Abstract: A camera system for collecting image data in a clinical environment includes a camera configured to acquire color (RGB), near-infrared, and depth image data and a plurality of LED modules held proximate to the camera such that the LED modules are configured to illuminate a subject while image data is collected by the camera. The camera system also includes a microcontroller in electrical communication with the camera and the plurality of LED modules. The microcontroller is configured to actuate the plurality of LED modules and the camera such that the camera acquires the image data. A computing device is in electrical communication with the camera and the microcontroller.

Abstract: A general framework for building a biometrics system capable of capturing multispectral data from a series of sensors synchronized with active illumination sources is provided. The framework unifies the system design for different biometric modalities and its realization on face, finger and iris data is described in detail. To the best of our knowledge, the presented design is the first to employ such a diverse set of electromagnetic spectrum bands, ranging from visible to long-wave-infrared wavelengths, and is capable of acquiring large volumes of data in seconds. Having performed a series of data collections, we run a comprehensive analysis on the captured data using a deep-learning classifier for presentation attack detection. The invention follows a data-centric approach attempting to highlight the strengths and weaknesses of each spectral band at distinguishing live from fake samples.

Abstract: Light-field data is masked to identify regions of interest, before applying to deep learning models. In one approach, a light-field camera captures a light-field image of an object to be classified. The light-field image includes a plurality of views of the object taken simultaneously from different viewpoints. The light-field image is pre-processed, with the resulting data provided as input to a deep learning model. The pre-processing includes determining and then applying masks to select regions of interest within the light-field data. In this way, less relevant data can be excluded from the deep learning model. Based on the masked data, the deep learning model produces a decision classifying the object.

Abstract: Light-field data is masked to identify regions of interest, before applying to deep learning models. In one approach, a light-field camera captures a light-field image of an object to be classified. The light-field image includes a plurality of views of the object taken simultaneously from different viewpoints. The light-field image is pre-processed, with the resulting data provided as input to a deep learning model. The pre-processing includes determining and then applying masks to select regions of interest within the light-field data. In this way, less relevant data can be excluded from the deep learning model. Based on the masked data, the deep learning model produces a decision classifying the object.

Abstract: A three-dimensional image of the eardrum is automatically registered. In one aspect, a three-dimensional image (e.g., depth map) of an eardrum is produced from four-dimensional light field data captured by a light field otoscope. The three-dimensional image is registered according to a predefined standard form. The eardrum is then classified based on the registered three-dimensional image. Registration may include compensation for out-of-plane rotation (tilt), for in-plane rotation, for center location, for translation, and/or for scaling.

Abstract: Eardrums are automatically classified based on a feature set from a three-dimensional image of the eardrum, such as might be derived from a plenoptic image captured by a light field otoscope. In one aspect, a grid is overlaid onto the three-dimensional image of the eardrum. The grid partitions the three-dimensional image into cells. One or more descriptors are calculated for each of the cells, and the feature set includes the calculated descriptors. Examples of descriptors include various quantities relating to the depth and/or curvature of the eardrum. In another aspect, isocontour lines (of constant depth) are calculated for the three-dimensional image of the eardrum. One or more descriptors are calculated for the isocontour lines, and the feature set includes the calculated descriptors. Examples of descriptors include various quantities characterizing the isocontour lines.

Abstract: Described are imaging systems that employ diffractive structures as focusing optics optimized to detect visual edges (e.g., slits or bars). The diffractive structures produce edge responses that are relatively insensitive to wavelength, and can thus be used to precisely measure edge position for panchromatic sources over a wide angle of view. Simple image processing can improve measurement precision. Field-angle measurements can be made without the aid of lenses, or the concomitant cost, bulk, and complexity.

Abstract: Eardrums are automatically classified based on a feature set from a three-dimensional image of the eardrum, such as might be derived from a plenoptic image captured by a light field otoscope. In one aspect, a grid is overlaid onto the three-dimensional image of the eardrum. The grid partitions the three-dimensional image into cells. One or more descriptors are calculated for each of the cells, and the feature set includes the calculated descriptors. Examples of descriptors include various quantities relating to the depth and/or curvature of the eardrum. In another aspect, isocontour lines (of constant depth) are calculated for the three-dimensional image of the eardrum. One or more descriptors are calculated for the isocontour lines, and the feature set includes the calculated descriptors. Examples of descriptors include various quantities characterizing the isocontour lines.

Abstract: A three-dimensional image of the eardrum is automatically registered. In one aspect, a three-dimensional image (e.g., depth map) of an eardrum is produced from four-dimensional light field data captured by a light field otoscope. The three-dimensional image is registered according to a predefined standard form. The eardrum is then classified based on the registered three-dimensional image. Registration may include compensation for out-of-plane rotation (tilt), for in-plane rotation, for center location, for translation, and/or for scaling.

Abstract: Described are imaging systems that employ diffractive structures as focusing optics optimized to detect visual edges (e.g., slits or bars). The diffractive structures produce edge responses that are relatively insensitive to wavelength, and can thus be used to precisely measure edge position for panchromatic sources over a wide angle of view. Simple image processing can improve measurement precision. Field-angle measurements can be made without the aid of lenses, or the concomitant cost, bulk, and complexity.