Point Cloud Classification Algorithm Based on the Fusion of the Local Binary Pattern Features and Structural Features of Voxels

Point cloud classification is a key technology for point cloud applications and point cloud feature extraction is a key step towards achieving point cloud classification. Although there are many point cloud feature extraction and classification methods, and the acquisition of colored point cloud data has become easier in recent years, most point cloud processing algorithms do not consider the color information associated with the point cloud or do not make full use of the color information. Therefore, we propose a voxel-based local feature descriptor according to the voxel-based local binary pattern (VLBP) and fuses point cloud RGB information and geometric structure features using a random forest classifier to build a color point cloud classification algorithm. The proposed algorithm voxelizes the point cloud; divides the neighborhood of the center point into cubes (i.e., multiple adjacent sub-voxels); compares the gray information of the voxel center and adjacent sub-voxels; performs voxel global thresholding to convert it into a binary code; and uses a local difference sign–magnitude transform (LDSMT) to decompose the local difference of an entire voxel into two complementary components of sign and magnitude. Then, the VLBP feature of each point is extracted. To obtain more structural information about the point cloud, the proposed method extracts the normal vector of each point and the corresponding fast point feature histogram (FPFH) based on the normal vector. Finally, the geometric mechanism features (normal vector and FPFH) and color features (RGB and VLBP features) of the point cloud are fused, and a random forest classifier is used to classify the color laser point cloud. The experimental results show that the proposed algorithm can achieve effective point cloud classification for point cloud data from different indoor and outdoor scenes, and the proposed VLBP features can improve the accuracy of point cloud classification.

Non-local Solvable Birth–Death Processes

In this paper, we study strong solutions of some non-local difference–differential equations linked to a class of birth–death processes arising as discrete approximations of Pearson diffusions by means of a spectral decomposition in terms of orthogonal polynomials and eigenfunctions of some non-local derivatives. Moreover, we give a stochastic representation of such solutions in terms of time-changed birth–death processes and study their invariant and their limit distribution. Finally, we describe the correlation structure of the aforementioned time-changed birth–death processes.

A percepção ambiental de professores do ensino fundamental sobre o tráfico e a proteção à fauna em três municípios do Rio Grande do Sul, Brasil

O Projeto Liberdade e Saúde, pioneiro no Brasil, que utiliza a educação ambiental como ferramenta de combate ao tráfico de animais silvestres é desenvolvido pelo IBAMA (PI) e IBAMA (RS). Este trabalha na formação de professores e alunos do ensino fundamental sobre a problemática do tráfico, suas modalidades, consequências, a legislação brasileira e o risco das zoonoses associadas. Verificou-se o que os professores compreendem sobre o tema, através de um questionário semi-estruturado de percepção ambiental aplicado aos professores de Guaíba, Porto Alegre e São Sebastião do Caí (RS). Analisou-se por estatística descritiva não paramétrica, a fim de estimar a frequência, as relações entre as respostas dos professores das distintas cidades. Participaram 149 professores e, destes, 146 reconheceram que o tráfico de animais silvestres é danoso ao ambiente. As características locais apareceram em algumas questões, representando assim os contextos de onde estão inseridas de maior e menor proximidade com o ambiente natural e com menos ou mais sinais de antropização. The environmental perception about the wildlife traffic and wildlife protection in three cities in the Rio Grande do Sul, Brazil The Liberty and Health project, developed by IBAMA (PI) and IBAMA (RS), is one of pioneer projects that use environmental education to elementary teachers and students, to prevent the wildlife trafficking in Brazil. It trains teachers and students for elementary schools about this issue, its forms, consequences, Brazilian laws and the risk about the zoonoses in this practice. For verify the comprehension of this public about this theme, applied a semi-strcutured questionnaire of environmental perception in the Guaíba, Porto Alegre and São Sebastião do Caí cities. The data was analyzed with non-parametric descriptive statistical analysis to estimate the frequencies about answers and cities, considered significant ( < 0,05). The most important answer with 149 teachers was the recognition about the damages of the wildlife trafficking. The local difference was seen in some questions, demonstrate the contexts of the proximity with the urbanization. This work demonstrate the necessity of constant formation of the teachers and public agents about the wildlife trafficking. Keywords: Environmental perception. Animal trafficking. Biodiversity. Wildlife. Environmental education. Elementary school.