Abstract: A three-dimensional depiction of an object to be tracked may be tracked using a depth sensing camera. An indication of the object's movement is developed. Also, an amount of pixels in the depiction that are not part of the object is estimated. Then the indication is corrected based on said amount of pixels that are not part of the object.

Abstract: Systems and methods may provide for projection of a plurality of structured light patterns. In one example, the method may include generating a low-resolution pattern image utilizing a returned image, wherein the low-resolution pattern image is an approximation of an image that would have been generated utilizing a low-resolution pattern and generating a high-resolution pattern image utilizing a preprocessed returned image and a preprocessed low-resolution pattern image, wherein high-resolution pattern image is an approximation of an image that would have been generated utilizing a high-resolution pattern.

Abstract: Techniques to determine a search range for a stereo based matching pyramid. A first disparity estimation value for a first level in a stereo based matching pyramid based on an image may be received. A search range for a second level may be determined using the first disparity estimation value. The search range based on a pyramid level of a second level may be increased. The search range may be increased based on a pyramid level of the second level. A second disparity estimation value may be selected from the search area for the second level. A depth map for the second level may be determined based on the second disparity estimation value. Other embodiments are described and claimed.

Abstract: Embodiments of systems, apparatuses, and methods for performing in a computer processor vector packed horizontal partial sum of packed data elements in response to a single vector packed horizontal sum instruction that includes a destination vector register operand, a source vector register operand, and an opcode are described.

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: Systems and methods may provide for projection of a plurality of structured light patterns. In one example, the method may include generating a low-resolution pattern image utilizing a returned image, wherein the low-resolution pattern image is an approximation of an image that would have been generated utilizing a low-resolution pattern and generating a high-resolution pattern image utilizing a preprocessed returned image and a preprocessed low-resolution pattern image, wherein high-resolution pattern image is an approximation of an image that would have been generated utilizing a high-resolution pattern.

Abstract: Techniques to determine a search range for a stereo based matching pyramid. A first disparity estimation value for a first level in a stereo based matching pyramid based on an image may be received. A search range for a second level may be determined using the first disparity estimation value. The search range based on a pyramid level of a second level may be increased. The search range may be increased based on a pyramid level of the second level. A second disparity estimation value may be selected from the search area for the second level. A depth map for the second level may be determined based on the second disparity estimation value. Other embodiments are described and claimed.

Abstract: Systems and methods may provide for projection of a plurality of structured light patterns. In one example, the method may include generating a low-resolution pattern image utilizing a returned image, wherein the low-resolution pattern image is an approximation of an image that would have been generated utilizing a low-resolution pattern and generating a high-resolution pattern image utilizing a preprocessed returned image and a preprocessed low-resolution pattern image, wherein high-resolution pattern image is an approximation of an image that would have been generated utilizing a high-resolution pattern.

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: A processor includes a face recognition block and a face detection block. The face detection block includes a scan block and control logic. The scan block may divide an image into a plurality of rectangles, determine whether at least some pixel values in each of the plurality of rectangles is within an allowable skin tone range, reject one or more of the plurality of rectangles, which do not contain pixels having a value within the allowable skin tone range, and mark remaining of the plurality of rectangles as component rectangles of a human face. Further, the scan block is to determine variability of the component rectangles, compare the variability of the component rectangles with a variability threshold, reject one or more of the component rectangles whose variability is less than the variability threshold, and retain remaining component rectangles as features of the human face.

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: Embodiments of systems, apparatuses, and methods for performing in a computer processor vector packed horizontal partial sum of packed data elements in response to a single vector packed horizontal sum instruction that includes a destination vector register operand, a source vector register operand, and an opcode are described.

Abstract: Techniques to determine a search range for a stereo based matching pyramid. A first disparity estimation value for a first level in a stereo based matching pyramid based on an image may be received. A search range for a second level may be determined using the first disparity estimation value. The search range based on a pyramid level of a second level may be increased. The search range may be increased based on a pyramid level of the second level. A second disparity estimation value may be selected from the search area for the second level. A depth map for the second level may be determined based on the second disparity estimation value. Other embodiments are described and claimed.

Abstract: Systems and methods may include utilizing a structured light pattern that may be, among other things, decoded in the three directions (e.g., vertical, horizontal, and diagonal). In one example, the method may include detecting a first feature of a target image in a return image, designating a feature type of the first feature, and an index with the letter, wherein the index is associated with the pattern slide. The method may also include calculating a horizontal position in the pattern slide of the first feature, calculating a vertical position in the pattern slide of the first feature, and calculating a depth of the first feature.

Abstract: An apparatus may include an emitter to project a low resolution optical pattern and a high resolution optical pattern having a finer resolution than the low resolution optical pattern and a sensor to detect a composite image, where the composite image comprises a low resolution optical reflection pattern comprising reflection of the projected low resolution optical pattern and a high resolution optical reflection pattern comprising a reflection of the projected high resolution optical pattern. The apparatus may also include logic to determine object depth in a first depth range and object depth in a second depth range based upon the detected composite image. Other embodiments are disclosed and claimed.

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: Systems and methods may provide for determining a one-dimensional (1D) disparity between a plurality of rectified images, and extracting depth information from the plurality of rectified images based at least in part on the 1D disparity. In one example, the 1D disparity is in the horizontal direction and the images are rectified with respect to one another in the vertical direction.

Abstract: Systems and methods may provide for projection of a plurality of structured light patterns. In one example, the method may include generating a low-resolution pattern image utilizing a returned image, wherein the low-resolution pattern image is an approximation of an image that would have been generated utilizing a low-resolution pattern and generating a high-resolution pattern image utilizing a preprocessed returned image and a preprocessed low-resolution pattern image, wherein high-resolution pattern image is an approximation of an image that would have been generated utilizing a high-resolution pattern.

Abstract: Systems and methods may include utilizing a structured light pattern that may be, among other things, decoded in the three directions (e.g., vertical, horizontal, and diagonal). In one example, the method may include detecting a first feature of a target image in a return image, designating a feature type of the first feature, and an index with the letter, wherein the index is associated with the pattern slide. The method may also include calculating a horizontal position in the pattern slide of the first feature, calculating a vertical position in the pattern slide of the first feature, and calculating a depth of the first feature.

Abstract: A processor includes a face recognition block and a face detection block. The face detection block includes a scan block and control logic. The scan block may divide an image into a plurality of rectangles, determine whether at least some pixel values in each of the plurality of rectangles is within an allowable skin tone range, reject one or more of the plurality of rectangles, which do not contain pixels having a value within the allowable skin tone range, and mark remaining of the plurality of rectangles as component rectangles of a human face. Further, the scan block is to determine variability of the component rectangles, compare the variability of the component rectangles with a variability threshold, reject one or more of the component rectangles whose variability is less than the variability threshold, and retain remaining component rectangles as features of the human face.