Korea Institute of Science and Technology

Electronics and Telecommunications Research Institute

Center of Human-centered Interaction for Coexistence

Sang Chul Ahn

Ig Jae Kim

Hyoung Gon Kim

Jaewon Kim

Yong-Moo Kwon

Hwasup Lim

Jong-Ho Lee

Youngmin Kim

Jae Won Kim

Ju Young Park

Bum Jae You

Nam Kyu Kim

Jin Uk Kwon

Jae-In Hwang

Nam Ho Kim

Seok Joon Kwon

Hwa Sup Lim

Sung Min Chu

Sungkuk Chun

You Hwa Oh

Ui Kyu Song

Junseok Kang

Sung Eun Choi

The present disclosure relates to an object detection and classification system with higher accuracy and resolution in a less computer memory environment. The system comprises an input value generation unit to receive an input image and generate an input value including feature information; a memory value generation unit to receive a reference image and generate a memory value including feature information; a memory management unit to select information having high importance from the memory values and store in a computer memory; an aggregated value generation unit to compute similarity between the input value and the memory value, calculate a weighted sum to generate an integrated value, and aggregate the integrated value and the input value; and an object detection unit to detect or classify the object from the input image using the aggregated value.

Object/region detection and classification system with improved computer memory efficiency

Facial expressions and whole-body gestures of a 3D character are provided based on facial expressions of a user and gestures of a hand puppet perceived using a depth camera.

Method and device for controlling 3D character using user's facial expressions and hand gestures

Disclosed is a method for facial age simulation based on an age of each facial part and environmental factors, which includes: measuring an age of each facial part on the basis of an input face image; designating a personal environmental factor; transforming an age of each facial part by applying an age transformation model according to the age of each facial part and the environmental factor; reconstructing the image transformed for each facial part; and composing the reconstructed images to generate an age-transformed face. Accordingly, it is possible to transform a face realistically based on an age measured for each facial part and an environmental factor.

Method for facial age simulation based on age of each part and environmental factors, recording medium and device for performing the method

Provided is an in vivo bioimaging method including irradiating near-infrared (NIR) light onto a living body, converting the NIR light passed through the living body, into visible light using upconversion nanoparticles (UCNPs), and generating a bioimage of the living body by receiving the visible light using a complementary metal-oxide-semiconductor (CMOS) image sensor.

In vivo bioimaging method and system using near-infrared light and complementary metal-oxide-semiconductor image sensor

In a remote interaction method performed by a remote collaboration system comprising a robot device and a head mounted display, the robot device is located in a remote space and comprising a projector, a panoramic camera and a high resolution camera. The head mounted display is located in a local space apart from the remote space. A communication between the robot device and the head mounted display is established based on a communication request. The remote space is observed by the head mounted display based on first image information and second image information. The first image information is collected by the panoramic camera and received from the robot device. The second image information is collected by the high resolution camera and received from the robot device. Necessary information to be provided to the remote space is searched by the head mounted display based on a result of observing the remote space. The necessary information received from the head mounted display is provided by the robot device to the remote space using the projector.

Remote collaboration system with projector-camera based robot device and head mounted display, and remote interaction method using the same

Disclosed is an apparatus for hand gesture recognition based on a depth image, which includes a depth image acquiring unit configured to acquire a depth image including a hand region, a depth point classifying unit configured to classify depth points of a hand region in the depth image according to a corresponding hand portion by means of a machine studying method, and a hand model matching unit configured to match a three-dimensional hand model with the classified depth points by using distances between the classified depth points and a hand portion respectively corresponding to the depth points. A recognition method using the apparatus is also disclosed.

Apparatus and method of hand gesture recognition based on depth image

A device for dynamic reconfiguration of robot components includes: a resource monitoring unit for monitoring resources of a plurality of boards on which components for executing tasks of a robot are loaded; a dynamic reconfiguration unit for dynamically reconfiguring components of the boards, in the case at least one of the boards is at a risk for a scarcity of resources; and a log managing unit for storing a configuration of present components and a configuration of reconfigured components. Accordingly, it is possible to recognize a scarcity of resources in advance while the robot is operating and prevent the robot from malfunctioning by distributing the components.

