Abstract: A method for touch classification includes obtaining frame data representative of a plurality of frames captured by a touch-sensitive device, analyzing the frame data to define a respective blob in each frame of the plurality of frames, the blobs being indicative of a touch event, computing a plurality of feature sets for the touch event, each feature set specifying properties of the respective blob in each frame of the plurality of frames, and determining a type of the touch event via machine learning classification configured to provide multiple non-bimodal classification scores based on the plurality of feature sets for the plurality of frames, each non-bimodal classification score being indicative of an ambiguity level in the machine learning classification.

Abstract: A system includes a memory and a processor configured to select a set of scene point pairs, to determine a respective feature vector for each scene point pair, to find, for each feature vector, a respective plurality of nearest neighbor point pairs in feature vector data of a number of models, to compute, for each nearest neighbor point pair, a respective aligning transformation from the respective scene point pair to the nearest neighbor point pair, thereby defining a respective model-transformation combination for each nearest neighbor point pair, each model-transformation combination specifying the respective aligning transformation and the respective model with which the nearest neighbor point pair is associated, to increment, with each binning of a respective one of the model-transformation combinations, a respective bin counter, and to select one of the model-transformation combinations in accordance with the bin counters to detect an object and estimate a pose of the object.

Abstract: A system includes a memory and a processor configured to select a set of scene point pairs, to determine a respective feature vector for each scene point pair, to find, for each feature vector, a respective plurality of nearest neighbor point pairs in feature vector data of a number of models, to compute, for each nearest neighbor point pair, a respective aligning transformation from the respective scene point pair to the nearest neighbor point pair, thereby defining a respective model-transformation combination for each nearest neighbor point pair, each model-transformation combination specifying the respective aligning transformation and the respective model with which the nearest neighbor point pair is associated, to increment, with each binning of a respective one of the model-transformation combinations, a respective bin counter, and to select one of the model-transformation combinations in accordance with the bin counters to detect an object and estimate a pose of the object.

Abstract: A system includes a memory and a processor configured to select a set of scene point pairs, to determine a respective feature vector for each scene point pair, to find, for each feature vector, a respective plurality of nearest neighbor point pairs in feature vector data of a number of models, to compute, for each nearest neighbor point pair, a respective aligning transformation from the respective scene point pair to the nearest neighbor point pair, thereby defining a respective model-transformation combination for each nearest neighbor point pair, each model-transformation combination specifying the respective aligning transformation and the respective model with which the nearest neighbor point pair is associated, to increment, with each binning of a respective one of the model-transformation combinations, a respective bin counter, and to select one of the model-transformation combinations in accordance with the bin counters to detect an object and estimate a pose of the object.

Abstract: A method for touch classification includes obtaining frame data representative of a plurality of frames captured by a touch-sensitive device, analyzing the frame data to define a respective blob in each frame of the plurality of frames, the blobs being indicative of a touch event, computing a plurality of feature sets for the touch event, each feature set specifying properties of the respective blob in each frame of the plurality of frames, and determining a type of the touch event via machine learning classification configured to provide multiple non-bimodal classification scores based on the plurality of feature sets for the plurality of frames, each non-bimodal classification score being indicative of an ambiguity level in the machine learning classification.

Abstract: A method for touch classification includes obtaining frame data representative of a plurality of frames captured by a touch-sensitive device, analyzing the frame data to define a respective blob in each frame of the plurality of frames, the blobs being indicative of a touch event, computing a plurality of feature sets for the touch event, each feature set specifying properties of the respective blob in each frame of the plurality of frames, and determining a type of the touch event via machine learning classification configured to provide multiple non-bimodal classification scores based on the plurality of feature sets for the plurality of frames, each non-bimodal classification score being indicative of an ambiguity level in the machine learning classification.

Abstract: A system includes a memory and a processor configured to select a set of scene point pairs, to determine a respective feature vector for each scene point pair, to find, for each feature vector, a respective plurality of nearest neighbor point pairs in feature vector data of a number of models, to compute, for each nearest neighbor point pair, a respective aligning transformation from the respective scene point pair to the nearest neighbor point pair, thereby defining a respective model-transformation combination for each nearest neighbor point pair, each model-transformation combination specifying the respective aligning transformation and the respective model with which the nearest neighbor point pair is associated, to increment, with each binning of a respective one of the model-transformation combinations, a respective bin counter, and to select one of the model-transformation combinations in accordance with the bin counters to detect an object and estimate a pose of the object.

Abstract: Global and local light detection techniques in optical sensor systems are described. In one or more implementations, a global lighting value is generated that describes a global lighting level for a plurality of optical sensors based on a plurality of inputs received from the plurality of optical sensors. An illumination map is generated that describes local lighting conditions of respective ones of the plurality of optical sensors based on the plurality of inputs received from the plurality of optical sensors. Object detection is performed using an image captured using the plurality of optical sensors along with the global lighting value and the illumination map.

Abstract: Object detection techniques for use in conjunction with optical sensors is described. In one or more implementations, a plurality of inputs are received, each of the inputs being received from a respective one of a plurality of optical sensors. Each of the plurality of inputs are classified using machine learning as to whether the inputs are indicative of detection of an object by a respective said optical sensor.

Abstract: A method for touch classification includes obtaining frame data representative of a plurality of frames captured by a touch-sensitive device, analyzing the frame data to define a respective blob in each frame of the plurality of frames, the blobs being indicative of a touch event, computing a plurality of feature sets for the touch event, each feature set specifying properties of the respective blob in each frame of the plurality of frames, and determining a type of the touch event via machine learning classification configured to provide multiple non-bimodal classification scores based on the plurality of feature sets for the plurality of frames, each non-bimodal classification score being indicative of an ambiguity level in the machine learning classification.

Abstract: Global and local light detection techniques in optical sensor systems are described. In one or more implementations, a global lighting value is generated that describes a global lighting level for a plurality of optical sensors based on a plurality of inputs received from the plurality of optical sensors. An illumination map is generated that describes local lighting conditions of respective ones of the plurality of optical sensors based on the plurality of inputs received from the plurality of optical sensors. Object detection is performed using an image captured using the plurality of optical sensors along with the global lighting value and the illumination map.

Abstract: Object detection techniques for use in conjunction with optical sensors is described. In one or more implementations, a plurality of inputs are received, each of the inputs being received from a respective one of a plurality of optical sensors. Each of the plurality of inputs are classified using machine learning as to whether the inputs are indicative of detection of an object by a respective said optical sensor.