In this work, we propose to use 3D facial landmarks for the task of subject identification, over a range expressed emotion. Landmarks are detected, using Temporal Deformable Shape Model and used train Support Vector Machine (SVM), Random Forest (RF), Long Short-term Memory (LSTM) neural network identification. As interested in identification with large variations expression, conducted experimen...

This research is motivated by the need for 3D GIS data models that allow for 3D spatial query, analysis and visualization of the subunits and internal network structure of ‘micro-spatial environments’ (the 3D spatial structure within buildings). It explores a new way of representing the topological relationships among 3D geographical features such as buildings and their internal partitions or s...

Aggregation of local features is a well-studied approach for image as well as 3D model retrieval (3DMR). A carefully designed local 3D geometric feature is able to describe detailed local geometry of 3D model, often with invariance to geometric transformations that include 3D rotation of local 3D regions. For efficient 3DMR, these local features are aggregated into a feature per 3D model. A rec...

The aim of this yticle is to describe and compare the methods based on random graphs (RGs) which are applied to learn and recognize 3D objects represented by multiple views. These methods me based on modelling the objects by means of probabilistic structures that keep 1'' and Zndorder probabilities. That is, multiple views of a 3D object are represented by few RGs. The most important probabilis...

Airborne hyperspectral data has high spectral-spatial information. However, how to mine and use this information effectively is still a great challenge. Recently, three-dimensional convolutional neural network (3D-CNN) provides new effective way of classification. its capability mining in complex urban areas, especially cloud shadow areas not been validated. Therefore, 3D-1D-CNN model was propo...