RFID-Based Digital Board Game Platforms

This paper presents digital board games built upon RFID-based platforms. The platforms consist of RFID tag-embedded physical objects and RFID reader boards. A library is built upon the platforms for recognizing data, locations, and movements of the physical game objects. Three kinds of game prototypes are designed and developed for use in young children's edutainment. The user tests prove that a natural type of interactivity can be achieved for digital board games, and it can contribute to establishing paradigms for next-generation edutainment

Computing global shape measures

Global shape measures are a convenient way to describe regions. They are generally simple and efficient to extract, and provide an easy means for high level tasks such as classification as well as helping direct low-level computer vision processes such as segmentation. In this chapter a large selection of global shape measures (some from the standard literature as well as other newer methods) are described and demonstrated

Geometric distortion measurement for shape coding: a contemporary review

Geometric distortion measurement and the associated metrics involved are integral to the rate-distortion (RD) shape coding framework, with importantly the efficacy of the metrics being strongly influenced by the underlying measurement strategy. This has been the catalyst for many different techniques with this paper presenting a comprehensive review of geometric distortion measurement, the diverse metrics applied and their impact on shape coding. The respective performance of these measuring strategies is analysed from both a RD and complexity perspective, with a recent distortion measurement technique based on arc-length-parameterisation being comparatively evaluated. Some contemporary research challenges are also investigated, including schemes to effectively quantify shape deformation

Image matching of firearm fingerprints

A spent cartridge case exhibits characteristic markings (firearm fingerprint) that can be used to identify the type and possibly make of weapon in which the cartridge was fired. This report details research into the use of discriminant analysis for the purpose of matching spent rim-fire cartridge cases to specific make and model firearms. The discrimination and classification are based on several scalar shape parameters for the two-dimensional silhouette of the firing pin (FP) impression-- shape factor calculated from the second order moment of inertia, G factor calculated from the distance transform, and the P2A factor- as well as the distance between the centre of the cartridge case and the centroid of the FP impression, and the orientation of the principal centroidal axes associated with the FP impression. Classification results for two case studies are detailed: (i) 3 different make/model weapons producing different shaped FP impressions, and (ii) 5 different make/model weapons each producing a rectangular FP impression

Symbol descriptor based on shape context and vector model of information retrieval

International audienceIn this paper we present an adaptative method for graphic symbol representation based on shape contexts. The proposed descriptor is invariant under classical geometric transforms (rotation, scale) and based on interest points. To reduce the complexity of matching a symbol to a large set of candidates we use the popular vector model for information retrieval. In this way, on the set of shape descriptors we build a visual vocabulary where each symbol is retrieved on visual words. Experimental results on complex and occluded symbols show that the approach is very promising

Two-dimensional object recognition through two-stage string matching

[[abstract]]A two-stage string matching method for the recognition of two-dimensional (2-D) objects is proposed in this work. The first stage is a global cyclic string matching. The second stage is a local matching with local dissimilarity measure computing. The dissimilarity measure function of the input shape and the reference shape is obtained by combining the global matching cost and the local dissimilarity measure. The proposed method has the advantage that there is no need to set any parameter in the recognition process. Experimental results indicate that the two-stage string matching approach significantly improves the recognition rates while comparing to the one-stage string matching method.[[fileno]]2020405010059[[department]]工工

Template Based Recognition of On-Line Handwriting

Software for recognition of handwriting has been available for several decades now and research on the subject have produced several different strategies for producing competitive recognition accuracies, especially in the case of isolated single characters. The problem of recognizing samples of handwriting with arbitrary connections between constituent characters (emph{unconstrained handwriting}) adds considerable complexity in form of the segmentation problem. In other words a recognition system, not constrained to the isolated single character case, needs to be able to recognize where in the sample one letter ends and another begins. In the research community and probably also in commercial systems the most common technique for recognizing unconstrained handwriting compromise Neural Networks for partial character matching along with Hidden Markov Modeling for combining partial results to string hypothesis. Neural Networks are often favored by the research community since the recognition functions are more or less automatically inferred from a training set of handwritten samples. From a commercial perspective a downside to this property is the lack of control, since there is no explicit information on the types of samples that can be correctly recognized by the system. In a template based system, each style of writing a particular character is explicitly modeled, and thus provides some intuition regarding the types of errors (confusions) that the system is prone to make. Most template based recognition methods today only work for the isolated single character recognition problem and extensions to unconstrained recognition is usually not straightforward. This thesis presents a step-by-step recipe for producing a template based recognition system which extends naturally to unconstrained handwriting recognition through simple graph techniques. A system based on this construction has been implemented and tested for the difficult case of unconstrained online Arabic handwriting recognition with good results