We present a design of a form-changing female robotic mannequin for garment fitting. The fitting robot can change itself to imitate the body shape of a female person and replace her to finish the fitting process remotely. It can be used for reducing the return rate of garment e-commerce and increasing the order quantity of made-to-measure by avoiding the geographical restriction and the complicated process of trying on different clothes personally for a customer. In this paper, we mainly focus on the design of the mechanical structure and control system for the female fitting robot. By analyzing the characteristics of the skeleton structure and shape of female bodies, we design a block model for representing the surface of the fitting robot and an internal motion mechanism which is driven by a control system after inputting parameters of body sizes. On the basis of the design, a prototype of the form-changing female fitting robot is developed. Experimental results show that it can imitate a range of female body types.

Learning an image filter from a pair of images composed of an original image and its filtered version has attracted increasing attention in the image processing region. However, the existing methods only take the color information into consideration to learn an image filter, which cannot accurately depict the luminance and some style changes of images so that the generated learning results are inaccurate. This paper proposes a Laplace operator based multi-channel learning approach to learn a multi-channel filter from an original image and its filtered version. Different from previous method, we adopt a strategy to learn a multi-channel filter which can accurately reconstruct the filtered image. In addition, benefiting from the Laplace regularization based multi-channel learning model, we can make the neighbor pixels of each channel have the similar weights and learn more homogeneous weight maps for each channel filter, thereby generating more accurate results with less time. Due to the multi-channel representation of our learnt filter, we can edit the luminance style and the color style of images by changing the weights of different channel filter. Furthermore, we can edit the effect of the image filters by tuning the weights of some basis filters. At last, extensive experiments well validate the performance of our method over state-of-the-art methods in terms of accuracy and speed.

We propose an enhanced method for feature-based simplification of feature-based CAD models. In previous feature-based simplification methods, a CAD model is progressively simplified by suppressing individual features with low importance one at a time, whereas in the proposed method, each feature itself is progressively simplified based on its feature type. The proposed method provides a user with a further subdivided level of simplification. In the proposed method, features are categorized into sketch-based features and non-sketch-based features. The sketch-based features are simplified using three sketch simplification operations. The non-sketch-based features are simplified by converting them into simpler features, or changing their parameters. To demonstrate the proposed method, we present the implementation and experiment results.

It is usually difficult to correctly segment medical images with intensity inhomogeneity, which is of great significance in understanding of medical images. The local image intensity features play a vital role in accurately segmenting medical images with intensity inhomogeneity. Therefore, it is crucial to acquire the local intensity features for a deeper understanding of medical images. The main idea of this paper is to construct an efficient similarity-based level set model, which synthesizes the similarity theory, curve evolution and level set. Firstly, a local statistical function is modeled as different scales of Gaussian distributions to estimate bias fields, in which a real image can be approximately obtained for a more accurate medical image segmentation. Secondly, a new potential function is constructed to maintain the stability of the curve evolution, especially the signed distance profile in the neighborhood of the zero level set, which plays an important role in the correct segmentation. Thirdly, an adaptive condition criterion has been proposed to accelerate the convergence in the curve processing. Finally, the experiments on artificial and medical images and comparisons with the current well-known region-based models are discussed in details. Our extensive experimental results demonstrate that the proposed method can correctly segment medical images with intensity inhomogeneity in a few iterations and also is less sensitive to the initial contour.

Energy and environmental issues have become pertinent to all industries in the globe because of sustainable development issues. This paper systematically investigates the turning process of the hardened material via process modeling, numerical experiments, and a hybrid algorithm. The objectives of this work are to reduce the specific cutting energy (SCE) and improve the energy efficiency (EF) based on the turning conditions optimization. The machining simulations were performed in conjunction response surface methodology (RSM) to generate the quadratic mathematical models of the specific cutting energy and energy efficiency in terms of machining parameters, including cutting speed, feed rate, nose radius, edge radius, and rake angle. An analysis of variance (ANOVA) was then adopted to examine the model adequacy and significant parameters. Subsequently, an evolutionary algorithm, namely non-dominated sorting genetic algorithm-II (NSGA-II) was used to find a much better spread of design solutions and better convergence near the true Pareto optimal front. A quantitative approach, namely entropy method was conducted to calculate the weight factors of multiple responses. In the last step, a TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) was applied as to determine the best compromise solution. It was indicated that the energy efficiency was significantly improved using the optimal machining parameters and the specific cutting energy was effectively decreased in comparison with initial values. Moreover, the integrative approach performed very well in optimum performance of the machining process. Therefore, this work is expected as a contribution to improve the machining efficiency of the turning process of hardened steels.

Parsing indoor scenes from RGB-D imagery is a significant and challenging task in image understanding and computer vision. Since the walls, floor and ceiling are the main structures of indoor scenes, we propose a simple and efficient method to find them from a single depth image. We use the prior knowledge of the walls, floor and ceiling and distance histogram to obtain their planes, and delimit their extents by ray-casting. Our method can also detect the open windows or doors and other planes which have large area in indoor scenes. Experimental results show that our method is more robust to find the main structures of the indoor scenes than the existing method.