Abstract: Image zooming is described. In one or more implementations, zoomed croppings of an image are scored. The scores calculated for the zoomed croppings are indicative of a zoomed cropping's inclusion of content that is captured in the image. For example, the scores are indicative of a degree to which a zoomed cropping includes salient content of the image, a degree to which the salient content included in the zoomed cropping is centered in the image, and a degree to which the zoomed cropping preserves specified regions-to-keep and excludes specified regions-to-remove. Based on the scores, at least one zoomed cropping may be chosen to effectuate a zooming of the image. Accordingly, the image may be zoomed according to the zoomed cropping such that an amount the image is zoomed corresponds to a scale of the zoomed cropping.

Abstract: Image cropping suggestion using multiple saliency maps is described. In one or more implementations, component scores, indicative of visual characteristics established for visually-pleasing croppings, are computed for candidate image croppings using multiple different saliency maps. The visual characteristics on which a candidate image cropping is scored may be indicative of its composition quality, an extent to which it preserves content appearing in the scene, and a simplicity of its boundary. Based on the component scores, the croppings may be ranked with regard to each of the visual characteristics. The rankings may be used to cluster the candidate croppings into groups of similar croppings, such that croppings in a group are different by less than a threshold amount and croppings in different groups are different by at least the threshold amount. Based on the clustering, croppings may then be chosen, e.g., to present them to a user for selection.

Abstract: Saliency map computation is described. In one or more implementations, a base saliency map is generated for an image of a scene. The base saliency map may be generated from intermediate saliency maps computed for boundary regions of the image. Each of the intermediate saliency maps may represent visual saliency of portions of the scene that are captured in the corresponding boundary region. The boundary regions may include, for instance, a top boundary region, a bottom boundary region, a left boundary region, and a right boundary region. Further, the intermediate saliency maps may be combined in such a way that an effect of a foreground object on the saliency map is suppressed. The foreground objects for which the effect is suppressed are those that occupy a majority of one of the boundary regions.

Abstract: Cropping boundary simplicity techniques are described. In one or more implementations, multiple candidate cropping s of a scene are generated. For each of the candidate croppings, a score is calculated that is indicative of a boundary simplicity for the candidate cropping. To calculate the boundary simplicity, complexity of the scene along a boundary of a respective candidate cropping is measured. The complexity is measured, for instance, using an average gradient, an image edge map, or entropy along the boundary. Values indicative of the complexity may be derived from the measuring. The candidate croppings may then be ranked according to those values. Based on the scores calculated to indicate the boundary simplicity, one or more of the candidate croppings may be chosen e.g., to present the chosen croppings to a user for selection.

Abstract: In techniques for image foreground detection, a foreground detection module is implemented to generate varying levels of saliency thresholds from a saliency map of an image that includes foreground regions. The saliency thresholds can be generated based on an adaptive thresholding technique applied to the saliency map of the image and/or based on multi-level segmentation of the saliency map. The foreground detection module applies one or more constraints that distinguish the foreground regions in the image, and detects the foreground regions of the image based on the saliency thresholds and the constraints. Additionally, different ones of the constraints can be applied to detect different ones of the foreground regions, as well as to detect multi-level foreground regions based on the saliency thresholds and the constraints.

Abstract: Methods and apparatus for procedural modeling are described for generating procedural models and exploring procedural model space. Common procedural components (models, modules, and methods) and a hierarchical framework are described that may be used to construct hierarchical procedural models. A model may comprise a hierarchy of one or more modules. Each module includes a sequence of methods and an optional geometry for display. A method may be controlled by one or more parameters. The values of the parameters may be specified via curves. A module description may be condensed into a single array of values for parameters of the module. Thus, a model or module may be specified by filling in values for arrays of parameters for the sequence of methods. An interpolation technique for interpolating between models is described in which models are matched according to a cost function so that ancestry is preserved.

