activity
20142026
most citedAn RIP-based approach to quantization for compressed sensing

15 citations · 24 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Stability and Concentration in Nonlinear Inverse Problems with Block-Structured Parameters: Lipschitz Geometry, Identifiability, and an Application to Gaussian Splatting

Joe-Mei Feng, Hsin-Hsiung Kao

We develop an operator-theoretic framework for stability and statistical concentration in nonlinear inverse problems with block-structured parameters. Under a unified set of assump…

cs.CV2026

The Geometric Observability Index: Influence, Fisher Information, and Weak Observability in SE(3) Pose Estimation

Joe-Mei Feng, Hsin-Hsiung Kao, Sheng-Wei Yu

We introduce the Geometric Observability Index (GOI), a per-feature sensitivity measure for pose estimation on SE(3): the metric norm of the pose perturbation that a single measure…

cs.CV2022★ 1 cited

A Novel Image Descriptor with Aggregated Semantic Skeleton Representation for Long-term Visual Place Recognition

Nie Jiwei, Feng Joe-Mei, Xue Dingyu +4

In a Simultaneous Localization and Mapping (SLAM) system, a loop-closure can eliminate accumulated errors, which is accomplished by Visual Place Recognition (VPR), a task that retr…

cs.IT2017★ 8 cited

Quantized Compressed Sensing for Partial Random Circulant Matrices

Joe-Mei Feng, Felix Krahmer, Rayan Saab

We provide the first analysis of a non-trivial quantization scheme for compressed sensing measurements arising from structured measurements. Specifically, our analysis studies comp…

cs.IT2014★ 15 cited

An RIP-based approach to quantization for compressed sensing

Joe-Mei Feng, Felix Krahmer

In this paper, we provide a new approach to estimating the error of reconstruction from quantized compressed sensing measurements. Our method is based on the restricted isomet…