Search Results for author: Jaeho Shin

Found 6 papers, 1 papers with code

Unbiased Estimator for Distorted Conics in Camera Calibration

1 code implementation7 Mar 2024 Chaehyeon Song, Jaeho Shin, Myung-Hwan Jeon, Jongwoo Lim, Ayoung Kim

Although conics are more informative features than points, the loss of the conic property under distortion has critically limited the utility of conic features in camera calibration.

Camera Calibration

TRansPose: Large-Scale Multispectral Dataset for Transparent Object

no code implementations11 Jul 2023 Jeongyun Kim, Myung-Hwan Jeon, Sangwoo Jung, Wooseong Yang, Minwoo Jung, Jaeho Shin, Ayoung Kim

Transparent objects are encountered frequently in our daily lives, yet recognizing them poses challenges for conventional vision sensors due to their unique material properties, not being well perceived from RGB or depth cameras.

Object Transparent objects

Realizable Sticky Matroid Conjecture

no code implementations30 Nov 2020 Jaeho Shin

We give a criterion for modular extension of rank-4 hypermodular matroids, and prove a weakening of Kantor's conjecture for rank-4 realizable matroids.

Combinatorics Algebraic Geometry 05B35 (Primary) 52B40, 14N20 (Secondary)

Biconvex Polytopes and Tropical Linear Spaces

no code implementations26 Feb 2020 Jaeho Shin

A biconvex polytope is a classical and tropical convex hull of finitely many points.

Algebraic Geometry Combinatorics Metric Geometry 14T15 (Primary) 05B35, 05C30, 52B40 (Secondary)

Incremental Knowledge Base Construction Using DeepDive

no code implementations3 Feb 2015 Jaeho Shin, Sen Wu, Feiran Wang, Christopher De Sa, Ce Zhang, Christopher Ré

Populating a database with unstructured information is a long-standing problem in industry and research that encompasses problems of extraction, cleaning, and integration.

Feature Engineering for Knowledge Base Construction

no code implementations24 Jul 2014 Christopher Ré, Amir Abbas Sadeghian, Zifei Shan, Jaeho Shin, Feiran Wang, Sen Wu, Ce Zhang

Our approach to KBC is based on joint probabilistic inference and learning, but we do not see inference as either a panacea or a magic bullet: inference is a tool that allows us to be systematic in how we construct, debug, and improve the quality of such systems.

Feature Engineering

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