Recycling and remanufacturing returned goods are economically beneficial for companies since the cost of obtaining used parts is lower in many cases and selling price is close to that of a new product. This leads to decreased costs and thereby increased profits for the company. In addition, there are also great environmental benefits by keeping the structural integrity of a part; the energy used for disassembly and refurbishing is much lower than the energy required for raw material extraction and machining. Encompassing the returned goods makes the supply chain to closed loop supply chain, which is different from the traditional supply chain due to reverse logistics. A reverse flow of material is however usually more complex than a forward flow of parts and components from suppliers. This means that inventory management becomes critical and needs to be viewed from a new perspective. The purpose of the report is to study FAW Co., Ltd’s inventory situation in reverse logistics. The report analysed the inventory management in the company, specifically focusing on one product as the instance Motor Engine LFTS-2000since it is in the maturity stage of product life cycle. Two scenarios were designed to consider how different parameters affect inventory levels in reverse logistics. The report analysed how different parameters affect the inventory levels and minimum cost. With the increasing returned goods are processed, inventory levels and minimum cost will decrease correspondingly.

This work focuses on analysing and improving feature detection and matching. After creating an initial framework of study, four main areas of work are researched. These areas make up the main chapters within this thesis and focus on using the Scale Invariant Feature Transform (SIFT).The preliminary analysis of the SIFT investigates how this algorithm functions. Included is an analysis of the SIFT feature descriptor space and an investigation into the noise properties of the SIFT. It introduces a novel use of the a contrario methodology and shows the success of this method as a way of discriminating between images which are likely to contain corresponding regions from images which do not. Parameter analysis of the SIFT uses both parameter sweeps and genetic algorithms as an intelligent means of setting the SIFT parameters for different image types utilising a GPGPU implementation of SIFT. The results have demonstrated which parameters are more important when optimising the algorithm and the areas within the parameter space to focus on when tuning the values. A multi-exposure, High Dynamic Range (HDR), fusion features process has been developed where the SIFT image features are matched within high contrast scenes. Bracketed exposure images are analysed and features are extracted and combined from different images to create a set of features which describe a larger dynamic range. They are shown to reduce the effects of noise and artefacts that are introduced when extracting features from HDR images directly and have a superior image matching performance. The final area is the development of a novel, 3D-based, SIFT weighting technique which utilises the 3D data from a pair of stereo images to cluster and class matched SIFT features. Weightings are applied to the matches based on the 3D properties of the features and how they cluster in order to attempt to discriminate between correct and incorrect matches using the a contrario methodology. The results show that the technique provides a method for discriminating between correct and incorrect matches and that the a contrario methodology has potential for future investigation as a method for correct feature match prediction.