An augmented reality operating system based on augmented reality software as a service (SaaS) comprises an augmented reality management system providing a pre-assigned 3D virtual image to a web browser which has transmitted a URL address in a distribution mode and in supporting creation of augmented reality content based on augmented reality software as a service in an authoring mode, providing a template for creating the augmented reality content on a web browser authorized as a manager and billing a payment according to the type of template used; a user terminal receiving the 3D virtual image from the augmented reality content management system by transmitting the URL address through an installed web browser and displaying each physical object of actual image information displayed on the web browser by augmenting the physical object with a pre-assigned virtual object of the 3D virtual image in a distribution mode; and a manager terminal accessing augmented reality software as a service of the augmented reality content management system via an installed web browser, creating the augmented reality content by determining an augmentation position on a map, a physical object of actual image information located at the augmentation position, and a virtual object assigned to the physical object respectively in an authoring mode.

An augmented reality operating system based on augmented reality software as a service (SaaS) comprises an augmented reality management system providing a pre-assigned 3D virtual image to a web browser which has transmitted a URL address in a distribution mode and in supporting creation of augmented reality content based on augmented reality software as a service in an authoring mode, providing a template for creating the augmented reality content on a web browser authorized as a manager and billing a payment according to the type of template used; a user terminal receiving the 3D virtual image from the augmented reality content management system by transmitting the URL address through an installed web browser and displaying each physical object of actual image information displayed on the web browser by augmenting the physical object with a pre-assigned virtual object of the 3D virtual image in a distribution mode; and a manager terminal accessing augmented reality software as a service of the augmented reality content management system via an installed web browser, creating the augmented reality content by determining an augmentation position on a map, a physical object of actual image information located at the augmentation position, and a virtual object assigned to the physical object respectively in an authoring mode.

Techniques are provided that include obtaining a vocabulary including a set of content indices that reference corresponding cells in a descriptor space based on an input set of descriptors. A plurality of local features of an image are identified based on the vocabulary, the local features being represented by a plurality of local descriptors. An associated visual word in the vocabulary is determined for each of the plurality of local descriptors. A plurality of global signatures for the image are generated based on the associated visual words, wherein some of the plurality of global signatures are generated using local descriptors corresponding to different cropped versions of the image, two or more of the different cropped versions of the image being centered at a same pixel location of the image, and an image recognition search is facilitated using the plurality of global signatures to search a document image dataset.

Embodiments of the invention provide a computer-readable medium of visual saliency estimation comprising receiving an input video of image frames. Each image frame has one or more channels, and each channel has one or more pixels. The computer-readable medium further comprises, for each channel of each image frame, generating corresponding neural spiking data based on a pixel intensity of each pixel of the channel, generating a corresponding multi-scale data structure based on the corresponding neural spiking data, and extracting a corresponding map of features from the corresponding multi-scale data structure. The multi-scale data structure comprises one or more data layers, wherein each data layer represents a spike representation of pixel intensities of a channel at a corresponding scale. The computer-readable medium further comprises encoding each map of features extracted as neural spikes.

Aspects of the subject disclosure may include, for example, a method performed by a processing system of determining a present orientation of a display region presented at a first time on a display of a video viewer, predicting a future orientation of the display region occurring at a second time based on data collected, to obtain a predicted orientation of the display region to be presented at the second time on the display of the video viewer, identifying, based on the predicted orientation of the display region, a first group of tiles from a video frame of a panoramic video being displayed by the video viewer, wherein the first group of tiles covers the display region in the video frame at the predicted orientation, and a plurality of objects moving in the video frame from the first time to the second time, wherein each object of the plurality of objects is located in a separate spatial region of the video frame at the second time, wherein a second group of tiles collectively covers the separate spatial regions, wherein tiles in the first group of tiles and tiles in the second group of tiles are different, and facilitating wireless transmission of the first group of tiles and a second tile from the second group of tiles, for presentation at the video viewer at the second time. Other embodiments are disclosed.

The present invention is adapted for recognition technology improvement, which provides a memory identification and recovery method based on recognition, including: S1. collecting the data information from the scene of activity through a recognition device; S2. conducting salient feature extraction to the data information collected from the scene and generating feature marks; S3. building mapping relations between the generated feature marks and the extracted data information, automatically generating memory information in the database, and storing the information in the database; S4. inputting related data information for searching; S5. selecting a corresponding method to search the generated memory information in the database based on the input data information; S6. determining if there is related data information in the memory data. The method can helps to enhance memory of the user, recover memory after forget it, recover effectively through recognition technology, improve memory, and retrieve memory quickly after memory loss, which is convenient and efficient.

Provided is an image processing apparatus, an image processing method, and a program, which are capable of accurate collation even when an image contains a number of identical or similar subjects. The image processing apparatus of the present invention has: first feature amount generating means for generating, with respect to a plurality of feature points to be detected from a first image, a first local feature amount group including local feature amounts representing feature amounts of a plurality of local regions containing the respective feature points, and a first coordinate position information group including coordinate position information; region dividing means for clustering the feature points of the first image based on the first coordinate position information group; and collation means for collating, in units of clusters, the first local feature amount group with a second local feature amount group formed from local feature amounts of feature points detected from a second image.

Mechanisms are provided to implement a generative adversarial network (GAN) for natural language processing. With these mechanisms, a generator neural network of the GAN is configured to generate a bag-of-ngrams (BoN) output based on a noise vector input and a discriminator neural network of the GAN is configured to receive a BoN input, where the BoN input is either the BoN output from the generator neural network or a BoN input associated with an actual portion of natural language text. The mechanisms further configure the discriminator neural network of the GAN to output an indication of a probability as to whether the input BoN is from the actual portion of natural language text or is the BoN output of the generator neural network. Moreover, the mechanisms train the generator neural network and discriminator neural network based on a feedback mechanism that compares the output indication from the discriminator neural network to an indicator of whether the input BoN is from the actual portion of natural language text of the BoN output of the generator neural network.

The present disclosure attempts to evaluate how the texture of an object is perceived based on visual features of the topological skeleton of the object. A camera S1 obtains a color image by taking an image of an object, which serves as an evaluation target. Within the image obtained, a visual feature area, which is likely to strike a person's eye when the person claps his/her eyes on the object, and an intensity of a visual stimulus of each pixel of the visual feature area are extracted. Visual skeleton features of each pixel of the image are determined within a contour region which is composed of the visual feature areas extracted. The visual skeleton features determined are shown on a display.

Embodiments of the invention provide a method of visual saliency estimation comprising receiving an input video of image frames. Each image frame has one or more channels, and each channel has one or more pixels. The method further comprises, for each channel of each image frame, generating corresponding neural spiking data based on a pixel intensity of each pixel of the channel, generating a corresponding multi-scale data structure based on the corresponding neural spiking data, and extracting a corresponding map of features from the corresponding multi-scale data structure. The multi-scale data structure comprises one or more data layers, wherein each data layer represents a spike representation of pixel intensities of a channel at a corresponding scale. The method further comprises encoding each map of features extracted as neural spikes.