Abstract:
Various embodiments are generally directed to techniques for employing a hybrid of sequential and parallel processing to perform random sample and consensus (RANSAC). A device to perform RANSAC includes a derivation component to derive a first set of proposed models in parallel from a first set of minimal sample sets of a data set; and a comparison component to recalculate a required quantity of proposed models to derive an accurate model if a proposed model of the first set of proposed models better fits the data set than any proposed model derived prior to derivation of the first set of proposed models, and to determine whether to derive a second set of proposed models following derivation of the first set of proposed models based on a comparison of the required quantity to a quantity of previously derived proposed models that includes the first set. Other embodiments are described and claimed.
Abstract:
Apparatuses, methods and storage medium associated with processing an image are disclosed herein. In embodiments, a method for processing one or more images may include generating a plurality of pairs of keypoint features for a pair of images. Each pair of keypoint features may include a keypoint feature from each image. Further, for each pair of keypoint features, corresponding adjoin features may be generated. Additionally, for each pair of keypoint features, whether the adjoin features are similar may be determined. Whether the pair of images have at least one similar object may also be determined, based at least in part on a result of the determination of similarity between the corresponding adjoin features. Other embodiments may be described and claimed.
Abstract:
A multi-core processor system may support 3D image rendering on an autostereoscopic display. The 3D image rendering includes pre-processing of depth map and 3D image wrapping tasks. The pre-processing of depth map may include a foreground prior depth image smoothing technique, which may perform a depth gradient detection and a smoothing task. The depth gradient detection task may detect areas with large depth gradient and the smoothing task may transform the large depth gradient into a linearly changing shape using low-strength, low-pass filtering techniques. The 3D image wrapping may include vectorizing the code for 3D image wrapping of row pixels using an efficient single instruction multiple data (SIMD) technique. After vectorizing, an API such as OpenMP may be used to parallelize the 3D image wrapping procedure. The 3D image wrapping using OpenMP may be performed on rows of the 3D image and on images of the multiple view images.
Abstract:
Apparatuses, methods and storage medium associated with creating an avatar video are disclosed herein. In embodiments, the apparatus may one or more facial expression engines, an animation-rendering engine, and a video generator. The one or more facial expression engines may be configured to receive video, voice and/or text inputs, and, in response, generate a plurality of animation messages having facial expression parameters that depict facial expressions for a plurality of avatars based at least in part on the video, voice and/or text inputs received. The animation-rendering engine may be configured to receive the one or more animation messages, and drive a plurality of avatar models, to animate and render the plurality of avatars with the facial expression depicted. The video generator may be configured to capture the animation and rendering of the plurality of avatars, to generate a video. Other embodiments may be described and/or claimed.
Abstract:
Apparatuses, methods and storage medium associated with animating and rendering an avatar are disclosed herein. In embodiments, the apparatus may include a gesture tracker and an animation engine. The gesture tracker may be configured to detect and track a user gesture that corresponds to a canned facial expression, the user gesture including a duration component corresponding to a duration the canned facial expression is to be animated. Further, the gesture tracker may be configured to respond to a detection and tracking of the user gesture, and output one or more animation messages that describe the detected/tracked user gesture or identify the canned facial expression, and the duration. The animation engine may be configured to receive the one or more animation messages, and drive an avatar model, in accordance with the one or more animation messages, to animate the avatar with animation of the canned facial expressions for the duration. Other embodiments may be described and/or claimed.
Abstract:
Various embodiments are generally directed to techniques for employing a hybrid of sequential and parallel processing to perform random sample and consensus (RANSAC). A device to perform RANSAC includes a derivation component to derive a first set of proposed models in parallel from a first set of minimal sample sets of a data set; and a comparison component to recalculate a required quantity of proposed models to derive an accurate model if a proposed model of the first set of proposed models better fits the data set than any proposed model derived prior to derivation of the first set of proposed models, and to determine whether to derive a second set of proposed models following derivation of the first set of proposed models based on a comparison of the required quantity to a quantity of previously derived proposed models that includes the first set. Other embodiments are described and claimed.
Abstract:
Stereo image reconstruction techniques are described. An image from a root viewpoint is translated to an image from another viewpoint. Homography fitting is used to translate the image between viewpoints. Inverse compositional image alignment is used to determine a homography matrix and determine a pixel in the translated image.
Abstract:
Stereo image reconstruction techniques are described. An image from a root viewpoint is translated to an image from another viewpoint. Homography fitting is used to translate the image between viewpoints. Inverse compositional image alignment is used to determine a homography matrix and determine a pixel in the translated image.