A system and method for stipulation collection using a Short Message Service (SMS) and mobile device cameras with real-time analysis of documents is disclosed. A particular embodiment is configured to: establish, by use of a data processor and a data network, a data connection with at least one dealer/lender platform and at least one applicant platform; receive a transaction application corresponding to a transaction for a particular applicant from the dealer/lender platform via the data network, the transaction application identifying a list of stipulation documents required from the particular applicant at the applicant platform; generate a link to the transaction application received from the dealer/lender platform and send the link to the dealer/lender platform; transmit the transaction application with the list of required stipulation documents as a Short Message Service (SMS) message to a mobile device of the particular applicant at the applicant platform; receive an upload of the required stipulation documents from the applicant platform via the data network, the uploaded stipulation documents including at least one stipulation document image captured with a camera of the mobile device of the particular applicant; perform stipulation document analysis and verification operations on the uploaded stipulation documents, the analysis and verification operations including optical character recognition and the application of one or more machine learning models; transmit at least one link of a verified stipulation document to the dealer/lender platform; and transmit an error message to the particular applicant at the applicant platform if a stipulation document cannot be verified.

In some implementations, a computer-implemented method includes: obtaining a captured image including, (i) a latent fingerprint to be captured, and (ii) a template, surrounding the latent fingerprint, that contains plurality of symbols; extracting, using an image processing technique, one or more characteristics for each of the plurality of symbols; calculating a resolution for the captured image based at least on the extracted one or more characteristics for each of the plurality of symbols; generating a reconstructed desired image, based at least on (i) the calculated resolution of the captured image, and (ii) the one or more extracted characteristics for each of the plurality of symbols; and providing the reconstructed desired image to a fingerprint identification system to perform a particular fingerprint operation.

A method of detecting a radiographic object is proposed. The method includes receiving, by a feature processing module, an input of radiographic image obtained by irradiating a region containing a plurality of tubes including at least one target tube as a radiographic object and at least one untargeted tube that is not a radiographic object, extracting, by the feature processing module, feature values of extremal points from the radiographic image to detect feature vectors, and analyzing, by a detection module, the feature vectors using a training model to detect a region in the radiographic image where the at least one target tube as a radiographic object exists.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network that is configured to process an input image to generate a network output for the input image. In one aspect, a method comprises, at each of a plurality of training steps: obtaining a plurality of training images for the training step; obtaining, for each of the plurality of training images, a respective target output; and selecting, from a plurality of image patch generation schemes, an image patch generation scheme for the training step, wherein, given an input image, each of the plurality of image patch generation schemes generates a different number of patches of the input image, and wherein each patch comprises a respective subset of the pixels of the input image.

Methods, systems, articles of manufacture, and apparatus to recalibrate confidences for image classification are disclosed. An example apparatus to classify an image includes an image crop detector to detect a first image crop from the image, the first image crop corresponding to a first object, a grouping controller to select a second image crop corresponding to a second object at a location of the first object, a prediction generator to, in response to executing a trained model, determine a label corresponding to the first object and a confidence level associated with the label, and a confidence recalibrator to recalibrate the confidence level based on a probability of the first object having a first attribute based on the second object having a second attribute, the confidence level recalibrated to increase an accuracy of the image classification.

This disclosure relates to systems, methods, and devices that can identify products on a shelf, and determine item descriptions and pricing from associated labels. In one embodiment, an image processing system for providing an indication about shelf label accuracy in a store is provided. The image processing system comprises at least one processor configured to receive, from a hand-held device, an image depicting products on store shelves, and associated labels coupled to the store shelves. The processor can be configured to process the image to identify at least some of the products and to access a database to determine associated product ID numbers. The processor can be further configured to determine product identifier and displayed price from the associated labels. The processor can also be configured to determine one or more product-label mismatches related to incorrect product placement on the shelf and to provide electronic notification of the mismatches.

An object detection device includes a prediction unit which predicts an object presence area, which is an area where an object is present in a current image, based on results of object detection in a past image, a generation unit which generates an object presence partial area that includes a partial area of the predicted object presence area together with partial information, which is information indicating a position of the partial area in the predicted object presence area and a size of the partial area, a detection unit which detects partial objects that are part of the object by performing object detection on the generated object presence partial area, and a restoration unit which restores results of object detection in the current image by placing the partial objects detected using the generated partial information in the predicted object presence area.

Techniques are described for using computing devices to perform automated operations involved in analysis of images acquired in a defined area, as part of generating mapping information of the defined area for subsequent use (e.g., for controlling navigation of devices, for display on client devices in corresponding GUIs, etc.). The defined area may include an interior of a multi-room building, and the generated information including a floor map of the building, such as from an analysis of multiple 360? spherical panorama images acquired at various viewing locations within the building (e.g., using an image acquisition device with a spherical camera having one or more fisheye lenses to capture a panorama image that extends 360 degrees around a vertical axis)the generating may be further performed without detailed information about distances from the images' viewing locations to objects in the surrounding building.

A medical information processing device of an embodiment includes processing circuitry. The processing circuitry acquires a medical image to be processed, divides the medical image into a plurality of regions, calculates an image feature amount for each of the plurality of regions, generates a first cluster in which at least some of the plurality of regions are collected on the basis of the image feature amount, and identifies a sampling position on the basis of the first cluster.

Patterning device modulates a light beam intensity distribution according to two-gradation first image data having a first number of pixels. Processing device calculates a correlation between detection intensity b based on output of photodetector and m-gradation (m?2) random second image data having a second number of pixels smaller than the first number, to generate a reconstructed image G(x,y) of an object. When the first and second image data are scaled to the same size and overlapped, each pixel is associated with a pixel group including multiple pixels of the overlapping first image data. With the normalized gradation value of a given pixel as k, the number of pixels included in the pixel group as L, and the number of pixels having a value of 1 in the pixel group as l, l=L?k holds true.