Centroid

About: Centroid is a research topic. Over the lifetime, 4110 publications have been published within this topic receiving 53637 citations. The topic is also known as: barycenter (geometry) & geometric center of a plane figure.

Speech Recognition Device and Speech Recognition Method

Abstract: There is provided a voice recognition device and a voice recognition method that enhance the function of noise adaptation processing in voice recognition processing and reduce the capacity of a memory being used. Acoustic models are subjected to clustering processing to calculate the centroid of each cluster and the differential vector between the centroid and each model, model composition between each kind of assumed noise model and the calculated centroid is carried out, and the centroid of each composition model and the differential vector are stored in a memory. In the actual recognition processing, the centroid optimal to the environment estimated by the utterance environmental estimation is extracted from the memory, model restoration is carried out on the extracted centroid by using the differential vector stored in the memory, and noise adaptation processing is executed on the basis of the restored model.

3D Point Cloud Super-Resolution via Graph Total Variation on Surface Normals

Abstract: Point cloud is a collection of 3D coordinates that are discrete geometric samples of an object’s 2D surfaces. Using a low-cost 3D scanner to acquire data means that point clouds are often in lower resolution than desired for rendering on high-resolution displays. Building on recent advances in graph signal processing, we design a local algorithm for 3D point cloud super-resolution (SR). First, we initialize new points at centroids of local triangles formed using the low-resolution point cloud, and connect all points using a k-nearest-neighbor graph. Then, to establish a linear relationship between surface normals and 3D point coordinates, we perform bipartite graph approximation to divide all nodes into two disjoint sets, which are optimized alternately until convergence. For each node set, to promote piecewise smooth (PWS) 2D surfaces, we design a graph total variation (GTV) objective for nearby surface normals, under the constraint that coordinates of the original points are preserved. We pursue an augmented Lagrangian approach to tackle the optimization, and solve the unconstrained equivalent using the alternating method of multipliers (ADMM). Extensive experiments show that our proposed point cloud SR algorithm outperforms competing schemes objectively and subjectively for a large variety of point clouds.

An EM-based Ensemble Learning Algorithm on Piecewise Surface Regression Problem

Abstract: A multi-step Expectation-Maximization based (EM-based) algorithm is proposed to solve the piecewise surface regression problem which has typical applications in market segmentation research, identification of consumer behavior patterns, weather patterns in meteorological research, and so on. The multiple steps involved are local regression on each data point of the training data set and a small set of its closest neighbors, clustering on the feature vector space formed from the local regression, regression learning for each individual surface, and classification to determine the boundaries for each individual surface. An EM-based iteration process is introduced in the regression learning phase to improve the learning outcome. In this phase, ensemble learning plays an important role in the reassignment of the cluster index for each data point. The reassignment of cluster index is determined by the majority voting of predictive error of sub-models, the distance between the data point and regressed hyperplane, and the distance between the data point and centroid of each clustered surface. Classification is performed at the end to determine the boundaries for each individual surface. Clustering quality validity techniques are applied to the scenario in which the number of surfaces for the input domain is not known in advance. A set of experiments based on both artificial generated and benchmark data source are conducted to compare the proposed algorithm and widely-used regression learning packages to show that the proposed algorithm outperforms those packages in terms of root mean squared errors, especially after ensemble learning has been applied.