The invention relates to a computer-implemented method comprising:—acquiring (406) first training images (108) using a first image acquisition technique (104), each first training image depicting a plant-related motive; —acquiring (402) second training images (106) using a second image acquisition technique (102), each second training image depicting the motive depicted in a respective one of the first training images; —automatically assigning (404) at least one label (150, 152, 154) to each of the acquired second training images; —spatially aligning (408) the first and second training images which are depicting the same one of the motives into an aligned training image pair; —training (410) a machine-learning model (132) as a function of the aligned training image pairs and the labels, wherein during the training the machine-learning model (132) learns to automatically assign one or more labels (250, 252, 254) to any test image (205) acquired with the first image acquisition technique which depicts a plant-related motive; and—providing (412) the trained machine-learning model (132).

The invention relates to a computer-implemented method comprising:—acquiring (406) first training images (108) using a first image acquisition technique (104), each first training image depicting a plant-related motive; —acquiring (402) second training images (106) using a second image acquisition technique (102), each second training image depicting the motive depicted in a respective one of the first training images; —automatically assigning (404) at least one label (150, 152, 154) to each of the acquired second training images; —spatially aligning (408) the first and second training images which are depicting the same one of the motives into an aligned training image pair; —training (410) a machine-learning model (132) as a function of the aligned training image pairs and the labels, wherein during the training the machine-learning model (132) learns to automatically assign one or more labels (250, 252, 254) to any test image (205) acquired with the first image acquisition technique which depicts a plant-related motive; and—providing (412) the trained machine-learning model (132).

A bulk sorting system for sorting objects in bulk is provided. The bulk sorting system includes: at least one radiation source arranged to radiate the objects, at least one optical sensor arranged to capture reflected radiation of the objects and acquire the reflected radiation as multi- or hyperspectral data; a processing circuit configured to analyze the reflected radiation of the objects by inputting the multi- or hyperspectral data into a convolutional neural network (CNN) with at least two convolutional layers in order to either detect and classify the objects in the multi- or hyperspectral data and/or semantically segment the multi- or hyperspectral data; and a mechanical sorter configured to sort the objects according to their classification and/or segmentation using the analysis of the processing circuit such that different overlapping and/or stacked objects are separated or treated as a single group of objects.

A bulk sorting system for sorting objects in bulk is provided. The bulk sorting system includes: at least one radiation source arranged to radiate the objects, at least one optical sensor arranged to capture reflected radiation of the objects and acquire the reflected radiation as multi- or hyperspectral data; a processing circuit configured to analyze the reflected radiation of the objects by inputting the multi- or hyperspectral data into a convolutional neural network (CNN) with at least two convolutional layers in order to either detect and classify the objects in the multi- or hyperspectral data and/or semantically segment the multi- or hyperspectral data; and a mechanical sorter configured to sort the objects according to their classification and/or segmentation using the analysis of the processing circuit such that different overlapping and/or stacked objects are separated or treated as a single group of objects.

Embodiments of the invention provide a method and system that allows parameters of a desired target image to be determined from hyperspectral imagery of scene. The parameters may be representative of various aspects of the scene being imaged, particularly representative of physical properties of the scene. For example, in some medical imaging contexts, the property being imaged may be blood perfusion or oxygenation saturation level information per pixel. In one embodiment the parameters are obtained by collecting lower temporal and spatial resolution hyperspectral imagery, and then building a virtual hypercube of the information having a higher spatial resolution using a spatiospectral aware demosaicking process, the virtual hypercube then being used for estimation of the desired parameters at the higher spatial resolution. Alternatively, in another embodiment, instead of building the virtual hypercube and then performing the estimation, a joint demosaicking and parameter estimation operation is performed to obtain the parameters. Various white level and spectral calibration operations may also be performed to improve the results obtained. While establishing functional and technical requirements of an intraoperative system for surgery, we present iHSI system embodiments that allows for real-time wide-field HSI and responsive surgical guidance in a highly constrained operating theatre. Two exemplar embodiments exploiting state-of-the-art industrial HSI cameras, respectively using linescan and snapshot imaging technology, were investigated by performing assessments against established design criteria and ex vivo tissue experiments. We further report the use of one real-time iHSI embodiment during an ethically-approved in-patient clinical feasibility case study as part of a spinal fusion surgery therefore successfully validating our assumptions that our invention can be seamlessly integrated into the operating theatre without interrupting the surgical workflow.

