Abstract: Certain aspects of the present disclosure provide techniques for automatically detecting and classifying tumor regions in a tissue slide. The method generally includes obtaining a digitized tissue slide from a tissue slide database and determining, based on output from a tissue classification module, a type of tissue of shown in the digitized tissue slide. The method further includes determining, based on output from a tumor classification model for the type of tissue, a region of interest (ROI) of the digitized tissue slide and generating a classified slide showing the ROI of the digitized tissue slide and an estimated diameter of the ROI. The method further includes displaying on an image display unit, the classified slide and user interface (UI) elements enabling a pathologist to enter input related to the classified slide.

Abstract: Certain aspects of the present disclosure provide techniques for automatically detecting and classifying tumor regions in a tissue slide. The method generally includes obtaining a digitized tissue slide from a tissue slide database and determining, based on output from a tissue classification module, a type of tissue of shown in the digitized tissue slide. The method further includes determining, based on output from a tumor classification model for the type of tissue, a region of interest (ROI) of the digitized tissue slide and generating a classified slide showing the ROI of the digitized tissue slide and an estimated diameter of the ROI. The method further includes displaying on an image display unit, the classified slide and user interface (UI) elements enabling a pathologist to enter input related to the classified slide.

Abstract: Certain aspects of the present disclosure provide techniques for automatically detecting and classifying tumor regions in a tissue slide. The method generally includes obtaining a digitized tissue slide from a tissue slide database and determining, based on output from a tissue classification module, a type of tissue of shown in the digitized tissue slide. The method further includes determining, based on output from a tumor classification model for the type of tissue, a region of interest (ROI) of the digitized tissue slide and generating a classified slide showing the ROI of the digitized tissue slide and an estimated diameter of the ROI. The method further includes displaying on an image display unit, the classified slide and user interface (UI) elements enabling a pathologist to enter input related to the classified slide.

Abstract: Certain aspects of the present disclosure provide techniques for automatically detecting and classifying tumor regions in a tissue slide. The method generally includes obtaining a digitized tissue slide from a tissue slide database and determining, based on output from a tissue classification module, a type of tissue of shown in the digitized tissue slide. The method further includes determining, based on output from a tumor classification model for the type of tissue, a region of interest (ROI) of the digitized tissue slide and generating a classified slide showing the ROI of the digitized tissue slide and an estimated diameter of the ROI. The method further includes displaying on an image display unit, the classified slide and user interface (UI) elements enabling a pathologist to enter input related to the classified slide.

Abstract: Certain aspects of the present disclosure provide techniques for automatically detecting and classifying tumor regions in a tissue slide. The method generally includes obtaining a digitized tissue slide from a tissue slide database and determining, based on output from a tissue classification module, a type of tissue of shown in the digitized tissue slide. The method further includes determining, based on output from a tumor classification model for the type of tissue, a region of interest (ROI) of the digitized tissue slide and generating a classified slide showing the ROI of the digitized tissue slide and an estimated diameter of the ROI. The method further includes displaying on an image display unit, the classified slide and user interface (UI) elements enabling a pathologist to enter input related to the classified slide.

Abstract: An electronic device is provided. The electronic device includes a camera module and an image processing module. The image processing module may obtains a first image and a second image for the scene including a first object and a second object, identifies a first motion amount corresponding to the first object and a second motion amount corresponding to the second object using the first image and the second image, and applies different effects to a first image area corresponding to the first object and a second image area corresponding to the second object, at least, based on the first motion amount and the second motion amount.

Abstract: A method of automatically focusing on a region of interest (ROI) by an electronic device is provided. The method includes extracting at least one feature from at least one candidate ROI in a field of view (FOV) in the electronic device, displaying at least one indicia for the at least one candidate ROI based on the at least one feature, receiving a selection of at least one ROI from among the at least one candidate ROI for which the at least one indicia is displayed; and focusing on the at least one ROI according to the selection.

Abstract: An electronic device is provided. The electronic device includes a camera module and an image processing module. The image processing module may obtains a first image and a second image for the scene including a first object and a second object, identifies a first motion amount corresponding to the first object and a second motion amount corresponding to the second object using the first image and the second image, and applies different effects to a first image area corresponding to the first object and a second image area corresponding to the second object, at least, based on the first motion amount and the second motion amount.

Abstract: A method and apparatus are provided for enhancing the local dynamic range of an image using the contents of exposure bracketed image set without affecting overall dynamic range of the image. The method allows a user to select at least one region of a reference image for enhancement after receiving the reference image from the user. Further the method comprises segmenting the reference image by using exposure weights, and selects an enhancement support image from an exposure bracketed image set. Furthermore the method determines weight maps of reference and enhancement support images and generates dynamic range enhanced reference image using determined weight maps.

Abstract: A method and apparatus are provided for enhancing the local dynamic range of an image using the contents of exposure bracketed image set without affecting overall dynamic range of the image. The method allows a user to select at least one region of a reference image for enhancement after receiving the reference image from the user. Further the method comprises segmenting the reference image by using exposure weights, and selects an enhancement support image from an exposure bracketed image set. Furthermore the method determines weight maps of reference and enhancement support images and generates dynamic range enhanced reference image using determined weight maps.

Abstract: A method and an apparatus are provided for eliminating noise in a digital image. An ambient image and at least one flash image are captured in succession of a given location. At least one image alignment technique is applied for the ambient image and the at least one flash image. Joint mean shift filtering is applied to the at least one flash image to obtain filter weights. The filter weights are applied to the ambient image to eliminate noise in the ambient image.

Abstract: A method and an apparatus are provided for eliminating noise in a digital image. An ambient image and at least one flash image are captured in succession of a given location. At least one image alignment technique is applied for the ambient image and the at least one flash image. Joint mean shift filtering is applied to the at least one flash image to obtain filter weights. The filter weights are applied to the ambient image to eliminate noise in the ambient image.