A framework of web-based conceptual design

A web-based conceptual design prototype system is presented. The system consists of four parts which interpret on-line sketches as 2D and 3D geometry, extract 3D hierarchical configurations, allow editing of component behaviours, and produce VRML-based behavioural simulations for design verification and web-based application. In the first part, on-line freehand sketched input is interpreted as 2D and 3D geometry, which geometrically represents conceptual design. The system then infers 3D configuration by analysing 3D modelling history. The configuration is described by a parent–child hierarchical relationship and relative positions between two geometric components. The positioning information is computed with respect to the VRML97 specification. In order to verify the conceptual design of a product, the behaviours can be specified interactively on different components. Finally, the system creates VRML97 formatted files for behavioural simulation and collaborative design application over the Internet. The paper gives examples of web-based applications. This work forms a part of a research project into the design and establishing of modular machines for automation manufacture. A consortium of leading automotive companies is collaborating on the research project

Categorization of indoor places using the Kinect sensor

The categorization of places in indoor environments is an important capability for service robots working and interacting with humans. In this paper we present a method to categorize different areas in indoor environments using a mobile robot equipped with a Kinect camera. Our approach transforms depth and grey scale images taken at each place into histograms of local binary patterns (LBPs) whose dimensionality is further reduced following a uniform criterion. The histograms are then combined into a single feature vector which is categorized using a supervised method. In this work we compare the performance of support vector machines and random forests as supervised classifiers. Finally, we apply our technique to distinguish five different place categories: corridors, laboratories, offices, kitchens, and study rooms. Experimental results show that we can categorize these places with high accuracy using our approach

Local wavelet features for statistical object classification and localisation

This article presents a system for texture-based probabilistic classification and localisation of 3D objects in 2D digital images and discusses selected applications. The objects are described by local feature vectors computed using the wavelet transform. In the training phase, object features are statistically modelled as normal density functions. In the recognition phase, a maximisation algorithm compares the learned density functions with the feature vectors extracted from a real scene and yields the classes and poses of objects found in it. Experiments carried out on a real dataset of over 40000 images demonstrate the robustness of the system in terms of classification and localisation accuracy. Finally, two important application scenarios are discussed, namely classification of museum artefacts and classification of metallography images