Abstract: An example of an apparatus including a communication interface to receive an image file is provided. The image file represents a scanned image of a output generated by a printing device. The apparatus further includes an identification engine to process the image file with a convolutional neural network model to identify a feature. The feature may be indicative of a potential failure. The apparatus also includes an image analysis engine to indicate a life expectancy of a part associated with the potential failure based on the feature. The image analysis engine uses the convolutional neural network model to determine life expectancy.

Abstract: An example of an apparatus including a communication interface to receive an image file is provided. The image file represents a scanned image of a output generated by a printing device. The apparatus further includes an identification engine to process the image file with a convolutional neural network model to identify a feature. The feature may be indicative of a potential failure. The apparatus also includes an image analysis engine to indicate a life expectancy of a part associated with the potential failure based on the feature. The image analysis engine uses the convolutional neural network model to determine life expectancy.

Abstract: Example implementations relate to detecting document objects. For example, detecting document objects may include a system comprising a pre-processing engine to establish a threshold for a document, wherein a structure of the document is unknown, a detection engine to detect a candidate area in the document, using a Hough transform and connected component analysis to merge detected candidate areas in the Hough transform to a same document object in the document, and a classification module to classify the candidate area as a document object or not a document object.

Abstract: In one implementation, a system for interpolating pixel values includes a receiver engine to receive a plurality of scan lines each captured using only two of three different colors of lights from a scanner, wherein each scan line comprises received pixel values for the two different colors of lights used to illuminate the plurality of scan lines, an interpolate engine to interpolate, using the received pixel values for the two different colors, pixel values for the third one of the three different colors by for each scan line to result in a color map, and a display engine to cause to display information relating to an image including the received pixel values and the interpolated pixel values for the three different colors for the plurality of scan lines.

Abstract: Example implementations relate to detecting document objects. For example, detecting document objects may include a system comprising a pre-processing engine to establish a threshold for a document, wherein a structure of the document is unknown, a detection engine to detect a candidate area in the document, using a Hough transform and connected component analysis to merge detected candidate areas in the Hough transform to a same document object in the document, and a classification module to classify the candidate area as a document object or not a document object.

Abstract: An example involves identifying a first artifact in an image on a display, the first artifact caused by a first subpixel of a first pixel, determining a type of the first artifact based on a location of the first artifact relative to content of the image, and correcting the first artifact to remove the appearance of the first artifact from the display based on the type of the first artifact.

Abstract: An example involves identifying a first artifact in an image on a display, the first artifact caused by a first subpixel of a first pixel, determining a type of the first artifact based on a location of the first artifact relative to content of the image, and correcting the first artifact to remove the appearance of the first artifact from the display based on the type of the first artifact.

Abstract: A multi-resolution color image segmentation algorithm which takes advantage of gradient information in an adaptive and progressive framework is described. A gradient-based segmentation method is initiated with a dyadic wavelet decomposition scheme of an arbitrary input image, accompanied by a vector gradient calculation of its color converted counterpart. The resultant gradient map is used to automatically and adaptively generate thresholds for segregating regions of varying gradient densities, at different resolution levels of the input image pyramid. In combination with a confidence map and non-linear spatial filtering techniques, regions of high confidence are passed from one resolution level to another until the final segmentation at highest (original) resolution is achieved.

Abstract: A method for segmenting an image includes computing a color gradient map based on an inputted image and selecting at least one initial seed of at least one pixel based on the color gradient map. The method further includes growing a region of pixels adjacent to the initial seed and merging adjacent regions of pixels using a measure of similarity.

Abstract: A multi-resolution color image segmentation algorithm which takes advantage of gradient information in an adaptive and progressive framework is described. A gradient-based segmentation method is initiated with a dyadic wavelet decomposition scheme of an arbitrary input image, accompanied by a vector gradient calculation of its color converted counterpart. The resultant gradient map is used to automatically and adaptively generate thresholds for segregating regions of varying gradient densities, at different resolution levels of the input image pyramid. In combination with a confidence map and non-linear spatial filtering techniques, regions of high confidence are passed from one resolution level to another until the final segmentation at highest (original) resolution is achieved.

Abstract: A method for segmenting an image receives the image. The image has a number of pixels and a number of color channels. The image is initially segmented into a number of initial regions at least by dynamically selecting a plurality of seeds within the image using a dynamic color gradient threshold and growing the initial regions from the seeds until the initial regions encompass all the pixels of the image. A texture channel of the image is generated at least by applying an entropy filter to each of a plurality of quantized colors of the image. The initial regions into which the image has been initially segmented are multimodal-merged based on the color channels and the texture channel of the image, to yield a number of merged regions corresponding to segmentation of the image.

Abstract: A method for segmenting an image includes computing a color gradient map based on an inputted image and selecting at least one initial seed of at least one pixel based on the color gradient map. The method further includes growing a region of pixels adjacent to the initial seed and merging adjacent regions of pixels using a measure of similarity.

Abstract: Tone reproduction curves for calibrating a marking system are obtained using a test pattern with a plurality of patches, preferably a plurality of first test patches extending in a first direction and at least one second test patch extending in a second direction crossing the first direction. Reflectance values are obtained from the first and second test patches. A set of gray balanced tone reproduction curves are obtained based on the reflectance values of the first test patches, and a set of spatial gray balanced tone reproduction curves are obtained based on the reflectance values of the second test patches.

Abstract: A method for sharpening the corners of digital image data within an anti-aliasing image path so as to overcome corner rounding when displayed or printed. The method comprises stepping a window across the image data and comparing that windowed data to templates or performing Boolean logic and arithmetic operations on the image data. Upon determining a match or a corner detection, pixel values are substituted in the identified pixel locations to achieve a clustering of the substituted pixel values about the corner structure producing a corner-enhanced digital image. Filtering and sampling are then performed according to an anti-alias operation to the corner-enhanced image to produce an anti-aliased corner enhanced image. The data substitution will achieve a localized clustering or “ear” of toner/ink, or in the alternative for inside corners the localized clustered absence of toner/ink. The result of this clustering is a sharpening of the corners so that they display or print as intended by the bitmap.

Abstract: Tone reproduction curves for calibrating a marking system are obtained using a test pattern with a plurality of patches, preferably a plurality of first test patches extending in a first direction and at least one second test patch extending in a second direction crossing the first direction. Reflectance values are obtained from the first and second test patches. A set of gray balanced tone reproduction curves are obtained based on the reflectance values of the first test patches, and a set of spatial gray balanced tone reproduction curves are obtained based on the reflectance values of the second test patches.