This paper describes the development of an approach for determining uncertainty and integrity for a vision based, precision relative navigation system. Integrity ultimately relies on the ability to determine rigorous knowledge of the probability density function (pdf) for the estimated relative state or state error; which is based on a known set of models or assumptions and conditioned upon a set of observed measurements. The research is based on the concept of using a vision system, such as a electro-optical (EO) or infrared imaging (IR) sensor, to compute run-time confidence intervals or protection levels for a high precision, safety-critical relative navigation system. Previous work demonstrated the ability to provide an integrity monitor using vision based techniques, this paper presents research taking those approaches further to provide dynamic run-time protection levels for the navigation solution. The research utilizes a generalized Bayesian inference approach, in which a full pdf determination of the state estimate is realized. The paper describes the development a flexible approach for vision based integrity, applicable to a variety of image feature approaches. Results demonstrate how utilization of additional pixel measurements have significant impact on estimation uncertainty.