Abstract: In a method of generating a three dimensional representation of a human body shape from a depth image of a clothed human subject is disclosed.

Abstract: A system for capturing 3D image data of a scene, including three light sources, each configured to emit light at a different wavelength to the other two sources and to illuminate the scene to be captured; a first video camera configured to receive light from the light sources which has been reflected from the scene, to isolate light received from each of the light sources, and to output data relating to the image captured for each of the three light sources; a depth sensor configured to capture depth map data of the scene; and an analysis unit configured to receive data from the first video camera and process the data to obtain data relating to a normal field obtained from the images captured for each of the three light sources, and to combine the normal field data with the depth map data to capture 3D image data of the scene.

Abstract: In one embodiment a method of generating a three dimensional model of a subject is disclosed. The method comprises receiving point cloud data for a subject; receiving user inputs indicating a plurality of cubes, and a grouping of the cubes into a plurality of blocks to form a representation of a class of subjects; generating a first mesh comprising a plurality of quadrilaterals by subdividing patches corresponding to faces of blocks of the plurality of blocks; fitting the first mesh to the point cloud data to generate a fitted mesh; iteratively generating further meshes, each comprising a plurality of quadrilaterals by subdividing patches of the fitted mesh from the previous iteration and fitting the further mesh to the point cloud data; and outputting as the three dimensional model of the subject the iteratively generated fitted mesh.

Abstract: A method of object recognition and/or registration includes receiving a point cloud, arranging the points of the point cloud into a hierarchical search tree, and determining geometric information of the points located within a region, by identifying a highest level tree nodes where all of descendent leaf nodes are contained within the region and selecting the leaf nodes for the points where no sub-tree is entirely contained within the region, such that the points falling within the region are represented by the smallest number of nodes and performing statistical operations on the nodes representing the points in the region. The geometric information includes descriptors of features in the point cloud. The method further includes comparing the feature descriptors with a database of feature descriptors for a plurality of objects.

Abstract: In a method of generating a three dimensional representation of a human body shape from a depth image of a clothed human subject is disclosed.

Abstract: According to one embodiment, a method of generating a three dimensional representation of a subject from a depth image, comprises comparing a depth image of the subject with a plurality of representative images, wherein each representative image is associated with a respective parametric model of a subject; identifying a representative image of the plurality of representative images as a closest representative image to the depth image of the subject; selecting the parametric model associated with the closest representative image to the depth image; and generating a three dimensional representation of the subject by fitting the selected parametric model to the depth image of the subject.

Abstract: A method of calculating a similarity measure between first and second image patches, which include respective first and second intensity values associated with respective elements of the first and second image patches, and which have a corresponding size and shape such that each element of the first image patch corresponds to an element on the second image patch. The method: determines a set of sub-regions on the second image patch corresponding to elements of the first image patch and having first intensity values within a range defined for that sub-region; calculates variance, for each sub-region of the set over all of the elements of that sub-region, of a function of the second intensity value associated with that element and the first intensity value associated with the corresponding element of the first image patch; and calculates similarity measure as the sum over all sub-regions of the calculated variances.

Abstract: In one embodiment a method of generating a three dimensional model of a subject is disclosed. The method comprises receiving point cloud data for a subject; receiving user inputs indicating a plurality of cubes, and a grouping of the cubes into a plurality of blocks to form a representation of a class of subjects; generating a first mesh comprising a plurality of quadrilaterals by subdividing patches corresponding to faces of blocks of the plurality of blocks; fitting the first mesh to the point cloud data to generate a fitted mesh; iteratively generating further meshes, each comprising a plurality of quadrilaterals by subdividing patches of the fitted mesh from the previous iteration and fitting the further mesh to the point cloud data; and outputting as the three dimensional model of the subject the iteratively generated fitted mesh.

Abstract: A method for analysing a point cloud, the method comprising: receiving a point cloud, comprising a plurality of points, each point representing a spatial point in an image; arranging the points into a hierarchical search tree, with a lowest level comprising a plurality of leaf nodes, where each leaf node corresponds to a point of the point cloud, the search tree comprising a plurality of hierarchical levels with tree nodes in each of the hierarchical levels, the nodes being vertically connected to each other though the hierarchy by branches, wherein at least one moment of the property of the descendant nodes is stored in each tree node; and determining geometric information of the points located within a region, by identifying the highest level tree nodes where all of the descendent leaf nodes are contained within the region and selecting the leaf nodes for the points where no sub-tree is entirely contained within the region, such that such that the points falling within the region are represented by the s

Abstract: A method for comparing a plurality of objects, the method comprising representing at least one feature of each object as a 3D ball representation, the radius of each ball representing the scale of the feature in the with respect to the frame of the object, the position of each ball representing the translation the feature in the frame of the object, the method further comprising comparing the objects by comparing the scale and translation as represented by the 3D balls to determine similarity between objects and their poses.

