Understanding Ourselves is the Holy Grail of the Human-Kind

Richard Feynman once said \u27what I cannot create, I do not understand\u27. It follows, that building intelligent robots that emulate us is the best way to understand the human mind

Two Correspondence Problems Easier Than One

Computer vision research rarely makes use of symmetry in stereo reconstruction despite its established importance in perceptual psychology. Such stereo reconstructions produce visually satisfying figures with precisely located points and lines, even when input images have low or moderate resolution. However, because few invariants exist, there are no known general approaches to solving symmetry correspondence on real images. The problem is significantly easier when combined with the binocular correspondence problem, because each correspondence problem provides strong non-overlapping constraints on the solution space. We demonstrate a system that leverages these constraints to produce accurate stereo models from pairs of binocular images using standard computer vision algorithms

The Role of Problem Representation in Producing Near-Optimal TSP Tours

Gestalt psychologists pointed out about 100 years ago that a key to solving difficult insight problems is to change the mental representation of the problem, as is the case, for example, with solving the six matches problem in 2D vs. 3D space. In this study we ask a different question, namely what representation is used when subjects solve search, rather than insight problems. Some search problems, such as the traveling salesman problem (TSP), are defined in the Euclidean plane on the computer monitor or on a piece of paper, and it seems natural to assume that subjects who solve a Euclidean TSP do so using a Euclidean representation. It is natural to make this assumption because the TSP task is defined in that space. We provide evidence that, on the contrary, subjects may produce TSP tours in the complex-log representation of the TSP city map. The complex-log map is a reasonable assumption here, because there is evidence suggesting that the retinal image is represented in the primary visual cortex as a complex-log transformation of the retina. It follows that the subject’s brain may be “solving” the TSP using complex-log maps. We conclude by pointing out that solving a Euclidean problem in a complex-log representation may be acceptable, even desirable, if the subject is looking for near-optimal, rather than optimal solutions

Multiple alcohol septal ablations in a young patient with hypertrophic cardiomyopathy

A 16 year old female with hypertrophic cardiomyopathy was treated with alcohol ablation for NYHA class III symptoms on medical therapy. Three months later, patient underwent a second alcohol ablation procedure for continued symptoms. Follow-up, for 4 years now, continues to show resolution of symptoms. (Cardiol J 2007; 14: 301-304

3-D Shape Recovery from a Single Camera Image

3-D shape recovery is an ill-posed inverse problem which must be solved by using a priori constraints. We use symmetry and planarity constraints to recover 3-D shapes from a single image. Once we assume that the object to be reconstructed is symmetric, all that is left to do is to estimate the plane of symmetry and establish the symmetry correspondence between the various parts of the object. The edge map of the image of an object serves as a good representation of its 2-D shape and establishing symmetry correspondence means identifying pairs of symmetric curves in the edge map. The vanishing points define the symmetry planes up to a scale factor. In this work, we have assumed that we know the vanishing points. In order to be able to match curves, we should first extract some meaningful curves, where the word meaningful implies that the curve should make sense to a human observer. Connected components obtained after canny edge detection are broken down, based on gradient orientation, to get small curve pieces which can be then combined to form meaningful curves. In order to obtain longer pieces of curves, we find the shortest paths between all pairs of short pieces of curves with a cost function that penalizes spatial separation and large turning angles. In the next step, we find the optimal curve matches that minimize the number of planes required to fit the final 3-D reconstruction while simultaneously ensuring that a substantial portion of the object is reconstructed. This optimization problem is converted to a binary integer program which is then solved using the Gurobi optimization framework. Symmetry and planarity in many ways represent the simplicity of an object and by applying these constraints we are attempting to reconstruct a simple 3-D shape that can explain the image