Global ETD Search

Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.

Results

Showing 1 to 8 of 8 for “"Object Understanding"”.

  1. Object understanding in generalized video segmentation

    Spatio-temporal object representation is a key component in video understanding and is important for tasks like generalized video segmentation. Current methods often fail to represent objects spatio-temporally, by failing to capture the motion and interactions of objects over time, leading to …

    uiuc Repository record for Object understanding in generalized video segmentation (opens in a new tab)

  2. Optimization Techniques for Trustworthy 3D Object Understanding

    Autonomous machines require reliable 3D object understanding to interpret and interact with their environment. In this thesis, we consider two tightly coupled 3D object understanding problems. Shape estimation seeks a consistent 3D model of an object given sensor data and some set of priors. Pose …

    mit Repository record for Optimization Techniques for Trustworthy 3D Object Understanding (opens in a new tab)

  3. VICTORIOUS : video indexing with combined tracking and object recognition for improved object understanding in scenes

    Automatic understanding of video content is a problem which grows in importance every day. Video understanding algorithms require accuracy, robustness, speed, and scalability. Accuracy generates user confidence in usage. Robustness enables greater autonomy and reduced human intervention. …

    mit Repository record for VICTORIOUS : video indexing with combined tracking and object recognition for improved object understanding in scenes (opens in a new tab)

  4. Inferring object states and articulation modes from egocentric videos

    We develop algorithms for understanding objects from the point of view of interacting with them. There are two key aspects to obtaining such an understanding. First, objects can occur in different states and we need features that are sensitive to such states. Second, different objects can be …

    uiuc Repository record for Inferring object states and articulation modes from egocentric videos (opens in a new tab)

  5. Mapping the time-course and content of visual predictions with a novel object-scene associative memory paradigm

    … of contextual facilitation effects in visual object processing. In all three experiments, participants studied novel object-scene pairs in a paired associate memory paradigm. At test, we presented the scene first, followed after a delay by the test object, which either matched or mismatched …

    uiuc Repository record for Mapping the time-course and content of visual predictions with a novel object-scene associative memory paradigm (opens in a new tab)

  6. Building rearticulable models for 3D articulated objects from multi-view RGB videos

    Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01

    uiuc Repository record for Building rearticulable models for 3D articulated objects from multi-view RGB videos (opens in a new tab)

  7. Toward visual understanding of everyday object

    … vision community has made impressive progress on object recognition using large scale data. However, for any visual system to interact with objects, it needs to understand much more than simply recognizing where the objects are. The goal of my research is to explore and solve object understanding

    mit Repository record for Toward visual understanding of everyday object (opens in a new tab)

  8. Generalizable Representations for Vision in Biological and Artificial Neural Networks

    … the persistent challenges of vision science is understanding the underlying representations that allow us to recognize objects and scene attributes across a diversity of environments. A central framework for identifying such representations is inverse graphics, which hypothesizes that the brain …

    mit Repository record for Generalizable Representations for Vision in Biological and Artificial Neural Networks (opens in a new tab)