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Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

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Related Experiment Video

Updated: Aug 10, 2025

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

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Automatic depth map retrieval from digital holograms using a deep learning approach.

Nabil Madali, Antonin Gilles, Patrick Gioia

|February 14, 2023

Summary

This summary is machine-generated.

This study introduces novel learning-based methods for extracting depth information from holograms, outperforming traditional techniques. These advanced approaches offer faster and more accurate holographic depth extraction.

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Last Updated: Aug 10, 2025

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Area of Science:

Computer Vision
Holography
Machine Learning

Background:

Information extraction from computer-generated holograms is an emerging research area.
Classical depth from focus (DFF) methods have limitations in holographic data processing.

Purpose of the Study:

To propose and evaluate two learning-based methods for extracting depth information from holograms.
To compare the performance of these new methods against classical DFF techniques.

Main Methods:

Development of two novel learning-based algorithms for holographic depth extraction.
Comparative analysis of proposed methods with existing DFF approaches.
Investigation into hologram characteristics influencing model training.

Main Results:

Demonstrated feasibility of extracting depth information from holograms with well-posed problem formulation.
Proposed learning-based methods show superior speed and accuracy compared to state-of-the-art DFF methods.
Hologram characteristics significantly impact model training effectiveness.

Conclusions:

Learning-based methods provide a viable and efficient solution for holographic depth extraction.
The proposed techniques represent a significant advancement over traditional DFF methods for holograms.
Further research into well-posed problem formulations can enhance holographic depth information extraction.