3D Object Modeling Using Spatial and Pictographic Gestures

Summary:

Nishino et al. designed a 3D object modeling system that uses stereoscopic glasses, CyberGloves, and polhemus trackers.

The system allows the creation of superellipsoids that can have smooth or squarish parameters. These primitive shapes can be bent, stretched, twisted, and merged with other shapes. Hand postures control these actions, such as grasping and pointing. Virtual hands are displayed on a 200-inch arched screen, along with the object, in stereoscopic mode. The virtual hands allow the user to easily see where they can touch and modify the 3D model.

The authors tested the system by having users attempt two types of objects: symmetric and asymmetric. The symmetric object was a bottle, and the asymmetric object was a teapot.
Creation of the objects took up to 120 minutes. The size of the stored objects was much less than a competing program, Open Inventor.


Discussion:

For a paper in 1998, this was a pretty advanced system and seemed to offer some benefits over other systems. I would have liked to have seen feedback from the users, though, since I'm not sure how hard the system is to use.

Feature selection for grasp recognition from optical markers

Summary:

Chang et al. reduced the number of markers needed on a vision-based hand grasp system from 30 to 5 while retaining around a 90% recognition rate.

Six different grasps are used for classification: cylindrical, spherical, lumbrical, two-finger pinch, tripod, and lateral tripod. The posterior probabilities for a class yk are modeled with a softmax function, which divides the exp value of an observation sequence with the class weights, divided by the sum of all exp(weights * obs) values.

The weight values are determined by maximum conditional likelihood estimation from the training set of observations and classes (X, Y). Gradient descent is used to find the log likelihood with respect to the weights. Input features are found using a "sequential wrapper algorithm" that examines one feature at a time with respect to a target class.

Grasp data measured 38 objects being grasped with a full set of 30 markers. An "optimal", small set of markers was chosen by forward and backward selection.

The results indicate that the small marker set of 5 markers has between a 92-97% "accuracy retention" rate.


Discussion:

Reducing the number of sensors using the forward and backward selection is nice, but simply having a few more sensors increases the accuracy to the actual plateau point. From 10 on there is almost no change in accuracy, but between 5 and 10 sensors the accuracy can jump 5%, or 1/20, which is a huge percentage when taking into account user frustration.