Abstract: In a graphics processing unit (GPU), receiving an input image comprising an array of pixels. Each pixel having a grayscale value from a range of N grayscale values. For each particular input patch of pixels of a set of input patches partitioning the input image and in parallel for each particular grayscale value the range, counting the number of pixels in the particular input patch having the particular grayscale value. In parallel for each particular input patch of pixels of a set of input patches partitioning the input image, creating an output image patch as an ordered sequence of N pixels, with the color value of the nth pixel in each corresponding output patch representing the count of pixels in the particular input patch having the nth grayscale value. Combining the output image patches into a single composite output image of N pixels, the pixel value of the nth pixel in the single composite output image corresponding to the count of pixels in the input image having the nth grayscale value.

Abstract: In a graphics processing unit (GPU), receiving an input image comprising an array of pixels. Each pixel having a gray scale value from a range of N grayscale values. For each particular input patch of pixels of a set of input patches partitioning the input image and in parallel for each particular grayscale value the range, counting the number of pixels in the particular input patch having the particular grayscale value. In parallel for each particular input patch of pixels of a set of input patches partitioning the input image, creating an output image patch as an ordered sequence of N pixels, with the color value of the nth pixel in each corresponding output patch representing the count of pixels in the particular input patch having the nth grayscale value. Combining the output image patches into a single composite output image of N pixels, the pixel value of the nth pixel in the single composite output image corresponding to the count of pixels in the input image having the nth grayscale value.

Abstract: Implementations generally relate to object based image editing. In some implementations, a method includes segmenting an image into object data by identifying one or more object classifications in the image and storing at least one locator for one or more regions of the image corresponding to each instance of the object classification. The method further includes receiving a selection of a representative portion of the segmented image from a user, and matching the representative portion with the object data to determine at least one matched object classification associated with the representative portion. The method further includes presenting the user with one or more of the matched object classifications for the user to instruct one or more edit operations to be applied to at least one object represented by the matched object classification.

Abstract: Implementations generally relate to object based image editing. In some implementations, a method includes segmenting an image into object data by identifying one or more object classifications in the image and storing at least one locator for one or more regions of the image corresponding to each instance of the object classification. The method further includes receiving a selection of a representative portion of the segmented image from a user, and matching the representative portion with the object data to determine at least one matched object classification associated with the representative portion. The method further includes presenting the user with one or more of the matched object classifications for the user to instruct one or more edit operations to be applied to at least one object represented by the matched object classification.

Abstract: Implementations generally relate to object based image editing. In some implementations, a method includes segmenting an image into object data by identifying one or more object classifications in the image and storing at least one locator for one or more regions of the image corresponding to each instance of the object classification. The method further includes receiving a selection of a representative portion of the segmented image from a user, and matching the representative portion with the object data to determine at least one matched object classification associated with the representative portion. The method further includes presenting the user with one or more of the matched object classifications for the user to instruct one or more edit operations to be applied to at least one object represented by the matched object classification.

Abstract: Implementations generally relate to object based image editing. In some implementations, a method includes segmenting an image into object data by identifying one or more object classifications in the image and storing at least one locator for one or more regions of the image corresponding to each instance of the object classification. The method further includes receiving a selection of a representative portion of the segmented image from a user, and matching the representative portion with the object data to determine at least one matched object classification associated with the representative portion. The method further includes presenting the user with one or more of the matched object classifications for the user to instruct one or more edit operations to be applied to at least one object represented by the matched object classification.