Abstract: Cropping boundary simplicity techniques are described. In one or more implementations, multiple candidate croppings of a scene are generated. For each of the candidate croppings, a score is calculated that is indicative of a boundary simplicity for the candidate cropping. To calculate the boundary simplicity, complexity of the scene along a boundary of a respective candidate cropping is measured. The complexity is measured, for instance, using an average gradient, an image edge map, or entropy along the boundary. Values indicative of the complexity may be derived from the measuring. The candidate croppings may then be ranked according to those values. Based on the scores calculated to indicate the boundary simplicity, one or more of the candidate croppings may be chosen e.g., to present the chosen croppings to a user for selection.

Abstract: Image cropping suggestion is described. In one or more implementations, multiple croppings of a scene are scored based on parameters that indicate visual characteristics established for visually pleasing croppings. The parameters may include a parameter that indicates composition quality of a candidate cropping, for example. The parameters may also include a parameter that indicates whether content appearing in the scene is preserved and a parameter that indicates simplicity of a boundary of a candidate cropping. Based on the scores, image croppings may be chosen, e.g., to present the chosen image croppings to a user for selection. To choose the croppings, they may be ranked according to the score and chosen such that consecutively ranked croppings are not chosen. Alternately or in addition, image croppings may be chosen that are visually different according to scores which indicate those croppings have different visual characteristics.

Abstract: In techniques for category histogram image representation, image segments of an input image are generated and bounding boxes are selected that each represent a region of the input image, where each of the bounding boxes include image segments of the input image. A saliency map of the input image can also be generated. A bounding box is applied as a query on an images database to determine database image regions that match the region of the input image represented by the bounding box. The query can be augmented based on saliency detection of the input image region that is represented by the bounding box, and a query result is a ranked list of the database image regions. A category histogram for the region of the input image is then generated based on category labels of each of the database image regions that match the input image region.

Abstract: This document describes techniques and apparatuses for offset three-dimensional (3D) printing. These techniques are capable of creating smoother surfaces and more-accurate structures than many current techniques. In some cases, the techniques provide a first stage of filaments separated by offsets and, at a second stage, provide filaments over these offsets. In so doing, filaments of the second stage partially fill-in these offsets, which can remove steps, increase accuracy, or reduce undesired production artifacts.

Abstract: Selection of an area of an image can be received. Selection of a subset of a plurality of predefined patterns may be received. A plurality of patterns can be generated. At least one generated pattern in the plurality of patterns may be based at least in part on one or more predefined patterns in the subset. Selection of another subset of patterns may be received. At least one pattern in the other subset of patterns may be selected from the plurality of predefined patterns and/or the generated patterns. Another plurality of patterns can be generated. At least one generated pattern in this plurality of patterns may be based at least on part on one or more patterns in the other subset. Selection of a generated pattern from the generated other plurality of patterns may be received. The selected area of the image may be populated with the selected generated pattern.

Abstract: In techniques for category histogram image representation, image segments of an input image are generated and bounding boxes are selected that each represent a region of the input image, where each of the bounding boxes include image segments of the input image. A saliency map of the input image can also be generated. A bounding box is applied as a query on an images database to determine database image regions that match the region of the input image represented by the bounding box. The query can be augmented based on saliency detection of the input image region that is represented by the bounding box, and a query result is a ranked list of the database image regions. A category histogram for the region of the input image is then generated based on category labels of each of the database image regions that match the input image region.

Abstract: Selection of an area of an image can be received. Selection of a subset of a plurality of predefined patterns may be received. A plurality of patterns can be generated. At least one generated pattern in the plurality of patterns may be based at least in part on one or more predefined patterns in the subset. Selection of another subset of patterns may be received. At least one pattern in the other subset of patterns may be selected from the plurality of predefined patterns and/or the generated patterns. Another plurality of patterns can be generated. At least one generated pattern in this plurality of patterns may be based at least on part on one or more patterns in the other subset. Selection of a generated pattern from the generated other plurality of patterns may be received. The selected area of the image may be populated with the selected generated pattern.

