Netease hangzhou Inc.

Hangzhou, China

Netease hangzhou Inc.

Hangzhou, China
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Ren J.,Zhejiang University | Chen G.,Zhejiang University | Li X.,Netease hangzhou Inc. | Mao K.,Netease hangzhou Inc.
Multimedia Tools and Applications | Year: 2017

In today’s rapid growth of volume of multimedia data, security is important yet challenging problem in multimedia applications. Image, which covers the highest percentage of the multimedia data, it is very important for multimedia security. Image segmentation is utilized as a fundamental preprocessing of various multimedia applications such as surveillance for security by breaking a given image into multiple salient regions. In this paper, we present a new image segmentation approach based on frequency-domain filtering for images with stripe texture, and generalize it to lattice fence images. Our method significantly reduces the impact of stripes on segmentation performance. The approach proposed in this paper consists of three phases. Given the images, we weaken the effect of stripe texture by filtering in the frequency domain automatically. Then, structure-preserving image smoothing is employed to remove texture details and extract the main image structures. Last, we use an effective threshold method to produce segmentation results. Our method achieves very promising results for the test image dataset and could benefit a number of new multimedia applications such as public security. © 2017 Springer Science+Business Media New York


Ren J.,Zhejiang University | Chen G.,Zhejiang University | Li X.,Netease Hangzhou Inc.
Computers in Industry | Year: 2017

Printed fabrics differ from non-planar fabrics (e.g., embroidery) in their rich colours, various patterns and low costs. However, non-planar fabrics are considered more attractive, expensive in appearance and luxurious owing to their solid texture patterns, floats and luster contrasts. To produce fabrics that are both colorful and lavish and also have color gradients, this paper proposes a novel fine-grained digital printing system that combines digital textile printing and non-planar fabrics. Note that because distortion is one of the characteristics of the fabric, directly printing an ink-design pattern onto non-planar fabric gives poor performance. This work develops a solution to accurately align the ink-design pattern with the fabric, consisting of both hardware and software parts that are used to take pictures of the fabric, employ image registration-based computer vision technology to deform the ink-design pattern according to the deformability of the fabric, and print the deformed ink-design pattern onto the fabric. The system is flexible and highly automated, and it makes mass production possible. In the prototype, fine quality fabrics are produced efficiently (a fabric of 0.12 m2 in less than 4 s). © 2017 Elsevier B.V.

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