Abstract: An image processing system comprises an image processor configured to construct a designated functional based on a plurality of functions each associated with a corresponding portion of image information relating to at least first and second images, and to generate a target image utilizing the constructed functional. For example, the functions may comprise a set of functions ƒ1(A1), ƒ1(A1), . . . , ƒ1(A1) of pixels from respective input images A1, A2, AL of the image information, and the functional may be a function F(X) of the set of functions ƒ1,(A1) ƒ2(AL), ƒL(AL) where X denotes the target image and is generated by minimizing the functional F(X). The input images may be received from one or more image sources and the target image may be provided to one or more image destinations.

Abstract: In one embodiment, an image processor is configured to obtain a plurality of phase images for each of first and second depth frames. For each of a plurality of pixels of a given one of the phase images of the first depth frame, the image processor determines an amount of movement of a point of an imaged scene between the pixel of the given phase image and a pixel of a corresponding phase image of the second depth frame, and adjusts pixel values of respective other phase images of the first depth frame based on the determined amount of movement. A motion compensated first depth image is generated utilizing the given phase image and the adjusted other phase images of the first depth frame. Movement of a point of the imaged scene is determined, for example, between pixels of respective n-th phase images of the first and second depth frames.

Abstract: An image processor comprises image processing circuitry implementing a plurality of processing layers including at least an evaluation layer and a recognition layer. The evaluation layer comprises a software-implemented portion and a hardware-implemented portion, with the software-implemented portion of the evaluation layer being configured to generate first object data of a first precision level using a software algorithm, and the hardware-implemented portion of the evaluation layer being configured to generate second object data of a second precision level lower than the first precision level using a hardware algorithm. The evaluation layer further comprises a signal combiner configured to combine the first and second object data to generate output object data for delivery to the recognition layer. By way of example only, the evaluation layer may be implemented in the form of an evaluation subsystem of a gesture recognition system of the image processor.

Abstract: An image processing system comprises an image processor having image processing circuitry and an associated memory. The image processor is configured to implement a gesture recognition system comprising a static pose recognition module. The static pose recognition module is configured to identify a hand region of interest in at least one image, to extract a contour of the hand region of interest, to compute a feature vector based at least in part on the extracted contour, and to recognize a static pose of the hand region of interest utilizing a dynamic warping operation based at least in part on the feature vector.

Abstract: An image processor comprises image processing circuitry implementing a plurality of processing layers including at least an evaluation layer and a recognition layer. The evaluation layer comprises a software-implemented portion and a hardware-implemented portion, with the software-implemented portion of the evaluation layer being configured to generate first object data of a first precision level using a software algorithm, and the hardware-implemented portion of the evaluation layer being configured to eV generate second object data of a second precision level lower than the first precision level using a hardware algorithm. The evaluation layer further comprises a signal combiner configured to combine the first and second object data to generate output object data for delivery to the recognition layer. By way of example only, the evaluation layer may be implemented in the form of an evaluation subsystem of a gesture recognition system of the image processor.

Abstract: A depth imager is configured to generate a first depth image using a first depth imaging technique, and to generate a second depth image using a second depth imaging technique different than the first depth imaging technique. At least portions of the first and second depth images are merged to form a third depth image. The depth imager comprises at least one sensor including a single common sensor at least partially shared by the first and second depth imaging techniques, such that the first and second depth images are both generated at least in part using data acquired from the single common sensor. By way of example, the first depth image may comprise a structured light (SL) depth map generated using an SL depth imaging technique, and the second depth image may comprise a time of flight (ToF) depth map generated using a ToF depth imaging technique.

Abstract: An image processing system comprises an image processor configured to construct a designated functional based on a plurality of functions each associated with a corresponding portion of image information relating to at least first and second images, and to generate a target image utilizing the constructed functional. For example, the functions may comprise a set of functions f1(A1), f1(A1), . . . , f1(A1) of pixels from respective input images A1, A2, AL of the image information, and the functional may be a function F(X) of the set of functions f1,(A1) f2(AL), fL(AL) where X denotes the target image and is generated by minimizing the functional F(X). The input images may be received from one or more image sources and the target image may be provided to one or more image destinations.

Abstract: An image processing system comprises an image processor configured to determine velocity of a hand in a plurality of images, and to selectively enable dynamic gesture recognition for at least one image responsive to the determined velocity. By way of example, the image processor illustratively includes a dynamic gesture preprocessing detector and a dynamic gesture recognizer, with the dynamic gesture preprocessing detector being configured to determine the velocity of the hand for a current frame and to compare the determined velocity to a specified velocity threshold. If the determined velocity is greater than or equal to the velocity threshold, the dynamic gesture recognizer operates on the current frame, and otherwise the dynamic gesture recognizer is bypassed for the current frame. The dynamic gesture recognizer when enabled is configured to generate similarity measures for respective ones of a plurality of gestures of a gesture vocabulary for the current frame.

