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: 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 herein relate to generating a personalized model using a machine learning process, identifying a learning engagement state of a learner based at least in part on the personalized model, and tailoring computerized provision of an educational program to the learner based on the learning engagement state. An apparatus to provide a computer-aided educational program may include one or more processors operating modules that may receive indications of interactions of a learner and indications of physical responses of the learner, generate a personalized model using a machine learning process based at least in part on the interactions of the learner and the indications of physical responses of the learner during a calibration time period, and identify a current learning state of the learner based at least in part on the personalized model during a usage time period. Other embodiments may be described and/or claimed.

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: Embodiments herein relate to identifying a learning engagement state of a learner. A computing platform with one or more processors running modules may receive indications of interactions of a learner with an educational program as well as indications of physical responses of the learner collected substantially simultaneously as the learner interacts with the educational program. A current learning engagement state of the learner may be identified based at least in part on the received indications by using an artificial neural network associated that is calibrated to the learner. The artificial neural network may be trained and updated in part by human observation and learner self-reporting of the learner's current learning engagement state.

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 are disclosed for determining an affective state of a user. A user behavior characteristic is detected in response to content provided to the user. Content metadata indicates a context of the content provided to the user and a probability of the user experiencing at least one expected emotion in response to an interaction with the content. Based on the context and the at least one expected emotion indicated in the content metadata, one or more rules are applied to map the detected user behavior characteristic to an affective state of the user.

Abstract: A mechanism is described for facilitating affect-based adaptive representation of user behavior relating to user expressions on computing devices according to one embodiment. A method of embodiments, as described herein, includes receiving a plurality of expressions communicated by a user. The plurality of expressions may include one or more visual expressions or one or more audio expressions. The method may further include extracting a plurality of features associated with the plurality of expressions, where each feature reveals a behavior trait of the user when the user communicates a corresponding expression. The method may further include mapping the plurality of expressions on a model based on the plurality of features, and discovering a behavioral reasoning associated with each of the plurality of expressions communicated by the user based on a mapping pattern as inferred from the model.

Abstract: Generally, this disclosure provides methods and systems for video indexing systems with viewer reaction estimation based on visual cue detection. The method may include detecting visual cues generated by a user, the visual cues generated in response to the user viewing the video; mapping the visual cues to an emotion space associated with the user; estimating emotion events of the user based on the mapping; and indexing the video with metadata, the metadata comprising the estimated emotion events and timing data associated with the estimated emotion events. The method may further include summarization, partitioning and searching of videos based on the video index.

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: 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: Generally, this disclosure provides methods and systems for video indexing systems with viewer reaction estimation based on visual cue detection. The method may include detecting visual cues generated by a user, the visual cues generated in response to the user viewing the video; mapping the visual cues to an emotion space associated with the user; estimating emotion events of the user based on the mapping; and indexing the video with metadata, the metadata comprising the estimated emotion events and timing data associated with the estimated emotion events. The method may further include summarization, partitioning and searching of videos based on the video index.

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: Methods and systems may provide for an image processing pipeline having a hardware module to spatially filter a raw image in the horizontal direction to obtain intermediate image data. The pipeline can also include a set of instructions which, if executed by a processor, cause the pipeline to spatially filter the intermediate image data in the vertical direction.

Abstract: A 3-D camera is disclosed. The 3-D camera includes an optical system, a front-end block, and a processor. The front-end block further includes a combined image sensor to generate an image, which includes color information and near infra-red information of a captured object and a near infra-red projector to generate one or more patterns. The processor is to generate a color image and a near infra-red image from the image and then generate a depth map using the near infra-red image and the one or more patterns from a near infra-red projector. The processor is to further generate a full three dimensional color model based on the color image and the depth map, which may be aligned with each other.

Abstract: Methods and systems may provide for an image processing pipeline having a hardware module to spatially filter a raw image in the horizontal direction to obtain intermediate image data. The pipeline can also include a set of instructions which, if executed by a processor, cause the pipeline to spatially filter the intermediate image data in the vertical direction.