Apparatus and methods for the stitch zone calculation of a generated projection of a spherical image. In one embodiment, a computing device is disclosed which includes logic configured to: obtain a plurality of images; map the plurality of images onto a spherical image; re-orient the spherical image in accordance with a desired stitch line and a desired projection for the desired stitch line; and map the spherical image to the desired projection having the desired stitch line. In a variant, the desired stitch line is mapped onto an optimal stitch zone, the optimal stitch zone characterized as a set of points that defines a single line on the desired projection in which the set of points along the desired projection lie closest to the spherical image in a mean square sense.

Methods and systems are provided for performing material capture to determine properties of an imaged surface. A plurality of images can be received depicting a material surface. The plurality of images can be calibrated to align corresponding pixels of the images and determine reflectance information for at least a portion of the aligned pixels. After calibration, a set of reference materials from a material library can be selected using the calibrated images. The set of reference materials can be used to determine a material model that accurately represents properties of the material surface.

A method of quantifying changes in a visceral organ comprises acquiring first (310) and second (410) medical scans of a visceral organ at first and second timepoints. At least part of the visceral organ in the first medical scan is parcellated into a first set of one or more subregions (420), based on image content, each subregion comprising a plurality of voxels. The first medical scan (310) is aligned to the second medical scan (410), before or after parcellating the first medical scan (310). Then the second medical scan is parcellated into a second set of one or more subregions. A metric is evaluated for a subregion in the first medical scan (310), and for the corresponding subregion in the second medical scan (410). A difference in the metric values provides a measure of a change that has occurred in the subregion, between the first and second timepoints.

One variation of a method for predicting manufacturing defects includes: accessing a first set of inspection images of a first set of assembly units recorded by an optical inspection station over a first period of time; generating a first set of vectors representing features extracted from the first set of inspection images; grouping neighboring vectors in a multi-dimensional feature space into a set of vector groups; accessing a second inspection image of a second assembly recorded by the optical inspection station at a second time succeeding the first period of time; detecting a second set of features in the second inspection image; generating a second vector representing the second set of features in the multi-dimensional feature space; and, in response to the second vector deviating from the set of vector groups by more than a threshold difference, flagging the second assembly unit.

A method, system and computer program product are configured to analyze an image of a vehicle to determine a characteristic of the vehicle, such as may be represented by or otherwise at least partially defined by the shadow cast by the vehicle. In the context of a method, information is received identifying a vehicle from a raster image and the pixel values of the raster image are evaluated to identify pixels having pixel values representative of a shadow associated with the vehicle. The method also modifies a representation of the shadow by modifying the pixel values of the pixels based upon a shape of the vehicle such that the representation of the shadow, as modified, has a shape corresponding to the shape of the vehicle. The method additionally determines a characteristic of the vehicle based upon the representation of the shadow, as modified, that is associated with the vehicle.

A computer-implemented method according to one embodiment includes identifying a plurality of two-dimensional (2D) images illustrating a subject performing a rotation, selecting a representative image of the subject, cropping the plurality of 2D images, utilizing the representative image of the subject, to create a cropped plurality of 2D images, aligning each of the cropped plurality of 2D images, thereby creating an aligned plurality of 2D images, selecting a subset of the aligned plurality of 2D images that illustrate a predetermined amount of rotation of the subject, and creating a three-dimensional (3D) point cloud of the subject, utilizing the subset of the aligned plurality of 2D images.

Provided is a process executed by a robot, including: traversing, to a first position, a first distance in a backward direction; after traversing the first distance, rotating 180 degrees in a first rotation; after the first rotation, traversing, to a second position, a second distance in the second direction; and after traversing the second distance, rotating 180 degrees in a second rotation such that the field of view of the sensor points in the first direction.

A computer-implemented method of preoperatively planning a surgical procedure on a knee of a patient including determining femoral condyle vectors and tibial plateau vectors based on image data of the knee, the femoral condyle vectors and the tibial plateau vectors corresponding to motion vectors of the femoral condyles and the tibial plateau as they move relative to each other. The method may also include modifying a bone model representative of at least one of the femur and the tibia into a modified bone model based on the femoral condyle vectors and the tibial plateau vectors. And the method may further include determining coordinate locations for a resection of the modified bone model.

One or more neural networks generate a first vector field from an input image and a reference image. The first vector field is applied to the input image to generate a first warped image. The training of the neural networks is evaluated via one or more objective functions. The neural networks are updated in response to the evaluating. The neural networks generate a second vector field from the input image and the reference image. A number of degrees of freedom in the first vector field is less than a number of degrees of freedom in the second vector field. The second vector field is applied to the input image to generate a second warped image. The neural networks are evaluated via the one or more objective functions, the reference image and the second warped image. The networks are updated in response to the evaluating.

Multiple cameras are arranged in an array at a pitch, roll, and yaw that allow the cameras to have adjacent fields of view such that each camera is pointed inward relative to the array. The read window of an image sensor of each camera in a multi-camera array can be adjusted to minimize the overlap between adjacent fields of view, to maximize the correlation within the overlapping portions of the fields of view, and to correct for manufacturing and assembly tolerances. Images from cameras in a multi-camera array with adjacent fields of view can be manipulated using low-power warping and cropping techniques, and can be taped together to form a final image.