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In this paper, we design a new high-capacity constructive steganographic scheme by optimal texture image block synthesis. First, original texture pattern is divided into multiple seed patches, which are hashed into a blank stego pattern by the key. Furthermore, we employ generated seed patches to reconstruct a candidate pattern, which is scanned by sliding window to generate a series of candidate texture blocks. Subsequently, we sort these candidate texture blocks and select the appropriate ones that their serial number equals to the secret messages to achieve information hiding. After theses texture blocks containing secret messages are hashed into the stego pattern by the key, the remaining positions of stego pattern are sequentially filled with the optimal candidate texture patches by calculating the least square error. Finally, the stego pattern is completely synthesized as the constructive stego image. Compared with the existing schemes, our proposed scheme has a significant high embedding capacity, while keeping a higher visual quality of stego images. [ABSTRACT FROM AUTHOR]

Multiple research studies have recently demonstrated deep networks can generate realistic‐looking textures and stylised images from a single texture example. However, they suffer from some drawbacks. Generative adversarial networks are in general difficult to train. Multiple feature variations, encoded in their latent representation, require a priori information to generate images with specific features. The auto‐encoders are prone to generate a blurry output. One of the main reasons is the inability to parameterise complex distributions. The authors present a novel texture generative model architecture extending the variational auto‐encoder approach. It gradually increases the accuracy of details in the reconstructed images. Thanks to the proposed architecture, the model is able to learn a higher level of details resulting from the partial disentanglement of latent variables. The generative model is also capable of synthesising complex real‐world textures. The model consists of multiple separate latent layers responsible for learning the gradual levels of texture details. Separate training of latent representations increases the stability of the learning process and provides partial disentanglement of latent variables. The experiments with proposed architecture demonstrate the potential of variational auto‐encoders in the domain of texture synthesis and also tend to yield sharper reconstruction as well as synthesised texture images. [ABSTRACT FROM AUTHOR]

With the recent development of stains simulation on warp-weft style fabric materials, high realistic visual effects of real-life stains can be plausibly simulated effectively. However, the previous method relies on the limited single solvent dyeing assumption, while in the real world, the fabric is often contaminated by different stains simultaneously. To tackle the multi-stains simulation problem, we propose a novel duel-stage solvents computational model (DSSM-TLM), which essentially extended the triple-layer model (TLM) (Zheng et al. in IEEE Trans Vis Comput Graph 25(7):2471–2481, 2019. https://doi.org/10.1109/TVCG.2018.2832039) into a more general version. We demonstrated that various effects, such as oil-water stain mixing or alcohol-water stain mixing, can be simulated correctly first ever. Moreover, the simulation process of our algorithm is consistent with the real multi-solvent liquid diffusion process on a real fabric surface. [ABSTRACT FROM AUTHOR]

The thangka image inpainting method based on wavelet transform is not ideal for contour curves when the high frequency information is repaired. In order to solve the problem, a new image inpainting algorithm is proposed based on edge structural constraints and wavelet transform coefficients. Firstly, a damaged thangka image is decomposed into low frequency subgraphs and high frequency subgraphs with different resolutions using wavelet transform. Then, the improved fast marching method is used to repair the low frequency subgraphs which represent structural information of the image. At the same time, for the high frequency subgraphs which represent textural information of the image, the extracted and repaired edge contour information is used to constrain structure inpainting in the proposed algorithm. Finally, the texture part is repaired using texture synthesis based on the wavelet coefficient characteristic of each subgraph. In this paper, the improved method is compared with the existing three methods. It is found that the improved method is superior to them in inpainting accuracy, especially in the case of contour curve. The experimental results show that the hierarchical method combined with structural constraints has a good effect on the edge damage of thangka images. [ABSTRACT FROM AUTHOR]

This paper proposes a new constructive texture synthesis steganographic scheme by compressing original secret messages. First, we divide the original message into multiple bit blocks, which are transferred to decimal values and compressed into small decimal values by recording their interval sign characters. Then, a candidate pattern is generated by combining the given source pattern and boundary extension algorithm. Furthermore, we segment the candidate pattern into multiple candidate patches and use affine transformation algorithm to locate secret positions on a blank canvas, which are used to hide the sign characters by mapping the candidate patches. Finally, we select the candidate patches with minimal mean square error to represent secret bits to generate stego image by image quilting. Extensive experiments demonstrate that compared with existing texture steganographic methods, our method has a better visual quality, higher embedding capacity and security performance, while maintaining strong anti-steganalysis capability. [ABSTRACT FROM AUTHOR]

Aiming at the texture synthesis in new computer graphics technical conditions, this paper proposed a texture interpolation method to synthesize different resolution textures adaptively from a single real world texture. The synthesized texture's DPI was the same as the original texture example. Firstly, the proposed method used a high-dimensional interpolation algorithm to generate an intermediate guidance texture with target resolution by split the source texture. Secondly, it selected particular random patches to fill the gaps in the intermediate texture according to the self-similarity in the source texture. Finally, it seamlessly embedded these patches in the previous intermediate texture by the Poisson image-editing algorithm. Experimental results show that the proposed method can handle both stationary and non-stationary texture synthesis, and the synthetic results are more consistent in the visual properties of the source texture. In addition, the implement logic of the proposed method is enough simple to be programing and executing on a common mobile platform. [ABSTRACT FROM AUTHOR]

Field-guided parameterization methods have proven effective for quad meshing of surfaces; these methods compute smooth cross fields to guide the meshing process and then integrate the fields to construct a discrete mesh. A key challenge in extending these methods to three dimensions, however, is representation of field values. Whereas cross fields can be represented by tangent vector fields that form a linear space, the 3D analog—an octahedral frame field—takes values in a nonlinear manifold. In this work, we describe the space of octahedral frames in the language of differential and algebraic geometry. With this understanding, we develop geometry-aware tools for optimization of octahedral fields, namely geodesic stepping and exact projection via semidefinite relaxation. