Visual Field Reconstruction Using fMRI-Based Techniques

fMRI of peripheral visual field representation.

OBJECTIVE Despite mapping tools for central visual field, delineation of peripheral visual field representations in the human cortex has remained a challenge. Access to large visual field and differentiation of retinotopic areas with robust mapping procedures and automated analysis are beneficial in basic research and could accelerate development of clinical applications. METHODS We construct...

fMRI-Based Image Reconstruction Using the Elastic Net

Using Dense 3D Reconstruction for Visual Odometry Based on Structure from Motion Techniques

Aim of intense research in the field computational vision, dense 3D reconstruction achieves an important landmark with first methods running in real time with millimetric precision, using RGBD cameras and GPUs. However, these methods are not suitable for low computational resources. The goal of this work is to show a method of visual odometry using regular cameras, without using a GPU. The prop...

Sparse models for visual image reconstruction from fMRI activity.

Statistical model is essential for constraint-free visual image reconstruction, as it may overfit training data and have poor generalization. In this study, we investigate the sparsity of the distributed patterns of visual representation and introduce a suitable sparse model for the visual image reconstruction experiment. We use elastic net regularization to model the sparsity of the distribute...

fMRI Visual Image Reconstruction Using Sparse Logistic Regression with a Tunable Regularization Parameter

fMRI has been a popular way for encoding and decoding human visual cortex activity. A previous research reconstructed binary image using a sparse logistic regression (SLR) with fMRI activity patterns as its input. In this article, based on SLR, we propose a new sparse logistic regression with a tunable regularization parameter (SLR-T), which includes the SLR and maximum likelihood regression (M...