The detection and identification of extraneous metals in material conveyance systems

The aim of this work was to design and build an equipment which can detect ferrous and non-ferrous objects in conveyed commodities, discriminate between them and locate the object along the belt and on the width of the belt. The magnetic induction mechanism was used as a means of achieving the objectives of this research. In order to choose the appropriate geometry and size of the induction field source, the field distributions of different source geometries and sizes were studied in detail. From these investigations it was found the square loop geometry is the most appropriate as a field generating source for the purpose of this project. The phenomena of field distribution in the conductors was also investigated. An equipment was designed and built at the preliminary stages of thework based on a flux-gate magnetometer with the ability to detect only ferrous objects.The instrument was designed such that it could be used to detect ferrous objects in the coal conveyors of power stations. The advantages of employing this detector in the power industry over the present ferrous metal electromagnetic separators were also considered. The objectives of this project culminated in the design and construction of a ferrous and non-ferrous detector with the ability to discriminate between ferrous and non-ferrous metals and to locate the objects on the conveying system. An experimental study was carried out to test the performance of the equipment in the detection of ferrous and non-ferrous objects of a given size carried on the conveyor belt. The ability of the equipment to discriminate between the types of metals and to locate the object on the belt was also evaluated experimentally. The benefits which can be gained from the industrial implementations of the equipment were considered. Further topics which may be investigated as an extension of this work are given.

Late development of metric part-relational processing in object recognition

Four experiments with unfamiliar objects examined the remarkably late consolidation of part-relational relative to part-based object recognition (Jüttner, Wakui, Petters, Kaur, & Davidoff, 2013). Our results indicate a particularly protracted developmental trajectory for the processing of metric part relations. Schoolchildren aged 7 to 14 years and adults were tested in 3-Alternative-Forced-Choice tasks to judge the correct appearance of upright and inverted newly learned multipart objects that had been manipulated in terms of individual parts or part relations. Experiment 1 showed that even the youngest tested children were close to adult levels of performance for recognizing categorical changes of individual parts and relative part position. By contrast, Experiment 2 demonstrated that performance for detecting metric changes of relative part position was distinctly reduced in young children compared with recognizing metric changes of individual parts, and did not approach the latter until 11 to 12 years. A similar developmental dissociation was observed in Experiment 3, which contrasted the detection of metric relative-size changes and metric part changes. Experiment 4 showed that manipulations of metric size that were perceived as part (rather than part-relational) changes eliminated this dissociation. Implications for theories of object recognition and similarities to the development of face perception are discussed. © 2014 American Psychological Association.

A stable graph-based representation for object recognition through high-order matching

Many Object recognition techniques perform some flavour of point pattern matching between a model and a scene. Such points are usually selected through a feature detection algorithm that is robust to a class of image transformations and a suitable descriptor is computed over them in order to get a reliable matching. Moreover, some approaches take an additional step by casting the correspondence problem into a matching between graphs defined over feature points. The motivation is that the relational model would add more discriminative power, however the overall effectiveness strongly depends on the ability to build a graph that is stable with respect to both changes in the object appearance and spatial distribution of interest points. In fact, widely used graph-based representations, have shown to suffer some limitations, especially with respect to changes in the Euclidean organization of the feature points. In this paper we introduce a technique to build relational structures over corner points that does not depend on the spatial distribution of the features. © 2012 ICPR Org Committee.