3DPO: A three-dimensional part orientation system
International Journal of Robotics Research
Algorithms for clustering data
Algorithms for clustering data
A Real-Time Processor for the Hough Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
On the Sensitivity of the Hough Transform for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Complete and Extendable Approach to Visual Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding circles by an array of accumulators
Communications of the ACM
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Depth Map Processing for Recognizing Objects Modeled by Planes and Quadrics of Revolution
Intelligent Autonomous Systems, An International Conference
Geometric Primitive Extraction Using a Genetic Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
| Hi-index | 0.14 |
The multiwindow transform, an extension of parameter transform techniques that increase performance and scope by exploiting the long-range correlated information contained in multiple portions of an image, is presented. Multiple-window transforms allow the extraction of high-dimensional features with improvement in accuracy over conventional techniques while keeping linear to low-order-polynomial computational and space requirements with respect to image size and dimensionality of the features. Using correlated information provides a direct link between extracted features and supporting regions in the image. This, coupled with evidence integration techniques, is used to suppress noisy or nonexistent feature hypotheses. Parameter spaces are implemented as constraint satisfaction networks, where feature hypotheses with overlapping support in the image compete. After an iterative relaxation phase, surviving hypotheses have disjoint support, forming a segmentation of the image. Examples show the performance and provide insight about the behavior.