Abstract: A light pattern projector with a pattern mask to spatially modulate an intensity of a wideband illumination source, such as an LED, and a projector lens to reimage the spatially modulated emission onto regions of a scene that is to be captured with an image sensor. The projector lens may comprise a microlens array (MLA) including a first lenslet to reimage the spatially modulated emission onto a first portion of a scene, and a second lenslet to reimage the spatially modulated emission onto a first portion of a scene. The MLA may have a fly's eye architecture with convex curvature over a diameter of the projector lens in addition to the lenslet curvature. The pattern mask may be an amplitude mask comprising a mask pattern of high and low amplitude transmittance regions. In the alternative, the pattern mask may be a phase mask, such as a refractive or diffractive mask.

Abstract: A light pattern projector with a pattern mask to spatially modulate an intensity of a wideband illumination source, such as an LED, and a projector lens to reimage the spatially modulated emission onto regions of a scene that is to be captured with an image sensor. The projector lens may comprise a microlens array (MLA) including a first lenslet to reimage the spatially modulated emission onto a first portion of a scene, and a second lenslet to reimage the spatially modulated emission onto a first portion of a scene. The MLA may have a fly's eye architecture with convex curvature over a diameter of the projector lens in addition to the lenslet curvature. The pattern mask may be an amplitude mask comprising a mask pattern of high and low amplitude transmittance regions. In the alternative, the pattern mask may be a phase mask, such as a refractive or diffractive mask.

Abstract: A light pattern projector with a pattern mask to spatially modulate an intensity of a wideband illumination source, such as an LED, and a projector lens to reimage the spatially modulated emission onto regions of a scene that is to be captured with an image sensor. The projector lens may comprise a microlens array (MLA) including a first lenslet to reimage the spatially modulated emission onto a first portion of a scene, and a second lenslet to reimage the spatially modulated emission onto a first portion of a scene. The MLA may have a fly's eye architecture with convex curvature over a diameter of the projector lens in addition to the lenslet curvature. The pattern mask may be an amplitude mask comprising a mask pattern of high and low amplitude transmittance regions. In the alternative, the pattern mask may be a phase mask, such as a refractive or diffractive mask.

Abstract: A light pattern projector with a pattern mask to spatially modulate an intensity of a wideband illumination source, such as an LED, and a projector lens to reimage the spatially modulated emission onto regions of a scene that is to be captured with an image sensor. The projector lens may comprise a microlens array (MLA) including a first lenslet to reimage the spatially modulated emission onto a first portion of a scene, and a second lenslet to reimage the spatially modulated emission onto a first portion of a scene. The MLA may have a fly's eye architecture with convex curvature over a diameter of the projector lens in addition to the lenslet curvature. The pattern mask may be an amplitude mask comprising a mask pattern of high and low amplitude transmittance regions. In the alternative, the pattern mask may be a phase mask, such as a refractive or diffractive mask.

Abstract: Techniques are provided for range image generation. An input image and a reference image are used to compute raw and optimized disparity-space correlation disparity results for each pixel in the reference image. A bilateral filter is applied to both the raw and optimized disparity space correlation disparity results within a neighborhood of a given pixel. For each pixel in the neighborhood, a mean of sub-pixel disparity estimates includes the raw disparity-space correlation disparity result, and another mean of sub-pixel disparity estimates is computed using optimized disparity-space correlation disparity results. The output of the bilateral filter is a weighted sum of the means, with the weights being proportional to the ratio of valid, non-optimized sub-pixel correlation disparity results to optimized sub-pixel correlation disparity results, thereby favoring non-optimized estimates.

Abstract: Techniques are provided for range image generation. An input image and a reference image are used to compute raw and optimized disparity-space correlation disparity results for each pixel in the reference image. A bilateral filter is applied to both the raw and optimized disparity space correlation disparity results within a neighborhood of a given pixel. For each pixel in the neighborhood, a mean of sub-pixel disparity estimates includes the raw disparity-space correlation disparity result, and another mean of sub-pixel disparity estimates is computed using optimized disparity-space correlation disparity results. The output of the bilateral filter is a weighted sum of the means, with the weights being proportional to the ratio of valid, non-optimized sub-pixel correlation disparity results to optimized sub-pixel correlation disparity results, thereby favoring non-optimized estimates.

Abstract: Segmentation of background and foreground objects in an image is based upon the joint use of range and color data. Range-based data is largely independent of color image data, and hence not adversely affected by the limitations associated with color-based segmentation, such as shadows and similarly colored objects. Furthermore, color segmentation is complementary to range measurement in those cases where reliable range data cannot be obtained. These complementary sets of data are used to provide a multidimensional background estimation. The segmentation of a foreground object in a given frame of an image sequence is carried out by comparing the image frames with background statistics relating to range and normalized color, using the sets of statistics in a complementary manner. A background model is determined by estimating using a multidimensional histogram, recording pixel values, configuring the pixel values into a cluster, and selecting a largest cluster as representing the background model.

