A novel methodology for computing the medial axis/skeleton of a discrete binary object using a divide and conquer algorithm, in which any 3D object is first sliced into a series of 2D images in X, Y and Z directions. Then, a geometric (Voronoi) algorithm is applied on each 2D image to extract the respective medial axis. This information is then combined to reconstruct the medial axis of the original 3D object using an intersection technique. An optional 3D interpolation step to achieve continuous connected skeletons uses Delaunay triangles and a spherical search to establish the nearest neighboring points in 3D space to interpolate between. Test results show that the proposed 3D Voronoi and optional interpolation algorithms are able to accurately and efficiently extract medial axes for complex 3D objects as well. Finally, an axis-smoothing algorithm using the same Delaunay triangle and spherical test is operable to remove unwanted noise from the extracted medial axis.

A computer implemented method for determining a centerline of a three-dimensional tubular structure is described. The method includes providing an edge-detected data set of voxels that characterize a boundary of the tubular structure according to a three-dimensional voxel data set for the tubular structure. A gradient field of a distance transformation is computed for the edge-detected dataset. A voxel data set corresponding to a centerline of the tubular structure is computed according to derivative of gradient field. A trajectory within the tubular structure is computed based on the centerline.

The invention belongs to the technical field of the quantitative statistical distribution analysis of the features from characteristic images of microstructures and precipitated phases in metal materials, and relates to a quantitative statistical distribution characterization method of precipitate particles with the full field of view in a metal material. The method comprises the following steps of electrolytic corrosion of a metallic material specimen, automatic collection of characteristic images of microstructure, automatic stitching and fusion of the full-view-field microstructure images, automatic identification and segmentation of the precipitate particles and quantitative distribution characterization of the precipitate particles with the full field of view in a large-range scale. By establishing a mathematic model, the large-range automatic stitching and fusion of the characteristic images of the full-view-field microstructures in a characteristic region and the automatic segmentation and identification of the precipitate particles are realized; and the quantitative statistical distribution characterization information of the full-view-field morphology, quantity, size, distribution and the like of plentiful precipitated phases in a larger range is quickly obtained. The method has the features of being accurate, high-efficiency and informative in quantitative distribution characterization, as well as has much more statistical representativeness compared with conventional single-view-field quantitative image analysis.

A method of localizing portable apparatus (100) in an environment, the method comprising obtaining captured image data representing an image captured by an imaging device (106) associated with the portable apparatus, and obtaining mesh data representing a 3-dimensional textured mesh of at least part of the environment. The mesh data is processed to generate a plurality of synthetic images, each synthetic image being associated with a pose within the environment and being a simulation of an image that would be captured by the imaging device from that associated pose. The plurality of synthetic images is analyzed to find a said synthetic image similar to the captured image data, and an indication is provided of a pose of the portable apparatus within the environment based on the associated pose of the similar synthetic image.

A computer implemented method for determining a centerline of a three-dimensional tubular structure is described. The method includes providing an edge-detected data set of voxels that characterize a boundary of the tubular structure according to a three-dimensional voxel data set for the tubular structure. A gradient field of a distance transformation is computed for the edge-detected dataset. A voxel data set corresponding to a centerline of the tubular structure is computed according to derivative of gradient field.

A device, system and method for automatic calibration of image devices is provided. Triplets of at least three image devices, including a projector, are in non-collinear arrangements, and pairs of the image devices have overlapping fields of view on a physical object. Pixel correspondences between the pairs are used to determine relative vectors between the image devices. Relative locations between each of the image devices are determined based on the relative vectors with a relative distance between one pair of the image devices is to an arbitrary distance, the relative locations being further relative to a cloud-of-points representing the object. A model of the object and the cloud-of-points are aligned to transform the relative locations of each of the image devices to locations relative to the model. The projector is controlled to project onto the object based at least on the locations relative to the model.

Techniques are provided for determining a cell count within a whole slide pathology image. The image is segmented using a global threshold value to define a tissue area. A plurality of patches comprising the tissue area are selected. Stain intensity vectors are determined within the plurality of patches to generate a stain intensity image. The stain intensity image is iteratively segmented to generate a cell mask using a local threshold value that is and gradually reduced after each iteration. A chamfer distance transform is applied to the cell mask to generate a distance map. Cell seeds are determined on the distance map. Cell segments are determined using a watershed transformation, and a whole cell count is calculated for the plurality of patches based on the cell segments. A client device may be configured for real-time cell counting based on the whole cell count.

A system for plant leaf identification includes: a plant image capturing unit which captures an image of a target plant to generate a plant image; a plant area image extraction unit which separates a background area and a plant area in the plant image to generate a plant area image including the plant area; a plant area image skeletonization unit which skeletonizes the plant area image to generate a skeletonized plant area image; a candidate leaf path generation unit which identifies a root vertex, a junction vertex and a leaf tip vertex in the skeletonized plant area image, and generates a plurality of candidate leaf paths by calculating all possible paths from the root vertex to the leaf tip vertex; and a final leaf path reconstruction unit which reconstructs a final leaf path matching the plant image by selecting the plurality of candidate leaf paths.

A method of localising portable apparatus (100) in an environment, the method comprising obtaining captured image data representing an image captured by an imaging device (106) associated with the portable apparatus, and obtaining mesh data representing a 3-dimensional textured mesh of at least part of the environment. The mesh data is processed to generate a plurality of synthetic images, each synthetic image being associated with a pose within the environment and being a simulation of an image that would be captured by the imaging device from that associated pose. The plurality of synthetic images is analysed to find a said synthetic image similar to the captured image data, and an indication is provided of a pose of the portable apparatus within the environment based on the associated pose of the similar synthetic image.

This specification describes technologies relating to biometric authentication based on images of the eye. In general, one aspect of the subject matter described in this specification can be embodied in methods that include obtaining a first image of an eye including a view of the white of the eye. The method may further include determining metrics for the first image, including a first metric for reflecting an extent of one or more connected structures in the first image that represents a morphology of eye vasculature and a second metric for comparing the extent of eye vasculature detected across different color components in the first image. A quality score may be determined based on the metrics for the first image. The first image may be rejected or accepted based on the quality score.