Bayesian fusion models based on multi-level 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（贝叶斯）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、多光谱或多模态图像。更准确地说，这个研究项目将尝试提出新的统计模型来协同整合多个图像线索(例如，颜色、纹理、边缘、兴趣点或对称检测、格式塔感知线索、等)，以可能不同的约束（可能在不同的抽象层次上表达），以便更好地模拟待估计（图像）解的内在和复杂属性，和/或融合几个弱解或不同互补的低级应用（分割，边缘映射，恢复图像等），以实现更可靠和准确的解或高级计算机视觉任务（3D重建，复杂形状定位等）。这些模型不仅在静止图像处理和计算机视觉中有广泛的应用，而且在其他几个领域也有广泛的应用，包括多模态医学图像应用，地球科学图像，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。***对于这些不同的研究模型，所采用的框架主要依赖于贝叶斯统计理论，该理论允许考虑到对所要发现的信息的一些可用的先验知识，并将该先验模型与描述隐藏变量和观察变量之间相互作用的（统计）模型（似然模型）相结合。在这个框架中，可用的先验信息的适当使用可以通过局部先验模型（如马尔可夫随机场（MRF）模型）来表达，上下文知识通常通过空间局部相互作用的规范或最近通过非局部（或远程）相互作用来捕获。此外，贝叶斯理论还可以应用全局先验（相互作用）或约束，例如要检测/重建的对象形状的自然可变性（通过全局参数概率先验模型或由较低抽象层次的解决方案知识表示的全局约束）

My research program investigates the use of new unsupervised (Bayesian) probabilistic or energy-based fusion models for understanding, analyzing, and manipulating still, moving and multidimensional, multispectral or multimodal images.*** More precisely, this research program will attempt to propose new statistical models to synergistically integrate multiple image cues (e.g., color, texture, edges, interest point or symmetry detection, Gestalt perceptual cues, etc.) with possibly different constraints (possibly expressed at different levels of abstraction) in order to better model the intrinsic and complex properties of the (image) solution to be estimated and/or to fuse several weak solutions or different complementary low-level applications (segmentation, edge map, restored image, etc.) in order to achieve either a more reliable and accurate solution or a high-level computer vision task (3D reconstruction, complex shape localization, etc.). *** These models can have a wide range of applications not only in still image processing and computer vision, but also in several other fields, including multi-modal medical image applications, Geoscience imagery and more generally in all multi-camera or multi-modal recognition and reconstruction systems of the next generation.***The adopted framework, for these different research models, mainly relies on the Bayesian statistical theory which allows to take into account some available prior knowledge on the information to be found and to combine this prior model with a (statistical) model describing the interactions between hidden and observed variables (likelihood model). In this framework, the proper use of the available prior information can be expressed by local prior models such as Markov Random Field (MRF) models and contextual knowledge is usually captured through the specification of spatially local interactions or recently through non local (or long-range) interactions. In addition, Bayesian theory makes it also possible to apply global prior (interactions) or constraints such as the natural variability of an object shape to be detected/reconstructed (via global parametric probabilistic prior models or global constraints expressed by the knowledge of a solution at a lower level of abstraction).**