 20 - 22 Feb 2022 Society of Photo-optical Instrumentation Engineers. https://doi.org/10.1117/12.2624423
Early detection of oesophageal cancer through colour contrast enhancement for data augmentation
Gao, X., Taylor, S., Pang, W., Lu, X. and Braden, B. 2022. Early detection of oesophageal cancer through colour contrast enhancement for data augmentation. SPIE Medical Imaging: Computer-Aided Diagnosis. San Diego, USA 21 - 24 Feb 2022 Society of Photo-optical Instrumentation Engineers. https://doi.org/10.1117/12.2611409
Gold-viral particle identification by deep learning in wide-field photon scattering parametric images
Zhao, H., Ni, B., Jin, X., Zhang, H., Hou, J., Hou, L., Marsh, J., Dong, L., Li, S., Gao, X., Shi, D., Liu, X. and Xiong, J. 2022. Gold-viral particle identification by deep learning in wide-field photon scattering parametric images. Applied Optics. 61 (2), pp. 546-553. https://doi.org/10.1364/AO.445953
Signal denoising of viral particle in wide-field photon scattering parametric images using deep learning
Zhao, H., Ni, B., Liu, W., Jin, X., Zhang, H., Gao, X., Wen, S., Shi, D., Dong, L., Xiong, J. and Liu, X. 2022. Signal denoising of viral particle in wide-field photon scattering parametric images using deep learning. Optics Communications. 503. https://doi.org/10.1016/j.optcom.2021.127463
Artificial intelligence in endoscopy: the challenges and future directions
Gao, X. and Braden, B. 2021. Artificial intelligence in endoscopy: the challenges and future directions. Artifical Intelligence in Gastrointestinal Endoscopy. 2 (4), pp. 117-126. https://doi.org/10.37126/aige.v2.i4.117
COVID-CBR: a deep learning architecture featuring case-based reasoning for classification of COVID-19 from chest x-ray images
Gao, X. and Gao, A. 2021. COVID-CBR: a deep learning architecture featuring case-based reasoning for classification of COVID-19 from chest x-ray images. 20th IEEE ICMLA 2021. Virtual online 13 - 16 Dec 2021 IEEE. pp. 1319-1324 https://doi.org/10.1109/ICMLA52953.2021.00214
Evaluation of GAN architectures for visualisation of HPV viruses from microscopic images
Gao, X., Wen, S., Li, D., Liu, W., Xiong, J., Xu, B., Liu, J., Zhang, H. and Liu, X. 2021. Evaluation of GAN architectures for visualisation of HPV viruses from microscopic images. 20th IEEE ICMLA 2021. Virtual online 13 - 16 Dec 2021 IEEE. pp. 829-833 https://doi.org/10.1109/ICMLA52953.2021.00137
Characterization of deep sub-wavelength nanowells by imaging the photon state scattering spectra
Liu, W., Liu, X. and Gao, X. 2021. Characterization of deep sub-wavelength nanowells by imaging the photon state scattering spectra. Optics Express. 29 (2), pp. 1221-1231. https://doi.org/10.1364/OE.413942
Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy
Ali, S., Dmitrieva, M., Ghatwary, N., Bano, S., Polat, G., Temizel, A., Krenzer, A., Hekalo, A., Guo, Y., Matuszewski, B., Gridach, M., Voiculescu, I., Yoganand, V., Chavan, A., Raj, A., Nguyen, N., Tran, D., Huynh, L., Boutry, N., Rezvy, S., Chen, H., Choi, Y., Subramanian, A., Balasubramanian, V., Gao, X., Hu, H., Liao, Y., Stoyanov, D., Daul, C., Realdon, S., Cannizzaro, R., Lamarque, D., Tran-Nguyen, T., Bailey, A., Braden, B., East, J. and Rittscher, J. 2021. Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy. Medical Image Analysis. 70. https://doi.org/10.1016/j.media.2021.102002
Transfer learning for endoscopy disease detection and segmentation with mask-RCNN benchmark architecture
Rezvy, S., Zebin, T., Pang, W., Taylor, S. and Gao, X. 2020. Transfer learning for endoscopy disease detection and segmentation with mask-RCNN benchmark architecture. 2nd International Workshop and Challenge on Computer Vision in Endoscopy. Iowa City, United States 03 Apr 2020 pp. 68-72
An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy
