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The paper aims to propose a novel method based on deep sparse convolutional neural network (CNN) for clothing recognition. A CNN based on inception module is applied to bridge pixel-level features and high-level category labels. In order to improve the robustness accuracy of the network, six transformation methods are used to preprocess images. To avoid representational bottlenecks, small-sized convolution kernels are adopted in the network. This method first pretrains the network on ImageNet and then fine-tune the model in clothing data set.

The paper opts for an exploratory study by using the control variable comparison method. To verify the rationality of the network structure, lateral contrast experiments with common network structures such as VGG, GoogLeNet and AlexNet, and longitudinal contrast tests with different structures from one another are performed on the created clothing image data sets. The indicators of comparison include accuracy, average recall, average precise and F-1 score.

Compared with common methods, the experimental results show that the proposed network has better performance on clothing recognition. It is also can be found that larger input size can effectively improve accuracy. By analyzing the output structure of the model, the model learns a certain “rules” of human recognition clothing.

Clothing analysis and recognition is a meaningful issue, due to its potential values in many areas, including fashion design, e-commerce and retrieval system. Meanwhile, it is challenging because of the diversity of clothing appearance and background. Thus, this paper raises a network based on deep sparse CNN to realize clothing recognition.

The purpose of this paper is to propose a novel method using support vector machine (SVM) classifiers for objective seam pucker evaluation. Features are extracted using wavelet analysis and gray-level co-occurrence matrix (GLCM), and the samples are evaluated using SVM classifiers. The study aims to solve the problem of inappropriate parameters and large required samples in objective seam pucker evaluation.

Initially, seam pucker image was captured, and Edge detection and Hough transform were utilized to normalize the seam position and orientation. After cropping the image, the intensity was adjusted to the same identical level through histogram specification. Then, the standard deviations of the horizontal image and diagonal image, reconstructed using wavelet decomposition and reconstruction, were calculated based on parameter optimization. Meanwhile, GLCM was extracted from the restructured horizontal detail image, then the contrast and correlation of GLCM were calculated. Finally, these four features were imported to SVM classifiers based on genetic algorithm for evaluation.

The four extracted features reflected linear relationships among five grades. The experimental results showed that the classification accuracy was 96 percent, which catches up to the performance of human vision, and resolves ambiguity and subjective of the manual evaluation.

There are large required samples in current research. This paper provides a novel method using finite samples, and the parameters of the methods were discussed for parameter optimization. The evaluation results can provide references for analyzing the reason of wrinkles during garment manufacturing.

The purpose of this paper is to research an auto generation method of developing FFT image and image pattern for textile based on FFT theory.

In the research, a program was developed to generate FFT images using the FFT algorithm. The process of auto generation FFT image can be divided into the following steps: initializing the size of image, painting source image, giving the color pattern, transforming FFT image by FFT, designing mask template, and image pattern combining by point diagram. These image patterns can be used to apply on the textile.

The results showed that the FFT images can be used for textile designer directly. The FFT images can also be used as elements for textile image design such as clothing. The auto generation FFT images by FFT reflect different modern sense of beauty from traditional geometric images.

There are many parameters that affect the art effect of FFT image generating by FFT algorithm. However, there is no discussion about the relationship between the parameters and art effect. Three dimension effects are not obvious in the simulation results by virtual clothing software.

The paper presents a fundamental understanding of the property of the FFT image generating by FFT algorithm and application method of the image pattern in clothing.

Examines the fifthteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

The purpose of this paper is to add to the understanding of Māori perspectives pertaining to the economic application of New Zealand's flora and fauna.

The body of literature consulted was a combination of works written about Rongoā Māori (Māori medicine) and Māori perspectives on the stewardship and management of New Zealand's natural resources. Empirical findings were obtained from focus groups and an interview with a practitioner of Rongoā Māori. All interviews were semi‐structured.

The findings indicate that Māori enterprise involving indigenous flora and fauna is likely to be community based; with a proportion of these being non‐profit in nature. The transmission and protection of traditional knowledge regarding the use of plants is a key issue. Māori iwi (tribes) would benefit from further research into their models of community‐based entrepreneurship.

The paper would be useful for academics considering further exploration of Māori participation in the bio‐economy.

The paper is an exploratory study that has captured some Māori perspectives regarding the use of indigenous flora and fauna.