Abstract: An image processing system comprises an image processor having image processing circuitry and an associated memory. The image processor is configured to implement a gesture recognition system. The gesture recognition system comprises a cursor detector, a dynamic gesture detector, a static pose recognition module, and a finite state machine configured to control selectively enabling of the cursor detector, the dynamic gesture detector and the static pose recognition module. By way of example, the finite state machine includes a cursor detected state in which cursor location and tracking are applied responsive to detection of a cursor in a current frame, a dynamic gesture detected state in which dynamic gesture recognition is applied responsive to detection of a dynamic gesture in the current frame, and a static pose recognition state in which static pose recognition is applied responsive to failure to detect a cursor or a dynamic gesture in the current frame.

Abstract: An image processing system comprises an image processor having image processing circuitry and an associated memory. The image processor is configured to implement a gesture recognition system comprising a static pose recognition module. The static pose recognition module is configured to identify a hand region of interest in at least one image, to perform a skeletonization operation on the hand region of interest, to determine a main direction of the hand region of interest utilizing a result of the skeletonization operation, to perform a scanning operation on the hand region of interest utilizing the determined main direction to estimate a plurality of hand features that are substantially invariant to hand orientation, and to recognize a static pose of the hand region of interest based on the estimated hand features.

Abstract: An image processing system comprises an image processor configured to identify a plurality of candidate boundaries in an image, to obtain corresponding modified images for respective ones of the candidate boundaries, to apply a mapping function to each of the modified images to generate a corresponding vector, to determine sets of estimates for respective ones of the vectors relative to designated class parameters, and to select a particular one of the candidate boundaries based on the sets of estimates. The designated class parameters may include sets of class parameters for respective ones of a plurality of classes each corresponding to a different gesture to be recognized. The candidate boundaries may comprise candidate palm boundaries associated with a hand in the image. The image processor may be further configured to select a particular one of the plurality of classes to recognize the corresponding gesture based on the sets of estimates.

Abstract: An image processing system comprises an image processor configured to identify one or more potentially defective pixels associated with at least one depth artifact in a first image, and to apply a super resolution technique utilizing a second image to reconstruct depth information of the one or more potentially defective pixels. Application of the super resolution technique produces a third image having the reconstructed depth information. The first image may comprise a depth image and the third image may comprise a depth image corresponding generally to the first image but with the depth artifact substantially eliminated. An additional super resolution technique may be applied utilizing a fourth image. Application of the additional super resolution technique produces a fifth image having increased spatial resolution relative to the third image.

Abstract: An apparatus generally having a first circuit and a second circuit is disclosed. The first circuit may be configured to synthesize a first vector by filtering a second vector based on a third vector. The second circuit may be configured to (i) generate a gain corresponding to a fourth vector, (ii) compare the gain to a plurality of thresholds and (iii) update the third vector as a function of the gain where the compare determines that the gain is not between the thresholds. The fourth vector may be received from a network as an echo of the second vector.

Abstract: In one embodiment, the invention is a method for performing preamble detection in a wireless communication network. The method performs a first dwell, wherein non-overlapping chunks of received data are processed to generate partial correlation values for each possible combination of a signature code and delay. Candidate selection is performed by comparing each of the partial correlation values to a candidate-selection threshold. For each detected candidate, the chunks of received data are processed to generate full correlation values. Each full correlation value is then compared to a preamble-detection threshold to detect a transmitted signature. Generating full correlation values for only the selected candidates reduces the computation complexity over prior-art methods that generate full correlation values for all signatures at all delays.

Abstract: An apparatus generally having a first circuit and a second circuit is disclosed. The first circuit may be configured to generate a first sample by filtering an input vector based on (a) a filter vector and (b) a stochastic vector. Each of a plurality of components in the stochastic vector generally has a respective random value. The first circuit may also be configured to generate a second sample as a difference between a third sample and the first sample. The third sample may be received from a network as an echo. The second circuit may be configured to update a subset of a plurality of taps of the filtering where a corresponding one of the components of the stochastic vector has a first value of the random values.

Abstract: In one embodiment, the invention is a method for performing preamble detection in a wireless communication network. The method performs a first dwell, wherein non-overlapping chunks of received data are processed to generate partial correlation values for each possible combination of a signature code and delay. Candidate selection is performed by comparing each of the partial correlation values to a candidate-selection threshold. For each detected candidate, the chunks of received data are processed to generate full correlation values. Each full correlation value is then compared to a preamble-detection threshold to detect a transmitted signature. Generating full correlation values for only the selected candidates reduces the computation complexity over prior-art methods that generate full correlation values for all signatures at all delays.

Abstract: An apparatus generally having a first circuit and a second circuit is disclosed. The first circuit may be configured to synthesize a first vector by filtering a second vector based on a third vector. The second circuit may be configured to (i) generate a gain corresponding to a fourth vector, (ii) compare the gain to a plurality of thresholds and (iii) update the third vector as a function of the gain where the compare determines that the gain is not between the thresholds. The fourth vector may be received from a network as an echo of the second vector.

Abstract: An apparatus generally having a first circuit and a second circuit is disclosed. The first circuit may be configured to generate a first sample by filtering an input vector based on (a) a filter vector and (b) a stochastic vector. Each of a plurality of components in the stochastic vector generally has a respective random value. The first circuit may also be configured to generate a second sample as a difference between a third sample and the first sample. The third sample may be received from a network as an echo. The second circuit may be configured to update a subset of a plurality of taps of the filtering where a corresponding one of the components of the stochastic vector has a first value of the random values.