Ali, S., Zhou, F., Braden, B., Bailey, A., Yang, S., Cheng, G., Zhang, P., Li, X., Kayser, M., Soberanis-Mukul, R., Albarqouni, S., Wang, X., Wang, C., Watanabe, S., Oksuz, I., Ning, Q., Yang, S., Khan, M., Gao, X., Realdon, S., Loshchenov, M., Schnabel, J., East, J., Wagnieres, G., Loschenov, V., Grisan, E., Daul, C., Blondel, W. and Rittscher, J. 2020. An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy. Scientific Reports. 10 (1), pp. 1-15. https://doi.org/10.1038/s41598-020-59413-5
An enhanced deep learning architecture for classification of Tuberculosis types from CT lung images
Gao, X., Comley, R. and Khan, M. 2020. An enhanced deep learning architecture for classification of Tuberculosis types from CT lung images. ICIP 2020: 27th IEEE International Conference on Image Processing. Abu Dhabi, Unites Arab Emirates (Virtual Conference) 25 - 28 Oct 2020 IEEE. pp. 2486-2490 https://doi.org/10.1109/ICIP40778.2020.9190815
Case-based reasoning of a deep learning network for prediction of early stage of oesophageal cancer
Gao, X., Braden, B., Zhang, L., Taylor, S., Pang, W. and Petridis, M. 2020. Case-based reasoning of a deep learning network for prediction of early stage of oesophageal cancer. 24th UK Symposium on Case-Based Reasoning (UKCBR 2019). Cambridge, UK 17 Dec 2019 BCS SGAI: The Specialist Group on Artificial Intelligence. pp. 1-12
Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture
Gao, X., James-Reynolds, C. and Currie, E. 2020. Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture. Neurocomputing. 392, pp. 233-244. https://doi.org/10.1016/j.neucom.2018.12.086
Patch-based deep learning approaches for artefact detection of endoscopic images
Gao, X. and Qian, Y. 2019. Patch-based deep learning approaches for artefact detection of endoscopic images. Endoscopic artefact detection challenge 2019 (EAD2019). Venice, Italy 08 Apr 2019 CEUR Workshop Proceedings.
Towards real-time detection of squamous pre-cancers from oesophageal endoscopic videos
Gao, X., Braden, B., Taylor, S. and Pang, W. 2019. Towards real-time detection of squamous pre-cancers from oesophageal endoscopic videos. ICMLA 2019. Boca Raton, Florida, USA 16 - 19 Dec 2019 IEEE. pp. 1606-1612 https://doi.org/10.1109/ICMLA.2019.00264
Analysing TB severity levels with an enhanced deep residual learning– depth-resnet
Gao, X., James-Reynolds, C. and Currie, E. 2018. Analysing TB severity levels with an enhanced deep residual learning– depth-resnet. Cappellato, L., Ferro, N., Nie, J-Y. and Soulier, L. (ed.) CLEF 2018 Conference and Labs of the Evaluation Forum - ImageCLEF-Multimedia Retrieval in CLEF. Avignon, France 10 - 14 Sep 2018 CEUR-WS.
Prediction of multidrug-resistant TB from CT pulmonary images based on deep learning techniques
Gao, X. and Qian, Y. 2018. Prediction of multidrug-resistant TB from CT pulmonary images based on deep learning techniques. Molecular Pharmaceutics. 15 (10), pp. 4326-4335. https://doi.org/10.1021/acs.molpharmaceut.7b00875
Segmentation of brain lesions from CT images based on deep learning techniques
Gao, X. and Qian, Y. 2018. Segmentation of brain lesions from CT images based on deep learning techniques. Gimi, B. and Krol, A. (ed.) SPIE Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging. Houston, Texas, United States 10 - 15 Feb 2018 Society of Photo-optical Instrumentation Engineers (SPIE). https://doi.org/10.1117/12.2286844
Application of deep learning neural network for classification of TB lung CT images based on patches
Gao, X. and Qian, Y. 2017. Application of deep learning neural network for classification of TB lung CT images based on patches. ImageCLEF / LifeCLEF - Multimedia Retrieval in CLEF: CLEF 2017: Conference and Labs of the Evaluation Forum. Dublin, Ireland 11 - 14 Sep 2017 CEUR Workshop Proceedings.
