Abstract: The method for item recognition can include: optionally calibrating a sampling system, determining visual data using the sampling system, determining a point cloud, determining region masks based on the point cloud, generating a surface reconstruction for each item, generating image segments for each item based on the surface reconstruction, and determining a class identifier for each item using the respective image segments.

Abstract: Methods, systems, and programs are presented for simultaneous recognition of objects within a detection space utilizing three-dimensional (3D) cameras configured for capturing 3D images of the detection space. One system includes the 3D cameras, calibrated based on a pattern in a surface of the detection space, a memory, and a processor. The processor combines data of the 3D images to obtain pixel data and removes, from the pixel data, background pixels of the detection space to obtain object pixel data associated with objects in the detection space. Further, the processor creates a geometric model of the object pixel data, the geometric model including surface information of the objects in the detection space, generates one or more cuts in the geometric model to separate objects and obtain respective object geometric models, and performs object recognition to identify each object in the detection space based on the respective object geometric models.

Abstract: In variants, the self-checkout method can include: acquiring measurements of a batch of items, automatically identifying each item based on the measurements, and repeating the above until a checkout condition is met.

Abstract: Methods, systems, and programs are presented for simultaneous recognition of objects within a detection space utilizing three-dimensional (3D) cameras configured for capturing 3D images of the detection space. One system includes the 3D cameras, calibrated based on a pattern in a surface of the detection space, a memory, and a processor. The processor combines data of the 3D images to obtain pixel data and removes, from the pixel data, background pixels of the detection space to obtain object pixel data associated with objects in the detection space. Further, the processor creates a geometric model of the object pixel data, the geometric model including surface information of the objects in the detection space, generates one or more cuts in the geometric model to separate objects and obtain respective object geometric models, and performs object recognition to identify each object in the detection space based on the respective object geometric models.

Abstract: In variants, a method for item recognition can include: optionally calibrating a sampling system, determining visual data using the sampling system, determining a point cloud, determining region masks based on the point cloud, generating a surface reconstruction for each item, generating image segments for each item based on the surface reconstruction, and determining a class identifier for each item using the respective image segments.

Abstract: Methods, systems, and programs are presented for simultaneous recognition of objects within a detection space utilizing three-dimensional (3D) cameras configured for capturing 3D images of the detection space. One system includes the 3D cameras, calibrated based on a pattern in a surface of the detection space, a memory, and a processor. The processor combines data of the 3D images to obtain pixel data and removes, from the pixel data, background pixels of the detection space to obtain object pixel data associated with objects in the detection space. Further, the processor creates a geometric model of the object pixel data, the geometric model including surface information of the objects in the detection space, generates one or more cuts in the geometric model to separate objects and obtain respective object geometric models, and performs object recognition to identify each object in the detection space based on the respective object geometric models.

Abstract: Methods, systems, and programs are presented for simultaneous recognition of objects within a detection space utilizing three-dimensional (3D) cameras configured for capturing 3D images of the detection space. One system includes the 3D cameras, calibrated based on a pattern in a surface of the detection space, a memory, and a processor. The processor combines data of the 3D images to obtain pixel data and removes, from the pixel data, background pixels of the detection space to obtain object pixel data associated with objects in the detection space. Further, the processor creates a geometric model of the object pixel data, the geometric model including surface information of the objects in the detection space, generates one or more cuts in the geometric model to separate objects and obtain respective object geometric models, and performs object recognition to identify each object in the detection space based on the respective object geometric models.

Abstract: The method for item identification preferably includes determining visual information for an item; calculating a first encoding using the visual information; calculating a second encoding using the first encoding; determining an item identifier for the item using the second encoding; optionally presenting information associated with the item to a user; and optionally registering a new item.

Abstract: The method for item recognition can include: optionally calibrating a sampling system, determining visual data using the sampling system, determining a point cloud, determining region masks based on the point cloud, generating a surface reconstruction for each item, generating image segments for each item based on the surface reconstruction, and determining a class identifier for each item using the respective image segments.

Abstract: The method for item identification preferably includes determining visual information for an item; calculating a first encoding using the visual information; calculating a second encoding using the first encoding; determining an item identifier for the item using the second encoding; optionally presenting information associated with the item to a user; and optionally registering a new item.

Abstract: The method for item recognition can include: optionally calibrating a sampling system, determining visual data using the sampling system, determining a point cloud, determining region masks based on the point cloud, generating a surface reconstruction for each item, generating image segments for each item based on the surface reconstruction, and determining a class identifier for each item using the respective image segments.

Abstract: The method for item recognition can include: optionally calibrating a sampling system, determining visual data using the sampling system, determining a point cloud, determining region masks based on the point cloud, generating a surface reconstruction for each item, generating image segments for each item based on the surface reconstruction, and determining a class identifier for each item using the respective image segments.

