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Dr Hossein Malekmohamadi

Job: VC2020 Lecturer in Games Programming

Faculty: Technology

School/department: School of Computer Science and Informatics

Address: De Montfort University, The Gateway, Leicester, LE1 9BH, UK

T: +44 (0)116 250 6182



Research interests/expertise

  • Computer Vision
  • Computer Graphics
  • Deep Learning
  • Multimedia Signal Processing
  • Smart systems
  • Quality of Experience (QoE)

Areas of teaching

  • Computer graphics
  • Computer vision



Courses taught

  • Introduction to shader programming
  • Shader programming
  • Introduction to computer vision
  • Final year project coordinator

Membership of professional associations and societies

Fellow of the Higher Education Academy

Recent research outputs

  • Vehicle detection and classification in difficult environmental conditions using deep learning. In Proceedings of SAI Intelligent Systems Conference (pp. 686-696). Springer, Cham.
  • Towards Semantic Segmentation Using Ratio Unpooling. In Proceedings of SAI Intelligent Systems Conference (pp. 111-123). Springer, Cham.
  • Surface Defect Detection Using YOLO Network. In Proceedings of SAI Intelligent Systems Conference (pp. 505-515). Springer, Cham.
  • Human Activity Identification in Smart Daily Environments. In Smart Assisted Living (pp. 91-118). Springer, Cham.
  • Improving a 3-D Convolutional Neural Network Model Reinvented from VGG16 with Batch Normalization. In 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 45-50). IEEE.
  • Deep Learning Based Photometric Stereo from Many Images and Under Unknown Illumination. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 785-790). IEEE.
  • Low-Cost Automatic Ambient Assisted Living System. In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (pp. 693-697). IEEE.
  • Automatic subjective quality estimation of 3D stereoscopic videos: NR-RR approach. In 2017 3DTV Conference: The True Vision-Capture, Transmission and Display of 3D Video (3DTV-CON) (pp. 1-4). IEEE.
  • Paper type classification based on a new 3D surface texture measure. Electronics letters50(8), pp.596-598.
  • A new reduced reference metric for color plus depth 3D video. Journal of Visual Communication and Image Representation, 25(3), pp.534-541.

Published patents

Paper classification based on three-dimensional characteristics. U.S. Patent 9,977,999.

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