A fused deep learning architecture for viewpoint classification of echocardiography
Gao, X., Li, W., Loomes, M. and Wang, L. 2017. A fused deep learning architecture for viewpoint classification of echocardiography. Information Fusion. 36, pp. 103-113. https://doi.org/10.1016/j.inffus.2016.11.007
Classification of CT brain images based on deep learning networks
Gao, X., Hui, R. and Tian, Z. 2017. Classification of CT brain images based on deep learning networks. Computer Methods and Programs in Biomedicine. 138 (2017), pp. 49-56. https://doi.org/10.1016/j.cmpb.2016.10.007
A deep learning based approach to classification of CT brain images
Gao, X. and Hui, R. 2016. A deep learning based approach to classification of CT brain images. SAI Computing Conference 2016. London, UK 13 - 15 Jul 2016 IEEE. https://doi.org/10.1109/sai.2016.7555958
A new approach to image enhancement for the visually impaired
Gao, X. and Loomes, M. 2016. A new approach to image enhancement for the visually impaired. IS&T International Symposium on Electronic Imaging 2016 - Color Imaging XXI: Displaying, Processing, Hardcopy, and Applications. San Francisco, CA, USA 14 - 18 Feb 2016 Society for Imaging Science and Technology. pp. 1-7 https://doi.org/10.2352/ISSN.2470-1173.2016.20.COLOR-325
Advancing ambient assisted living with caution
Huyck, C., Augusto, J., Gao, X. and Botia, J. 2015. Advancing ambient assisted living with caution. in: Helfert, M., Holzinger, A., Ziefle, M., Fred, A., O'Donoghue, J. and Röcker, C. (ed.) Information and Communication Technologies for Ageing Well and e-Health: First International Conference, ICT4AgeingWell 2015, Lisbon, Portugal, May 20-22, 2015. Revised Selected Papers Springer.
Evaluation of colour appearances displaying on smartphones
Gao, X., Khodamoradi, E., Guo, L., Yang, X., Tang, S., Guo, W. and Wang, Y. 2015. Evaluation of colour appearances displaying on smartphones. Yaguchi, H., Okajima, K., Ishida, T., Araki, K., Doi, M. and Manabe, Y. (ed.) AIC 2015, Color and Image, Midterm meeting of the International Colour Association (AIC). Tokyo, Japan 19 - 22 May 2015 The Color Science Association of Japan. pp. 539-544
The application of KAZE features to the classification echocardiogram videos
Li, W., Qian, Y., Loomes, M. and Gao, X. 2015. The application of KAZE features to the classification echocardiogram videos. First International Workshop Multimodal Retrieval in the Medical Domain (MRMD 2015). Vienna, Austria 29 Mar 2015 Springer. pp. 61-72 https://doi.org/10.1007/978-3-319-24471-6_6
Modelling of chromatic contrast for retrieval of wallpaper images
Gao, X., Wang, Y., Qian, Y. and Gao, A. 2015. Modelling of chromatic contrast for retrieval of wallpaper images. Color Research and Application. 40 (4), pp. 361-373. https://doi.org/10.1002/col.21897
Feature-wise representation for both still and motion 3D medical images
Gao, X. 2014. Feature-wise representation for both still and motion 3D medical images. 2014 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI). San Diego, USA 06 - 09 Apr 2014 IEEE. pp. 1-4 https://doi.org/10.1109/SSIAI.2014.6806014
Cardiac motion reconstruction using LKT algorithm from 2D and 3D echocardiography
Gao, A., Li, W., Lin, C., Loomes, M. and Gao, X. 2013. Cardiac motion reconstruction using LKT algorithm from 2D and 3D echocardiography. in: IPCV'13 - The 2013 International Conference on Image Processing, Computer Vision, and Pattern Recognition CSRES Press.
Content-based petrieval of 3D medical images
Qian, Y., Gao, X., Loomes, M., Comley, R., Barn, B., Hui, R. and Tian, Z. 2011. Content-based petrieval of 3D medical images. in: Gemert-Pijnen, L., Ossebaard, H. and Hämäläinen, P. (ed.) eTELEMED 2011, The Third International Conference on eHealth, Telemedicine, and Social Medicine IARIA. pp. 7-12
The synergy of 3D SIFT and sparse codes for classification of viewpoints from echocardiogram videos
Qian, Y., Wang, L., Wang, C. and Gao, X. 2013. The synergy of 3D SIFT and sparse codes for classification of viewpoints from echocardiogram videos. Greenspan, H., Müller, H. and Syeda-Mahmood, T. (ed.) 3rd MICCAI International Workshop on Medical Content-Based Retrieval for Clinical Decision Support. Nice, France 01 - 01 Oct 2012 Berlin, Heidelberg Springer. https://doi.org/10.1007/978-3-642-36678-9_7
3D CBIR with sparse coding for image-guided neurosurgery
Qian, Y., Hui, R. and Gao, X. 2013. 3D CBIR with sparse coding for image-guided neurosurgery. Signal Processing. 93 (6), pp. 1673-1683. https://doi.org/10.1016/j.sigpro.2012.10.020
Retrieval of 3D medical images via their texture features
Gao, X., Qian, Y., Loomes, M., Barn, B., Comley, R., Chapman, A., Rix, J., Hui, R. and Tian, Z. 2012. Retrieval of 3D medical images via their texture features. International Journal on Advances in Software. 4 (3&4), pp. 499-509.
