2018 Harvest Imaging Forum Agenda

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Albert Theuwissen announces agenda of Harvest Imaging Forum, to be held on December 6-7 in Delft, the Netherlands.

Day 1 of the forum is devoted to "Efficient embedded deep learning for vision applications," presented by Marian VERHELST (KU Leuven, Belgium):
  1. Introduction into deep learning
    From neural networks (NN) to deep NN
    Benefits & applications
    Training and inference with deep NN
    Types of deep NN
    Sparse connectivity
    Residual networks
    Separable models
    Key enablers & challenges
  2. Computer architectures for deep NN inference
    Benefits and limitations of CPU and GPUs
    Exploiting NN structure in custom processors
    Architecture level exploitation: spatial reuse in efficient datapaths
    Architecture level exploitation: temporal reuse in efficient memory hierarchies
    Circuit level exploitation: near/in memory compute
    Exploiting NN precision in custom processors
    Architecture level exploitation: reduced and variable precision processors
    Circuit level exploitation: mixed signal neural network processors
    Exploiting NN sparsity:
    Architecture level exploitation: computational and memory gating
    Architecture level exploitation: I/O compression
  3. HW and SW optimization for efficient inference
    Co-optimizing NN topology and precision with hardware architectures
    Hardware modeling
    Hardware-aware network optimization
    Network-aware hardware optimization
  4. Trends and outlook
    Dynamic application-pipelines
    Dynamic SoCs
    Beyond deep learning, explainable AI
    Outlook
Day 2 is devoted to "Image and Data Fusion," presented by Wilfried PHILIPS (imec and Ghent University, Belgium):
  1. Data fusion: principles and theory
    Bayesian estimation
    Priors and likelihood
    Information content, redundancy, correlation
    Application to image processing: recursive maximum likelihood tracking, pixel fusion
  2. Pixel level fusion
    Sampling grids and spatio-temporal aliasing
    Multi-modal sensors, interpolation
    Temporal fusion and superresolution
    Multi-focal fusion
  3. Multi-camera image fusion
    Occlusion and inpainting
    Uni and multimodal Inter-camera pixel fusion
    Fusion of heterogeneous sources: camera, lidar, radar
    Applications: time of flight, hyperspectral, hdr, multiview imaging
    Fusion of heterogeneous sources: radar, video, lidar
  4. Geometric fusion
    Multi-view geometry
    Fusion of point clouds
    Image stitching
    Simultaneous localization and mapping
    Applications: remote sensing from drones and vehicles
  5. Inference fusion in camera networks
    Multi-camera calibration
    Occlusion reasoning for multiple cameras with an overlapping viewpoint
    Multi-camera tracking
    Cooperative fusion and distributed processing

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