Certain embodiments describe a system, method, and apparatus for multi-spectral photoacoustic imaging. A method, for example, can include receiving multi-spectral photoacoustic image data from a photoacoustic imaging system. The method can also include pre-processing the multi-spectral photoacoustic image data. The pre-processing can comprise determining a number of significant components above a noise floor of the multi-spectral photoacoustic image data. In addition, the method can include detecting tissue chromophores based on the number of significant components from the multi-spectral photoacoustic image data using an unsupervised spectral unmixing process. The unsupervised spectral unmixing process can include clustering and windowing of the multi-spectral photoacoustic image data. The method can further include displaying the detected tissue chromophores in an abundance map.

A method for semantic annotation of sensor data may include obtaining sensor data representing an image of a geographic area. The boundary points defining a first polygon in the image of the geographic area may be determined based on the sensor data. An overlap between the first polygon and a second polygon in the image of the geographic area may be detected based at least on the boundary points defining the first polygon. At least one of the first polygon or the second polygon may be modified to remove the overlap between the first polygon and the second polygon. An annotation corresponding to the first polygon may be generated based on the modifying of at least one of the first polygon or the second polygon. The annotation may identify a physical feature within the geographic area. Related systems and computer program products are also provided.

Systems and methods for image processing are described. Embodiments of the present disclosure identify a first image depicting a first object; identify a plurality of candidate images depicting a second object; select a second image from the plurality of candidate images depicting the second object based on the second image and a sequence of previous images including the first image using a crop selection network trained to select a next compatible image based on the sequence of previous images; and generate a composite image depicting the first object and the second object based on the first image and the second image.

A neural network model that can perform highly accurate processing on input data is provided. The neural network model includes first and second neutral networks, and the first neural network includes a first layer, a second layer, and a third layer. A feature map output from the first layer is input to the second layer and the second neural network, and a feature map output from the second neural network is input to the third layer. Given that the feature map output from the first layer when first data is input to the first neural network is a correct feature map and that the feature map output from the first layer when second data obtained by adding noise to the first data is input to the first neural network is a learning feature map, the second neural network is learned so that a feature map output from the second neural network matches the correct feature map when the learning feature map is input.

[Object] To provide a display apparatus, an image generation method, and a program that are capable of displaying images such that an image displayed on a display unit and a scene outside the display apparatus appear to be continuous.

[Solving Means] The display apparatus includes a first image sensor, a first distance sensor, a second sensor, a display unit, and an image generation unit. The first image sensor is disposed on a first surface side of an apparatus main body. The first distance sensor is disposed on the first surface side. The second sensor is disposed on a second surface side opposite to the first surface side. The display unit is disposed on the second surface side. The image generation unit generates a display image to be displayed on the display unit, using a two-dimensional image of a subject and a distance image of the subject, the two-dimensional image being acquired by the first image sensor, the distance image being acquired by the first distance sensor, on the basis of three-dimensional position information of a viewpoint of a photographer, the three-dimensional position information being calculated on the basis of a sensing result acquired by the second sensor.

[Object] To provide a display apparatus, an image generation method, and a program that are capable of displaying images such that an image displayed on a display unit and a scene outside the display apparatus appear to be continuous.

[Solving Means] The display apparatus includes a first image sensor, a first distance sensor, a second sensor, a display unit, and an image generation unit. The first image sensor is disposed on a first surface side of an apparatus main body. The first distance sensor is disposed on the first surface side. The second sensor is disposed on a second surface side opposite to the first surface side. The display unit is disposed on the second surface side. The image generation unit generates a display image to be displayed on the display unit, using a two-dimensional image of a subject and a distance image of the subject, the two-dimensional image being acquired by the first image sensor, the distance image being acquired by the first distance sensor, on the basis of three-dimensional position information of a viewpoint of a photographer, the three-dimensional position information being calculated on the basis of a sensing result acquired by the second sensor.

The present disclosure generally pertains to image sensor control circuitry for event-based controlling of an image sensor, the image sensor control circuitry being configured to: obtain events from a plurality of event-based vision elements of an event-based vision sensor; determine event groups based on an event-detection property; and generate an imaging control signal for controlling the imaging elements of the image sensor based on the event groups, for imaging with imaging element groups corresponding to the event groups.

A neural network configured for classifying whether an image from an optical sensor characterizes an obstruction of the optical sensor or not. The classification is characterized by an output of the neural network for an input of the neural network and wherein the input is based on the image. The neural network comprises a first convolutional layer that characterizes a 1D-convolution along a vertical axis of a convolution output of a preceding convolutional layer and a second convolutional layer that characterizes a 1D-convolution along a horizontal axis of the convolution output. The output of the neural network is based on a first convolution output of the first convolutional layer and based on a second convolution output of the second convolutional layer.

A neural network configured for classifying whether an image from an optical sensor characterizes an obstruction of the optical sensor or not. The classification is characterized by an output of the neural network for an input of the neural network and wherein the input is based on the image. The neural network comprises a first convolutional layer that characterizes a 1D-convolution along a vertical axis of a convolution output of a preceding convolutional layer and a second convolutional layer that characterizes a 1D-convolution along a horizontal axis of the convolution output. The output of the neural network is based on a first convolution output of the first convolutional layer and based on a second convolution output of the second convolutional layer.

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.