A system and method for estimating dimensions of an approximately cuboidal object from a 3D image of the object acquired by an image sensor of the vision system processor is provided. An identification module, associated with the vision system processor, automatically identifies a 3D region in the 3D image that contains the cuboidal object. A selection module, associated with the vision system processor, automatically selects 3D image data from the 3D image that corresponds to approximate faces or boundaries of the cuboidal object. An analysis module statistically analyzes, and generates statistics for, the selected 3D image data that correspond to approximate cuboidal object faces or boundaries. A refinement module chooses statistics that correspond to improved cuboidal dimensions from among cuboidal object length, width and height. The improved cuboidal dimensions are provided as dimensions for the object. A user interface displays a plurality of interface screens for setup and runtime operation.

A system and method for estimating dimensions of an approximately cuboidal object from a 3D image of the object acquired by an image sensor of the vision system processor is provided. An identification module, associated with the vision system processor, automatically identifies a 3D region in the 3D image that contains the cuboidal object. A selection module, associated with the vision system processor, automatically selects 3D image data from the 3D image that corresponds to approximate faces or boundaries of the cuboidal object. An analysis module statistically analyzes, and generates statistics for, the selected 3D image data that correspond to approximate cuboidal object faces or boundaries. A refinement module chooses statistics that correspond to improved cuboidal dimensions from among cuboidal object length, width and height. The improved cuboidal dimensions are provided as dimensions for the object. A user interface displays a plurality of interface screens for setup and runtime operation.

A citrus identification method comprises: performing first-time image acquisition processing on a target citrus tree to obtain a first image; inputting the first image into a first citrus fruit identification model to be processed to obtain a first identification result sequence; performing area interception processing on the first image to obtain a citrus fruit area; obtaining roundness integrity numerical values; selecting an appointed roundness integrity numerical value, and acquiring a defect position of an appointed citrus fruit in the first image; determining a first spatial range and a second spatial range; performing both first spray injection treatment and second spray injection treatment; performing second-time image acquisition processing to obtain a second image; inputting the second image into a second citrus fruit identification model to be processed to obtain a second identification result; and generating a citrus fruit identification result.

A system and method include performing digital block-out of one or more digital preparation teeth.

A system and method include performing digital block-out of one or more digital preparation teeth.

The current document is directed to methods and systems that straighten curvature in the text lines of text-containing digital images, including text-containing digital images generated from the two pages of an open book. Initial processing of a text-containing image identifies the outline of a text-containing page. Next, contours are generated to represent each text line. The midpoints and inclination angles of the links or vectors that comprise the contour lines are determined. A model is constructed for the perspective-induced curvature within the text image. In one implementation, the model, essentially an inclination-angle map, allows for assigning local displacements to pixels within the page image which are then used to straighten the text lines in the text image. In another implementation, the model is essentially a pixel-displacement map which is used to straighten the text lines in the text image.

A system and method for gaging the shape of a curved panel includes, as components, (1) a system and method for acquiring three-dimensional surface data corresponding to the panel, and (2) a system and method for receiving the acquired surface data, comparing the acquired surface to a pre-defined surface description, and developing indicia of the level of conformance of the contoured panel to the pre-defined specification. The surface data acquisition system includes a conveyor for conveying the panel, at least one display projecting a preselected contrasting pattern, and at least one camera. The camera(s) and display(s) are uniquely paired and are mounted in a spaced-apart relationship a known distance and angle from the surface of the panel such that the camera detects the reflected image of the pattern projected on the surface of the panel from its associated display.

A system and method for gaging the shape of a curved panel includes, as components, (1) a system and method for acquiring three-dimensional surface data corresponding to the panel, and (2) a system and method for receiving the acquired surface data, comparing the acquired surface to a pre-defined surface description, and developing indicia of the level of conformance of the contoured panel to the pre-defined specification. The surface data acquisition system includes a conveyor for conveying the panel, at least one display projecting a preselected contrasting pattern, and at least one camera. The camera(s) and display(s) are uniquely paired and are mounted in a spaced-apart relationship a known distance and angle from the surface of the panel such that the camera detects the reflected image of the pattern projected on the surface of the panel from its associated display.

Examples disclosed herein relate to determining the shape of an object representation. In one implementation, a processor determines contours of a silhouette of an object representation above a contour degree threshold, where a contour degree is determined based on the area of the contour with respect to itself. The processor may identify a shape type of the object representation based on a comparison of the determined contours to shape definition information.

Examples disclosed herein relate to determining the shape of an object representation. In one implementation, a processor determines contours of a silhouette of an object representation above a contour degree threshold, where a contour degree is determined based on the area of the contour with respect to itself. The processor may identify a shape type of the object representation based on a comparison of the determined contours to shape definition information.