We present a human-centric method to sample and synthesize 3D room layouts and 2D
images thereof, for the purpose of obtaining large-scale 2D/3D image data with the perfect …

This exploratory paper quests for a stochastic and context sensitive grammar of images. The
grammar should achieve the following four objectives and thus serves as a unified …

In this paper, we propose a probabilistic model for detecting relevant changes in registered
aerial image pairs taken with the time differences of several years and in different seasonal …

Probabilistic models have been adopted for many computer vision applications, however
inference in high-dimensional spaces remains problematic. As the state-space of a model …

We propose a systematic learning-based approach to the generation of massive quantities
of synthetic 3D scenes and arbitrary numbers of photorealistic 2D images thereof, with …

This article proposes a generative image model, which is called “primal sketch,” following
Marr's insight and terminology. This model combines two prominent classes of generative …

In this paper, we present a mathematical theory for Marr's primal sketch. We first conduct a
theoretical study of the descriptive Markov random field model and the generative …

In this paper, we give a comparative study on three Multilayer Markov Random Field (MRF)
based solutions proposed for change detection in optical remote sensing images, called …

This paper presents a class of statistical models that integrate two statistical modeling
paradigms in the literature:(I) Descriptive methods, such as Markov random fields and …

Autonomous systems, including airborne, land-based, marine, and underwater vehicles, are
increasingly present in the world. One important aspect of autonomy is the capability to …