Automated Mathematical Equation Discovery for Visual Analysis
A novel frame- work for automatically discovering equations from scratch with little human intervention to deal with the different challenges encountered in real-world scenarios. In addition, our proposal can reduce human bias by proposing a search space design through generative network instead of hand-designed. As a proof of concept, the equations discovered by our framework are used to distinguish moving objects from the background in video sequences.
Real-Time Facial Expression Using Raspberry Pi
Detect seven expression and show on an 8X8 RGB LED matrix.
One-Class Wagging for Feature Selection
A novel online one-class ensemble based on wagging to select suitable features to each region of a certain scene to distinguish the foreground objects from the background. In addition, we propose a mechanism to update the importance of each feature discarding insignificantly features over time.
More informations can be found here.
Weighted Random Subspace for Feature Selection
An Online Weighted Ensemble of One-Class SVMs able to select suitable features for each pixel to distinguish the foreground objects from the background. In addition, our framework uses a mechanism to update these importances features over time. Moreover, a efficient heuristic approach is used to background model maintenance.
More informations can be found here.
XCS - LBP Descriptor for Background Subtraction
An eXtended Center-Symmetric Local Binary Pattern (XCS-LBP) descriptor for background modeling and subtraction in videos. By combining the strengths of the original LBP and the similar CS ones, it appears to be robust to illumination changes and noise, and produces short histograms, too.
More informations can be found here.
Facial Expression Recognition using Neural Networks
A system for automatic recognition of facial expressions. Initially 20 facial landmarks are extracted from each face¹. Subsequently, the GPA (Generalized Procrustes analysis) is applied to normalize all landmarks (i.e. translation, rotation, reflection and scaling). All landmarks are used to classify seven different facial expressions: neutral, angry, disgust, fear, happy, sad and surprise.
More informations can be found here.
In this project, the face detection is performed with the traditional Viola–Jones object detection framework. The Viola-Jones framework consists of Haar-like features extraction method and Adaboost classifier. After face detection, the same Viola-Jones framework is used to detect the facial regions.
More informations can be found here.
Facial Expression Public Databases
Here are found the main databases to evaluate the facial expression recognition algorithms. We shown a brief description and links to download each database. The databases are organized by alphabetically order.
More informations can be found here.
Algorithm for Mapping Indoor Environments
An Algorithm for Mapping Indoor Environments (in Virtual Environment). All tests were performed with Microsoft Robotics Studio.
More informations can be found here.
An Algorithm for Mapping Indoor Environments (in Real Environment).
More informations can be found here.