Browsing College of Engineering and Informatics by Author "Javidnia, Hossein"

Now showing items 1-8 of 8

    • The application of deep learning on depth from multi-array camera 

      Javidnia, Hossein; Bazrafkan, Shabab; Corcoran, Peter (Institute of Electrical and Electronics Engineers, 2018-01-02)
      Consumer-level multi-array cameras are a key enabling technology for next generation smartphones imaging systems. The present paper aims to analyze the accuracy of the depth estimation while using different camera combinations ...

    • Application of preconditioned alternating direction method of multipliers in depth from focal stack 

      Javidnia, Hossein; Corcoran, Peter (Society of Photo-optical Instrumentation Engineers (SPIE), 2018-04-06)
      Postcapture refocusing effect in smartphone cameras is achievable using focal stacks. However, the accuracy of this effect is totally dependent on the combination of the depth layers in the stack. The accuracy of the ...

    • Generative Augmented Dataset and Annotation Frameworks for Artificial Intelligence (GADAFAI) 

      Corcoran, Peter; Javidnia, Hossein; Lemley, Joseph E.; Varkarakis, Viktor (Institute of Electrical and Electronics Engineers (IEEE), 2020-08-31)
      Recent Advances in Artificial Intelligence (AI), particularly in the field of compute vision, have been driven by the availability of large public datasets. However, as AI begins to move into embedded devices there will ...

    • High-accuracy facial depth models derived from 3D synthetic data 

      Khan, Faisal; Basak, Shubhajit; Javidnia, Hossein; Schukat, Michael; Corcoran, Peter (Institute of Electrical and Electronics Engineers (IEEE), 2020-08-31)
      In this paper, we explore how synthetically generated 3D face models can be used to construct a high-accuracy ground truth for depth. This allows us to train the Convolutional Neural Networks (CNN) to solve facial depth ...

    • Latent space mapping for generation of object elements with corresponding data annotation 

      Bazrafkan, Shabab; Javidnia, Hossein; Corcoran, Peter (Elsevier, 2018-10-25)
      Deep neural generative models such as Variational Auto-Encoders (VAE) and Generative Adversarial Networks (GAN) give promising results in estimating the data distribution across a range of machine learning fields of ...

    • Real-time automotive street-scene mapping through fusion of improved stereo depth and fast feature detection algorithms 

      Javidnia, Hossein; Corcoran, Peter (Institute of Electrical and Electronics Engineers, 2017-01-08)
      The real-time tracking of street scenes as a vehicle is driving is a key enabling technology for autonomous vehicles. In this work we provide the basis for such a system through combining an improved advanced random walk ...

    • Semiparallel deep neural network hybrid architecture: first application on depth from monocular camera 

      Bazrafkan, Shabab; Javidnia, Hossein; Lemley, Joseph; Corcoran, Peter (Society of Photo-optical Instrumentation Engineers (SPIE), 2018-08-07)
      Deep neural networks have been applied to a wide range of problems in recent years. Convolutional neural network is applied to the problem of determining the depth from a single camera image (monocular depth). Eight different ...

    • Total variation-based dense depth from multicamera array 

      Javidnia, Hossein; Corcoran, Peter (Society of Photo-optical Instrumentation Engineers (SPIE), 2018-05-20)
      Multicamera arrays are increasingly employed in both consumer and industrial applications, and various passive techniques are documented to estimate depth from such camera arrays. Current depth estimation methods provide ...