Device and method for dynamic reconfiguration of robot components

An image generator is provided which obtains a specular image and a diffuse image from an image acquired by a polarized light field camera by separating two reflection components of a subject, and a control method thereof. The image generator may include a main lens, a polarizing filter part, a photosensor, a microlens array, and a controller that generates a single image in response to the electrical image signal and extracts, from the generated image, a specular image and a diffuse image that exhibit different reflection characteristics of the subject.

Specular and diffuse image generator using polarized light field camera and control method thereof

A method that highlights a depth-of-field (DOF) region of an image and performs additional image processing by using the DOF region. The method includes: obtaining a first pattern image and a second pattern image that are captured by emitting light according to different patterns from an illumination device; detecting a DOF region by using the first pattern image and the second pattern image; determining weights to highlight the DOF region; and generating the highlighted DOF image by applying the weights to a combined image of the first pattern image and the second pattern image.

Method and apparatus for generating image with highlighted depth-of-field

A method and system for reconstructing an image displayed on an electronic device connected to a network, to be a high resolution image. The method of reconstructing a selected area of the image displayed on the electronic device connected to a network, to be a high resolution image, includes: receiving a request to expand the selected area; collecting images including the selected area from the Internet; correcting the selected area to have a high resolution while expanding the selected area based on the collected images; and displaying the image expanded to have a high resolution on the electronic device.

Method and system for reconstructing image having high resolution

A method of reconstructing a three-dimensional (3D) facial shape with super resolution even from a short moving picture having a front facial image by acquiring a super-resolution facial image by applying, as a weighting factor, a per-unit-patch similarity between a target frame and frames remaining after excluding the target frame from among a plurality of continuous frames including the front facial image, and reconstructing the 3D facial shape based on the acquired super-resolution facial image.

Method of reconstructing three-dimensional facial shape

Disclosed herein are a UPnP control point and a UPnP device based on the UPnP network and a connecting method using the same, in which a service connection is performed. The service connection is restrictedly performed using a configuration file defined to allow or deny a connection with a specific device on the UPnP network, so that only devices specified by a user can be connected.

UPnP control point and UPnP device based on the UPnP network and connecting method using the same

Provided are a method and an apparatus for tagging a photograph with information. The method of tagging a photograph with information, which calculates a shooting position of an input image from reference images having shooting position information to tag the shooting position information, includes: selecting a plurality of reference images; calculating a relative shooting position of the input image to the shooting positions of the reference images; calculating the shooting position of the input image on the basis of the calculation result of the calculating; and storing the shooting position and shooting direction information on the input image in an exchangeable image file format (EXIF) tag of the input image.

Method for geotagging of pictures and apparatus thereof

Provided is an apparatus for recognizing an activity of daily living (ADL). The apparatus includes a radio frequency identification (RFID) reader for reading the information of an RFID tag to recognize a motion object, a motion detector attached on a moving subject for acquiring acceleration information and recognizing a motion characteristic, and a controller for receiving information on the motion object from the RFID reader and information on the motion characteristic from the motion detector and then recognizing an ADL.

Apparatus and a method for recognizing an activity of daily living

A method for obtaining a glassless image is disclosed. The image processing method comprises the steps of (a) receiving an RGB color frontal facial image; (b) extracting candidates of eye regions from the received RGB color frontal facial image; (c) determining an exact eye region out of the candidates and normalizing the received RGB color frontal facial image; (d) extracting a glasses frame region by using color information contained in the received RGB color frontal facial image and edge information of a glasses frame; (e) performing an RGB-HSI transformation on the normalized frontal facial image; (f) generating H', S&#x2032;, and I&#x2032; glassless compensated images; (g) obtaining R&#x2032;, G&#x2032;, and B&#x2032; compensated images by performing an HSI-RGB transformation on the H&#x2032;, S&#x2032;, and I&#x2032; glassless compensated images; and (h) creating a glassless final color facial image on the basis of the R&#x2032;, G&#x2032;, and B&#x2032; compensated images.

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Seoul, South Korea

Sang Chul Ahn