The Binford-Horn LINEFINDER

This paper briefly describes the processing performed in the course of producing a line drawing from vidisector information.MIT Artificial Intelligence Laboratory Vision Grou

The Facts of Light

This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-70-A-0362-0005.This is a random collection of facts about radiant and luminous energy. Some of this information may be useful in the design of photo-diode image sensors, in the set-up of lighting for television microscopes and the understanding of the characteristics of photographic image output devices. A definition of the units of measurement and the properties of lambertian surfaces is included.MIT Artificial Intelligence Laboratory Department of Defense Advanced Research Projects Agenc

Notes Relating to the Design of a High Quality Image Sensor

This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-70-A-0362-0005.Some of the information that as used in arriving at a design for a high quality image input device is documented. The device uses a PIN photo-diode directly coupled to an FET-input op-amp as the sensor and two moving-iron galvanometer-driven mirrors as the deflection system. The disadvantages of a system like this are its long random access time (about 4 milli-seconds) and the long settling time of the diode-amplifier system (about 1 milli-seconds). In almost all other respects such a sensor is superior to other known image sensors. Pictures taken with this device have shown that some of the difficulties experienced in image analysis can be directly traced to the low quality of images read in through vidicons and image dissectors.MIT Artificial Intelligence Laborator

Worms of Ganymedes - Hazards of Image "Restoration"

MIT Artificial Intelligence Laborator

Vision Review

This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-75-C-0643.MIT Artificial Intelligence Laboratory Department of Defense Advanced Research Projects Agenc

What is Delaying the Manipulator Revolution?

This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the Laboratory's artificial intelligence research is provided in part by the Office of Naval Research of the Department of Defense under ONR contract N00014-77-C-0389.Despite two decades of work on mechanical manipulators and their associated controls, we do not see wide-spread application of these devices to many of the tasks to which they seem so obviously suited. Somehow, a variety of interacting causes has conspired to prevent them from fulfilling their much talked about potential. In part, this appears to be the result of a research effort that was too small, too fragmented, and too discontinuous in time.MIT Artificial Intelligence Laboratory Department of Defense Office of Naval Researc

Kinematics, Statics, and Dynamics of Two-D Manipulators

This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-70-A-0362-0005.In order to get some feeling for the kinematics, statics, and dynamics of manipulators, it is useful to separate the problem of visualizing linkages in three-space from the basic mechanics. The general-purpose two-dimensional manipulator is analyzed in this paper in order to gain a basic understanding of the issues without the complications of three-dimensional geometry.MIT Artificial Intelligence Laborator

Direct Object Recognition Using No Higher Than Second or Third Order Statistics of the Image

Novel algorithms for object recognition are described that directly recover the transformations relating the image to its model. Unlike methods fitting the typical conventional framework, these new methods do not require exhaustive search for each feature correspondence in order to solve for the transformation. Yet they allow simultaneous object identification and recovery of the transformation. Given hypothesized % potentially corresponding regions in the model and data (2D views) --- which are from planar surfaces of the 3D objects --- these methods allow direct compututation of the parameters of the transformation by which the data may be generated from the model. We propose two algorithms: one based on invariants derived from no higher than second and third order moments of the image, the other via a combination of the affine properties of geometrical and the differential attributes of the image. Empirical results on natural images demonstrate the effectiveness of the proposed algorithms. A sensitivity analysis of the algorithm is presented. We demonstrate in particular that the differential method is quite stable against perturbations --- although not without some error --- when compared with conventional methods. We also demonstrate mathematically that even a single point correspondence suffices, theoretically at least, to recover affine parameters via the differential method

Indoor Localization Using Uncooperative Wi-Fi Access Points

Indoor localization using fine time measurement (FTM) round-trip time (RTT) with respect to cooperating Wi-Fi access points (APs) has been shown to work well and provide 1–2 m accuracy in both 2D and 3D applications. This approach depends on APs implementing the IEEE 802.11-2016 (also known as IEEE 802.11mc) Wi-Fi standard (“two-sided” RTT). Unfortunately, the penetration of this Wi-Fi protocol has been slower than anticipated, perhaps because APs tend not to be upgraded as often as other kinds of electronics, in particular in large institutions—where they would be most useful. Recently, Google released Android 12, which also supports an alternative “one-sided” RTT method that will work with legacy APs as well. This method cannot subtract out the “turn-around” time of the signal, and so, produces distance estimates that have much larger offsets than those seen with two-sided RTT—and the results are somewhat less accurate. At the same time, this method makes possible distance measurements for many APs that previously could not be used. This increased accessibility can compensate for the decreased accuracy of individual measurements. We demonstrate here indoor localization using one-sided RTT with respect to legacy APs that do not support IEEE 802.11-2016. The accuracy achieved is 3–4 m in cluttered environments with few line-of-sight readings (and using only 20 MHz bandwidths). This is not as good as for two-sided RTT, where 1–2 m accuracy has been achieved (using 80 MHz bandwidths), but adequate for many applications A wider Wi-Fi channel bandwidth would increase the accuracy further. As before, Bayesian grid update is the preferred method for determining position and positional accuracy, but the observation model now is different from that for two-sided RTT. As with two-sided RTT, the probability of an RTT measurement below the true distance is very low, but, in the other direction, the range of measurements for a given distance can be much wider (up to well over twice the actual distance). We describe methods for formulating useful observation models. As with two-sided RTT, the offset or bias in distance measurements has to be subtracted from the reported measurements. One difference is that here, the offsets are large (typically in the 2400–2700 m range) because of the “turn-around time” of roughly 16 μs (i.e., about two orders of magnitude larger than the time of flight one is attempting to measure). We describe methods for estimating these offsets and for minimizing the effort required to do so when setting up an installation with many APs