Abstract: Machine learning systems and methods are disclosed for prediction of wound healing, such as for diabetic foot ulcers or other wounds, and for assessment implementations such as segmentation of images into wound regions and non-wound regions. Systems for assessing or predicting wound healing can include a light detection element configured to collect light of at least a first wavelength reflected from a tissue region including a wound, and one or more processors configured to generate an image based on a signal from the light detection element having pixels depicting the tissue region, determine reflectance intensity values for at least a subset of the pixels, determine one or more quantitative features of the subset of the plurality of pixels based on the reflectance intensity values, and generate a predicted or assessed healing parameter associated with the wound over a predetermined time interval.

Abstract: Machine learning systems and methods are disclosed for prediction of wound healing, such as for diabetic foot ulcers or other wounds, and for assessment implementations such as segmentation of images into wound regions and non-wound regions. Systems for assessing or predicting wound healing can include a light detection element configured to collect light of at least a first wavelength reflected from a tissue region including a wound, and one or more processors configured to generate an image based on a signal from the light detection element having pixels depicting the tissue region, determine reflectance intensity values for at least a subset of the pixels, determine one or more quantitative features of the subset of the plurality of pixels based on the reflectance intensity values, and generate a predicted or assessed healing parameter associated with the wound over a predetermined time interval.

Abstract: Generally described, one or more aspects of the present application correspond to systems and techniques for spectral imaging using a multi-aperture system with curved multi-bandpass filters positioned over each aperture. The present disclosure further relates to techniques for implementing spectral unmixing and image registration to generate a spectral datacube using image information received from such imaging systems. Aspects of the present disclosure relate to using such a datacube to analyze the imaged object, for example to analyze tissue in a clinical setting, perform biometric recognition, or perform materials analysis.

Abstract: Machine learning systems and methods are disclosed for prediction of wound healing, such as for diabetic foot ulcers or other wounds, and for assessment implementations such as segmentation of images into wound regions and non-wound regions. Systems for assessing or predicting wound healing can include a light detection element configured to collect light of at least a first wavelength reflected from a tissue region including a wound or portion thereof, and one or more processors configured to generate an image based on a signal from the light detection element having pixels depicting the tissue region, automatically segment the pixels into wound pixels and non-wound pixels, determine one or more optically determined tissue features of the wound or portion thereof, and generate a predicted or assessed healing parameter associated with the wound or portion thereof over a predetermined time interval.

Abstract: Generally described, one or more aspects of the present application correspond to systems and techniques for spectral imaging using a multi-aperture system with curved multi-bandpass filters positioned over each aperture. The present disclosure further relates to techniques for implementing spectral unmixing and image registration to generate a spectral datacube using image information received from such imaging systems. Aspects of the present disclosure relate to using such a datacube to analyze the imaged object, for example to analyze tissue in a clinical setting, perform biometric recognition, or perform materials analysis.

Abstract: A sign-monitoring system includes at least one electronic sign and a controller comprising a processor and memory. The electronic sign includes a pixel array, the pixel array including a plurality of pixels. The electronic sign further includes an embedded controller coupled to the at least one electronic sign. The embedded controller develops diagnostic information for the at least one electronic sign, the diagnostic information including information related to a number of malfunctioning pixels in the plurality of pixels. The controller is communicably coupled to the embedded controller and receives at least a portion of the diagnostic information from the embedded controller. In addition, the controller assesses the at least a portion of the diagnostic information to develop health information. The assessment involves evaluating the information related to the number of malfunctioning pixels.

Abstract: Machine learning systems and methods are disclosed for prediction of wound healing, such as for diabetic foot ulcers or other wounds, and for assessment implementations such as segmentation of images into wound regions and non-wound regions. Systems for assessing or predicting wound healing can include a light detection element configured to collect light of at least a first wavelength reflected from a tissue region including a wound, and one or more processors configured to generate an image based on a signal from the light detection element having pixels depicting the tissue region, determine reflectance intensity values for at least a subset of the pixels, determine one or more quantitative features of the subset of the plurality of pixels based on the reflectance intensity values, and generate a predicted or assessed healing parameter associated with the wound over a predetermined time interval.

Abstract: Generally described, one or more aspects of the present application correspond to systems and techniques for spectral imaging using a multi-aperture system with curved multi-bandpass filters positioned over each aperture. The present disclosure further relates to techniques for implementing spectral unmixing and image registration to generate a spectral datacube using image information received from such imaging systems. Aspects of the present disclosure relate to using such a datacube to analyze the imaged object, for example to analyze tissue in a clinical setting, perform biometric recognition, or perform materials analysis.

Abstract: A sign-monitoring system includes at least one electronic sign and a controller comprising a processor and memory. The electronic sign includes a pixel array, the pixel array including a plurality of pixels. The electronic sign further includes an embedded controller coupled to the at least one electronic sign. The embedded controller develops diagnostic information for the at least one electronic sign, the diagnostic information including information related to a number of malfunctioning pixels in the plurality of pixels. The controller is communicably coupled to the embedded controller and receives at least a portion of the diagnostic information from the embedded controller. In addition, the controller assesses the at least a portion of the diagnostic information to develop health information. The assessment involves evaluating the information related to the number of malfunctioning pixels.

Abstract: Generally described, one or more aspects of the present application correspond to systems and techniques for spectral imaging using a multi-aperture system with curved multi-bandpass filters positioned over each aperture. The present disclosure further relates to techniques for implementing spectral unmixing and image registration to generate a spectral datacube using image information received from such imaging systems. Aspects of the present disclosure relate to using such a datacube to analyze the imaged object, for example to analyze tissue in a clinical setting, perform biometric recognition, or perform materials analysis.

