Adjacency list

About: Adjacency list is a research topic. Over the lifetime, 4419 publications have been published within this topic receiving 78449 citations.

Measuring the efficacy of adjacency constraint structure in forest planning models

Abstract: This paper examines various structural representations of adjacency conditions in forest planning problems. It will be shown that alternative representations can rival traditional approaches, which...

Connected Segmentation Tree — A joint representation of region layout and hierarchy

Abstract: This paper proposes a new object representation, called connected segmentation tree (CST), which captures canonical characteristics of the object in terms of the photometric, geometric, and spatial adjacency and containment properties of its constituent image regions. CST is obtained by augmenting the objectpsilas segmentation tree (ST) with inter-region neighbor links, in addition to their recursive embedding structure already present in ST. This makes CST a hierarchy of region adjacency graphs. A regionpsilas neighbors are computed using an extension to regions of the Voronoi diagram for point patterns. Unsupervised learning of the CST model of a category is formulated as matching the CST graph representations of unlabeled training images, and fusing their maximally matching subgraphs. A new learning algorithm is proposed that optimizes the model structure by simultaneously searching for both the most salient nodes (regions) and the most salient edges (containment and neighbor relationships of regions) across the image graphs. Matching of the category model to the CST of a new image results in simultaneous detection, segmentation and recognition of all occurrences of the category, and a semantic explanation of these results.

Cluster adjacency and the four-loop NMHV heptagon

Abstract: We exploit the recently described property of cluster adjacency for scattering amplitudes in planar $\mathcal{N}=4$ super Yang-Mills theory to construct the symbol of the four-loop NMHV heptagon amplitude. We use a manifestly cluster adjacent ansatz and describe how the parameters of this ansatz are determined using simple physical consistency requirements. We then specialise our answer for the amplitude to the multi-Regge limit, finding agreement with previously available results up to the next-to-leading logarithm, and obtaining new predictions up to (next-to)$^3$-leading-logarithmic accuracy.

Constraint-based channel routing for analog and mixed analog/digital circuits

Abstract: A well-defined methodology for mapping the constraints on a set of critical coupling capacitances into constraints in the vertical-constraint (VC) graph of a channel is presented. The approach involves directing undirected edges, adding directed edges, and increasing the weights of edges in the VC graph in order to meet crossover constraints between orthogonal segments and adjacency constraints between parallel segments while attempting to cause minimum increase in the channel height due to the constraints. Use is made of shield nets when necessary. A formal description of the conditions under which the crossover and the adjacency constraints are satisfied is provided and used to construct the appropriate mapping algorithms. The problem of imposing matching constraints on the routing parasitics in a channel with lateral symmetry is addressed. It is observed that perfect matching is not possible for a matched pair of nets with intersecting horizontal spans. A technique to achieve almost perfect mirror symmetry is presented for such pairs of nets. >

Geometrically consistent plane extraction for dense indoor 3D maps segmentation

Abstract: Modern SLAM systems with a depth sensor are able to reliably reconstruct dense 3D geometric maps of indoor scenes. Representing these maps in terms of meaningful entities is a step towards building semantic maps for autonomous robots. One approach is to segment the 3D maps into semantic objects using Conditional Random Fields (CRF), which requires large 3D ground truth datasets to train the classification model. Additionally, the CRF inference is often computationally expensive. In this paper, we present an unsupervised geometric-based approach for the segmentation of 3D point clouds into objects and meaningful scene structures. We approximate an input point cloud by an adjacency graph over surface patches, whose edges are then classified as being either on or off. We devise an effective classifier which utilises both global planar surfaces and local surface convexities for edge classification. More importantly, we propose a novel global plane extraction algorithm for robustly discovering the underlying planes in the scene. Our algorithm is able to enforce the extracted planes to be mutually orthogonal or parallel which conforms usually with human-made indoor environments. We reconstruct 654 3D indoor scenes from NYUv2 sequences to validate the efficiency and effectiveness of our segmentation method.