Abstract: A moving feature is recognized in a video sequence by comparing its movement with a characteristic pattern. Possible trajectories through the video sequence are generated for an object by identifying potential matches of points in pairs of frames of the video sequence. When looking for the characteristic pattern, a number of possible trajectories are analyzed. The possible trajectories may be selected so that they are suitable for analysis. This may include selecting longer trajectories that can be easier to analyze. Thereby where the object being tracked is momentarily behind another object a continuous trajectory is generated.

Abstract: A method of comparing two object poses, wherein each object pose is expressed in terms of position, orientation and scale with respect to a common coordinate system, the method comprising: calculating a distance between the two object poses, the distance being calculated using the distance function: d sRt ? ( X , Y ) = d s 2 ? ( X , Y ) ? s 2 + d r 2 ? ( X , Y ) ? r 2 + d t 2 ? ( X , Y ) ? t 2 .

Abstract: A method of comparing two object poses, wherein each object pose is expressed in terms of position, orientation and scale with respect to a common coordinate system, the method comprising: calculating a distance between the two object poses, the distance being calculated using the distance function: d sRt ? ( X , Y ) = d s 2 ? ( X , Y ) ? s 2 + d r 2 ? ( X , Y ) ? r 2 + d t 2 ? ( X , Y ) ? t 2 .

Abstract: According to one embodiment, a method of generating a three dimensional representation of a subject from a depth image, comprises comparing a depth image of the subject with a plurality of representative images, wherein each representative image is associated with a respective parametric model of a subject; identifying a representative image of the plurality of representative images as a closest representative image to the depth image of the subject; selecting the parametric model associated with the closest representative image to the depth image; and generating a three dimensional representation of the subject by fitting the selected parametric model to the depth image of the subject.

Abstract: A method of comparing two object poses, wherein each object pose is expressed in terms of position, orientation and scale with respect to a common coordinate system, the method comprising: calculating a distance between the two object poses, the distance being calculated using the distance function: d sRt ? ( X , Y ) = d s 2 ? ( X , Y ) ? s 2 + d r 2 ? ( X , Y ) ? r 2 + d t 2 ? ( X , Y ) ? t 2 .

Abstract: A characteristic motion in a video is identified by determining pairs of moving features that have an indicative relationship between the motions of the two moving features in the pair. For example, the motion of a pedestrian is identified by an indicative relationship between the motions of the pedestrian's feet. This indicative relationship may be that one of the feet moves relative to the surroundings while the other remains stationary.

Abstract: An object location method includes: analyzing data including plural objects each including plural features, and extracting the features from the data; matching features stored in a database with those extracted from the data, and deriving a prediction of the object, each feature extracted from the data providing a vote for at least one prediction; expressing the prediction to be analyzed in a Hough space, the objects to be analyzed being described by n parameters and each parameter defining a dimension of the Hough space, n is an integer of at least one; providing a constraint by applying a higher weighting to votes which agree with votes from other features than those votes which do not agree with votes from other features; finding local maxima in the Hough space using the weighted votes; and identifying the predictions associated with the local maxima to locate the objects provided in the data.

Abstract: According to one embodiment, a method of classifying a feature in a video sequence includes selecting a target region of a frame of the video sequence, where the target region contains the feature; dividing the target region into a plurality cells, calculating histograms of optic flow with the cells comparing the histograms of optic flow for pairs of cells; and assigning the feature to a class based at least in part on the result of the comparison.

Abstract: A method of calculating a similarity measure between first and second image patches, which include respective first and second intensity values associated with respective elements of the first and second image patches, and which have a corresponding size and shape such that each element of the first image patch corresponds to an element on the second image patch. The method: determines a set of sub-regions on the second image patch corresponding to elements of the first image patch and having first intensity values within a range defined for that sub-region; calculates variance, for each sub-region of the set over all of the elements of that sub-region, of a function of the second intensity value associated with that element and the first intensity value associated with the corresponding element of the first image patch; and calculates similarity measure as the sum over all sub-regions of the calculated variances.

Abstract: A method dividing an image into plural superpixels of plural pixels of the image. The method calculates an initial set of weights from a measure of similarity between pairs of pixels, from which a resultant set of weights is calculated for pairs of pixels that are less that a threshold distance apart on the image. The calculation calculates a weight for a pair of pixels as the sum over a set of third pixels of the product of initial weight of the first pixel of the pair of pixel with the third pixel and the weight of the third pixel with the second pixel. Each weight is then subjected to a power coefficient operation. The resultant set of weights and the initial set of weights are then compared to check for convergence. If the weights converge, the converged set of weights is used to divide the image into superpixels.