Abstract: An apparatus, system, and method are described herein. The apparatus includes an emitter and a plurality of sensors. The emitter and the sensors are asymmetrically placed in the system with respect to the emitter. Data from the emitter and sensors is used to generate a high accuracy depth map and a dense depth map. A high resolution and dense depth map is calculated using the high accuracy depth map and the dense depth map.

Abstract: An apparatus, system, and method are described herein. The apparatus includes an emitter and a plurality of sensors. The emitter and the sensors are asymmetrically placed in the system with respect to the emitter. Data from the emitter and sensors is used to generate a high accuracy depth map and a dense depth map. A high resolution and dense depth map is calculated using the high accuracy depth map and the dense depth map.

Abstract: An apparatus may include an image sensor that contains a multiplicity of pixel elements to detect one or more images and a processor circuit coupled to the image sensor. The apparatus may include a white balance module for execution on the processor circuit to receive, based upon a detected image of the one or more images, for a plurality of pixel elements of the multiplicity of pixel elements, three of more gray level values for a respective three or more color channels, to determine grayness likelihood functions for the respective three or more color channels, the three or more grayness likelihood functions comprising a proportional contribution to grey pixels of the detected image from one or more gray levels for each respective color channel, and to determine a white balance gain for two or more color channels based upon the determined grayness likelihood functions. Other embodiments are described and claimed.

Abstract: In accordance with an embodiment of the invention, an anisotropic denoising method is provided that removes sensor noise from a digital image while retaining edges, lines, and details in the image. In one embodiment, the method removes noise from a pixel of interest based on the detected type of image environment in which the pixel is situated. If the pixel is situated in an edge/line image environment, then denoising of the pixel is increased such that relatively stronger denoising of the pixel occurs along the edge or line feature. If the pixel is situated in a detail image environment, then denoising of the pixel is decreased such that relatively less denoising of the pixel occurs so as to preserve the details in the image. In one embodiment, detection of the type of image environment is accomplished by performing simple arithmetic operations using only pixels in a 9 pixel by 9 pixel matrix of pixels in which the pixel of interest is situated.

Abstract: An apparatus may include an image sensor that contains a multiplicity of pixel elements to detect one or more images and a processor circuit coupled to the image sensor. The apparatus may include a white balance module for execution on the processor circuit to receive, based upon a detected image of the one or more images, for a plurality of pixel elements of the multiplicity of pixel elements, three of more gray level values for a respective three or more color channels, to determine grayness likelihood functions for the respective three or more color channels, the three or more grayness likelihood functions comprising a proportional contribution to grey pixels of the detected image from one or more gray levels for each respective color channel, and to determine a white balance gain for two or more color channels based upon the determined grayness likelihood functions. Other embodiments are described and claimed.

Abstract: In an embodiment, a device comprises a plurality of elements configured to apply a filter to multiple groups of pixels in a neighborhood of pixels surrounding a particular pixel to generate a matrix of filtered values; compute, from the matrix of filtered values, a first set of gradients along a first direction and a second set of gradients along a second and different direction; determine how many directional changes are experienced by the gradients in the first set of gradients and the gradients in the second set of gradients; compute a first weighted value for a first direction and a second weighted value for a second direction; and based, at least in part, upon the first and second weighted values, compute an overall texture characterization value for the particular pixel, wherein the overall texture characterization value indicates a type of image environment in which the particular pixel is located.

Abstract: In accordance with an embodiment of the invention, an anisotropic denoising method is provided that removes sensor noise from a digital image while retaining edges, lines, and details in the image. In one embodiment, the method removes noise from a pixel of interest based on the detected type of image environment in which the pixel is situated. If the pixel is situated in an edge/line image environment, then denoising of the pixel is increased such that relatively stronger denoising of the pixel occurs along the edge or line feature. If the pixel is situated in a detail image environment, then denoising of the pixel is decreased such that relatively less denoising of the pixel occurs so as to preserve the details in the image. In one embodiment, detection of the type of image environment is accomplished by performing simple arithmetic operations using only pixels in a 9 pixel by 9 pixel matrix of pixels in which the pixel of interest is situated.

Abstract: A computer-implemented method includes receiving a depth map (30) of a scene containing a body of a humanoid subject (28). The depth map includes a matrix of pixels (32), each corresponding to a respective location in the scene and having a respective pixel value indicative of a distance from a reference location to the respective location. The depth map is segmented so as to find a contour (64) of the body. The contour is processed in order to identify a torso (70) and one or more limbs (76, 78, 80, 82) of the subject. An input is generated to control an application program running on a computer by analyzing a disposition of at least one of the identified limbs in the depth map.

Abstract: In an embodiment, a device comprises a plurality of elements configured to apply a filter to multiple groups of pixels in a neighborhood of pixels surrounding a particular pixel to generate a matrix of filtered values; compute, from the matrix of filtered values, a first set of gradients along a first direction and a second set of gradients along a second and different direction; determine how many directional changes are experienced by the gradients in the first set of gradients and the gradients in the second set of gradients; compute a first weighted value for a first direction and a second weighted value for a second direction; and based, at least in part, upon the first and second weighted values, compute an overall texture characterization value for the particular pixel, wherein the overall texture characterization value indicates a type of image environment in which the particular pixel is located.

Abstract: A method for facial feature detection. The method comprises the following steps: a) receiving a digital image depicting a human face, the digital image comprising a plurality of color pixels, each one of the color pixels comprising color information, b) segmenting a face segment based on the color information, the face segment delimiting the area of the face, c) identifying a centerline that approximately bisects a human face section, and d) using the centerline to segment a set of facial features of the human face.

Abstract: A computer-implemented method includes receiving a depth map (30) of a scene containing a body of a humanoid subject (28). The depth map includes a matrix of pixels (32), each corresponding to a respective location in the scene and having a respective pixel value indicative of a distance from a reference location to the respective location. The depth map is segmented so as to find a contour (64) of the body. The contour is processed in order to identify a torso (70) and one or more limbs (76, 78, 80, 82) of the subject. An input is generated to control an application program running on a computer by analyzing a disposition of at least one of the identified limbs in the depth map.

Abstract: A method for facial feature detection. The method comprises the following steps: a) receiving a digital image depicting a human face, the digital image comprising a plurality of color pixels, each one of the color pixels comprising color information, b) segmenting a face segment based on the color information, the face segment delimiting the area of the face, c) identifying a centerline that approximately bisects a human face section, and d) using the centerline to segment a set of facial features of the human face.

Abstract: A method for determining the orientation of a pattern on the surface of an object relative to the object. The pattern comprises an array of generally perpendicular grid lines intersecting at grid junctions, and the object comprises a marker located at an edge thereof. The method comprises: determining the directions of the grid lines relative to the direction of the reference coordinate system, determining the direction of object's orientation axis relative to the direction of the reference coordinate system, and determining the orientation of the grid lines relative to the object's orientation axis. The step of determining the direction of object's orientation axis comprises the steps of determining a location of the geometrical center of the object in the reference coordinate system and detecting and determining a location of the marker in the reference coordinate system.

Abstract: A method for determining the orientation of a pattern on the surface of an object relative to the object. The pattern comprises an array of generally perpendicular grid lines intersecting at grid junctions, and the object comprises a marker located at an edge thereof. The method comprises: determining the directions of the grid lines relative to the direction of the reference coordinate system, determining the direction of object's orientation axis relative to the direction of the reference coordinate system, and determining the orientation of the grid lines relative to the object's orientation axis. The step of determining the direction of object's orientation axis comprises the steps of determining a location of the geometrical center of the object in the reference coordinate system and detecting and determining a location of the marker in the reference coordinate system.