Memory-Efficient Symbolic Heuristic Search

A promising approach to solving large state-space search problems is to integrate heuristic search with symbolic search. Recent work shows that a symbolic A * search al-gorithm that uses binary decision diagrams to compactly rep-resent sets of states outperforms traditional A * in many do-mains. Since the memory requirements of A * limit its scal-ability, we show how to integrate symbolic search with a memory-efficient strategy for heuristic search. We analyze the resulting search algorithm, consider the factors that affect its behavior, and evaluate its performance in solving bench-mark problems that include STRIPS planning problems

Minimum spanning tree reconstruction using autoencoders

Minimum spanning trees are widely used graph structures used to find relationships between data organized in a graph. This work proposes a new approach based on neural networks, in particular, autoencoders, to extrapolate this tree from the dissimilarity representation of the data, seen as a noisy version of the minimum spanning tree. After many tests done on different network, the final results confirm the validity of the idea

Video copy detection by fast sequence matching

ABSTRACT Sequence matching techniques are effective for comparing two videos. However, existing approaches suffer from demanding computational costs and thus are not scalable for large-scale applications. In this paper we view video copy detection as a local alignment problem between two frame sequences and propose a two-level filtration approach which achieves significant acceleration to the matching process. First, we propose to use an adaptive vocabulary tree to index all frame descriptors extracted from the video database. In this step, each video is treated as a "bag of frames." Such an indexing structure not only provides a rich vocabulary for representing videos, but also enables efficient computation of a pyramid matching kernel between videos. This vocabulary tree filters those videos that are dissimilar to the query based on their histogram pyramid representations. Second, we propose a fast edit-distance-based sequence matching method that avoids unnecessary comparisons between dissimilar frame pairs. This step reduces the quadratic runtime to a linear time with respect to the lengths of the sequences under comparison. Experiments on the MUSCLE VCD benchmark demonstrate that our approach is effective and efficient. It is 18X faster than the original sequence matching algorithms. This technique can be applied to several other visual retrieval tasks including shape retrieval. We demonstrate that the proposed method can also achieve a significant speedup for the shape retrieval task on the MPEG-7 shape dataset

Evaluation of RSL history as a tool for assistance in the development and evaluation of computer vision algorithms

A revision of Recognition Strategy Language (RSL), a domain-specific language for pattern recognition algorithm development, is in development. This language provides several tools for pattern recognition algorithm implementation and analysis, including composition of operations and a detailed history of those operations and their results. This research focuses on that history and shows that for some problems it provides an improvement over traditional methods of gathering information. When designing a pattern recognition algorithm, bookkeeping code in the form of copious logging and tracing code must be written and analyzed in order to test the effectiveness of procedures and parameters. The amount of data grows when dealing with video streams; new organization and searching tools need to be designed in order to manage the large volume of data. General purpose languages have techniques like Aspect Oriented Programming intended to address this problem, but a general approach is limited because it does not provide tools that are useful to only one problem domain. By incorporating support for this bookkeeping work directly into the language, RSL provides an improvement over the general approach in both development time and ability to evaluate the algorithm being designed for some problems. The utility of RSL is tested by evaluating the implementation process of a computer vision algorithm for recognizing American Sign Language (ASL). RSL history is examined in terms of its use in the development and evaluation stages of the algorithm, and the usefulness of the history is stated based on the benefit seen at each stage. RSL is found to be valuable for a portion of the algorithm involving distinct steps that provide opportunity for comparison. RSL was less beneficial for the dynamic programming portion of the algorithm. Compromises were made for performance reasons while implementing the dynamic programming solution and the inspection at every step of what amounts to a brute-force search was less informative. We suggest that this investigation could be continued by testing with a larger data set and by comparing this ASL recognition algorithm with another

Error processes in the integration of digital cartographic data in geographic information systems.

Errors within a Geographic Information System (GIS) arise from several factors. In the first instance receiving data from a variety of different sources results in a degree of incompatibility between such information. Secondly, the very processes used to acquire the information into the GIS may in fact degrade the quality of the data. If geometric overlay (the very raison d'etre of many GISs) is to be performed, such inconsistencies need to be carefully examined and dealt with. A variety of techniques exist for the user to eliminate such problems, but all of these tend to rely on the geometry of the information, rather than on its meaning or nature. This thesis explores the introduction of error into GISs and the consequences this has for any subsequent data analysis. Techniques for error removal at the overlay stage are also examined and improved solutions are offered. Furthermore, the thesis also looks at the role of the data model and the potential detrimental effects this can have, in forcing the data to be organised into a pre-defined structure

Applications and Experiences of Quality Control

The rich palette of topics set out in this book provides a sufficiently broad overview of the developments in the field of quality control. By providing detailed information on various aspects of quality control, this book can serve as a basis for starting interdisciplinary cooperation, which has increasingly become an integral part of scientific and applied research

A Multi-Modal Incompleteness Ontology model (MMIO) to enhance 4 information fusion for image retrieval

This research has been supported in part by National Science and Technology Development (NSTDA), Thailand. Project No: SCH-NR2011-851

The machine refinement of raw graphic data for translation into a low level data base for computer aided architectural design (CAAD).

It is argued that a significant feature which acts as a disincentive against the adoption of CAAD systems by small private architectural practices, is the awkwardness of communicating with computers when compared with traditional drawing board techniques. This consideration, although not perhaps the dominant feature, may be mitigated by the development of systems in which the onus of communicating is placed on the machine, through the medium of an architect's sketch plan drawing. In reaching this conclusion, a design morphology is suggested, in which the creative generation of building designs is set in the context of the development of a 'data-base' of information which completely and consistently describes the architect's hypothetical building solution. This thesis describes research carried out by the author between 1981 and 1984, and describes the theory, development and application of algorithms to interpret architect's sketch plan drawings, and hence permit the encoding of building geometries for CAAD applications programs