Cognitive computer vision is a collection of computing methodologies aimed to model visual perception when working in real conditions. The fundamental idea of cognitive vision is to use cognitive research in order to emulate the human and living beings-like sense of perceiving images. Object recognition is the capacity of perceiving the object’s physical properties (such as shapes, colors, corners, textures, salience) and apply semantic, sometimes gradual, or fuzzy attributes to the object. This usually implies understanding of an object’s use (functional object recognition), previous experience with the object and how it relates to the environment. Extrapolating from cognitive sciences to automatics we are aware that designing a robot able “to see” and “understand” depends on the task complexity to be accomplished, which leads one to the concept of task driven or attention driven design.

Interdisciplinary elements of artificial intelligence, knowledge formalization and scenarios with dedicated frames, rule-based systems, inference engines and specialized mathematical functions, corroborate in modeling this.

Due to the large variety and complexity of the domain, the constituting methods of Cognitive Computer Vision are not competing for a comprehensive universally acceptable solution. Rather, these methods are complementing each other, for dedicated solutions adapted to each specific problem. Hundreds of concrete applications are already available in control, decision making, pattern recognition and robotics. However, Cognitive Computer Vision is a major new development.

The aim of this workshop is to bring together active researchers interested in the joint areas of vision and image processing, knowledge formalization and cognitive sciences to discuss current research, results, and problems of both theoretical and practical nature. Taking advantage of the reliable communication developments, this workshop will be organized in video-presentations and discussions by participants located in very diverse geographical areas.