Call For Papers

We invite original research papers that present innovative ideas, methods, and applications in the area of phenotyping using computer vision. HTVP’23 invites previously unpublished and original work. 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. 

The submissions will undergo single blind review, where the referees remain anonymous for the authors throughout the process. Papers should not exceed 6 pages and use single column format(including references). 

Topics of Interest

● Detection, multi-scale instance and semantic segmentation.

● 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. 

Paper submission instructions

• We invite papers upto 6 pages including references (single column).

• The review process is single blind i.e. the authors can include their names and affiliation in the submission at the time of review.  

• The template is available at Springer Conference Proceedings page

• Paper can be submitted at CMT (https://cmt3.research.microsoft.com/htvp2023)