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Simulation, Consciousness, Existence -- Hans Moravec, 1998
A simulated world hosting a simulated person can be a closed self-contained entity. It might exist as a program on a computer processing data quietly in some dark corner, giving no external hint of the joys and pains, successes and frustrations of the person inside. Inside the simulation events unfold according to the strict logic of the program, which defines the ``laws of physics'' of the simulation. The inhabitant might, by patient experimentation and inference, deduce some representation of the simulation laws, but not the nature or even existence of the simulating computer. The simulation's internal relationships would be the same if the program were running correctly on any of an endless variety of possible computers, slowly, quickly, intermittently, or even backwards and forwards in time, with the data stored as charges on chips, marks on a tape, or pulses in a delay line, with the simulation's numbers represented in binary, decimal, or Roman numerals, compactly or spread widely across the machine. There is no limit, in principle, on how indirect the relationship between simulation and simulated can be.
Coevolution of Active Vision and feature Selection - Dario Floreano
We show that complex visual tasks, such as position- and size-invariant shape recognition and navigation in the environment, can be tackled with simple architectures generated by a coevolutionary process of active vision and feature selection. Behavioral machines equipped with primitive vision systems and direct pathways between visual and motor neurons are evolved while they freely interact with their environments.
RobotVision@ICPR | ImageCLEF - Image Retrieval in CLEF
The RobotVision task has been proposed to the ImageCLEF participants for the first time in 2009. The task attracted considerable attention, with 19 inscribed research groups, 7 groups eventually participating and a total of 27 submitted runs. The second edition of the RobotVision task will be part of the ImageCLEF@ICPR contest introducing new interesting challenges to the pattern recognition community.
The second edition of the RobotVision task addresses the problem of visual place classification. Specifically, participants will be asked to classify rooms and areas on the basis of image sequences, captured by a stereo camera mounted on a mobile robot within an office environment, under varying illumination conditions. The system built by the participants should be able to answer the question "where are you?" when presented with a test sequence imaging rooms seen during training (from different viewpoints and under different conditions) or additional rooms that were not imaged in the training sequence.
Artificial Intelligence and Robotics: A biologically-inspired vision system
Researchers at the Rowland Institute, Harvard, and McGovern Institute for Brain Sciences, MIT, are developing new, biologically-inspired vision systems taking advantage of faster computers. Their goal is to create vision systems for image understanding that can be as accurate as biological systems and more specifically the human visual system. The researchers have developed a new method that allows them to evaluate many different vision systems and quickly determine which are best suited for scene understanding. In a PLoS Computational Biology paper, the researchers show that their method performs better than current state-of-the-art computer vision systems when tested using standard data sets.
If you don't want to read the paper, then you should at least watch the below video in its entirety. In the video, lead researcher David Cox explains at a high level how biological vision works and how their computational system mimics it to achieve the results presented in the paper.
robots.net - Robust Optical Flow Model Derived From Biology
Brinkworth and O'Carroll have come up with a new optical flow model, described in their paper, Robust Models for Optic Flow Coding in Natural Scenes Inspired by Insect Biology (PDF format). Their new system is based on the actual neural processing pathways of the fly which could prove to be a very robust velocity estimator and accurate sensor for self-motion in robots. CC-licensed image of female Tabanus Horse Fly by flickr user Thomas Shahan.
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
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