Abstract: The method for item identification preferably includes determining visual information for an item; calculating a first encoding using the visual information; calculating a second encoding using the first encoding; determining an item identifier for the item using the second encoding; optionally presenting information associated with the item to a user; and optionally registering a new item.

Abstract: Methods, systems, and programs are presented for simultaneous recognition of objects within a detection space utilizing three-dimensional (3D) cameras configured for capturing 3D images of the detection space. One system includes the 3D cameras, calibrated based on a pattern in a surface of the detection space, a memory, and a processor. The processor combines data of the 3D images to obtain pixel data and removes, from the pixel data, background pixels of the detection space to obtain object pixel data associated with objects in the detection space. Further, the processor creates a geometric model of the object pixel data, the geometric model including surface information of the objects in the detection space, generates one or more cuts in the geometric model to separate objects and obtain respective object geometric models, and performs object recognition to identify each object in the detection space based on the respective object geometric models.

Abstract: Methods, systems, and programs are presented for simultaneous recognition of objects within a detection space utilizing three-dimensional (3D) cameras configured for capturing 3D images of the detection space. One system includes the 3D cameras, calibrated based on a pattern in a surface of the detection space, a memory, and a processor. The processor combines data of the 3D images to obtain pixel data and removes, from the pixel data, background pixels of the detection space to obtain object pixel data associated with objects in the detection space. Further, the processor creates a geometric model of the object pixel data, the geometric model including surface information of the objects in the detection space, generates one or more cuts in the geometric model to separate objects and obtain respective object geometric models, and performs object recognition to identify each object in the detection space based on the respective object geometric models.

Abstract: Methods, systems, and computer programs are presented for on-the-fly image recognition of an item in motion. One method includes an operation for periodically capturing images, by several cameras, of a recognition area. Further, the method includes detecting that the item is present in the recognition area and selecting a recognition window that defines a period of time for analysis. The recognition window defines recognition frames corresponding to the images captured within the recognition window. Each recognition frame is analyzed to determine if an identity of the item has been obtained for the recognition frame, the analysis being based on image recognition of the recognition frame to identify the item based on, at least, a shape of the item and coloring of the item. Further, the method includes operations for determining if the item has been identified based on the analysis for the recognition frames, and for displaying the item identification.

Abstract: Methods, systems, and computer programs are presented for capturing color images of items for a machine-learning model. In one method, a request, for adding an item to be visually recognized during sales transactions, is detected at a checkout apparatus. Further, the method includes capturing and storing a color image, by each of one or more cameras of the checkout apparatus, of an examination space when the item is present. Further, the capturing and storing of color images of the item is repeated at different poses until at a predetermined number of color images of the item are captured. Further, the method includes generating, by a processor of the checkout apparatus, additional images of the item by modifying the captured color images. A machine-learning model for item recognition is built by training a machine-learning tool with the captured images and the additional images.

Abstract: Methods, systems, and computer programs are presented for object recognition performed by electronic devices. One method includes an operation for capturing three-dimensional (3D) images of a region over a surface using 3D cameras, the surface having a pattern and each 3D camera defining a respective camera coordinate system. For each camera, the 3D image is analyzed to identify a location of the pattern indicating an origin of a common coordinate system, and a coordinate transformation function is defined to convert data to the common coordinate system. Each 3D camera captures a 3D object image of an object on the surface that includes 3D object data. The 3D object data is transformed to the common coordinate system to obtain transformed 3D object data. The 3D object data is combined to obtain a composite 3D object data, and object recognition of the object is performed based on the composite 3D object data.

Abstract: Methods, systems, and computer programs are presented for capturing color images of items for a machine-learning model. In one method, a request, for adding an item to be visually recognized during sales transactions, is detected at a checkout apparatus. Further, the method includes capturing and storing a color image, by each of one or more cameras of the checkout apparatus, of an examination space when the item is present. Further, the capturing and storing of color images of the item is repeated at different poses until at a predetermined number of color images of the item are captured. Further, the method includes generating, by a processor of the checkout apparatus, additional images of the item by modifying the captured color images. A machine-learning model for item recognition is built by training a machine-learning tool with the captured images and the additional images.

Abstract: Methods, systems, and computer programs are presented for providing a feedback loop to improve object image-based recognition based on transaction data. In one method, instructions are received defining items to be visually recognized by a terminal. For each item, a check is made to determine if item image information is in a global database or if it is a new item. The global database includes item images captured during transactions performed at several terminals. For each item in the global database, item image information is downloaded from the global database. For new items, terminal cameras capture pose images for several poses of the new items, each camera taking an image for each pose. A machine-learning program is trained with the downloaded image information and the pose images, where the machine-learning program performs image-based recognition of the items that are presented at the terminal, based on images captured by the cameras.