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The Art of Jim Campbell: Seeing In Pixels - Boing Boing
Movement makes up for the lack of other visual information. Your brain can read and understand a video at much lower resolution than it would need to make equal sense of a still frame.
LPP - J. Kevin O'Regan
Papers about sensorimotor loop, situated perception, situated vision, enactive vision, visual contingencies
The evolution of object categorization and the challenge of image abstraction Sven Dickinson
PDF: Analyzing Vision at the Computational Level. J. K. Tsotsos
Excellent paper trying to apply computational complexity to analyze some visual tasks and a deep discussion following it.
Review of Visual Tracking
This report contains a review of visual tracking in monocular video sequences. For the purpose of this review, the majority of the visual trackers in the literature are divided into three tracking categories: discrete feature trackers, contour trackers, and region-based trackers. This categorization was performed based on the features used and the algorithms employed by the various visual trackers. The first class of trackers represents targets as discrete features (e.g. points, sets of points, lines) and performs data association using a distance metric that accommodates the particular feature.
Situated Vision in a Dynamic World: Chasing Objects
We describe a system that approaches and follows arbitrary moving objects in real time using vision as its only sense. The system uses multiple simple vision computations which, although individually unreliable, complement each other in a manner mediated by a situated control network.
Visual Attention Research: The FINST Visual Indexing Theory
Work related to Aaron Sloman ideas of registration along the optic array and also situated vision.
The Evolution of Visual Processing and the Construction of Seeing Systems — Proceedings B
This paper is concerned with the evolution of visual mechanisms and the possibility of copying their principles at different levels of sophistication. It is an old question how the complex interaction between eye and brain evolved when each needs the other as a test-bed for successive improvements. I propose that the primitive mechanism for the separation of stationary objects relies on their relative movement against a background, normally caused by the animal's own movement. Apparently insects and many lower animals use little more than this for negotiating through a three-dimensional world, making adequate responses to individual objects which they `see' without a cortical system or even without a large brain. In the development of higher animals such as birds or man, additional circuits store memories of the forms of objects that have been frequently inspected from all angles or handled. Simple visual systems, however, are tuned to a feature of the world by which objects separate themselves by movement relative to the eye. In making simple artificial visual systems which `see', as distinct from merely projecting the image, it is more hopeful to copy the `ambient' vision of lower animals than the cortical systems of birds or mammals.
CiteSeerX — The evolution of visual processing and the construction of seeing systems (trying to get this paper)
The evolution of visual processing and the construction of seeing systems (1987)
Laboratory for Active and Attentive Vision -
Some J.K Tsotsos papers relating vision and complexity (I'm still unsure what kind of complexity)
Journal of Vision - Yarbus lives: a foveated exploration of how task influences saccadic eye movement, by Nelson, Cottrell, Movellan, & Sereno
Yarbus and others have shown that viewers' eye movements with respect to a particular stimulus image differ according to the viewer's task. We revisited Yarbus' work, giving different subjects different tasks, with the same stimulus images. Tasks varied from ascertaining the weather, to free viewing, to inferring the thoughts of people depicted in a scene. Subjects controlled their viewing time. Subjects usually viewed an image for just a few seconds. As expected, eye movements differed according to task.
Indoor Scene Recognition, CVPR 09
Indoor scene recognition is a challenging open problem in high level vision. Most scene recognition models that work well for outdoor scenes perform poorly in the indoor domain. The main difficulty is that while some indoor scenes (e.g. corridors) can be well characterized by global spatial properties, others (e.g., bookstores) are better characterized by the objects they contain. More generally, to address the indoor scenes recognition problem we need a model that can exploit local and global discriminative information.
Keith Price Bibliography Integration of Vision Modules
Some papers on organisation of (artificial) visual systems
Ullman's Visual Routines x CMU Tekkotsu's Sketches
Describes some of the resources available in CMU Tekkotsu robotics simulator and how they relate to Ullman's Visual Routines
To afford or not to afford: A new formalization of affordance towards affordance-based robot control
The affordances of the environment are what it offers the animal, what it provides or furnishes, either for good or ill. The verb to afford is found in the dictionary, but the noun affordance is not. I have made it up. I mean by it something that refers to both the environment and the animal in a way that no existing term does. It implies the complementarity of the animal and the environment.”“... an affordance is neither an objective property nor a subjective property; or both if you like. An affordance cuts across the dichotomy of subjective-objective and helps us to understand its inadequacy. It is equally a fact of the environment and a fact of behavior. It is both physical and psychical, yet neither. An affordance points bothways, to the environment and to the observer.”“The perceiving of an affordance is not a process of perceiving a value-free physical object to which meaning is somehow added in a way that no one has been able to agree upon; it is a process of perceiving a value-rich ecological object.”“The theory of affordances rescues us from the philosophical muddle of assumingfixed classes of objects, each defined by its common features and then given a name...You do not have to classify and label things in order to perceive what they afford.”
MeMoMan — Markerless Tracking from Videos
We are developing new computational models and a system for accurate measurement of human motion. Our primary goal is to develop markerless vision-based tracking algorithms for use with the industry-proven anthropometric human model RAMSIS (in collaboration with the TUM Ergonomics Department/Faculty of Mechanical Engineering). By providing RAMSIS with markerless tracking capabilities, we open up new fields of application in ergonomic studies and industrial design. On the other hand, we believe that a far-developed, flexible and accurate model such as RAMSIS is beneficial for human motion tracking given the ergonomic expertise that has affected its design.
Computer Vision Dataset Detailed References
Test data is available in bits and pieces and in several larger repositories, These datasets are selected from references included in the Computer Vision Bibliography. The links on the Author and Journal references in the list point to entries in that database. Generally, to avoid confusion, in this bibliography database is used for database systems or research and would apply to image database query techniques rather than a database containing images for use in specific applications. I have thus chosen dataset to describe collections of images used by researchers in some domain. Current research and applications are highlighted in various Computer Vision and Image Processing Conferences. Some of these have evaluation sessions with related datasets.
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