Discovering the Invisible from Visual Data

This thesis attempts at discovering features from visual data which cannot be obtained or observed directly using standard computer vision algorithms. We refer to these features as Invisible features. In particular, we focus on two types of invisible features. First, the physics of a scene which governs the visual cues for the objects in the scene. In this part of the thesis, we teach a machine to discover the laws of physics from video streams. We assume no prior knowledge of physics, beyond a temporal stream of bounding boxes. The problem is very difficult because a machine must learn not only a governing equation (e.g. projectile motion) but also the existence of governing parameters (e.g. velocities). Second, texture information that is invisible in standard RGB images but can be seen in other imaging modalities such as polarization. Texture in some scenes is challenging for intensity images to capture. Imagine a black car in shadow or an oil slick on road. We exploit this adversity of contrast in the intensity domain to adapt a new representation for polarization cues, proposing a new degree of linear polarization (DOLP) that has favorable statistical properties. The new representation of DOLP we obtain is not only more robust in the context of noise, but can also preserve the scientific information in the original DOLP that allows geometry and photoelastic effects to be discerned. We hope this work lays a foundation for the future of a Polarized ISP process, particularly for sensor fusion applications

Discovering the Invisible from Visual Data

This thesis attempts at discovering features from visual data which cannot be obtained or observed directly using standard computer vision algorithms. We refer to these features as Invisible features. In particular, we focus on two types of invisible features. First, the physics of a scene which governs the visual cues for the objects in the scene. In this part of the thesis, we teach a machine to discover the laws of physics from video streams. We assume no prior knowledge of physics, beyond a temporal stream of bounding boxes. The problem is very difficult because a machine must learn not only a governing equation (e.g. projectile motion) but also the existence of governing parameters (e.g. velocities). Second, texture information that is invisible in standard RGB images but can be seen in other imaging modalities such as polarization. Texture in some scenes is challenging for intensity images to capture. Imagine a black car in shadow or an oil slick on road. We exploit this adversity of contrast in the intensity domain to adapt a new representation for polarization cues, proposing a new degree of linear polarization (DOLP) that has favorable statistical properties. The new representation of DOLP we obtain is not only more robust in the context of noise, but can also preserve the scientific information in the original DOLP that allows geometry and photoelastic effects to be discerned. We hope this work lays a foundation for the future of a Polarized ISP process, particularly for sensor fusion applications

tardis-sn/tardis: TARDIS v2023.11.05

<p>This release has been created automatically by the TARDIS continuous delivery pipeline.</p> <p>A complete list of changes for this release is available at <a href="https://github.com/tardis-sn/tardis/blob/master/CHANGELOG.md">CHANGELOG.md</a>.</p&gt

tardis-sn/tardis: TARDIS v2023.10.20

<p>This release has been created automatically by the TARDIS continuous delivery pipeline.</p> <p>A complete list of changes for this release is available at <a href="https://github.com/tardis-sn/tardis/blob/master/CHANGELOG.md">CHANGELOG.md</a>.</p&gt