2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IP-3.6
Paper Title PROVABLE TRANSLATIONAL ROBUSTNESS FOR OBJECT DETECTION WITH CONVOLUTIONAL NEURAL NETWORKS
Authors Axel Vierling, Charu James, Technische Universität Kaiserslautern, Germany; Nikoletta Katsaouni, Goethe University Frankfurt am Main, Germany; Karsten Berns, Technische Universität Kaiserslautern, Germany
SessionMLR-APPL-IP-3: Machine learning for image processing 3
LocationArea F
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In the following work object detection approaches with Convolutional Neural Networks (CNNs), which have provable characteristics regarding translational robustness, are proposed, evaluated in an application scenario, and compared to state of the art approaches. The provable characteristics are achieved by transferring theoretical results from wavelet theory and scattering networks to common CNNs used for classification. Therefore first a CNN is modeled as a scattering network. Needed parameters are estimated with data relevant for application scenarios. With the obtained information first the best feature extractor for a given application scenario is chosen. Afterward, the theory is extended to cover object detection networks. The proposed approaches are trained on simulated and real datasets and evaluated on real datasets.