Abstract: Disclosed is an image processing device, which includes a plurality of sensors that output two or more different types of sensing data, a processor that synchronizes the sensing data, extracts image processing-related information from the synchronized sensing data, and generates a gigapixel texture and a three-dimensional (3D) mesh based on the image processing-related information, a user interface that visualizes and outputs 3D cultural asset data generated based on the gigapixel texture and the 3D mesh, and a memory that stores the image processing-related information and the 3D cultural asset data. The image processing-related information includes a multi-viewpoint image, a calibration image, and a camera parameter.

Abstract: The present disclosure includes a diorama space generation and construction module configured to construct an AR map by using a user camera image or image data received through a sensor, a diorama environment tracking and location optimization module configured to load the constructed AR map onto an AR device and track a location of the AR device in a diorama space in real time based on image data received in real time, and a multi-user association and event synchronization module including a user interaction module configured to perform user interaction processing, user event information processing and management, and AR content playback configured to perform interaction synchronization processing, content synchronization processing, and user information synchronization processing.

Abstract: A clustering method includes configuring a network with clustering target objects; collecting significance of the clustering target objects; performing network embedding for outputting a set of vectors representing neighboring objects of the clustering target objects constituting the network using a neural network; and performing clustering on the clustering target objects using the set of vectors and information on each of the clustering target objects, wherein the neural network is trained so that neighboring objects having high significance are output with higher probability.

Abstract: A clustering method includes configuring a network with clustering target objects; collecting significance of the clustering target objects; performing network embedding for outputting a set of vectors representing neighboring objects of the clustering target objects constituting the network using a neural network; and performing clustering on the clustering target objects using the set of vectors and information on each of the clustering target objects, wherein the neural network is trained so that neighboring objects having high significance are output with higher probability.

Abstract: The present invention relates to an apparatus and method for providing personalized adaptive e-learning, and more specifically, to an apparatus and method for estimating a learner's proficiency to each attribute and providing adaptive e-learning by taking into consideration an item answering result of an individual learner in a personalized learning environment.

Abstract: Disclosed is an apparatus for analyzing a game update effect according to a change in a gamer action sequence, the apparatus including: a gamer action information collector configured to collect action information of a gamer from a game operating server that provides a gamer terminal with a game and stores the action information of the gamer therein; a gamer action information sequence identifier configured to detect an action sequence of the gamer from the collected action information of the gamer; and an update result analyzer configured to analyze an consequence on behavior of the gamer with respect to an update of a game service by comparing action sequences of the gamer detected through the gamer action information sequence identifier on the basis of a time point of the update of the game service.

Abstract: Provided are an apparatus and method for learner diagnosis using reliability of a cognitive diagnostic model, which estimates the reliability of the cognitive diagnostic model that estimates a concept vector (?) of a learner through a Q-matrix regarding a question and an R-matrix regarding a response to a question, the method including assuming a probability (P(X|?)) of a learner response (X) when a concept vector (?) of a learner is given; obtaining a concept pattern-specific probability (P(?|X)) of the learner from the assumed concept vector and learner response of the learner; obtaining an information entropy (H) value of the learner from the concept pattern-specific probability (P(?|X)) of the learner; and obtaining reliability (?) of an estimated result of a learner-specific concept understanding using the information entropy value of the learner and a number of concepts.

Abstract: Disclosed is an apparatus for analyzing a game update effect according to a change in a gamer action sequence, the apparatus including: a gamer action information collector configured to collect action information of a gamer from a game operating server that provides a gamer terminal with a game and stores the action information of the gamer therein; a gamer action information sequence identifier configured to detect an action sequence of the gamer from the collected action information of the gamer; and an update result analyzer configured to analyze an consequence on behavior of the gamer with respect to an update of a game service by comparing action sequences of the gamer detected through the gamer action information sequence identifier on the basis of a time point of the update of the game service.

Abstract: Provided are a mobile-based personal content protection apparatus and method, in which a user sets a mobile terminal to prevent another user from photographing and using personal content with a camera included in the mobile terminal. The mobile-based personal content protection apparatus may include a camera view estimator, a peripheral information receiver, a personal content detector, and a personal content masking unit. A view which is to be photographed by a user is estimated in a space where photographing is allowed by using sensor information included in a mobile terminal, information about whether to allow photographing by each mobile device is collected by cooperating all mobile terminals in a camera view, and a disallowed area is masked in photographing, thereby fundamentally protecting personal content.

Abstract: Provided is an alternative text generating method. The alternative text generating method includes recognizing input visual content, generating input information corresponding to a recognition result of the recognition of the visual content, generating an editing window including an input item to which the input information is automatically input, automatically generating an alternative text, based on an alternative text generation rule and the input information, and displaying the generated alternative text on a text box of the editing window.