Abstract: Cropping boundary simplicity techniques are described. In one or more implementations, multiple candidate croppings of a scene are generated. For each of the candidate croppings, a score is calculated that is indicative of a boundary simplicity for the candidate cropping. To calculate the boundary simplicity, complexity of the scene along a boundary of a respective candidate cropping is measured. The complexity is measured, for instance, using an average gradient, an image edge map, or entropy along the boundary. Values indicative of the complexity may be derived from the measuring. The candidate croppings may then be ranked according to those values. Based on the scores calculated to indicate the boundary simplicity, one or more of the candidate croppings may be chosen e.g., to present the chosen croppings to a user for selection.

Abstract: Image cropping suggestion is described. In one or more implementations, multiple croppings of a scene are scored based on parameters that indicate visual characteristics established for visually pleasing croppings. The parameters may include a parameter that indicates composition quality of a candidate cropping, for example. The parameters may also include a parameter that indicates whether content appearing in the scene is preserved and a parameter that indicates simplicity of a boundary of a candidate cropping. Based on the scores, image croppings may be chosen, e.g., to present the chosen image croppings to a user for selection. To choose the croppings, they may be ranked according to the score and chosen such that consecutively ranked croppings are not chosen. Alternately or in addition, image croppings may be chosen that are visually different according to scores which indicate those croppings have different visual characteristics.

Abstract: This document describes techniques and apparatuses for 3D printing with small geometric offsets to affect surface characteristics. These techniques are capable of enabling fused-deposition printers to create 3D objects having desired surface characteristics, such as particular colors, images and image resolutions, textures, and luminosities. In some cases, the techniques do so using a single filament head with a single filament material. In some other cases, the techniques do so using multiple heads each with different filaments, though the techniques can forgo many switches between these heads. Each printing layer may use even a single filament from one head, thereby enabling surface characteristics while reducing starts and stops for filaments heads, which enables fewer artifacts or increases printing speed.

Abstract: In techniques of combined composition and change-based models for image cropping, a composition application is implemented to apply one or more image composition modules of a learned composition model to evaluate multiple composition regions of an image. The learned composition model can determine one or more cropped images from the image based on the applied image composition modules, and evaluate a composition of the cropped images and a validity of change from the image to the cropped images. The image composition modules of the learned composition model include a salient regions module that iteratively determines salient image regions of the image, and include a foreground detection module that determines foreground regions of the image. The image composition modules also include one or more imaging models that reduce a number of the composition regions of the image to facilitate determining the cropped images from the image.

Abstract: In techniques for image foreground detection, a foreground detection module is implemented to generate varying levels of saliency thresholds from a saliency map of an image that includes foreground regions. The saliency thresholds can be generated based on an adaptive thresholding technique applied to the saliency map of the image and/or based on multi-level segmentation of the saliency map. The foreground detection module applies one or more constraints that distinguish the foreground regions in the image, and detects the foreground regions of the image based on the saliency thresholds and the constraints. Additionally, different ones of the constraints can be applied to detect different ones of the foreground regions, as well as to detect multi-level foreground regions based on the saliency thresholds and the constraints.

Abstract: In techniques for iterative saliency map estimation, a salient regions module applies a saliency estimation technique to compute a saliency map of an image that includes image regions. A salient image region of the image is determined from the saliency map, and an image region that corresponds to the salient image region is removed from the image. The salient regions module then iteratively determines subsequent salient image regions of the image utilizing the saliency estimation technique to recompute the saliency map of the image with the image region removed, and removes the image regions that correspond to the subsequent salient image regions from the image. The salient image regions of the image are iteratively determined until no salient image regions are detected in the image, and a salient features map is generated that includes each of the salient image regions determined iteratively and combined to generate the final saliency map.

Abstract: This document describes techniques and apparatuses for smooth 3D printing using multi-stage filaments. These techniques are capable of creating smoother surfaces than many current techniques. In some cases, the techniques determine a portion of a surface of a 3D object that includes, or will include, a printing artifact or is otherwise not smooth, and then applies multi-stage filaments to provide a smoothing surface over that portion.