Abstract: Ancient books are significant culture inheritors and their background textures convey the potential history information. However, multi-style texture recovery of ancient books has received little attention. Restricted by insufficient ancient textures and complex handling process, the generation of ancient textures confronts with new challenges. For instance, training without sufficient data usually brings about overfitting or mode collapse, so some of the outputs are prone to be fake. Recently, image generation and style transfer based on deep learning are widely applied in computer vision. Breakthroughs within the field make it possible to conduct research upon multi-style texture recovery of ancient books. Under the circumstances, we proposed a network of layout analysis and image fusion system. Firstly, we trained models by using Deep Convolution Generative against Networks (DCGAN) to synthesize multi-style ancient textures; then, we analyzed layouts based on the Position Rearrangement (PR) algorithm that we proposed to adjust the layout structure of foreground content; at last, we realized our goal by fusing rearranged foreground texts and generated background. In experiments, diversified samples such as ancient Yi, Jurchen, Seal were selected as our training sets. Then, the performances of different fine-turning models were gradually improved by adjusting DCGAN model in parameters as well as structures. In order to evaluate the results scientifically, cross entropy loss function and Fréchet Inception Distance (FID) are selected to be our assessment criteria. Eventually, we got model M8 with lowest FID score. Compared with DCGAN model proposed by Radford at el., the FID score of M8 improved by 19.26%, enhancing the quality of the synthetic images profoundly.
Abstract: Computer aided design accounts with the support of
parametric software in the design of machine components as well as
of any other pieces of interest. The complexities of the element under
study sometimes offer certain difficulties to computer design, or ever
might generate mistakes in the final body conception. Reverse
engineering techniques are based on the transformation of already
conceived body images into a matrix of points which can be
visualized by the design software. The literature exhibits several
techniques to obtain machine components dimensional fields, as
contact instrument (MMC), calipers and optical methods as laser
scanner, holograms as well as moiré methods. The objective of this
research work was to analyze the moiré technique as instrument of
reverse engineering, applied to bodies of nom complex geometry as
simple solid figures, creating matrices of points. These matrices were
forwarded to a parametric software named SolidWorks to generate
the virtual object. Volume data obtained by mechanical means, i.e.,
by caliper, the volume obtained through the moiré method and the
volume generated by the SolidWorks software were compared and
found to be in close agreement. This research work suggests the
application of phase shifting moiré methods as instrument of reverse
engineering, serving also to support farm machinery element designs.
Abstract: This paper proposes a copyright protection scheme for color images using secret sharing and wavelet transform. The scheme contains two phases: the share image generation phase and the watermark retrieval phase. In the generation phase, the proposed scheme first converts the image into the YCbCr color space and creates a special sampling plane from the color space. Next, the scheme extracts the features from the sampling plane using the discrete wavelet transform. Then, the scheme employs the features and the watermark to generate a principal share image. In the retrieval phase, an expanded watermark is first reconstructed using the features of the suspect image and the principal share image. Next, the scheme reduces the additional noise to obtain the recovered watermark, which is then verified against the original watermark to examine the copyright. The experimental results show that the proposed scheme can resist several attacks such as JPEG compression, blurring, sharpening, noise addition, and cropping. The accuracy rates are all higher than 97%.