Bayesian fusion models based on multi-level constraints and multiple criteria in image processing and computer vision

我的研究计划研究使用新的无监督（贝叶斯）概率或基于能量的融合模型来理解，分析和操作静态，移动和多维，多光谱或多模式图像。 更确切地说，这项研究计划将试图提出新的统计模型，以协同整合多个图像线索（例如，颜色、纹理、边缘、兴趣点或对称性检测、完形感知线索等）具有可能不同的约束（可能以不同的抽象级别表示），以便更好地对要估计的（图像）解的内在和复杂属性建模和/或融合几个弱解或不同的互补低级应用（分割、边缘图、恢复图像等）。以实现更可靠和准确的解决方案或高级计算机视觉任务（3D重建，复杂形状定位等）。 这些模型不仅在静态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，地球科学成像，更一般地说，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，该理论允许考虑关于待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的相互作用的（统计）模型（似然模型）联合收割机结合。在这个框架中，可用的先验信息的正确使用可以表示为本地先验模型，如马尔可夫随机场（MRF）模型和上下文的知识通常是通过空间本地的相互作用的规范或最近通过非本地（或远程）的相互作用。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变化（通过全局参数概率先验模型或由较低抽象级别的解决方案的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).