An object is recognized from image data as a target object and linked to a user based on an interaction by the user, information about the target object is obtained and a purchase of the target object is initiated.

Various embodiments disclosed herein are directed to methods of capturing Vehicle Identification Numbers (VIN) from images captured by a mobile device. Capturing VIN data can be useful in several applications, for example, insurance data capture applications. There are at least two types of images supported by this technology: (1) images of documents and (2) images of non-documents.

An apparatus and a method are provided for detecting entities in a numerical image, wherein the apparatus includes a computing unit configured for detecting, based on a histogram vector determined on the basis of gradient and partitioning information, the presence of at least one of the entities in the image, a signaling unit in signal communication with the computing unit, and configured for being activated when the computing unit detects the presence of at least one of the entities in the image, memory containing partitioning information, and configured for allowing access to the partitioning information on the basis of the gradient information, wherein each piece of partitioning information identifies at least one of the partitioning elements that allow the computing unit to quantize the gradient information.

A system and method that allows the capture of a series of images to create a single linear panoramic image is disclosed. The method includes capturing an image, dynamically comparing a previously captured image with a preview image on a display of a capture device until a predetermined overlap threshold is satisfied, generating a user interface to provide feedback on the display of the capture device to guide a movement of the capture device, and capturing the preview image with enough overlap with the previously captured image with little to no tilt for creating a linear panorama.

A method comprising: obtaining a three-dimensional (3D) point cloud about an object; obtaining binary feature descriptors for feature points in a 2D image about the object; assigning a plurality of index values for each feature point as multiple bits of the corresponding binary feature descriptor; storing the binary feature descriptor in a table entry of a plurality of hash key tables of a database image; obtaining query binary feature descriptors for feature points in a query image; matching the query binary feature descriptors to the binary feature descriptors of the database image; reselecting one bit of the hash key of the matched database image; and re-indexing the feature points in the table entries of the hash key table of the database image.

A server device transmits picture image data to an image printing device, and transmits, to a portable terminal, moving image data corresponding to the picture image data. The server device includes a storage unit storing the picture image data and the moving image data, a picture-image data transmission unit transmitting the picture image data to the image printing device, a storage location information transmission unit transmitting storage location information of the moving image data to the image printing device, and a moving-image data transmission unit transmitting the moving image data to the portable terminal that has read, from a printed matter on which the picture image data and a code image obtained by coding the storage location information are printed by the image printing device, the code image, that has acquired the storage location information from the read code image, and that has accessed the server device.

A method for three-dimensional (3D) modeling using two-dimensional (2D) image data includes obtaining a first image of an object oriented in a first direction and a second image of the object oriented in a second direction, determining a plurality of feature points of the object in the first image, and determining a plurality of matching feature points of the object in the second image that correspond to the plurality of feature points of the object in the first image. The method further includes calculating similarity values between the plurality of feature points and the corresponding plurality of matching feature points, calculating depth values of the plurality of feature points, calculating weighted depth values based on the similarity values and depth values, and performing 3D modeling of the object based on the weighted depth values.

In one embodiment of the present invention, a native resolution analyzer generates a log-magnitude spectrum that elucidates sampling operations that have been performed on a scene. In operation, the native resolution analyzer performs a transform operation of a color component associated with a frame included in the scene to generate a frame spectrum. The native resolution analyzer then normalizes the magnitudes associated with the frame spectrum and logarithmically scales the normalized magnitudes to create a log-magnitude frame spectrum. This two dimensional log-magnitude frame spectrum serves as a frequency signature for the frame. More specifically, patterns in the log-magnitude spectrum reflect re-sampling operations, such as a down-sampling and subsequent up-sampling, that may have been performed on the frame. By analyzing the log-magnitude spectrum, discrepancies between the display resolution of the scene and the lowest resolution with which the scene has been processed may be detected in an automated fashion.

An image classification method is provided. The method includes: inputting a to-be-classified image into a plurality of neural network models; obtaining data output by multiple non-input layers specified by each neural network model to generate a plurality of image features corresponding to the plurality of neural network models; respectively inputting the plurality of corresponding image features into linear classifiers, each of the linear classifiers being trained by one of the plurality of neural network models for determining whether an image belongs to a preset class; obtaining, using each neural network model, a corresponding probability that the to-be-classified image comprises an object image of the preset class; and determining, according to each obtained probability, whether the to-be-classified image includes the object image of the preset class.

A system may comprise a registration device configured to register patterns for users; a recording device configured to record a received pattern, as an electronic pattern, wherein the recording device recognizes the received pattern as one of the registered patterns; a receiving device configured to observe human movement patterns with a camera, transform the observed human movement patterns to an electronic signal, and receive the recognized registered pattern from the recording device by a first wireless transmission; a forwarding device configured to transmit the electronic signal, and the received recognized registered pattern to an alert service by a second wireless transmission; and an alert service, configured to receive the electronic signal and the received recognized registered pattern from the forwarding device and configured to transmit the electronic signal and the received recognized registered pattern to a second electronic device by a third wireless transmission.