Abstract: Dimensions of an object associated with an electro-optically readable code are estimated by aiming a handheld device at a scene containing the object supported on a base surface. A scanner on the device scans the scene over a field of view to obtain a position of a reference point of the code associated with the object, and reads the code. A dimensioning sensor on the device captures a three-dimensional (3D) point cloud of data points of the scene in automatic response to the reading of the code. A controller clusters the point cloud into data clusters, locates the reference point of the code in one of the data clusters, extracts from the point cloud the data points of the one data cluster belonging to the object, and processes the extracted data points belonging to the object to estimate the dimensions of the object.

Abstract: An example disclosed apparatus includes memory configured to store a mapping of items to respective ones of a plurality of minimum pixel-resolutions, wherein the minimum pixel-resolutions correspond to an image resolution required to identify a corresponding one of the items using an image; and a processor configured to obtain identifying information corresponding to one of the items; select, using the mapping and based on the identifying information, one of the plurality of minimum pixel-resolutions; determine a value of an output parameter based on the selected one of the plurality of minimum pixel resolutions and a camera position relative to a shelf, wherein the output parameter is at least one of an elevation angle of the camera, a vertical resolution, a horizontal resolution, a vertical field of view, and a horizontal field of view; and output the determined value of the output parameter to facilitate positioning of the camera.

Abstract: Dimensions of an object associated with an electro-optically readable code are estimated by aiming a handheld device at a scene containing the object supported on a base surface. A scanner on the device scans the scene over a field of view to obtain a position of a reference point of the code associated with the object, and reads the code. A dimensioning sensor on the device captures a three-dimensional (3D) point cloud of data points of the scene in automatic response to the reading of the code. A controller clusters the point cloud into data clusters, locates the reference point of the code in one of the data clusters, extracts from the point cloud the data points of the one data cluster belonging to the object, and processes the extracted data points belonging to the object to estimate the dimensions of the object.

Abstract: An example disclosed apparatus includes memory configured to store a mapping of items to respective ones of a plurality of minimum pixel-resolutions, wherein the minimum pixel-resolutions correspond to an image resolution required to identify a corresponding one of the items using an image; and a processor configured to obtain identifying information corresponding to one of the items; select, using the mapping and based on the identifying information, one of the plurality of minimum pixel-resolutions; determine a value of an output parameter based on the selected one of the plurality of minimum pixel resolutions and a camera position relative to a shelf, wherein the output parameter is at least one of an elevation angle of the camera, a vertical resolution, a horizontal resolution, a vertical field of view, and a horizontal field of view; and output the determined value of the output parameter to facilitate positioning of the camera.

Abstract: An apparatus including a display screen displaying input fields for accepting user-input a list of input parameters that includes a height of the camera, a distance from the camera to the shelf, at least one of a width-span and a height-span. The apparatus also includes a memory to store a required minimum pixel-resolution of a selected label and to store a list of camera types, and includes a microprocessor configured to determine at least an elevation angle from the list of input parameters. The display screen can also be configured for displaying at least one camera type selected from the list of camera types based a selection criterion that depends upon at least some of said output parameters.

Abstract: An example disclosed method includes receiving an image containing shelf edge content displayed on a shelf edge; determining a location of the shelf edge content and an apparent orientation of the shelf edge content; determining, based on the location of the shelf edge content and the apparent orientation of the shelf edge content, a region in the image corresponding to the shelf edge; and providing an output based on the image that identifies the region in the image corresponding to the shelf edge.

Abstract: A non-parametric method of, and system for, dimensioning an object of arbitrary shape, captures a three-dimensional (3D) point cloud of data points over a field of view containing the object and a base surface on which the object is positioned, detects a base plane indicative of the base surface from the point cloud, extracts the data points of the object from the point cloud, processes the extracted data points of the object to obtain a convex hull, and fits a bounding box of minimum volume to enclose the convex hull. The bounding box has a pair of mutually orthogonal planar faces, and the fitting is performed by orienting one of the faces to be generally perpendicular to the base plane, and by simultaneously orienting the other of the faces to be generally parallel to the base plane.

Abstract: An example disclosed method includes receiving an image containing shelf edge content displayed on a shelf edge; determining a location of the shelf edge content and an apparent orientation of the shelf edge content; determining, based on the location of the shelf edge content and the apparent orientation of the shelf edge content, a region in the image corresponding to the shelf edge; and providing an output based on the image that identifies the region in the image corresponding to the shelf edge.

