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2016 | OriginalPaper | Chapter

Gaussian Process Density Counting from Weak Supervision

Authors : Matthias von Borstel, Melih Kandemir, Philip Schmidt, Madhavi K. Rao, Kumar Rajamani, Fred A. Hamprecht

Published in: Computer Vision – ECCV 2016

Publisher: Springer International Publishing

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Abstract

As a novel learning setup, we introduce learning to count objects within an image from only region-level count information. This level of supervision is weaker than earlier approaches that require segmenting, drawing bounding boxes, or putting dots on centroids of all objects within training images. We devise a weakly supervised kernel learner that achieves higher count accuracies than previous counting models. We achieve this by placing a Gaussian process prior on a latent function the square of which is the count density. We impose non-negativeness and smooth the GP response as an intermediary step in model inference. We illustrate the effectiveness of our model on two benchmark applications: (i) synthetic cell and (ii) pedestrian counting, and one novel application: (iii) erythrocyte counting on blood samples of malaria patients.

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previous chapter Efficient Multi-view Surface Refinement with Adaptive Resolution Control

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Our definition of MIR differs from that put forward in [18], where a single instance in a bag determines the entire count for the bag.

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Title: Gaussian Process Density Counting from Weak Supervision
Authors: Matthias von Borstel
Melih Kandemir
Philip Schmidt
Madhavi K. Rao
Kumar Rajamani
Fred A. Hamprecht
Publisher: Springer International Publishing
Book: Computer Vision – ECCV 2016
Print ISBN: 978-3-319-46447-3

Electronic ISBN: 978-3-319-46448-0

Copyright Year: 2016
DOI: https://doi.org/10.1007/978-3-319-46448-0_22

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