A stored volumetric scene model of a real scene is generated from data defining digital images of a light field in a real scene containing different types of media. The digital images have been formed by a camera from opposingly directed poses and each digital image contains image data elements defined by stored data representing light field flux received by light sensing detectors in the camera. The digital images are processed by a scene reconstruction engine to form a digital volumetric scene model representing the real scene. The volumetric scene model (i) contains volumetric data elements defined by stored data representing one or more media characteristics and (ii) contains solid angle data elements defined by stored data representing the flux of the light field. Adjacent volumetric data elements form corridors, at least one of the volumetric data elements in at least one corridor represents media that is partially light transmissive. The constructed digital volumetric scene model data is stored in a digital data memory for subsequent uses and applications.

A system for managing treatment of a person in need of emergency assistance is provided. The system includes at least one camera configured to be mounted to a person in need of medical assistance. The system also includes an image processing device, which can be configured to receive images captured by the at least one camera and to process the images to generate a representation of a rescue scene surrounding the person. The system further includes an analysis device. The analysis device can be configured to determine a characteristic associated with a resuscitation activity based on analysis of the representation of the rescue scene generated by the image processing device. A computer-implemented method for managing treatment of a person in need of emergency assistance is also provided.

A stored volumetric scene model of a real scene is generated from data defining digital images of a light field in a real scene containing different types of media. The digital images have been formed by a camera from opposingly directed poses and each digital image contains image data elements defined by stored data representing light field flux received by light sensing detectors in the camera. The digital images are processed by a scene reconstruction engine to form a digital volumetric scene model representing the real scene. The volumetric scene model (i) contains volumetric data elements defined by stored data representing one or more media characteristics and (ii) contains solid angle data elements defined by stored data representing the flux of the light field. Adjacent volumetric data elements form corridors, at least one of the volumetric data elements in at least one corridor represents media that is partially light transmissive. The constructed digital volumetric scene model data is stored in a digital data memory for subsequent uses and applications.

A system and method for an imaging circadiometer that measures the spatial distribution of eye-mediated, non-image-forming optical radiation within the visible spectrum.

Embodiments of systems and methods for multi-aperture ranging are disclosed. An embodiment of an image processing system includes at least one processor and memory configured to receive a multi-aperture image set that includes a high-resolution subaperture image and a low-resolution subaperture image, wherein the high-resolution subaperture image and the low-resolution subaperture image were captured simultaneously from a camera using dissimilar focal lengths, predict a high-resolution predicted disparity map from the high-resolution subaperture image using a neural network, predict a low-resolution predicted disparity map from the low-resolution subaperture image using the neural network, and generate an integrated range map from the high-resolution and low-resolution predicted disparity maps, wherein the integrated range map includes an array of range information that corresponds to the multi-aperture image set and that is generated by overlaying common points in both the high-resolution predicted disparity map and the low-resolution predicted disparity map.

A light field image processing method is disclosed for removing occluding foreground and blurring uninterested objects, by differentiating objects located at different depths of field and objects belonging to distinct categories, to create see-through effects. In various embodiments, the image processing method may blur a background object behind a specified object of interest. The image processing method may also at least partially remove from the rendered image any occluding object that may prevent a viewer from viewing the object of interest. The image processing method may further blur areas of the rendered image that represent an object in the light field other than the object of interest. The method includes steps of constructing a light field weight function comprising a depth component and a semantic component, where the weight function assigns a ray in the light field with a weight; and conducting light field rendering using the weight function.

The light field reconstruction method includes: obtaining a human segmentation result via a pre-trained semantic segmentation network, and obtaining an object segmentation result according to a pre-obtained scene background; fusing multiple frames of depth maps to obtain a geometric model, obtaining a complete human model according to a pre-trained human model completion network, and registering the models by point cloud registration and fusing the registered models to obtain an object model, so as to obtain a complete human model with geometric details and the object model; tracking motion of a rigid object through point cloud registration; reconstructing the complete human model with geometric details through a human skeleton tracking and a non rigid tracking of human surface nodes; and performing a fusion operation in time sequence and obtaining a reconstructed human model and a reconstructed rigid object model through the fusion operation.

An active sensor for performing active measurements of a scene is presented. The active sensor includes at least one transmitter configured to emit light pulses toward at least one target object in the scene, wherein the at least one target object is recognized in an image acquired by a passive sensor; at least one receiver configured to detect light pulses reflected from the at least one target object; a controller configured to control an energy level, a direction, and a timing of each light pulse emitted by the transmitter, wherein the controller is further configured to control at least the direction for detecting each of the reflected light pulses; and a distance measurement circuit configured to measure a distance to each of the at least one target object based on the emitted light pulses and the detected light pulses.

Various examples of the present disclosure include a stereoscopic deep neural network (DNN) that produces accurate and reliable results in real-time. Both LIDAR data (supervised training) and photometric error (unsupervised training) may be used to train the DNN in a semi-supervised manner. The stereoscopic DNN may use an exponential linear unit (ELU) activation function to increase processing speeds, as well as a machine learned argmax function that may include a plurality of convolutional layers having trainable parameters to account for context. The stereoscopic DNN may further include layers having an encoder/decoder architecture, where the encoder portion of the layers may include a combination of three-dimensional convolutional layers followed by two-dimensional convolutional layers.

A method of an optical sensor device used for detecting a color of a specific surface of an object includes: providing a sensor array having a plurality of pixel units each for receiving light reflected from the specific surface to generate a sensed pixel value; using the sensor array to generate a specific image formed by a plurality of sensed pixel values; determining whether the specific image is similar to a first solid color image corresponding to a first color according to the plurality of sensed pixel values of the specific image; and determining to execute a distance detection operation to calculate a specific distance from the optical sensor device to the specific surface according to a color saturation value of the first color in the specific image when it is determined that the specific image is similar to the first solid color image corresponding to the first color.