Showing papers by "David G. Lowe published in 1999"

Object recognition from local scale-invariant features

Abstract: An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low residual least squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.

Indexing without invariants in 3D object recognition

Abstract: We present a method of indexing 3D objects from single 2D images. The method does not rely on invariant features. This allows a richer set of shape information to be used in the recognition process. We also suggest the kd-tree as an alternative indexing data structure to the standard hash table. This makes hypothesis recovery more efficient in high-dimensional spaces, which are necessary to achieve specificity in large model databases. Search efficiency is maintained in these regimes by the use of best-bin first search. Neighbors recovered from the index are used to generate probability estimates, local within the feature space, which are then used to rank hypotheses for verification. On average, the ranking process greatly reduces the number of verifications required. Our approach is general in that it can be applied to any real-valued feature vector. In addition, it is straightforward to add to our index information from real images regarding the true probability distributions of the feature groupings used for indexing.

Programming contact tasks using a reality-based virtual environment integrated with vision

Abstract: We present an integrated system in which an operator uses a simulated environment to program part-mating and contact tasks. Generation of models within this virtual environment is facilitated using a fast, occlusion tolerant, 3D grey-scale vision system which can recognize and accurately locate objects within the work site. A major goal of this work is to make robotic programming easy and intuitive for untrained users working with standard desktop hardware. Simulation offers the ease-of-use benefits of "programming by demonstration", coupled with the ability to create a programmer-friendly virtual environment. Within a simulated environment, it is also straightforward to track and interpret an operator's actions. The simulator models objects as polyhedra and implements full 3D contact dynamics. When a manipulation task is completed, local planning techniques are used to turn the virtual environment's motion sequence history into a set of robot motion commands capable of realizing the prescribed task.