Abstract: Provided are a mobile-based personal content protection apparatus and method, in which a user sets a mobile terminal to prevent another user from photographing and using personal content with a camera included in the mobile terminal. The mobile-based personal content protection apparatus may include a camera view estimator, a peripheral information receiver, a personal content detector, and a personal content masking unit. A view which is to be photographed by a user is estimated in a space where photographing is allowed by using sensor information included in a mobile terminal, information about whether to allow photographing by each mobile device is collected by cooperating all mobile terminals in a camera view, and a disallowed area is masked in photographing, thereby fundamentally protecting personal content.

Abstract: The present invention relates to an apparatus and method for providing personalized adaptive e-learning, and more specifically, to an apparatus and method for estimating a learner's proficiency to each attribute and providing adaptive e-learning by taking into consideration an item answering result of an individual learner in a personalized learning environment.

Abstract: The present invention relates to a learning diagnosis apparatus, a method of diagnosing a learner's learning ability, and an adaptive learning system for providing adaptive learning using the diagnosis result. The learning diagnosis apparatus includes a database (DB) configured to store and manage adaptive learning data and store a program for estimating a learner's proficiency to each attribute and recommending learning content and a processor configured to execute the program, wherein the processor calculates a attribute proficiency vector estimation result of a specific learner using information about a solution to a personalized item together with group test solution information.

Abstract: Provided is a system for detecting an object from a depth image. The system includes a communication module, a memory, and a processor. By executing the object detection program, the processor extracts a first object area and a second object area from the depth image, based on a predetermined floor plane and an outer plane which is set with respect to the predetermined floor plane. The processor extracts a target area including pixels of the second object area which are spaced apart from the first object area by a predetermined interval. The processor samples a pixel, which is not included in the target area, to extract a floor area from the second object area, calculates a boundary value of an object and a floor, based on the floor area and the target area, and extracts a foreground pixel from the target area, based on the calculated boundary value.

Abstract: Provided is a system for detecting an object from a depth image. The system includes a communication module, a memory, and a processor. By executing the object detection program, the processor extracts a first object area and a second object area from the depth image, based on a predetermined floor plane and an outer plane which is set with respect to the predetermined floor plane. The processor extracts a target area including pixels of the second object area which are spaced apart from the first object area by a predetermined interval. The processor samples a pixel, which is not included in the target area, to extract a floor area from the second object area, calculates a boundary value of an object and a floor, based on the floor area and the target area, and extracts a foreground pixel from the target area, based on the calculated boundary value.

Abstract: Disclosed are an apparatus and a method for providing mixed reality for a virtual experience service, and more particularly, an apparatus and a method for providing mixed reality contents for learning a virtual experience based on a story that provides a service in which a user in a real world appears in a virtual world having the story to undergo a virtual experience. The method picks up the user image in the real world and synthesizes the picked-up image with a desired position of a 3D virtual world. According to the present disclosure, a realistic experience based learning can be provided while a spatial limit is overcome.

Abstract: Disclosed herein is a content playback apparatus and method for providing an interactive augmented space. The content playback apparatus includes an augmented space recognition unit and an interaction processing unit. The augmented space recognition unit reconfigures a real space into a virtual space based on one depth image extracted from the real space, matching one or more primitive objects to the virtual space, and projects one or more virtual 3D objects, mapped to the primitive objects and combined with the reconfigured virtual space, onto the real space. The interaction processing unit recognizes one or more gesture interactions of a user related to the virtual 3D objects projected onto the real space and projects the virtual 3D objects onto the real space by incorporating deformations of the virtual 3D objects resulting from the recognized gesture interactions.

Abstract: A face recognition and apparatus are provided. According to the method, an SVM classifier is created through machine learning on the basis of a degree of similarity of a divided facial image, and a facial image to be authenticated is normalized to a predetermined size using a center between two eyes. The normalized image is divided into more than one image in horizontal and vertical directions, respectively. Next, predetermined characteristic vectors from the divided images are extracted and a similarity vector based on a degree of similarity with respect to a registered characteristic vector is created. The similarity vector is input to the SVM classifier, so that authentication is performed.

Abstract: An educational system using virtual reality includes an image computer for extracting an image of a student from an entire image acquired by capturing the student; a remote lecturer computer for extracting an image of a lecturer at a remote place from an entire image acquired by capturing the remote lecturer. Further, the educational system using the virtual reality includes a classroom lecturer computer for executing learning content in which a virtual space is implemented, and performing real-time communication with the remote lecturer computer; and a virtual reality computer for combining the image of the student and the image of the remote lecturer with the virtual space of the learning content.

Abstract: A face recognition and apparatus are provided. According to the method, an SVM classifier is created through machine learning on the basis of a degree of similarity of a divided facial image, and a facial image to be authenticated is normalized to a predetermined size using a center between two eyes. The normalized image is divided into more than one image in horizontal and vertical directions, respectively. Next, predetermined characteristic vectors from the divided images are extracted and a similarity vector based on a degree of similarity with respect to a registered characteristic vector is created. The similarity vector is input to the SVM classifier, so that authentication is performed.