Abstract:
An apparatus and method for analyzing body part association. The apparatus and method may recognize at least one body part from a user image extracted from an observed image, select at least one candidate body part based on association of the at least one body part, and output a user pose skeleton related to the user image based on the selected at least one candidate body part.
Abstract:
Provided is a method of creating a body pose cluster, including performing feature extraction from pose data about at least one pose, classifying, as a single cluster, similar poses from a feature vector space using a similarity measure, and configuring the number of poses included in each cluster from the feature vector space to be uniform using an imbalance measure.
Abstract:
A method of generating three-dimensional (3D) volumetric data may be performed by generating a multilayer image, generating volume information and a type of a visible part of an object, based on the generated multilayer image, and generating volume information and a type of an invisible part of the object, based on the generated multilayer image. The volume information and the type of each of the visible part and invisible part may be generated based on the generated multilayered image which may be include at least one of a ray-casting-based multilayer image, a chroma key screen-based multilayer image, and a primitive template-based multilayer image.
Abstract:
An object recognition apparatus, a classification tree learning apparatus, an operation method of the object recognition apparatus, and an operation method of the classification tree learning apparatus are provided. The object recognition apparatus may include an input unit to receive, as an input, a depth image representing an object to be analyzed, and a processing unit to recognize a visible object part and a hidden object part of the object, from the depth image, using a classification tree.
Abstract:
An object recognition system is provided. The object recognition system for recognizing an object may include an input unit to receive, as an input, a depth image representing an object to be analyzed, and a processing unit to recognize a visible object part and a hidden object part of the object, from the depth image, by using a classification tree. The object recognition system may include a classification tree learning apparatus to generate the classification tree.
Abstract:
An apparatus recognizes an object using a hole in a depth image. An apparatus may include a foreground extractor to extract a foreground from the depth image, a hole determiner to determine whether a hole is present in the depth image, based on the foreground and a color image, a feature vector generator to generate a feature vector, by generating a plurality of features corresponding to the object based on the foreground and the hole, and an object recognizer to recognize the object, based on the generated feature vector and at least one reference feature vector.
Abstract:
A system and method for learning a pose classifier based on a distributed learning architecture. A pose classifier learning system may include an input unit to receive an input of a plurality of pieces of learning data, and a plurality of pose classifier learning devices to receive an input of a plurality of learning data sets including the plurality of pieces of learning data, and to learn each pose classifier. The pose classifier learning devices may share learning information in each stage, using a distributed/parallel framework.