Bridging the abridged – the diffusion of Telemedicine in Europe and China
Gao, X., Loomes, M. and Comley, R. 2012. Bridging the abridged – the diffusion of Telemedicine in Europe and China. in: Rodrigues, J., Díez, I. and Abajo, B. (ed.) Telemedicine and e-health services, policies, and applications: avancements and developments USA IGI Global. pp. 451-495
The state of the art of medical imaging technology: from creation to archive and back.
Gao, X., Qian, Y. and Hui, R. 2011. The state of the art of medical imaging technology: from creation to archive and back. The Open Medical Informatics Journal. 5 (1-M8), pp. 73-85. https://doi.org/10.2174/1874431101105010073
The anatomy of teleneurosurgery in China
Gao, X. 2011. The anatomy of teleneurosurgery in China. International Journal of Telemedicine and Applications. 2011. https://doi.org/10.1155/2011/353405
Texture-based 3d image retrieval for medical applications
Gao, X., Qian, Y., Hui, R., Loomes, M., Comley, R., Barn, B., Chapman, A. and Rix, J. 2010. Texture-based 3d image retrieval for medical applications. Macedo, M. (ed.) IADIS International Conference e-Health 2010. Freiburg, Germany 29 - 31 Jul 2010 IADIS. pp. 101-108
Application of mesh morphing techniques in modelling 3D objects
Gao, X. and Hassan, M. 2010. Application of mesh morphing techniques in modelling 3D objects. Annual International Conference on Computer Games Multimedia and Allied Technology. Singapore 06 - 07 Apr 2010 Global Science and Technology Forum. https://doi.org/10.5176/978-981-08-5480-5_048
Road sign recognition by one fixation of space-variant sensor.
Gao, X., Shaposhnikov, D., Podladchikova, L., Golovan, A., Shevtsova, N. and Hong, K. 2002. Road sign recognition by one fixation of space-variant sensor. in: Gorodnich, D. and Zhang, H. (ed.) Vision Interface ’2002: proceedings Quebec Canadian Image Processing and Pattern Recognition Society.
Invariant recognition of traffic signs
Gao, X., Shaposhnikov, D., Podladchikova, L., Shevtsova, N. and Golovan, A. 2002. Invariant recognition of traffic signs.
Application of the behavioural model of vision for invariant recognition of facial and traffic sign images.
Gao, X., Shaposhnikov, D., Podladchikova, L., Golovan, A., Shevtsova, N., Gusakova, V. and Gizatdinova, Y. 2003. Application of the behavioural model of vision for invariant recognition of facial and traffic sign images. in: Gulaev, Y. and Galushkin, A. (ed.) Neurocomputers and their application. [In Russian] Moscow Radiotechnics.
Image classification based on the informative regions properties.
Gao, X., Podladchikova, L. and Shaposhnikov, D. 2003. Image classification based on the informative regions properties. in: Proceedings of PRIA-6-2002 : 6th International conference on pattern recognition and image analysis: new information technologies. MAIK Nauka/Interperiodica. pp. 439-441
Image retrieval through perceptual shape modelling.
Gao, X., Ren, M., Riley, K., Eakins, J. and Briggs, P. 2001. Image retrieval through perceptual shape modelling. London Council for Museums, Archives and Libraries.
Telemedicine in Europe.
Gao, X. 2006. Telemedicine in Europe. ChinaPacs. Beijing 14 - 16 Apr 2006
A new approach to traffic sign recognition
Gao, X., Podladchikova, L., Shaposhnikov, D., Hong, K., Batty, S., Golovan, A., Gusakova, V. and Shevtsova, N. 2002. A new approach to traffic sign recognition. in: Arabnia, H. and Mun, Y. (ed.) Proceedings of the international conference on imaging science, systems, and technology: CISST'02. Athens CSREA Press.
Road sign recognition by means of the behavioural model of vision.
