Browsing by Author "Cooke, Colin"

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Gigapixel imaging with a novel multi-camera array microscope.
    (eLife, 2022-12) Thomson, Eric E; Harfouche, Mark; Kim, Kanghyun; Konda, Pavan C; Seitz, Catherine W; Cooke, Colin; Xu, Shiqi; Jacobs, Whitney S; Blazing, Robin; Chen, Yang; Sharma, Sunanda; Dunn, Timothy W; Park, Jaehee; Horstmeyer, Roarke W; Naumann, Eva A
    The dynamics of living organisms are organized across many spatial scales. However, current cost-effective imaging systems can measure only a subset of these scales at once. We have created a scalable multi-camera array microscope (MCAM) that enables comprehensive high-resolution recording from multiple spatial scales simultaneously, ranging from structures that approach the cellular scale to large-group behavioral dynamics. By collecting data from up to 96 cameras, we computationally generate gigapixel-scale images and movies with a field of view over hundreds of square centimeters at an optical resolution of 18 µm. This allows us to observe the behavior and fine anatomical features of numerous freely moving model organisms on multiple spatial scales, including larval zebrafish, fruit flies, nematodes, carpenter ants, and slime mold. Further, the MCAM architecture allows stereoscopic tracking of the z-position of organisms using the overlapping field of view from adjacent cameras. Overall, by removing the bottlenecks imposed by single-camera image acquisition systems, the MCAM provides a powerful platform for investigating detailed biological features and behavioral processes of small model organisms across a wide range of spatial scales.
  • Thumbnail Image
    ItemOpen Access
    Mesoscopic photogrammetry with an unstabilized phone camera
    (CVPR 2021, 2020-12-10) Zhou, Kevin C; Cooke, Colin; Park, Jaehee; Qian, Ruobing; Horstmeyer, Roarke; Izatt, Joseph A; Farsiu, Sina
    We present a feature-free photogrammetric technique that enables quantitative 3D mesoscopic (mm-scale height variation) imaging with tens-of-micron accuracy from sequences of images acquired by a smartphone at close range (several cm) under freehand motion without additional hardware. Our end-to-end, pixel-intensity-based approach jointly registers and stitches all the images by estimating a coaligned height map, which acts as a pixel-wise radial deformation field that orthorectifies each camera image to allow homographic registration. The height maps themselves are reparameterized as the output of an untrained encoder-decoder convolutional neural network (CNN) with the raw camera images as the input, which effectively removes many reconstruction artifacts. Our method also jointly estimates both the camera's dynamic 6D pose and its distortion using a nonparametric model, the latter of which is especially important in mesoscopic applications when using cameras not designed for imaging at short working distances, such as smartphone cameras. We also propose strategies for reducing computation time and memory, applicable to other multi-frame registration problems. Finally, we demonstrate our method using sequences of multi-megapixel images captured by an unstabilized smartphone on a variety of samples (e.g., painting brushstrokes, circuit board, seeds).