Abstract: A method and apparatus for image processing to avoid counting shelf edge promotional labels when counting product labels. Shelf edges are identified by detecting shelf edge content in a captured image by comparing the image to reference images of shelf edge content. Detected occurrences of shelf edge content are demarcated using a geometric pattern having corners at coordinates corresponding to positions around the identified shelf edge content. Corresponding corners are grouped into clusters, and the clusters are analyzed to define the upper and lower bounds of a shelf region in the image.

Abstract: Dimensions of an object associated with an electro-optically readable code are estimated by aiming a handheld device at a scene containing the object supported on a base surface. A scanner on the device scans the scene over a field of view to obtain a position of a reference point of the code associated with the object, and reads the code. A dimensioning sensor on the device captures a three-dimensional (3D) point cloud of data points of the scene in automatic response to the reading of the code. A controller clusters the point cloud into data clusters, locates the reference point of the code in one of the data clusters, extracts from the point cloud the data points of the one data cluster belonging to the object, and processes the extracted data points belonging to the object to estimate the dimensions of the object.

Abstract: Dimensions of an object associated with an electro-optically readable code are estimated by aiming a handheld device at a scene containing the object supported on a base surface. A scanner on the device scans the scene over a field of view to obtain a position of a reference point of the code associated with the object, and reads the code. A dimensioning sensor on the device captures a three-dimensional (3D) point cloud of data points of the scene in automatic response to the reading of the code. A controller clusters the point cloud into data clusters, locates the reference point of the code in one of the data clusters, extracts from the point cloud the data points of the one data cluster belonging to the object, and processes the extracted data points belonging to the object to estimate the dimensions of the object.

Abstract: A non-parametric method of, and system for, dimensioning an object of arbitrary shape, captures a three-dimensional (3D) point cloud of data points over a field of view containing the object and a base surface on which the object is positioned, detects a base plane indicative of the base surface from the point cloud, extracts the data points of the object from the point cloud, processes the extracted data points of the object to obtain a convex hull, and fits a bounding box of minimum volume to enclose the convex hull. The bounding box has a pair of mutually orthogonal planar faces, and the fitting is performed by orienting one of the faces to be generally perpendicular to the base plane, and by simultaneously orienting the other of the faces to be generally parallel to the base plane.

Abstract: An apparatus including a display screen displaying input fields for accepting user-input a list of input parameters that includes a height of the camera, a distance from the camera to the shelf, at least one of a width-span and a height-span. The apparatus also includes a memory to store a required minimum pixel-resolution of a selected label and to store a list of camera types, and includes a microprocessor configured to determine at least an elevation angle from the list of input parameters. The display screen can also be configured for displaying at least one camera type selected from the list of camera types based a selection criterion that depends upon at least some of said output parameters.

Abstract: An improved object recognition method is provided that enables the recognition of many objects in a single image. Multiple instances of an object in an image can now be detected with high accuracy. The method receives a plurality of matches of feature points between a database image and a query image and determines a kernel bandwidth based on statistics of the database image. The kernel bandwidth is used in clustering the matches. The clustered matches are then analyzed to determine the number of instances of the object within each cluster. A recursive geometric fitting can be applied to each cluster to further improve accuracy.

Abstract: A method and apparatus for image processing to avoid counting shelf edge promotional labels when counting product labels. Shelf edges are identified by detecting shelf edge content in a captured image by comparing the image to reference images of shelf edge content. Detected occurrences of shelf edge content are demarcated using a geometric pattern having corners at coordinates corresponding to positions around the identified shelf edge content. Corresponding corners are grouped into clusters, and the clusters are analyzed to define the upper and lower bounds of a shelf region in the image.

Abstract: An improved object recognition method is provided that enables the recognition of many objects in a single image. Multiple instances of an object in an image can now be detected with high accuracy. The method receives a plurality of matches of feature points between a database image and a query image and determines a kernel bandwidth based on statistics of the database image. The kernel bandwidth is used in clustering the matches. The clustered matches are then analyzed to determine the number of instances of the object within each cluster. A recursive geometric fitting can be applied to each cluster to further improve accuracy.