Abstract: A sign-monitoring system includes at least one electronic sign and a controller comprising a processor and memory. The electronic sign includes a pixel array, the pixel array including a plurality of pixels. The electronic sign further includes an embedded controller coupled to the at least one electronic sign. The embedded controller develops diagnostic information for the at least one electronic sign, the diagnostic information including information related to a number of malfunctioning pixels in the plurality of pixels. The controller is communicably coupled to the embedded controller and receives at least a portion of the diagnostic information from the embedded controller. In addition, the controller assesses the at least a portion of the diagnostic information to develop health information. The assessment involves evaluating the information related to the number of malfunctioning pixels.

Abstract: Machine learning systems and methods are disclosed for prediction of wound healing, such as for diabetic foot ulcers or other wounds, and for assessment implementations such as segmentation of images into wound regions and non-wound regions. Systems for assessing or predicting wound healing can include a light detection element configured to collect light of at least a first wavelength reflected from a tissue region including a wound, and one or more processors configured to generate an image based on a signal from the light detection element having pixels depicting the tissue region, determine reflectance intensity values for at least a subset of the pixels, determine one or more quantitative features of the subset of the plurality of pixels based on the reflectance intensity values, and generate a predicted or assessed healing parameter associated with the wound over a predetermined time interval.

Abstract: Generally described, one or more aspects of the present application correspond to systems and techniques for spectral imaging using a multi-aperture system with curved multi-bandpass filters positioned over each aperture. The present disclosure further relates to techniques for implementing spectral unmixing and image registration to generate a spectral datacube using image information received from such imaging systems. Aspects of the present disclosure relate to using such a datacube to analyze the imaged object, for example to analyze tissue in a clinical setting, perform biometric recognition, or perform materials analysis.

Abstract: A sign-monitoring system includes at least one electronic sign and a controller comprising a processor and memory. The electronic sign includes a pixel array, the pixel array including a plurality of pixels. The electronic sign further includes an embedded controller coupled to the at least one electronic sign. The embedded controller develops diagnostic information for the at least one electronic sign, the diagnostic information including information related to a number of malfunctioning pixels in the plurality of pixels. The controller is communicably coupled to the embedded controller and receives at least a portion of the diagnostic information from the embedded controller. In addition, the controller assesses the at least a portion of the diagnostic information to develop health information. The assessment involves evaluating the information related to the number of malfunctioning pixels.

Abstract: Machine learning systems and methods are disclosed for prediction of wound healing, such as for diabetic foot ulcers or other wounds, and for assessment implementations such as segmentation of images into wound regions and non-wound regions. Systems for assessing or predicting wound healing can include a light detection element configured to collect light of at least a first wavelength reflected from a tissue region including a wound, and one or more processors configured to generate an image based on a signal from the light detection element having pixels depicting the tissue region, determine reflectance intensity values for at least a subset of the pixels, determine one or more quantitative features of the subset of the plurality of pixels based on the reflectance intensity values, and generate a predicted or assessed healing parameter associated with the wound over a predetermined time interval.

Abstract: Generally described, one or more aspects of the present application correspond to systems and techniques for spectral imaging using a multi-aperture system with curved multi-bandpass filters positioned over each aperture. The present disclosure further relates to techniques for implementing spectral unmixing and image registration to generate a spectral datacube using image information received from such imaging systems. Aspects of the present disclosure relate to using such a datacube to analyze the imaged object, for example to analyze tissue in a clinical setting, perform biometric recognition, or perform materials analysis.

Abstract: A sign-monitoring system includes at least one electronic sign and a controller comprising a processor and memory. The electronic sign includes a pixel array, the pixel array including a plurality of pixels. The electronic sign further includes an embedded controller coupled to the at least one electronic sign. The embedded controller develops diagnostic information for the at least one electronic sign, the diagnostic information including information related to a number of malfunctioning pixels in the plurality of pixels. The controller is communicably coupled to the embedded controller and receives at least a portion of the diagnostic information from the embedded controller. In addition, the controller assesses the at least a portion of the diagnostic information to develop health information. The assessment involves evaluating the information related to the number of malfunctioning pixels.

Abstract: Generally described, one or more aspects of the present application correspond to systems and techniques for spectral imaging using a multi-aperture system with curved multi-bandpass filters positioned over each aperture. The present disclosure further relates to techniques for implementing spectral unmixing and image registration to generate a spectral datacube using image information received from such imaging systems. Aspects of the present disclosure relate to using such a datacube to analyze the imaged object, for example to analyze tissue in a clinical setting, perform biometric recognition, or perform materials analysis.

Abstract: Machine learning systems and methods are disclosed for prediction of wound healing, such as for diabetic foot ulcers or other wounds, and for assessment implementations such as segmentation of images into wound regions and non-wound regions. Systems for assessing or predicting wound healing can include a light detection element configured to collect light of at least a first wavelength reflected from a tissue region including a wound, and one or more processors configured to generate an image based on a signal from the light detection element having pixels depicting the tissue region, determine reflectance intensity values for at least a subset of the pixels, determine one or more quantitative features of the subset of the plurality of pixels based on the reflectance intensity values, and generate a predicted or assessed healing parameter associated with the wound over a predetermined time interval.

Abstract: A sign-monitoring system includes at least one electronic sign and a controller comprising a processor and memory. The electronic sign includes a pixel array, the pixel array including a plurality of pixels. The electronic sign further includes an embedded controller coupled to the at least one electronic sign. The embedded controller develops diagnostic information for the at least one electronic sign, the diagnostic information including information related to a number of malfunctioning pixels in the plurality of pixels. The controller is communicably coupled to the embedded controller and receives at least a portion of the diagnostic information from the embedded controller. In addition, the controller assesses the at least a portion of the diagnostic information to develop health information. The assessment involves evaluating the information related to the number of malfunctioning pixels.