Ricoh Company, Ltd.

Cimpress Schweiz Gmbh

Anshul Garg

Sri Kaushik Pavani

Bharathi Shekar

Jorge Moraleda

Srikrishna Bhat

Ajay Joshi

Michael J Gormish

Kathrin Berkner

Ramya Narasimha

Vikramaditya Khemka

Debashish Sahoo

Rohan Halasur

Vijay S Reddy Pochampally

Ankur Tiwari

Ramizuddin Khan

Saurabh Kumar

Jonathan Cho

Systems and methods for visually rendering a set of characters in a visual design are disclosed. According to certain aspects, an electronic device may determine a position for a character sizing box within a container element of the visual design. Further, the electronic device may determine a placement location for a set of characters within the character sizing box. Additionally, the electronic device may generate and display a visual rendering of the visual design that may comprise the set of characters within the character sizing box which is positioned within the container element, where the visual rendering may comprise additional content positioned relative to the set of characters according to a layout format.

Techniques for visually rendering a set of characters and other elements in a visual design

Systems and methods for detecting crop marks depicted in digital images are disclosed. According to certain aspects, an electronic device detects a set of crop marks in a reference digital image and aligns the reference digital image with an input digital image. Based on locations of the detected crop marks as aligned to the input digital image, the electronic device detects a set of partial crop marks in the input digital image. As a result, the partial crop marks in the input digital image may be removed.

Technologies for detecting crop marks in electronic documents using reference images

Systems and methods for automatically determining and displaying salient portions of images are disclosed. According to certain aspects, an electronic device may support a design application that may apply a saliency detection learning model to a digital image, resulting in the application generating one or more salient portions of the digital image. The electronic device may generate a digital rendering of the salient portion of the image on digital models of items or products, and may enable a user to review the digital rendering. The user may also choose alternative salient portions of the digital image and/or aspect ratios for those salient portions for inclusion on a digital model of the item or product.

Technologies for automatically determining and displaying salient portions of images

Systems and methods for detecting crop marks depicted in digital images are disclosed. According to certain aspects, an electronic device may, in conjunction with enabling a user to customize a product design, facilitate a set of digital image processing functionalities to detect a set of crop marks depicted in a digital image associated with the product design. In particular, the systems and methods may detect a first set of lines depicted in a processed digital image indicative of content of interest extracted from the digital image, and detect a second set of lines depicted in a subtracted digital resulting from subtracting a set of contours from the processed digital image. The systems and methods may determine the set of crop marks from the first and second sets of lines.

Technologies for detecting crop marks in electronic documents

The disclosure includes a system and method for distinguishing between stock keeping units of similar appearance that vary in size. An image recognition application receives an image depicting a plurality of items, the image including a reference marker with a known physical dimension. The image recognition application performs image recognition to identify an item in the image and a region of interest for the identified image. The image recognition application further determines a pixel-to-physical dimension ratio using the dimension of a region of interest of the reference marker and the known physical dimension of the reference marker. Finally, the image recognition application determines a stock keeping unit identifier of the identified item in the image based on the pixel-to-physical dimension ratio and a dimension of the region of interest of the identified item.

Distinguishing between stock keeping units using marker based methodology

The disclosure includes a system and method for indexing synthetically modified images of a high quality image. An image recognition application receives images of a product, crops background regions from the images, scales the image based on a minimum value among width and height of the image and generates multiple image sizes, blurs the images, brightens the image and indexes the images as being associated with the product. The images can be of box-shaped packages that include four or six images or cylindrical packages that include, for example, eight images of the packages. The images can be indexed in a k-dimensional tree for faster retrieval.

Three-dimensional indexing protocol

The disclosure includes a system and method for distinguishing between stock keeping units of similar appearance that vary in size. An image recognition application receives an image including a shelving unit stocking a plurality of items, identifies each item in the image, generates a region of interest for each identified item in the image, identifies a physical dimension of a portion of region depicted in the image, determines a dimension of the region of interest for each identified item and the portion of region in pixels, determines a pixel-to-physical dimension ratio using the dimension in pixels of the portion of region and the physical dimension of the portion of region depicted in the image, and determines a stock keeping unit identifier of each identified item in the image based on the pixel-to-physical dimension ratio and the dimension of the region of interest for each identified item.

Distinguishing between stock keeping units using a physical dimension of a region depicted in an image

A system and method that generates a candidate list of indexed images that potentially match an object in a query image is disclosed. The method includes receiving a query image including an object, receiving a plurality of indexed images that match the object, computing a region of interest for the object, computing an overlap between a first region of interest corresponding to a first indexed image and a second region of interest corresponding to a second indexed image, determining that the overlap between the first region of interest and the second region of interest satisfies a threshold and including the first indexed image and the second indexed image in a candidate list of indexed images.

Candidate list generation

The disclosure includes a system and method for distinguishing objects of similar appearance that vary in color. An image recognition application receives a query image of a plurality of objects, determines a candidate list of indexed images that match an object in the query image based on luminance features, crops an image of the object from the query image to generate a cropped image, computes color features for the object, ranks the candidate list of indexed images based on the color features, and provides the candidate list of indexed images to a user.

Color-based post-processing of images

A system and method that computes a quality score for an index image is disclosed. The method includes receiving an index image, computing a blurriness score of the index image based on variance associated with the index image, computing an image resolution score of the index image based on an area of the index image and a threshold area, computing a feature spread score for the index image using a first plurality of features associated with the index image, computing a feature uniqueness score for the index image using a description associated with each of a second plurality of features and determining a quality score for the index image using the blurriness score, the image resolution score, the feature spread score, and the feature uniqueness score.

Index image quality metric

The disclosure includes a system and method for distinguishing between items of similar appearance that vary in size. An image recognition application receives an image including a plurality of items, identifies each item in the image, generates a region of interest for each identified item in the image, determines a dimension of the region of interest for each identified item in pixels, determines a plurality of pixel-to-physical dimension ratios for each identified item, identifies a selected pixel-to-physical dimension ratio among the plurality of pixel-to-physical dimension ratios, and determines a stock keeping unit identifier of each identified item in the image based on the selected pixel-to-physical dimension ratio and the dimension of the region of interest for each identified item.

Distinguishing between stock keeping units using hough voting methodology

A method for sequencing products on a retail shelf, or other objects, recognized in an image of the retail shelf includes detecting products in the received image and calculating a final sequence position for each detected product. The final sequence position is calculated by generating a region of interest for each detected product in the received image, generating a plurality of unique raster numbers for each region of interest using a raster number equation by varying parameters of the raster number equation based on pixel coordinates of each region of interest, determining a plurality of sequence positions for each region of interest using the plurality of unique raster numbers, and determining the final sequence position for each region of interest using the plurality of sequence positions for each region of interest. The disclosure also includes systems for implementing the method.

Sequencing products recognized in a shelf image

The disclosure includes a system and method for distinguishing between stock keeping units of similar appearance that vary in size. An image recognition application receives an image depicting a plurality of items, identifies each item in the image, generates a region of interest for each identified item in the image, determines a pixel dimension of the region of interest for each identified item, determines whether one of the identified items in the image is a reference marker with a stored physical dimension, determines a pixel-to-physical dimension ratio for the reference marker using the dimension in pixels and stored physical dimension of the reference marker, and determines a stock keeping unit identifier of each identified item in the image based on the pixel-to-physical dimension ratio and the pixel dimension of the region of interest for each identified item.

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Haryana, India

Anshul Garg