June 7th, 2023 
Daniela Lopez de Luise “>● Object tracking, optical flow and/or scene flow estimation.
● 3D modeling and segmentation.
● Denoising and Multi-modal image registration.
● Statistical shape analysis.
● Evaluation and benchmarking methodologies of automated image algorithms.
● Interactive Image Analysis.
● Learning in the face of little to no training data.
● Acquisition and analysis techniques.
The list is not comprehensive. We invite papers that present innovative ideas, methods, and applications in the area of phenotyping using computer vision. We encourage submissions that have interdisciplinary collaborations between machine learning/computer vision and problem domain experts. We especially encourage work where machine learning and in particular representation learning could meaningfully amplify existing efforts for the phenotyping area.
We invite papers upto 6 pages including references (single column).
Please see https://sites.google.com/view/htvp2023/home for more information.
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