Gao, X., Golovan, A., Hong, K., Podladchikova, L. and Shevtsova, N. 2002. Road sign recognition by means of the behavioural model of vision. in: Proceedings of the third all-Russian conference on neuroinformatics [In Russian]. Moscow. pp. 63-69
Colour reproduction for tele-imaging systems
Gao, X. and He, P. 2006. Colour reproduction for tele-imaging systems. Computerized medical imaging and graphics. 30 (6-7), pp. 79-84.
Measurement of vessel diameters on retinal for cardiovascular studies.
Gao, X., Bharath, A., Stanton, A., Hughes, A., Chapman, N. and Thom, S. 2001. Measurement of vessel diameters on retinal for cardiovascular studies. in: Claridge, E., Bamber, J. and Marlow, K. (ed.) Medical image understanding and analysis 2001. Medical Imaging Understanding and Analysis.
Extraction of sagittal symmetry planes from PET images.
Gao, X., Batty, S., Clark, J., Fryer, T., Blandford, A. and International Association of Science and Technology for Development. 2001. Extraction of sagittal symmetry planes from PET images. in: Hamza, M. (ed.) Visualization, imaging and image processing: proceedings of the IASTED international conference. Calgary IASTED. pp. 428-433
The state of art of medical displays.
Gao, X. 2006. The state of art of medical displays. EuroPacs 2006. Trondheim, Norway 14 - 17 Jun 2006
Towards archiving wallpaper images
Gao, X., Qian, Y., Tully, T. and Hendon, Z. 2004. Towards archiving wallpaper images. in: Hamza, M. (ed.) Proceedings of the seventh IASTED international conference on computer graphics and imaging. Anaheim Acta Press. pp. 305-309
High-precision detection of facial landmarks to estimate head motions based on vision models
Gao, X., Anishenko, S., Shaposhnikov, D., Podladchikova, L., Batty, S. and Clark, J. 2007. High-precision detection of facial landmarks to estimate head motions based on vision models. Journal of computer sciences. 3 (7), pp. 528-532.
Content-based retrieval of PET images via localised anatomical texture measurements and mean activity levels
Gao, X., Batty, S., Clark, J. and Fryer, T. 2006. Content-based retrieval of PET images via localised anatomical texture measurements and mean activity levels. Computerized medical imaging and graphics. 30 (6-7), pp. 70-74.
Extraction of physiological information from 3D PET brain images.
Gao, X., Batty, S., Fryer, T., Clark, J., Turkheimer, F. and International Association of Science and Technology for Development. 2003. Extraction of physiological information from 3D PET brain images. in: Villanueva, J. (ed.) Visualization imaging and image processing. Acta Press. pp. 401-405
Extraction of features from 3D PET images.
Gao, X., Batty, S., Clark, J. and Fryer, T. 2002. Extraction of features from 3D PET images. in: Houston, A. and Zwiggelar, R. (ed.) Medical image understanding and analysis 2002. BMVA.
Towards archiving 3D PET brain images based on their physiological and visual content.
Gao, X., Batty, S., Clark, J., Fryer, T. and Turkheimer, F. 2002. Towards archiving 3D PET brain images based on their physiological and visual content. International conference on diagnostic imaging and analysis. Shanghai, China 18 - 20 Aug 2002
A new approach to estimation of non-isotropic scale factors for correction of MR distortion
Gao, X., Hui, R., White, A.S. and Tian, Z. 2009. A new approach to estimation of non-isotropic scale factors for correction of MR distortion. International Journal of Computer Assisted Radiology and Surgery. 4 (s1), pp. s349-s350.
Classification of images on the basis of the properties of informative regions.
Gao, X., Shaposhnikov, D. and Podladchikova, L. 2003. Classification of images on the basis of the properties of informative regions. Pattern Recognition and Image Analysis. 13 (2), pp. 349-352.
