Disclosed is a computer-implemented method for determining a position of an anatomical tracking structure in a tracking image usable for controlling a radiation treatment such as at least one of radiotherapy or radio surgery of a patient, a corresponding computer program, a non-transitory program storage medium storing such a program and a computer for executing the program, as well as a system for the position of an anatomical tracking structure in a tracking image usable for controlling a radiation treatment such as at least one of radiotherapy or radio surgery of a patient, a system comprising an electronic data storage device and the aforementioned computer.

Disclosed is a computer-implemented method for determining a position of an anatomical tracking structure in a tracking image usable for controlling a radiation treatment such as at least one of radiotherapy or radio surgery of a patient, a corresponding computer program, a non-transitory program storage medium storing such a program and a computer for executing the program, as well as a system for the position of an anatomical tracking structure in a tracking image usable for controlling a radiation treatment such as at least one of radiotherapy or radio surgery of a patient, a system comprising an electronic data storage device and the aforementioned computer.

A processor unit configured to identify blocks of a frame of a video sequence to be excluded from a motion-compensated operation, the processor unit comprising: a frame processor configured to process pixel values of a first frame to characterise blocks of one or more pixels of the first frame as representing at least a portion of a graphic object; a frame-difference processor configured to determine difference values between blocks of the first frame and corresponding blocks of a second frame, and to process said difference values to characterise blocks of the first frame as representing an image component that is static between the first and second frames; a block identifier configured to identify blocks of the first frame as protected blocks in dependence on blocks characterised as: (i) representing at least a portion of a graphic object; and (ii) representing an image component that is static between the first and second frames, wherein the identified protected blocks are to be excluded from the motion compensated operation.

A processor unit configured to identify blocks of a frame of a video sequence to be excluded from a motion-compensated operation, the processor unit comprising: a frame processor configured to process pixel values of a first frame to characterise blocks of one or more pixels of the first frame as representing at least a portion of a graphic object; a frame-difference processor configured to determine difference values between blocks of the first frame and corresponding blocks of a second frame, and to process said difference values to characterise blocks of the first frame as representing an image component that is static between the first and second frames; a block identifier configured to identify blocks of the first frame as protected blocks in dependence on blocks characterised as: (i) representing at least a portion of a graphic object; and (ii) representing an image component that is static between the first and second frames, wherein the identified protected blocks are to be excluded from the motion compensated operation.

A method of generating and optimizing a codebooks for document analysis comprises: receiving a first set of document images; extracting a plurality of keypoint regions from each document image of the first set of document images; calculating local descriptors for each keypoint region of the extracted keypoint regions; clustering the local descriptors such that each center of a cluster of local descriptors corresponds to a respective visual word; generating a codebook containing a set of visual words; and optimizing the codebook by maximizing mutual information (MI) between a target field of a second set of document images and at least one visual word of the set of visual words.

A method of generating and optimizing a codebooks for document analysis comprises: receiving a first set of document images; extracting a plurality of keypoint regions from each document image of the first set of document images; calculating local descriptors for each keypoint region of the extracted keypoint regions; clustering the local descriptors such that each center of a cluster of local descriptors corresponds to a respective visual word; generating a codebook containing a set of visual words; and optimizing the codebook by maximizing mutual information (MI) between a target field of a second set of document images and at least one visual word of the set of visual words.

Systems and methods for hybrid depth regularization in accordance with various embodiments of the invention are disclosed. In one embodiment of the invention, a depth sensing system comprises a plurality of cameras; a processor; and a memory containing an image processing application. The image processing application may direct the processor to obtain image data for a plurality of images from multiple viewpoints, the image data comprising a reference image and at least one alternate view image; generate a raw depth map using a first depth estimation process, and a confidence map; and generate a regularized depth map. The regularized depth map may be generated by computing a secondary depth map using a second different depth estimation process; and computing a composite depth map by selecting depth estimates from the raw depth map and the secondary depth map based on the confidence map.

Systems and methods for hybrid depth regularization in accordance with various embodiments of the invention are disclosed. In one embodiment of the invention, a depth sensing system comprises a plurality of cameras; a processor; and a memory containing an image processing application. The image processing application may direct the processor to obtain image data for a plurality of images from multiple viewpoints, the image data comprising a reference image and at least one alternate view image; generate a raw depth map using a first depth estimation process, and a confidence map; and generate a regularized depth map. The regularized depth map may be generated by computing a secondary depth map using a second different depth estimation process; and computing a composite depth map by selecting depth estimates from the raw depth map and the secondary depth map based on the confidence map.

Systems and methods for digitized document image data spillage recovery are provided. One or more memories may be coupled to one or more processors, the one or more memories including instructions operable to be executed by the one or more processors. The one or more processors may be configured to capture an image; process the image through at least a first pass to generate a first contour; remove a preprinted bounding region of the first contour to retain text; generate one or more pixel blobs by applying one or more filters to smudge the text; identify the one or more pixel blobs that straddle one or more boundaries of the first contour; resize the first contour to enclose spillage of the one or more pixel blobs; overlay the text from the image within the resized contour; and apply pixel masking to the resized contour.

Systems and methods for digitized document image data spillage recovery are provided. One or more memories may be coupled to one or more processors, the one or more memories including instructions operable to be executed by the one or more processors. The one or more processors may be configured to capture an image; process the image through at least a first pass to generate a first contour; remove a preprinted bounding region of the first contour to retain text; generate one or more pixel blobs by applying one or more filters to smudge the text; identify the one or more pixel blobs that straddle one or more boundaries of the first contour; resize the first contour to enclose spillage of the one or more pixel blobs; overlay the text from the image within the resized contour; and apply pixel masking to the resized contour.