Abstract: An example apparatus includes: a camera to record an image; memory to store instructions; and a processor in circuit with the memory, the processor to execute the instructions to: determine a depth based on: (a) the image and (b) a calibration parameter of the camera; and adjust the calibration parameter based on a temperature of the camera and the depth.

Abstract: A method and apparatus for auto range control are described. In one embodiment, the apparatus comprises a projector configured to project a sequence of light patterns on an object; a first camera configured to capture a sequence of images of the object illuminated with the projected light patterns; a controller coupled to the projector and first camera and operable to receive the sequence of images and perform range control by controlling power of the sequence of light patterns being projected on the object and exposure time of a camera based on information obtained from the sequence of images captured by the camera.

Abstract: A method of processing an image is disclosed. The method comprises decomposing the image into a plurality of channels, each being characterized by a different depth-of-field, and accessing a computer readable medium storing an in-focus dictionary defined over a plurality of dictionary atoms, and an out-of-focus dictionary defined over a plurality of sets of dictionary atoms, each set corresponding to a different out-of-focus condition. The method also comprises computing one or more sparse representations of the decomposed image over the dictionaries.

Abstract: A diagnostic method includes applying a vibration at a selected frequency to a location within a brain of a living subject (32). Electrical signals resulting from the vibration are measured at multiple positions on a scalp of the subject. The measured electrical signals are processed in order to compute an electrical gain matrix between the location within the brain and the positions on the scalp. Electroencephalogram (EEG) signals are measured at the multiple positions on the scalp. The EEG signals are filtered using the gain matrix in order to identify brain electrical activity originating from the location.

Abstract: An example apparatus for calibrating texture cameras includes an image receiver to receive a depth image from a depth camera and a color image from a texture camera. The apparatus also includes a feature extractor to extract features from the depth image and the color image. The apparatus further includes a feature tester to detect that the extracted features from the depth image and the color image exceed a quality threshold. The apparatus includes a misalignment detector to detect a misalignment between the extracted features from depth image and the extracted features from color image exceeds a misalignment threshold. The apparatus also further includes a calibrator to modify calibration parameters for the texture camera to reduce the detected misalignment between the extracted features from the depth image and the extracted features from the color image below a misalignment threshold.

Abstract: An example apparatus includes: a camera to record an image; memory to store instructions; and a processor in circuit with the memory, the processor to execute the instructions to: determine a depth based on: (a) the image and (b) a calibration parameter of the camera; and adjust the calibration parameter based on a temperature of the camera and the depth.

Abstract: A method and apparatus for auto range control are described. In one embodiment, the apparatus comprises a projector configured to project a sequence of light patterns on an object; a first camera configured to capture a sequence of images of the object illuminated with the projected light patterns; a controller coupled to the projector and first camera and operable to receive the sequence of images and perform range control by controlling power of the sequence of light patterns being projected on the object and exposure time of a camera based on information obtained from the sequence of images captured by the camera.

Abstract: Apparatus, including a set of N electrodes (22), configured to be located in proximity to an epidermis (24) of a subject, and to acquire signals generated by electric sources within the subject. The apparatus also includes a set of M channels, configured to transfer the signals, where M is less than N, and a switch (40), configured to select, repetitively and randomly, M signals from the N electrodes and to direct the M signals to the M channels. The apparatus further includes a processor (28), configured to activate the switch, and to receive and analyze the M signals from the M channels so as to determine respective positions of the electric sources within the subject.

Abstract: A diagnostic system includes an array of electrodes, which are coupled to a body surface of a living subject at different, respective positions in proximity to a region of interest within the body. A switched impedance network applies varying loads to the electrodes. A processor is coupled to receive and measure electrical signals from the electrodes as a function of the varying loads, and to analyze the measured signals so as to compute a local electrical characteristic of one or more locations within the region of interest.

Abstract: A method and apparatus for performing temperature compensation for thermal distortions in a camera system. In one embodiment, the system comprises a first camera configured to capture a sequence of images of the object; a second device; a processing unit to receive the sequence of images and determine depth information in response to parameters of the camera and the second device; one or more temperature sensors; and a thermal correction unit responsive to temperature information from the one or more temperature sensors to adjust one or more of the calibration parameters of the first camera and the second device.

Abstract: A method for image reconstruction includes defining a dictionary including a set of atoms selected such that patches of natural images can be represented as linear combinations of the atoms. A binary input image, including a single bit of input image data per input pixel, is captured using an image sensor. A maximum-likelihood (ML) estimator is applied, subject to a sparse synthesis prior derived from the dictionary, to the input image data so as to reconstruct an output image comprising multiple bits per output pixel of output image data.

