Computer vision and Mathematica

Computer vision algorithms are strongly based on advanced mathematical methods. Tools to efficiently develop new methods are increasingly based on computer algebra systems, such as Matlab, Maple and Mathematica. For rapid prototyping both symbolic as numerical capability is required. However, the application to large datasets such as images has always been frustated by the expensive use of memory or long computing times. Today such programs have improved substantially in numerical data handling, making them an ideal tool for rapid prototyping in computer vision. This paper gives a short variety of examples of the use of Mathematica in computer vision research.     

The role of computer vision in prosthetic vision

The cost of vision loss worldwide has been estimated at nearly $3 trillion (http://www.amdalliance.org/cost-of-blindness.html). Non-preventable diseases cause a significant proportion of blindness in developed nations and will become more prevalent as people live longer. Prosthetic vision technologies including retinal implants will play an important therapeutic role. Retinal implants convert an input image stream to visual percepts via stimulation of the retina. This paper highlights some barriers to restoring functional human vision for current generation visual prosthetic devices that computer vision can help overcome. Such computer vision is interactive, aiming to restore function including visuo-motor tasks and recognition.     

General-purpose vision chip architecture for real-time machine vision

To solve the I/O bottleneck problem in existing vision systems and to realize versatile processing adaptive to various and changing environments, we propose a new vision chip architecture for applications such as robot vision. The chip has general-purpose processing elements (PEs) with each PE being directly connected to a photo detector (PD) and can implement various visual processing algorithms. We developed and simulated some sample programs for the chip and proved that they can be processed within 1 ms/frame, a rate that is high enough for high-speed visual feedback for robot control. Aiming to complete the chip, we are now developing test chips based on the architecture. The latest design has 8 x 8 PEs and PDs in an area 3.3 mm x 3.0 mm using a 0.8 μm CMOS process.     

Active vision

We investigate several basic problems in vision under the assumption that the observer is active. An observer is called active when engaged in some kind of activity whose purpose is to control the geometric parameters of the sensory apparatus. The purpose of the activity is to manipulate the constraints underlying the observed phenomena in order to improve the quality of the perceptual results. For example a monocular observer that moves with a known or unknown motion or a binocular observer that can rotate his eyes and track environmental objects are just two examples of an observer that we call active. We prove that an active observer can solve basic vision problems in a much more efficient way than a passive one. Problems that are ill-posed and nonlinear for a passive observer become well-posed and linear for an active observer. In particular, the problems of shape from shading and depth computation, shape from contour, shape from texture, and structure from motion are shown to be much easier for an active observer than for a passive one. It has to be emphasized that correspondence is not used in our approach, i.e., active vision is not correspondence of features from multiple viewpoints. Finally, active vision here does not mean active sensing, and this paper introduces a general methodology, a general framework in which we believe low-level vision problems should be addressed.The author is Yiannis     

Robot vision

Robot vision is widely studied in Japan to realize flexible manufacturing systems. The study of robot vision includes the development of the following themes: input devices for range information, feature extraction for inspection or positioning, high-speed image processors, stereo vision, three-dimensional shape recovery, object recognition by model matching, and so forth. This paper describes some interesting work in these fields.     

Language,vision and metaphor

The integration of language and vision capabilities in computers can be seen purely as a multi-media task without any theoretical assumptions being required. However, it is worth exploring whether the modalities have anything serious in common, in particular in the light of claim that most non-technical language use is metaphorical. What consequences would that have for the underlying relationship of language and vision: is it possible that vision is largely metaphorical? The conclusion (see also, Wilks 1978b and Wilks and Okada (in press) is that visual processing can embody structural ambiguity (whether compositional or not), but not anything analogous to metaphor. Metaphor is essentially connected with the extension of sense and only symbols can have senses. But if it makes no sense to say a figure can be metaphorical (unless it embodies symbolic elements) that must also mean, alas, that it makes no sense to say it is literally anything either. Only a symbol can be literally something. A hat is a hat is a hat, but never, ever literally so.     

Mathematical statistics and computer vision

In this discussion paper, I present my views on the role on mathematical statistics for solving computer vision problems.     

Competent vision and navigation systems

In this article, we describe how flying insects use vision for guidance, especially in the contexts of regulating flight speed, negotiating narrow gaps, avoiding obstacles, and performing smooth landings. We show that many of these maneuvers, which were traditionally believed to involve relatively complex and high-level perception, can be achieved through the use of low-level cues and relatively simple computation. We also describe tests of the effectiveness of some of these strategies for autonomous guidance of small-scale terrestrial and aerial vehicles in the contexts of corridor navigation, altitude control, and terrain following and landing. We also describe a novel, mirror- based imaging system that is tailored for these tasks and facilitates the requisite visual computations.     

CAGD-based computer vision

The authors explore the connection between CAGD (computer-aided geometric design) and computer vision. A method for the automatic generation of recognition strategies based on the 3-D geometric properties of shape has been devised and implemented. It uses a novel technique to quantify the following properties of features which compose models used in computer vision: robustness, completeness, consistency, cost, and uniqueness. By utilizing this information, the automatic synthesis of a specialized recognition scheme, called a strategy tree, is accomplished. Strategy trees describe, in a systematic and robust manner, the search process used for recognition and localization of particular objects in the given scene. The consist of selected 3-D features which satisfy system constraints and corroborating evidence subtrees which are used in the formation of hypotheses. Verification techniques, used to substantiate or refute these hypotheses are explored. Experiments utilizing 3-D data are presented