Telecystoscopy can lower the barrier to access critical urologic diagnostics for patients around the world. A major challenge for robotic control of flexible cystoscopes and intuitive teleoperation is the pose estimation of the scope tip. We propose a novel real-time camera localization method using video recordings from a prior cystoscopy and 3D bladder reconstruction to estimate cystoscope pose within the bladder during follow-up telecystoscopy. We map prior video frames into a low-dimensional space as a dictionary so that a new image can be likewise mapped to efficiently retrieve its nearest neighbor among the dictionary images. The cystoscope pose is then estimated by the correspondence among the new image, its nearest dictionary image, and the prior model from 3D reconstruction. We demonstrate performance of our methods using bladder phantoms with varying fidelity and a servo-controlled cystoscope to simulate the use case of bladder surveillance through telecystoscopy. The servo-controlled cystoscope with 3 degrees of freedom (angulation, roll, and insertion axes) was developed for collecting cystoscope videos from bladder phantoms. Cystoscope videos were acquired in a 2.5D bladder phantom (bladder-shape cross-section plus height) with a panorama of a urothelium attached to the inner surface. Scans of the 2.5D phantom were performed in separatemore »
Using text to teach image retrieval
Image retrieval relies heavily on the quality of the data
modeling and the distance measurement in the feature
space. Building on the concept of image manifold, we first
propose to represent the feature space of images, learned
via neural networks, as a graph. Neighborhoods in the feature space are now defined by the geodesic distance between
images, represented as graph vertices or manifold samples.
When limited images are available, this manifold is sparsely
sampled, making the geodesic computation and the corresponding retrieval harder. To address this, we augment the
manifold samples with geometrically aligned text, thereby
using a plethora of sentences to teach us about images. In
addition to extensive results on standard datasets illustrating the power of text to help in image retrieval, a new public dataset based on CLEVR is introduced to quantify the
semantic similarity between visual data and text data. The
experimental results show that the joint embedding manifold is a robust representation, allowing it to be a better
basis to perform image retrieval given only an image and
a textual instruction on the desired modifications over the
image.
- Publication Date:
- NSF-PAR ID:
- 10347297
- Journal Name:
- CVPR 2021 Workshop
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1109/CVPRW53098.2021.00180
