Tatsuo Kozakaya — accepted AI-conference papers

2019

Deep Depth From Aberration Map

Masako Kashiwagi, Nao Mishima, Tatsuo Kozakaya, Shinsaku Hiura

ICCV 2019poster

Passive and convenient depth estimation from single-shot image is still an open problem. Existing depth from defocus methods require multiple input images or special hardware customization. Recent deep monocular depth estimation is also limited to an image with sufficient contextual information. In…

Cited by 21 PDF ↗Scholar ↗

2015

COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation

Viet-Quoc Pham, Tatsuo Kozakaya, Osamu Yamaguchi, Ryuzo Okada

ICCV 2015poster

This paper presents a patch-based approach for crowd density estimation in public scenes. We formulate the problem of estimating density in a structured learning framework applied to random decision forests. Our approach learns the mapping between patch features and relative locations of all objects…

Cited by 440 PDF ↗Scholar ↗