Abstract: In various embodiments, an image processing method is provided. In those embodiments, a first image and a second image are obtained. A conjugate image is obtained based on the second image. In the conjugate image, a given pixel has a luminance less than a corresponding pixel in the second image. In those embodiments, a weight is obtained based on the conjugate image. During an image fusion of the first and the second image, the weight is applied - for example for assigning percentages of luminance coming from the first and second images. In those embodiments, as a result of the weight, for certain parts like vegetation, the fused image is biased towards the color image, while for some other parts, the fused image is biased towards the NIR image. Other embodiments may include computer systems, apparatus, and computer programs stored storage medium, each configured to perform the image processing method.

Abstract: In some embodiments, an image processing application receives a first image and a second image for fusing. The image processing application generates a foreground object mask for a foreground object in the first image and dilates the foreground object mask to generate a dilated mask. The image processing application further integrates the image content of the first image in at least the dilated mask with the second image to generate an updated second image.

Abstract: A method is disclosed of capturing an image of a wound on a subject for wound assessment. The method includes obtaining an image of a portion of the subject with one or more cameras; displaying the image on a display panel on an imaging device; obtaining a stored condition from a memory; obtaining a present condition; comparing the stored condition and the present condition; displaying a crosshair over the image on the display panel when it is decided that the present condition corresponds to the stored condition on the basis of the comparison; receiving an instruction for capturing; and capturing an image of the wound in response to the received instruction.

Abstract: A method is disclosed of capturing an image of a wound on a subject for wound assessment. The method includes obtaining an image of a portion of the subject with one or more cameras; displaying the image on a display panel on an imaging device; obtaining a stored condition from a memory; obtaining a present condition; comparing the stored condition and the present condition; displaying a crosshair over the image on the display panel when it is decided that the present condition corresponds to the stored condition on the basis of the comparison; receiving an instruction for capturing; and capturing an image of the wound in response to the received instruction.

Abstract: A ghost detection method for high-dynamic range (HDR) image creation using multi-level median threshold bitmapping. At each of multiple levels, median threshold bitmaps (MTBs) are generated for each original low-dynamic range image. At each level, the MTBs of the multiple original images are compared to generate a ghost map for each original image which indicates the locations of ghost pixels in the original image. For each original image, the ghost maps generated at the multiple levels are combined to generate a combined ghost map. The multiple combined ghost maps can then be used in creating the HDR image, for example, by discarding pixels in an original image that are indicated by the corresponding combined ghost map as being a ghost pixel.

Abstract: A ghost detection method for high-dynamic range (HDR) image creation using multi-level median threshold bitmapping. At each of multiple levels, median threshold bitmaps (MTBs) are generated for each original low-dynamic range image. At each level, the MTBs of the multiple original images are compared to generate a ghost map for each original image which indicates the locations of ghost pixels in the original image. For each original image, the ghost maps generated at the multiple levels are combined to generate a combined ghost map. The multiple combined ghost maps can then be used in creating the HDR image, for example, by discarding pixels in an original image that are indicated by the corresponding combined ghost map as being a ghost pixel.

Abstract: A multiple video stream capture and encoding apparatus produces compressed data that represents multiple video streams capturing a common scene. Images from multiple video streams are analyzed to identify image color segments that are encoded into a composite graph data structure. Corresponding image segments across the multiple video streams are also identified and represented by one node in the composite graph data structure. The composite graph data structure also includes links between pairs of nodes that describe the relationship between the image segments associated with those nodes. The composite graph data structure is updated to represent changes to the image segments in the multiple video streams over time. The composite graph data structure is used to create compressed encoded data for storage and/or transmission.

Abstract: An image processing system and method for smoothing irregularities from 3D image information that was reconstructed from a plurality of 2D views of a scene, and particularly from homogeneous surfaces of objects in a scene. The method defines a window that overlaps a plurality of pixels of one of a plurality of 2D image views of a scene. Each pixel is associated with predefined 3D depth information, and further is associated with a matching curve. A subject pixel is located within the plurality of pixels overlapped by the window. The method calculates an average 3D depth information associated with the plurality of pixels overlapped by the window, and assigns the calculated average 3D depth information to the 3D depth information of the subject pixel, if the calculated average 3D depth information is within an error region of a matching curve associated with the subject pixel.

Abstract: An image processing system and method reconstructs 3D image information corresponding to a scene from a plurality of 2D images of the scene. The method receives a plurality of image features corresponded between different 2D views of the scene, the corresponded image features deviating between different views as a result of camera relative motion. The method determines image features of the received plurality of image features that are occluded views, determines image features of the received plurality of image features that are confident seeds associated with 3D depth information, and propagates 3D depth information from the confident seeds to neighboring image features, while avoiding image features that have been determined to be occluded views. The 3D image information can be rendered and displayed such as for a virtual walkthrough of the scene.

Abstract: An image processing system and method reconstructs 3D image information corresponding to a scene from a plurality of 2D images of the scene. The method receives a plurality of image features corresponded between different 2D views of the scene, the corresponded image features deviating between different views as a result of camera relative motion. The method determines image features of the received plurality of image features that are occluded views, determines image features of the received plurality of image features that are confident seeds associated with 3D depth information, and propagates 3D depth information from the confident seeds to neighboring image features, while avoiding image features that have been determined to be occluded views. The 3D image information can be rendered and displayed such as for a virtual walkthrough of the scene.

Abstract: An image processing system and method for smoothing irregularities from 3D image information that was reconstructed from a plurality of 2D views of a scene, and particularly from homogeneous surfaces of objects in a scene. The method defines a window that overlaps a plurality of pixels of one of a plurality of 2D image views of a scene. Each pixel is associated with predefined 3D depth information, and further is associated with a matching curve. A subject pixel is located within the plurality of pixels overlapped by the window. The method calculates an average 3D depth information associated with the plurality of pixels overlapped by the window, and assigns the calculated average 3D depth information to the 3D depth information of the subject pixel, if the calculated average 3D depth information is within an error region of a matching curve associated with the subject pixel.