A fast approach to segmentation of PET brain images for extraction of features
Gao, X. and Clark, J. 2008. A fast approach to segmentation of PET brain images for extraction of features. Gao, X., Loomes, M., Comley, R., Muller, H. and Luo, S. (ed.) International Conference on Medical Imaging and Informatics (MIMI 2007). Beijing, China 14 - 16 Aug 2007 Berlin, Heidelberg Springer. https://doi.org/10.1007/978-3-540-79490-5_25
Prototype system for semantic retrieval of neurological PET images
Batty, S., Clark, J., Fryer, T. and Gao, X. 2008. Prototype system for semantic retrieval of neurological PET images. Gao, X., Muller, H., Loomes, M., Comley, R. and Luo, S. (ed.) International Conference on Medical Imaging and Informatics (MIMI 2007). Beijing, China 14 - 16 Aug 2007 Berlin, Heidelberg Springer. https://doi.org/10.1007/978-3-540-79490-5_23
Colour vision model-based approach for segmentation of traffic signs
Gao, X., Hong, K., Passmore, P., Podladchikova, L. and Shaposhnikov, D. 2008. Colour vision model-based approach for segmentation of traffic signs. EURASIP Journal on Image and Video Processing. 2008. https://doi.org/10.1155/2008/386705
Toward a robust system to monitor the head motions during PET based on facial landmarks detection: a new approach
Anishenko, S., Osimov, V., Shaposhnikov, D., Podladchikova, L., Comley, R. and Gao, X. 2008. Toward a robust system to monitor the head motions during PET based on facial landmarks detection: a new approach. Puuronen, S., Pechenizkiy, M., Tsymbal, A. and Lee, D. (ed.) 21st IEEE International Symposium on Computer-Based Medical Systems. Jyvaskyla, Finland 17 - 19 Jun 2008 IEEE Computer Society. pp. 50-52 https://doi.org/10.1109/CBMS.2008.19
Recognition of traffic signs based on their colour and shape features extracted using human vision models
Gao, X., Podladchikova, L., Shaposhnikov, D., Hong, K. and Shevtsova, N. 2006. Recognition of traffic signs based on their colour and shape features extracted using human vision models. Journal of Visual Communication and Image Representation. 17 (4), pp. 675-685. https://doi.org/10.1016/j.jvcir.2005.10.003
Colour management in telemedicine
Gao, X. 2004. Colour management in telemedicine. Hamza, M. (ed.) 7th IASTED International Conference on Computer Graphics and Imaging. Kauai, Hawaii, United States 16 - 18 Aug 2004 Anaheim, CA Acta Press. pp. 361-364
Application of vision models to traffic sign recognition
Gao, X., Shaposhnikov, D. and Podladchikova, L. 2004. Application of vision models to traffic sign recognition. Kaynak, O., Alpaydin, E., Oja, E. and Xu, L. (ed.) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. Istanbul, Turkey 26 - 29 Sep 2003 Berlin, Heidelberg Springer. https://doi.org/10.1007/3-540-44989-2_131
Towards content-based retrieval for wallpaper images
Qian, Y., Tully, C., Hendon, Z. and Gao, X. 2003. Towards content-based retrieval for wallpaper images. 7th IASTED International Conference on Computer Graphics and Imaging. Kauai, Hawaii, United States 16 - 18 Aug 2004 pp. 305-309
Vision models based identification of traffic signs
Gao, X., Podladchikova, L., Shaposhnikov, D., Shevtsova, N., Hong, K., Batty, S., Golovan, A. and Gusakova, V. 2002. Vision models based identification of traffic signs. 1st European Conference on Colour Graphics, Imaging, and Vision. University of Poitiers, France 02 - 05 Apr 2002 Society for Imaging Science and Technology.
Content based retrieval of lesioned brain images
Batty, S., Blandford, A., Clark, J., Fryer, T. and Gao, X. 2002. Content based retrieval of lesioned brain images. Siegel, E. and Huang, H. (ed.) SPIE Medical Imaging 2002. San Diego, California, United States 23 - 28 Feb 2002 Bellingham Society of Photo-optical Instrumentation Engineers (SPIE). https://doi.org/10.1117/12.466997
A method of vessel tracking for vessel diameter measurement on retinal images
Gao, X., Bharath, A., Stanton, A., Hughes, A., Chapman, N. and Thom, S. 2001. A method of vessel tracking for vessel diameter measurement on retinal images. 2001 International Conference on Image Processing. Thessaloniki, Greece 07 - 10 Oct 2001 IEEE.
Computer algorithms for the automated measurement of retinal arteriolar diameters
Chapman, N., Witt, N., Gao, X., Bharath, A., Stanton, A., Thom, S. and Hughes, A. 2001. Computer algorithms for the automated measurement of retinal arteriolar diameters. British Journal of Ophthalmology. 85 (1), pp. 74-79. https://doi.org/10.1136/bjo.85.1.74
Quantification and characterization of arteries in retinal images
Gao, X., Bharath, A., Stanton, A., Hughes, A., Chapman, N. and Thom, S. 2000. Quantification and characterization of arteries in retinal images. Computer Methods and Programs in Biomedicine. 63 (2), pp. 133-146. https://doi.org/10.1016/S0169-2607(00)00082-1