Bayesian statistical fusion models based on constraints and multiple criteria in image processing and computer vision

我的研究项目调查使用新的无监督概率或基于能量的融合模型来理解、分析和操纵静止、移动和多维或多模态图像。更准确地说，本研究计划将尝试提出新的统计模型，以整合多个（低或高水平）和互补的图像线索（例如，颜色，纹理，边缘，角落和兴趣点检测，对称检测，感知组织的格式塔定律等）和/或利用一个或几个低级应用（分割，边缘图，恢复图像等）来可靠地估计最终的高级计算机视觉任务(例如，运动检测/分割、遮挡图、复杂形状定位、三维重建等)。这些问题在几个领域有广泛的应用，包括多模态医学图像应用、地球科学和高光谱图像，更普遍的是在下一代的所有多相机或多模态识别和重建系统中。

My research program investigates the use of new unsupervised probabilistic or energy-based fusion models for understanding, analyzing, and manipulating still, moving and multidimensional or multi-modal images. More precisely, this research program will attempt to propose new statistical models to integrate multiple (low or higher level) and complementary image cues (e.g., color, texture, edges, corner and interest point detection, symmetry detection, Gestalt laws of perceptual organization, etc.) and/or to exploit one or several low-level applications (segmentation, edge map, restored image, etc.) for the reliable estimation of a final high-level computer vision task (e.g., motion detection/segmentation, occlusion map, complex shape localization, 3D reconstruction, etc.). These problems have a wide range of applications in several fields, including multi-modal medical image applications, geoscience and hyperspectral imagery and more generally in all multi-camera or multi-modal recognition and reconstruction systems of the next generation.