Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
A method of recognizing a hand shape by using a database including a plurality of hand shape depth images includes receiving a motion of a user, extracting a hand shape depth image of the user from the received motion, normalizing a size and depth values of the extracted hand shape depth image to conform to criteria of a size and depth values of the hand shape depth images stored in the database, and detecting from the database a hand shape depth image corresponding to the normalized hand shape depth image. It is possible to detect a hand shape depth image in a rapid and accurate way with the disclosed method.