Abstract: A diagnostic method includes applying a vibration at a selected frequency to a location within a brain of a living subject (32). Electrical signals resulting from the vibration are measured at multiple positions on a scalp of the subject. The measured electrical signals are processed in order to compute an electrical gain matrix between the location within the brain and the positions on the scalp. Electroencephalogram (EEG) signals are measured at the multiple positions on the scalp. The EEG signals are filtered using the gain matrix in order to identify brain electrical activity originating from the location.

Abstract: Example range estimation apparatus disclosed herein include a first signal processor to process first data output from a light capturing device of a LIDAR system to estimate signal and noise power parameters of the LIDAR system. Disclosed example range estimation apparatus also include a second signal processor to generate templates corresponding to different possible propagation delays associated with second data output from the light capturing device while a modulated light beam is projected by the LIDAR system, the templates generated based on the signal and noise power parameters, and the second data having a higher sampling rate and a lower quantization resolution than the first data. In some examples, the second signal processor also cross-correlates the templates with the second data to determine an estimated propagation delay associated with the second data, the estimated propagation delay convertible to an estimated range to an object that reflected the modulated light beam.

Abstract: A mechanism is described for facilitating maximizing efficiency of time-of-flight optical depth sensors in computing environments according to one embodiment. An apparatus of embodiments, as described herein, includes detection and observation logic to facilitate a camera to detect and observe a scene and one or more objects in the scene. The apparatus may further include generation, transmission, and reception (GTR) logic to generate a beam of photons based on a transmitted code stored at a memory device, where the GTR logic to transmit the beam of photons to the one or more objects and capture a beam of photons bouncing back from the one or more objects. The apparatus may further include computation and correlation logic to correlate first values of the transmitted code with second values of a returned signal from the one or more objects as the beam of photons.

Abstract: Arrangements (e.g., apparatus, system, method, article of manufacture) for reconstructing a depth image of a scene. Some embodiments include: a processor; and a non-transitory computer readable medium to store a set of instructions for execution by the processor, the set of instructions to cause the processor to perform various operations. Operations include: collecting multiple data sets for a code-modulated light pulse reflected from an object in a scene, with each data set associated with a direction in a set of directions for the reflected code-modulated light pulse; assigning a fitness value to each data set based on one or more parameters of a model; and reconstructing a depth image providing a depth at each direction based on a corresponding data set and fitness value, the depth to correspond with a round-trip delay time of the code-modulated light pulse.

Abstract: An example apparatus for calibrating texture cameras includes an image receiver to receive a depth image from a depth camera and a color image from a texture camera. The apparatus also includes a feature extractor to extract features from the depth image and the color image. The apparatus further includes a feature tester to detect that the extracted features from the depth image and the color image exceed a quality threshold. The apparatus includes a misalignment detector to detect a misalignment between the extracted features from depth image and the extracted features from color image exceeds a misalignment threshold. The apparatus also further includes a calibrator to modify calibration parameters for the texture camera to reduce the detected misalignment between the extracted features from the depth image and the extracted features from the color image below a misalignment threshold.

Abstract: A method and apparatus for performing a single view depth and texture calibration are described. In one embodiment, the apparatus comprises a calibration unit operable to perform a single view calibration process using a captured single view a target having a plurality of plane geometries having detectable features and being at a single orientation and to generate calibration parameters to calibrate one or more of the projector and multiple cameras using the single view of the target.

Abstract: A method and apparatus for performing temperature compensation for thermal distortions in a camera system. In one embodiment, the system comprises a first camera configured to capture a sequence of images of the object; a second device; a processing unit to receive the sequence of images and determine depth information in response to parameters of the camera and the second device; one or more temperature sensors; and a thermal correction unit responsive to temperature information from the one or more temperature sensors to adjust one or more of the calibration parameters of the first camera and the second device.

Abstract: Embodiments of the present disclosure provide techniques and configurations for an optoelectronic three-dimensional object acquisition assembly configured to correct image distortions. In one instance, the assembly may comprise a first device configured to project a light pattern on an object at a determined angle; a second device configured to capture a first image of the object illuminated with the projected light pattern; a third device configured to capture a second image of the object; and a controller coupled to the first, second, and third devices and configured to reconstruct an image of the object from geometric parameters of the object obtained from the first image, and to correct distortions in the reconstructed image caused by a variation of the determined angle of the light pattern projection, based at least in part on the first and second images of the object. Other embodiments may be described and/or claimed.

Abstract: A camera intrinsic calibration may be performed using an object geometry. An intrinsic camera matrix may then be recovered. A homography is fit between object and camera coordinate systems. View transformations are finally recovered.