In the past decade, deep neural networks, particularly convolutional neural networks, have
revolutionised computer vision. However, all deep learning models may require a large …

This paper presents a novel method for detecting and localizing objects of a visual category
in cluttered real-world scenes. Our approach considers object categorization and figure …

This book contains both a synthesis and mathematical analysis of a wide set of algorithms
and theories whose aim is the automatic segmen tation of digital images as well as the …

A method for recognition of superquadrics from image contours is presented. The image
contour considered here is a 2-D occluding contour assuming parallel projection. The …

This paper addresses a problem arising in the reverse engineering of solid models from
depth-maps. We wish to identify and fit surfaces of known type wherever these are a good fit …

This paper addresses a common problem in the segmentation of range images. We present
methods for the least-squares fitting of spheres, cylinders, cones, and tori to 3D point data …

Segmentation of range images has long been considered in computer vision as an
important but extremely difficult problem. In this paper we present a new paradigm for the …

A novel approach to 3D part segmentation is presented. It is a well-known physical fact that
electrical charge on the surface of a conductor tends to accumulate at a sharp convexity and …

Data augmentation is a crucial technique for expanding training datasets, effectively
alleviating the overfitting issue that arises from limited training data in deep learning models …

Multi-image photogrammetry can in favorable conditions even under water generate large
clouds of 3D points which can be used for visualization of sunken heritage. For analysis of …