Abstract: A system for distance calculation is disclosed. The system includes an illuminator unit, one or more camera units, and a distance processor. The illuminator unit illuminates a scene in a target area using a textured pattern creator and wherein the textured pattern creator includes a diffractive optical element. The one or more camera units captures two or more images of the target area from two or more physical locations. A textured pattern illumination is visible in each of the two or more images of the target area. The images are used to calculate distances to one or more points in the scene in the target area.

Abstract: An integrated image processor implemented on a substrate is disclosed. An input interface is configured to receive pixel data from two or more images. A pixel handling processor disposed on the substrate is configured to convert the pixel data into depth and intensity pixel data. In some embodiments, a foreground detector processor disposed on the substrate is configured to classify pixels as background or not background. In some embodiments, a projection generator disposed on the substrate is configured to generate a projection in space of the depth and intensity pixel data.

Abstract: A powerful, scaleable, and reconfigurable image processing system and method of processing data therein is described. This general purpose, reconfigurable engine with toroidal topology, distributed memory, and wide bandwidth I/O are capable of solving real applications at real-time speeds. The reconfigurable image processing system can be optimized to efficiently perform specialized computations, such as real-time video and audio processing. This reconfigurable image processing system provides high performance via high computational density, high memory bandwidth, and high I/O bandwidth. Generally, the reconfigurable image processing system and its control structure include a homogeneous array of 16 field programmable gate arrays (FPGA) and 16 static random access memories (SRAM) arranged in a partial torus configuration. The reconfigurable image processing system also includes a PCI bus interface chip, a clock control chip, and a datapath chip. It can be implemented in a single board.

Abstract: Dense range data obtained at real-time rates is employed to estimate the pose of an articulated figure. In one approach, the range data is used in combination with a model of connected patches. Each patch is the planar convex hull of two circles, and a recursive procedure is carried out to determine an estimate of pose which most closely correlates to the range data. In another aspect of the invention, the dense range data is used in conjunction with image intensity information to improve pose tracking performance. The range information is used to determine the shape of an object, rather than assume a generic model or estimate structure from motion. In this aspect of the invention, a depth constraint equation, which is a counterpart to the classic brightness change constraint equation, is employed. Both constraints are used to jointly solve for motion estimates.

Abstract: Segmentation of background and foreground objects in an image is based upon the joint use of both range and color data. Range-based data is largely independent of color image data, and hence not adversely affected by the limitations associated with color-based segmentation, such as shadows and similarly colored objects. Furthermore, color segmentation is complementary to range measurement in those cases where reliable range data cannot be obtained. These complementary sets of data are used to provide a multidimensional background estimation. The segmentation of a foreground object in a given frame of an image sequence is carried out by comparing the image frames with background statistics relating to range and normalized color, using the sets of statistics in a complementary manner.

Abstract: Techniques from computer vision and computer graphics are combined to robustly track a target (e.g., a user) and perform a function based upon the image and/or the identity attributed to the target's face. Three primary modules are used to track a user's head: depth estimation, color segmentation, and pattern classification. The combination of these three techniques allows for robust performance despite unknown background, crowded conditions, and rapidly changing pose or expression of the user. Each of the modules can also provide an identity classification module with valuable information so that the identity of a user can be estimated. With an estimate of the position of a target in 3-D and the target's identity, applications such as individualized computer programs or graphics techniques to distort and/or morph the shape or apparent material properties of the user's face can be performed.

Abstract: Techniques from computer vision and computer graphics are combined to robustly track a target (e.g., a user) and perform a function based upon the image and/or the identity attributed to the target's face. Three primary modules are used to track a user's head: depth estimation, color segmentation, and pattern classification. The combination of these three techniques allows for robust performance despite unknown background, crowded conditions, and rapidly changing pose or expression of the user. Each of the modules can also provide an identity classification module with valuable information so that the identity of a user can be estimated. With an estimate of the position of a target in 3-D and the target's identity, applications such as individualized computer programs or graphics techniques to distort and/or morph the shape or apparent material properties of the user's face can be performed. The system can track and respond to a user's face in real-time using completely passive and non-invasive techniques.

Abstract: Techniques from computer vision and computer graphics are combined to robustly track a target (e.g., a user) and perform a function based upon the image and/or the identity attributed to the target's face. Three primary modules are used to track a user's head: depth estimation, color segmentation, and pattern classification. The combination of these three techniques allows for robust performance despite unknown background, crowded conditions, and rapidly changing pose or expression of the user. Each of the modules can also provide an identity classification module with valuable information so that the identity of a user can be estimated. With an estimate of the position of a target in 3-D and the target's identity, applications such as individualized computer programs or graphics techniques to distort and/or morph the shape or apparent material properties of the user's face can be performed.