Embodiments of the invention provide a method and system that allows parameters of a desired target image to be determined from hyperspectral imagery of scene. The parameters may be representative of various aspects of the scene being imaged, particularly representative of physical properties of the scene. For example, in some medical imaging contexts, the property being imaged may be blood perfusion or oxygenation saturation level information per pixel. In one embodiment the parameters are obtained by collecting lower temporal and spatial resolution hyperspectral imagery, and then building a virtual hypercube of the information having a higher spatial resolution using a spatiospectral aware demosaicking process, the virtual hypercube then being used for estimation of the desired parameters at the higher spatial resolution. Alternatively, in another embodiment, instead of building the virtual hypercube and then performing the estimation, a joint demosaicking and parameter estimation operation is performed to obtain the parameters. Various white level and spectral calibration operations may also be performed to improve the results obtained. While establishing functional and technical requirements of an intraoperative system for surgery, we present iHSI system embodiments that allows for real-time wide-field HSI and responsive surgical guidance in a highly constrained operating theatre. Two exemplar embodiments exploiting state-of-the-art industrial HSI cameras, respectively using linescan and snapshot imaging technology, were investigated by performing assessments against established design criteria and ex vivo tissue experiments. We further report the use of one real-time iHSI embodiment during an ethically-approved in-patient clinical feasibility case study as part of a spinal fusion surgery therefore successfully validating our assumptions that our invention can be seamlessly integrated into the operating theatre without interrupting the surgical workflow.

Provided is an object recognition method which includes obtaining a first visible-light image acquired by the first camera device and a second visible-light image acquired by the second camera device; performing exposure processing on the first visible-light image according to the luminance information of the bright area image of the first visible-light image and performing exposure processing on the second visible-light image according to the luminance information of the dark area images of the first visible-light image and/or the second visible-light image, where the dark area image is an area image having a luminance value less than or equal to the preset value; and performing target object detection on the first visible-light image obtained after exposure processing and the second visible-light image obtained after exposure processing and recognizing and verifying a target object according to the detection result.

Provided is an object recognition method which includes obtaining a first visible-light image acquired by the first camera device and a second visible-light image acquired by the second camera device; performing exposure processing on the first visible-light image according to the luminance information of the bright area image of the first visible-light image and performing exposure processing on the second visible-light image according to the luminance information of the dark area images of the first visible-light image and/or the second visible-light image, where the dark area image is an area image having a luminance value less than or equal to the preset value; and performing target object detection on the first visible-light image obtained after exposure processing and the second visible-light image obtained after exposure processing and recognizing and verifying a target object according to the detection result.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for improved image segmentation using hyperspectral imaging. In some implementations, a system obtains image data of a hyperspectral image, the image data comprising image data for each of multiple wavelength bands. The system accesses stored segmentation profile data for a particular object type that indicates a predetermined subset of the wavelength bands designated for segmenting different region types for images of an object of the particular object type. The system segments the image data into multiple regions using the predetermined subset of the wavelength bands specified in the stored segmentation profile data to segment the different region types. The system provides output data indicating the multiple regions and the respective region types of the multiple regions.

Image data is obtained that indicates an extent to which one or more objects reflect, scatter, or absorb light at each of multiple wavelength bands, where the image data was collected while a conveyor belt was moving the object(s). The image data is preprocessed by performing an analysis across frequencies and/or performing an analysis across a representation of a spatial dimension. A set of feature values is generated using the image preprocessed image data. A machine-learning model generates an output using to the feature values. A prediction of an identity of a chemical in the one or more objects or a level of one or more chemicals in the object(s) is generated using the output. Data is output indicating the prediction of the identity of the chemical in the object(s) or the level of the one or more chemicals in at least one of the one or more objects.