Exploratory study of logistics service quality scale based on online shopping malls

Online shopping has already become the new mode that a lot of customers try to adopt. At the same time, the online shopping could not be successfully completed without logistics service. Logistics service quality (LSQ) has significant impact on revenue and profitability. This paper presents the issue from the perspective of the customer, and explores the initial factors of LSQ based on the online shopping through in-depth interview and the Delphi method. The survey uses a standard 7-point Likert-type scale to measure the LSQ. Empirical research results are shown in detail to confirm seven LSQ dimensions with Chinese characteristics, including timeliness quality, personal contact quality, order quality, order discrepancy handling, order condition and convenience. Statistical analyses of the investigation were conducted to test the reliability and validity of the LSQ evaluation model.

A level set based segmentation approach for point-sampled surfaces

Segmenting a complex 3D surface model into some visually meaningful sub-parts is one of the fundamental problems in digital geometry processing. In this paper, a novel segmentation approach of point-sampled surfaces is proposed, which is based on the level set evolution scheme. To segment the model so as to align the patch boundaries with high curvature zones, the driven speed function for the zero level set inside narrow band is defined by the extended curvature field, which approaches zero speed as the propagating front approaches high curvature zone. The effectiveness of the proposed approach is demonstrated by our ex- perimental results. Furthermore, two applications of model segmentation are illustrated, such as piecewise parameterization and local editing for point-sampled geometry.

Pattern design on 3D triangular garment surfaces

This paper focuses on a pattern design method for a 3D triangular garment surface. Firstly, some definitions of 3D style lines are proposed for designing the boundaries of patterns as drawing straight lines or splines on the triangular surface. Additionally some commonly used style lines are automatically generated to enhance design efficiency. Secondly, after style lines are preprocessed, a searching method is presented for quickly obtaining the boundaries and patches of a pattern on the 3D trian- gular surface. Finally a new pattern design reuse method is introduced by encoding/decoding the style line information. After style lines are encoded, the pattern design information can be saved in a pattern template and when decoding this template on a new garment surface, it automates the pattern generation for made-to-measure apparel products.

Adaptive foreground and shadow segmentation using hidden conditional random fields

Video object segmentation is important for video surveillance, object tracking, video object recognition and video editing. An adaptive video segmentation algorithm based on hidden conditional random fields (HCRFs) is proposed, which models spatio-temporal constraints of video sequence. In order to improve the segmentation quality, the weights of spatio-temporal con- straints are adaptively updated by on-line learning for HCRFs. Shadows are the factors affecting segmentation quality. To separate foreground objects from the shadows they cast, linear transform for Gaussian distribution of the background is adopted to model the shadow. The experimental results demonstrated that the error ratio of our algorithm is reduced by 23% and 19% respectively, compared with the Gaussian mixture model (GMM) and spatio-temporal Markov random fields (MRFs).