Adeia Imaging LLC

Systems and methods for estimating depth from projected texture using camera arrays are described. A camera array includes a conventional camera and at least one two-dimensional array of cameras, where the conventional camera has a higher resolution than the cameras in the at least one two-dimensional array of cameras, an illumination system configured to illuminate a scene with a projected texture, where an image processing pipeline application directs the processor to: utilize the illumination system controller application to control the illumination system to illuminate a scene with a projected texture, capture a set of images of the scene illuminated with the projected texture, and determining depth estimates for pixel locations in an image from a reference viewpoint using at least a subset of the set of images.

Systems and methods for hybrid depth regularization in accordance with various embodiments of the invention are disclosed. In one embodiment of the invention, a depth sensing system comprises a plurality of cameras; a processor; and a memory containing an image processing application. The image processing application may direct the processor to obtain image data for a plurality of images from multiple viewpoints, the image data comprising a reference image and at least one alternate view image; generate a raw depth map using a first depth estimation process, and a confidence map; and generate a regularized depth map. The regularized depth map may be generated by computing a secondary depth map using a second different depth estimation process; and computing a composite depth map by selecting depth estimates from the raw depth map and the secondary depth map based on the confidence map.

In an embodiment, a 3D facial modeling system includes a plurality of cameras configured to capture images from different viewpoints, a processor, and a memory containing a 3D facial modeling application and parameters defining a face detector, wherein the 3D facial modeling application directs the processor to obtain a plurality of images of a face captured from different viewpoints using the plurality of cameras, locate a face within each of the plurality of images using the face detector, wherein the face detector labels key feature points on the located face within each of the plurality of images, determine disparity between corresponding key feature points of located faces within the plurality of images, and generate a 3D model of the face using the depth of the key feature points.

Systems and methods are provided for object extraction from images influenced by depth of field settings. Through control circuitry, an image subjected to a prior segmentation operation is acquired. A subsequent segmentation operation is performed, modulating the depth of field setting to its extreme values, producing two distinct segmented images. From these, an in-focus object is derived, forming delineated representations. A similarity index between representations is computed. If this index exceeds a specified threshold, the in-focus object is extracted from the original image using the control circuitry.

A region of interest is determined based on metadata associated with an image. The region of interest is then sliced into a plurality of slices. POI information is retrieved for each respective slice. An overlay for at least one POI is then generated for display over the image.

Systems in accordance with embodiments of the invention can perform parallax detection and correction in images captured using array cameras. Due to the different viewpoints of the cameras, parallax results in variations in the position of objects within the captured images of the scene. Methods in accordance with embodiments of the invention provide an accurate account of the pixel disparity due to parallax between the different cameras in the array, so that appropriate scene-dependent geometric shifts can be applied to the pixels of the captured images when performing super-resolution processing. In a number of embodiments, generating depth estimates considers the similarity of pixels in multiple spectral channels. In certain embodiments, generating depth estimates involves generating a confidence map indicating the reliability of depth estimates.

Systems and methods are provided for object extraction from images influenced by depth of field settings. Through control circuitry, an image subjected to a prior segmentation operation is acquired. A subsequent segmentation operation is performed, modulating the depth of field setting to its extreme values, producing two distinct segmented images. From these, an in-focus object is derived, forming delineated representations. A similarity index between representations is computed. If this index exceeds a specified threshold, the in-focus object is extracted from the original image using the control circuitry.

Systems in accordance with embodiments of the invention can perform parallax detection and correction in images captured using array cameras. Due to the different viewpoints of the cameras, parallax results in variations in the position of objects within the captured images of the scene. Methods in accordance with embodiments of the invention provide an accurate account of the pixel disparity due to parallax between the different cameras in the array, so that appropriate scene-dependent geometric shifts can be applied to the pixels of the captured images when performing super-resolution processing. In a number of embodiments, generating depth estimates considers the similarity of pixels in multiple spectral channels. In certain embodiments, generating depth estimates involves generating a confidence map indicating the reliability of depth estimates.

A method and system for detecting facial expressions in digital images and applications therefore are disclosed. Analysis of a digital image determines whether or not a smile and/or blink is present on a person's face. Face recognition, and/or a pose or illumination condition determination, permits application of a specific, relatively small classifier cascade.

Systems and methods are provided for object extraction from images influenced by depth of field settings. Through control circuitry, an image subjected to a prior segmentation operation is acquired. A subsequent segmentation operation is performed, modulating the depth of field setting to its extreme values, producing two distinct segmented images. From these, an in-focus object is derived, forming delineated representations. A similarity index between representations is computed. If this index exceeds a specified threshold, the in-focus object is extracted from the original image using the control circuitry.