Abstract: An image processing system comprises an image processor having image processing circuitry and an associated memory. The image processor is configured to identify a region of interest in an amplitude image, to detect one or more relatively low gradient regions in the region of interest, to reconstruct depth information for said one or more relatively low gradient regions, to extend the reconstructed depth information beyond said one or more relatively low gradient regions to additional pixels of the region of interest, and to generate a depth image utilizing at least portions of the reconstructed depth information and the extended reconstructed depth information. The image processor in some embodiments is adapted for coupling to an active lighting image sensor, such as an infrared sensor that does not provide depth information corresponding to the amplitude image, or an SL or ToF sensor that provides depth information corresponding to the amplitude image.

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 processing system comprises an image processor configured to identify edges in an image, to apply a first type of filtering operation to portions of the image associated with the edges, and to apply a second type of filtering operation to one or more other portions of the image. By way of example only, in a given embodiment a clustering operation is applied to the image to identify a plurality of clusters, a first set of edges comprising edges of the clusters is identified, an edge detection operation is applied to the image to identify a second set of edges, a third set of edges is identified based on the first and second sets of edges, and the first type of filtering operation is applied to portions of the image associated with one or more edges of the third set of edges.

Abstract: An image processing system comprises an image processor configured to obtain a first image stream having a first frame rate and a second image stream having a second frame rate lower than the first frame rate, to recover additional frames for the second image stream based on existing frames of the first and second image streams, and to utilize the additional frames to provide an increased frame rate for the second image stream. Recovering additional frames for the second image stream based on existing frames of the first and second image streams illustratively comprises determining sets of one or more additional frames for insertion between respective pairs of consecutive existing frames in the second image stream in respective iterations.

Abstract: An image processing system comprises an image processor configured to perform an edge detection operation on a first image to obtain a second image, to identify particular edges of the second image that exhibit at least a specified reliability, and to generate a third image comprising the particular edges and excluding other edges of the second image. By way of example only, in a given embodiment the first image may comprise a depth image generated by a depth imager, the second image may comprise an edge image generated by applying the edge detection operation to the depth image, and the third image may comprise a modified edge image having only the particular edges that exhibit at least the specified reliability.

Abstract: An image processing system comprises an image processor configured to perform first and second edge detection operations on respective first and second images to obtain respective first and second edge images, to apply a joint edge weighting operation using edges from the first and second edge images, to generate an edge mask based on results of the edge weighting operation, to utilize the edge mask to obtain a third edge image, and to generate a third image based on the third edge image. By way of example only, in a given embodiment the first image may comprise a first depth image generated by a depth imager, the second image may comprise a two-dimensional image of substantially the same scene as the first image, and the third image may comprise an enhanced depth image having enhanced edge quality relative to the first depth image.

Abstract: An image processing system comprises an image processor configured to identify edges in an image, to apply a first type of filtering operation to portions of the image associated with the edges, and to apply a second type of filtering operation to one or more other portions of the image. By way of example only, in a given embodiment a clustering operation is applied to the image to identify a plurality of clusters, a first set of edges comprising edges of the clusters is identified, an edge detection operation is applied to the image to identify a second set of edges, a third set of edges is identified based on the first and second sets of edges, and the first type of filtering operation is applied to portions of the image associated with one or more edges of the third set of edges.

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 configured to perform an edge detection operation on a first image to obtain a second image, to identify particular edges of the second image that exhibit at least a specified reliability, and to generate a third image comprising the particular edges and excluding other edges of the second image. By way of example only, in a given embodiment the first image may comprise a depth image generated by a depth imager, the second image may comprise an edge image generated by applying the edge detection operation to the depth image, and the third image may comprise a modified edge image having only the particular edges that exhibit at least the specified reliability.

Abstract: An image processing system comprises an image processor configured to perform first and second edge detection operations on respective first and second images to obtain respective first and second edge images, to apply a joint edge weighting operation using edges from the first and second edge images, to generate an edge mask based on results of the edge weighting operation, to utilize the edge mask to obtain a third edge image, and to generate a third image based on the third edge image. By way of example only, in a given embodiment the first image may comprise a first depth image generated by a depth imager, the second image may comprise a two-dimensional image of substantially the same scene as the first image, and the third image may comprise an enhanced depth image having enhanced edge quality relative to the first depth image.

Abstract: In one embodiment, an image processing system comprises an image processor configured to obtain depth and amplitude data associated with a depth image, to identify a region of interest based on the depth and amplitude data, to separately compress the depth and amplitude data based on the identified region of interest to form respective compressed depth and amplitude portions, and to combine the separately compressed portions to provide a compressed depth image. The image processor may additionally or alternatively be configured to obtain a compressed depth image, to divide the compressed depth image into compressed depth and amplitude portions, and to separately decompress the compressed depth and amplitude portions to provide respective depth and amplitude data associated with a depth image. Other embodiments of the invention can be adapted for compressing or decompressing only depth data associated with a given depth image or sequence of depth images.

Abstract: An image processing system comprises an image processor implemented using at least one processing device and adapted for coupling to an image source, such as a depth imager. The image processor is configured to compute a convergence matrix and a noise threshold matrix, to estimate background information of an image utilizing the convergence matrix, and to eliminate at least a portion of the background information from the image utilizing the noise threshold matrix. The background estimation and elimination may involve the generation of static and dynamic background masks that include elements indicating which pixels of the image are part of respective static and dynamic background information. The computing, estimating and eliminating operations may be performed over a sequence of depth images, such as frames of a 3D video signal, with the convergence and noise threshold matrices being recomputed for each of at least a subset of the depth images.

Abstract: A method for double precision approximation of a single precision operation is disclosed. The method may include steps (A) to (B). Step (A) may store an input value in a processor. The processor generally implements a plurality of first operations in hardware. Each first operation may receive a first variable as an argument. The first variable may be implemented in a fixed point format at a single precision. The input value may be implemented in the fixed point format at a double precision. Step (B) may generate an output value by emulating a selected one of the first operations using the input value as the argument. The emulation may utilize the selected first operation in hardware. The output value may be implemented in the fixed point format at the double precision. The emulation is generally performed by a plurality of instructions executed by the processor.

Abstract: An image processing system comprises an image processor configured to obtain a first image stream having a first frame rate and a second image stream having a second frame rate lower than the first frame rate, to recover additional frames for the second image stream based on existing frames of the first and second image streams, and to utilize the additional frames to provide an increased frame rate for the second image stream. Recovering additional frames for the second image stream based on existing frames of the first and second image streams illustratively comprises determining sets of one or more additional frames for insertion between respective pairs of consecutive existing frames in the second image stream in respective iterations.

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: A method for double precision approximation of a single precision operation is disclosed. The method may include steps (A) to (B). Step (A) may store an input value in a processor. The processor generally implements a plurality of first operations in hardware. Each first operation may receive a first variable as an argument. The first variable may be implemented in a fixed point format at a single precision. The input value may be implemented in the fixed point format at a double precision. Step (B) may generate an output value by emulating a selected one of the first operations using the input value as the argument. The emulation may utilize the selected first operation in hardware. The output value may be implemented in the fixed point format at the double precision. The emulation is generally performed by a plurality of instructions executed by the processor.

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.