Subgraph isomorphism can be determined by means of a brute-force tree-search enumeration procedure. In this paper a new algorithm is introduced that attains efficiency by inferentially eliminating successor nodes in the tree search. To assess the time actually taken by the new algorithm, subgraph isomorphism, clique detection, graph isomorphism, and directed graph isomorphism experiments have been carried out with random and with various nonrandom graphs. A parallel asynchronous logic-in-memory implementation of a vital part of the algorithm is also described, although this hardware has not actually been built. The hardware implementation would allow very rapid determination of isomorphism.

/pdf/an-algorithm-for-subgraph-isomorphism-1t0qre6b4v.pdf

An Algorithm for Subgraph Isomorphism

Visual Reconstruction

Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables systematic development of optimal vision algorithms when used with optimization principles. This detailed and thoroughly enhanced third edition presents a comprehensive study / reference to theories, methodologies and recent developments in solving computer vision problems based on MRFs, statistics and optimisation. It treats various problems in low- and high-level computational vision in a systematic and unified way within the MAP-MRF framework. Among the main issues covered are: how to use MRFs to encode contextual constraints that are indispensable to image understanding; how to derive the objective function for the optimal solution to a problem; and how to design computational algorithms for finding an optimal solution. Easy-to-follow and coherent, the revised edition is accessible, includes the most recent advances, and has new and expanded sections on such topics as: Discriminative Random Fields (DRF) Strong Random Fields (SRF) Spatial-Temporal Models Total Variation Models Learning MRF for Classification (motivation + DRF) Relation to Graphic Models Graph Cuts Belief Propagation Features: Focuses on the application of Markov random fields to computer vision problems, such as image restoration and edge detection in the low-level domain, and object matching and recognition in the high-level domain Presents various vision models in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation Uses a variety of examples to illustrate how to convert a specific vision problem involving uncertainties and constraints into essentially an optimization problem under the MRF setting Introduces readers to the basic concepts, important models and various special classes of MRFs on the regular image lattice and MRFs on relational graphs derived from images Examines the problems of parameter estimation and function optimization Includes an extensive list of references This broad-ranging and comprehensive volume is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It has been class-tested and is suitable as a textbook for advanced courses relating to these areas.

Markov Random Field Modeling in Image Analysis

Compliant motion of a manipulator occurs when the manipulator position is constrained by the task geometry. Compliant motion may be produced either by a passive mechanical compliance built in to the manipulator, or by an active compliance implemented in the control servo loop. The second method, called force control, is the subject of this paper. In particular a theory of force control based on formal models of the manipulator and the task geometry is presented. The ideal effector is used to model the manipulator, the ideal surface is used to model the task geometry, and the goal trajectory is used to model the desired behavior of the manipulator. Models are also defined for position control and force control, providing a precise semantics for compliant motion primitives in manipulation programming languages. The formalism serves as a simple interface between the manipulator and the programmer, isolating the programmer from the fundamental complexity of low-level manipulator control. A method of automatically synthesizing a restricted class of manipulator programs based on the formal models of task and goal trajectory is also provided by the formalism.

/pdf/compliance-and-force-control-for-computer-controlled-wdvtx8zovr.pdf

Compliance and Force Control for Computer Controlled Manipulators

A recent paper posed the question: "Graph Matching: What are we really talking about?". Far from providing a definite answer to that question, in this paper we will try to characterize the role that graphs play within the Pattern Recognition field. To this aim two taxonomies are presented and discussed. The first includes almost all the graph matching algorithms proposed from the late seventies, and describes the different classes of algorithms. The second taxonomy considers the types of common applications of graph-based techniques in the Pattern Recognition and Machine Vision field.

/pdf/thirty-years-of-graph-matching-in-pattern-recognition-58lvpdkcaj.pdf

Thirty years of graph matching in pattern recognition

https://groups.inf.ed.ac.uk/vision/ROBOTICS/FREDDY/Barrow/Documents/1975_ambler_popplestone_inferring_positions_of_bodies_AIJ.pdf

Inferring the positions of bodies from specified spatial relationships

An interpreter for a language for describing assemblies

A versatile assembly system, using TV cameras and oomputer-controlled arm and moving table, is described. It makes almple assemblies such aa a peg and rings and a toy car. It separates parts from a heap, recognising them with an overhead camera, then assembles them by feel. It can be instructed to perform a new task with different parte by spending an hour showing it the parts and a day or two programming the assembly manipulations. A hierarchical description of parts, views, outlines etc. is used to construct models, and a structure matching algorithm is used in recognition.

https://ijcai.org/Proceedings/73/Papers/033.pdf

A versatile computer-controlled assembly system

A versatile system for computer-controlled assembly☆

A system is described which uses light stripe input to construct 3-D body models useful for automatic assembly. Special hardware can extract a stripe from a TV picture in about 02 second. Clusters of stripes from single surfaces in the body, and hence the equations of the surfaces themselves, are found by a program which forms hypotheses based on clues in the data and then tries to establish them. The resulting surface equations and the stripe evidence for them are used to produce the body model, which is a union of convex subsets of three-space formed as intersections of (possibly complemented) half-spaces and solid cylinders.

/pdf/forming-models-of-plane-and-cylinder-faceted-bodies-from-4ag4t6923t.pdf

A. P. Ambler

Papers

Inferring the positions of bodies from specified spatial relationships

An interpreter for a language for describing assemblies

A versatile computer-controlled assembly system

A versatile system for computer-controlled assembly☆

Forming models of plane-and-cylinder faceted bodies from light stripes