SMART-BARN: Scalable multimodal arena for real-time tracking behavior of animals in large numbers.

The SMART-BARN (scalable multimodal arena for real-time tracking behavior of animals in large numbers) achieves fast, robust acquisition of movement, behavior, communication, and interactions of animals in groups, within a large (14.7 meters by 6.6 meters by 3.

Head-tracking of freely-behaving pigeons in a motion-capture system reveals the selective use of visual field regions.

Using a motion-capture system and custom head-calibration methods, we reconstructed the head-centric view of freely behaving pigeons and examined how they orient their head when presented with various types of attention-getting objects at various relative locations. Pigeons predominantly employed their retinal specializations to view a visual target, namely their foveas projecting laterally (at an azimuth of ± 75°) into the horizon, and their visually-sensitive "red areas" projecting broadly into the lower-frontal visual field. Pigeons used their foveas to view any distant object while they used their red areas to view a nearby object on the ground (< 50 cm).

Animals in Virtual Environments.

The core idea in an XR (VR/MR/AR) application is to digitally stimulate one or more sensory systems (e.g. visual, auditory, olfactory) of the human user in an interactive way to achieve an immersive experience.

DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning.

Quantitative behavioral measurements are important for answering questions across scientific disciplines-from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animal's body parts directly from images or videos. However, currently available animal pose estimation methods have limitations in speed and robustness.

On-site semi-automatic calibration and registration of a projector-camera system using arbitrary objects with known geometry.

In the Shader Lamps concept, a projector-camera system augments physical objects with projected virtual textures, provided that a precise intrinsic and extrinsic calibration of the system is available. Calibrating such systems has been an elaborate and lengthy task in the past and required a special calibration apparatus. Self-calibration methods in turn are able to estimate calibration parameters automatically with no effort.

Video-guided calibration of an augmented reality mobile C-arm.

Purpose: The augmented reality (AR) fluoroscope augments an X-ray image by video and provides the surgeon with a real-time in situ overlay of the anatomy. The overlay alignment is crucial for diagnostic and intra-operative guidance, so precise calibration of the AR fluoroscope is required. The first and most complex step of the calibration procedure is the determination of the X-ray source position.