Abstract: A method for correcting a distorted input image comprises determining a local region of an image to be displayed and dividing said region into an array of rectangular tiles, each tile corresponding to a distorted tile with a non-rectangular boundary within said input image. For each tile of the local region, maximum and minimum memory address locations of successive rows of said input image sufficient to span said boundary of said distorted tile are determined. Successive rows of the distorted input from between said maximum and minimum addresses are read. Distortion of the non-rectangular portion of said distorted input image is corrected to provide a tile of a corrected output image which is stored.

Abstract: A method for correcting a distorted input image includes determining a local region of an image to be displayed and dividing the region into an array of rectangular tiles, each tile corresponding to a distorted tile with a non-rectangular boundary within the input image. For each tile of the local region, maximum and minimum memory address locations of successive rows of the input image sufficient to span the boundary of the distorted tile are determined. Successive rows of the distorted input from between the maximum and minimum addresses are read. Distortion of the non-rectangular portion of the distorted input image is corrected to provide a tile of a corrected output image which is stored.

Abstract: A method for correcting a distorted input image comprises determining a local region of an image to be displayed and dividing said region into an array of rectangular tiles, each tile corresponding to a distorted tile with a non-rectangular boundary within said input image. For each tile of the local region, maximum and minimum memory address locations of successive rows of said input image sufficient to span said boundary of said distorted tile are determined. Successive rows of the distorted input from between said maximum and minimum addresses are read. Distortion of the non-rectangular portion of said distorted input image is corrected to provide a tile of a corrected output image which is stored.

Abstract: A template matching module is configured to program a processor to apply multiple differently-tuned object detection classifier sets in parallel to a digital image to determine one or more of an object type, configuration, orientation, pose or illumination condition, and to dynamically switch between object detection templates to match a determined object type, configuration, orientation, pose, blur, exposure and/or directional illumination condition.

Abstract: A method for correcting a distorted input image includes determining a local region of an image to be displayed and dividing the region into an array of rectangular tiles, each tile corresponding to a distorted tile with a non-rectangular boundary within the input image. For each tile of the local region, maximum and minimum memory address locations of successive rows of the input image sufficient to span the boundary of the distorted tile are determined. Successive rows of the distorted input from between the maximum and minimum addresses are read. Distortion of the non-rectangular portion of the distorted input image is corrected to provide a tile of a corrected output image which is stored.

Abstract: A dynamically reconfigurable heterogeneous systolic array is configured to process a first image frame, and to generate image processing primatives from the image frame, and to store the primatives and the corresponding image frame in a memory store. A characteristic of the image frame is determined. Based on the characteristic, the array is reconfigured to process a following image frame.

Abstract: A template matching module is configured to program a processor to apply multiple differently-tuned object detection classifier sets in parallel to a digital image to determine one or more of an object type, configuration, orientation, pose or illumination condition, and to dynamically switch between object detection templates to match a determined object type, configuration, orientation, pose, blur, exposure and/or directional illumination condition.

Abstract: In one example, a camera is provided that includes: a plurality of MEMS electrostatic comb actuators, each actuator operable to exert a force on at least one lens; and an optical image stabilization (OIS) algorithm module operable to command the plurality of actuators to actuate the at least one lens responsive to motion of the camera.

Abstract: A dynamically reconfigurable heterogeneous systolic array is configured to process a first image frame, and to generate image processing primatives from the image frame, and to store the primatives and the corresponding image frame in a memory store. A characteristic of the image frame is determined. Based on the characteristic, the array is reconfigured to process a following image frame.

Abstract: A method for correcting a distorted input image includes determining a local region of an image to be displayed and dividing the region into an array of rectangular tiles, each tile corresponding to a distorted tile with a non-rectangular boundary within the input image. For each tile of the local region, maximum and minimum memory address locations of successive rows of the input image sufficient to span the boundary of the distorted tile are determined. Successive rows of the distorted input from between the maximum and minimum addresses are read. Distortion of the non-rectangular portion of the distorted input image is corrected to provide a tile of a corrected output image which is stored.

Abstract: In one example, a camera is provided that includes: a plurality of MEMS electrostatic comb actuators, each actuator operable to exert a force on at least one lens; and an optical image stabilization (OIS) algorithm module operable to command the plurality of actuators to actuate the at least one lens responsive to motion of the camera.

Abstract: A method for correcting a distorted input image includes determining a local region of an image to be displayed and dividing the region into an array of rectangular tiles, each tile corresponding to a distorted tile with a non-rectangular boundary within the input image. For each tile of the local region, maximum and minimum memory address locations of successive rows of the input image sufficient to span the boundary of the distorted tile are determined. Successive rows of the distorted input from between the maximum and minimum addresses are read. Distortion of the non-rectangular portion of the distorted input image is corrected to provide a tile of a corrected output image which is stored.

Abstract: In one example, a camera is provided that includes: a plurality of MEMS electrostatic comb actuators, each actuator operable to exert a force on at least one lens; and an optical image stabilization (OIS) algorithm module operable to command the plurality of actuators to actuate the at least one lens responsive to motion of the camera.

Abstract: A forward interpolation approach is disclosed for enabling a second version of an image to be constructed from a first version of the image. According to one implementation, pixels from the first version of the image are mapped to pixels in the second version of the image, and pixel values are determined for the corresponding pixels in the second version of the image. In one implementation, the pixel mapping is performed using a lookup table and linear approximation. Performing the pixel mapping in this manner enables computations to be simplified and cost and gate count to be reduced.

Abstract: A template matching module is configured to program a processor to apply multiple differently-tuned object detection classifier sets in parallel to a digital image to determine one or more of an object type, configuration, orientation, pose or illumination condition, and to dynamically switch between object detection templates to match a determined object type, configuration, orientation, pose, blur, exposure and/or directional illumination condition.