A structural analysis computing device may generate a proposed insurance claim and/or generate a proposed insurance quote for an object pictured in a three-dimensional (3D) image. The structural analysis computing device may be coupled to a drone configured to capture exterior images of the object. The structural analysis computing device may include a memory, a user interface, an object sensor configured to capture the 3D image, and a processor in communication with the memory and the object sensor. The processor may access the 3D image including the object, and analyze the 3D images to identify features of the object—such as by inputting the 3D image into a trained machine learning or pattern recognition program. The processor may generate a proposed claim form for a damaged object and/or a proposed quote for an uninsured object, and display the form to a user for their review and/or approval.

Disclosed are method and apparatus to recognize actors during normal system operation. The method includes defining actor input such as hand gestures, executing and detecting input, and identifying salient features of the actor therein. A model is defined from salient features, and a data set of salient features and/or model are retained, and may be used to identify actors for other inputs. A command such as “unlock” may be executed in response to actor input. Parameters may be applied to further define where, when, how, etc. actor input is executed, such as defining a region for a gesture. The apparatus includes a processor and sensor, the processor defining actor input, identifying salient features, defining a model therefrom, and retaining a data set. A display may also be used to show actor input, a defined region, relevant information, and/or an environment. A stylus or other non-human actor may be used.

Embodiments include a method for self-rectification of a stereo camera, wherein the stereo camera comprises a first camera and a second camera, the method comprises creating image pairs from a first images taken by the first camera and second images taken by the second camera, respectively, such that each image pair comprises two images taken at essentially the same time by the first camera and the second camera, respectively. The method comprises creating, for each image pair, matching point pairs from corresponding points in the two images of each image pair, such that each matching point pair comprises one point from each of the first and second image of the respective image pair. For each matching point pair, a disparity is calculated and a plurality of disparities is created for each image pair, and the resulting plurality of disparities is taken into account for the self-rectification.

A method of determining the position of an object in a scene, comprising: receiving captured images of the scene, each image being captured from a different field of view of the scene, wherein a portion of the scene with a volume comprises a detectable object, the volume is divided into volume portions, and each volume portion is within the captured field of view of at least two of the captured images so that an image of each volume portion appears in the at least two of the captured images; detecting, for each volume portion in each of the captured images within which an image of that volume portion appears, whether or not an image of one of the detectable objects in the scene is positioned within a distance of the position of the image of that volume portion, a correspondence between the images of the detectable objects detected in the at least two of the images is established, the correspondence indicating that the images of the detectable objects detected in the at least two of the images correspond to a single detectable object in the scene, and the position in the scene of that volume portion is established as a position in the scene of the single detectable object.

An endoscope apparatus includes a display section with a touch panel having a left image display region and a right image display region and a control section. The control section is configured to perform display control so as to change, upon receiving a drag operation instruction while the left image display region selected, a position of a cursor displayed in the left image display region and the right image display region by an amount of variation da, and change, upon receiving a drag operation instruction while the right image display region selected, the position of the cursor displayed in the left image display region and right image display region by an amount of variation db.

Provided is an image processing device including a matching unit that performs matching processing between a predetermined pattern on a surface of a 3D model of a biological tissue including an operating site generated on the basis of a preoperative diagnosis image and a predetermined pattern on a surface of the biological tissue included in a captured image during surgery, a shift amount estimation unit that estimates an amount of deformation from a preoperative state of the biological tissue on the basis of a result of the matching processing and information regarding a three-dimensional position of a photographing region which is a region photographed during surgery on the surface of the biological tissue, and a 3D model update unit that updates the 3D model generated before surgery on the basis of the estimated amount of deformation of the biological tissue.

Certain aspects of the technology disclosed herein involve combining images to generate a wide view image of a surrounding environment. Images can be recorded using an stand-alone imaging device having wide angle lenses and/or normal lenses. Images from the imaging device can be combined using methods described herein. In an embodiment, a pixel correspondence between a first image and a second image can be determined, based on a corresponding overlap area associated with the first image and the second image. Corresponding pixels in the corresponding overlap area associated with the first image and the second image can be merged based on a weight assigned to each of the corresponding pixels.

An information processing apparatus includes an imaging apparatus that irradiates reference light in a predetermined wavelength band to a subject and captures reflection of the reference light from the subject to acquire data of captured images including a polarized image in multiple bearings (S30). Based on the polarized image, the imaging apparatus acquires a polarization degree image representing a distribution of polarization degrees (S32). The imaging apparatus extracts a region whose polarization degree falls within a predetermined range of polarization degrees as an image of the subject having a predetermined material (S34). The imaging apparatus performs relevant processing on the subject image to generate output data and outputs the generated data (S36).

A stereo image matching apparatus includes a processor which includes: a bit distributor distributing values of each pixel of stereo images into sequential N bits and outputting a plurality of stereo images including the sequential N bits; a plurality of cost calculators each receiving the plurality of stereo images and calculating matching cost values for each pixel of each of the stereo images; a confidence calculator calculating a matching confidence by using cost characteristics lit of the respective matching cost values calculated by the plurality of cost calculators; and a depth determiner determining that a depth value of which the matching confidence is high and the matching cost values are relatively low is a final depth value.

A system for executing a three-dimensional (3D) intraoperative scan of a patient is disclosed. A 3D scanner controller projects the object points included onto a first image plane and the object points onto a second image plane. The 3D scanner controller determines first epipolar lines associated with the first image plane and second epipolar lines associated with the second image plane based on an epipolar plane that triangulates the object points included in the first 2D intraoperative image to the object points included in the second 2D intraoperative image. Each epipolar lines provides a depth of each object as projected onto the first image plane and the second image plane. The 3D scanner controller converts the first 2D intraoperative image and the second 2D intraoperative image to the 3D intraoperative scan of the patient based on the depth of each object point provided by each corresponding epipolar line.