[show abstract][hide abstract] ABSTRACT: Humans selectively process and store details about the vicinity based on their knowledge about the scene, the world and their current task. In doing so, only those pieces of information are extracted from the visual scene that is required for solving a given task. In this paper, we present a flexible system architecture along with a control mechanism that allows for a task-dependent representation of a visual scene. Contrary to existing approaches, our system is able to acquire information selectively according to the demands of the given task and based on the system's knowledge. The proposed control mechanism decides which properties need to be extracted and how the independent processing modules should be combined, based on the knowledge stored in the system's long-term memory. Additionally, it ensures that algorithmic dependencies between processing modules are resolved automatically, utilizing procedural knowledge which is also stored in the long-term memory. By evaluating a proof-of-concept implementation on a real-world table scene, we show that, while solving the given task, the amount of data processed and stored by the system is considerably lower compared to processing regimes used in state-of-the-art systems. Furthermore, our system only acquires and stores the minimal set of information that is relevant for solving the given task.
[show abstract][hide abstract] ABSTRACT: A cognitive visual system is generally intended to work robustly under varying environmental conditions, adapt to a broad
range of unforeseen changes, and even exhibit prospective behavior like systematically anticipating possible visual events.
These properties are unquestionably out of reach of currently available solutions. To analyze the reasons underlying this
failure, in this paper we develop the idea of a vision system that flexibly controls the order and the accessibility of visual
processes during operation. Vision is hereby understood as the dynamic process of selective adaptation of visual parameters
and modules as a function of underlying goals or intentions. This perspective requires a specific architectural organization,
since vision is then a continuous balance between the sensory stimulation and internally generated information. Furthermore,
the consideration of intrinsic resource limitations and their organization by means of an appropriate control substrate become
a centerpiece for the creation of truly cognitive vision systems. We outline the main concepts that are required for the development
of such systems, and discuss modern approaches to a few selected vision subproblems like image segmentation, item tracking
and visual object classification from the perspective of their integration and recruitment into a cognitive vision system.
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