Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs (1307.0060v1)

Abstract: The idea of computer vision as the Bayesian inverse problem to computer graphics has a long history and an appealing elegance, but it has proved difficult to directly implement. Instead, most vision tasks are approached via complex bottom-up processing pipelines. Here we show that it is possible to write short, simple probabilistic graphics programs that define flexible generative models and to automatically invert them to interpret real-world images. Generative probabilistic graphics programs consist of a stochastic scene generator, a renderer based on graphics software, a stochastic likelihood model linking the renderer's output and the data, and latent variables that adjust the fidelity of the renderer and the tolerance of the likelihood model. Representations and algorithms from computer graphics, originally designed to produce high-quality images, are instead used as the deterministic backbone for highly approximate and stochastic generative models. This formulation combines probabilistic programming, computer graphics, and approximate Bayesian computation, and depends only on general-purpose, automatic inference techniques. We describe two applications: reading sequences of degraded and adversarially obscured alphanumeric characters, and inferring 3D road models from vehicle-mounted camera images. Each of the probabilistic graphics programs we present relies on under 20 lines of probabilistic code, and supports accurate, approximately Bayesian inferences about ambiguous real-world images.

• The paper introduces Generative Probabilistic Graphics Programs (GPGP) as a novel approach to invert rendering processes for probabilistic image interpretation.
• The paper demonstrates robust performance by achieving a 70.6% CAPTCHA recognition rate and 74.60% lane detection accuracy using less than 20 lines of probabilistic code.
• The paper simplifies Bayesian inference by integrating stochastic scene generators, approximate renderers, and likelihood models, paving the way for practical applications in computer vision.

Approximate Bayesian Image Interpretation Using Generative Probabilistic Graphics Programs

This paper presents a framework called Generative Probabilistic Graphics Programs (GPGP) for image interpretation tasks. GPGP combines probabilistic programming, computer graphics representations, and approximate Bayesian computation to generate high-quality inferences from real-world images that are typically plagued by ambiguity. The approach effectively inverts graphical models to deduce scene compositions from image data.

Key Components and Methodology

GPGP involves four basic components:

1. Stochastic Scene Generator: This creates probabilistic representations of the entities in a scene, encompassing their spatial coordinates, sizes, identities, rotations, etc.
2. Approximate Renderer: Leveraging pre-existing graphics software, this unit processes the scene generator's output to produce rendered images.
3. Stochastic Likelihood Model: This compares the rendered images against observed data outputs, supporting probabilistic evaluation of scene accuracy.
4. Latent Control Variables: These adjust the rendering fidelity and model tolerance, allowing flexible adaptation to different forms of visual input.

Inference within the GPGP framework relies on automatic Metropolis-Hastings transitions without the need for custom-tailored algorithms, thus simplifying the task and enhancing reliability.

Applications and Performance

The paper reports two application areas where GPGP is applied—textual data interpretation from degraded images and 3D road modeling for autonomous driving. Each application is facilitated by programs written with less than 20 lines of probabilistic code, showcasing a high degree of model efficiency.

1. Reading Degraded Text: GPGP was tested on a challenging CAPTCHA corpus and achieved a character recognition rate of 70.6%, surpassing existing OCR systems that need extensive engineering efforts.
2. 3D Road Modeling: Applied to the KITTI dataset, GPGP realizes robust scene interpretations by integrating geometric scene constraints with image appearance distributions. This approach outperformed some existing baseline models, reporting a lane detection accuracy of up to 74.60%.

Implications and Future Work

The combination of graphics representations and probabilistic programming is significant as it showcases a pathway for tackling intricate vision tasks without cumbersome engineering interventions. The framework encourages adaptability and pragmatic inference across varied image domains, such as robotics or autonomous systems.

Future research might explore advanced automatic inference strategies, potentially incorporating discriminative training and modern image features for appearance modeling. Additionally, graphics data structures could be integrated within probabilistic programming to further refine rendering efficacy and improve conditional independence exploitation—a development poised to enhance computational tractability.

Generative Probabilistic Graphics Programs thus represent a promising frontier in probabilistic vision analysis, merging visual synthesis traditions with probabilistic inference techniques to advance the state of computer vision fundamentally. This paper lays foundational insights inviting further exploration and optimization in vision and graphics intersections.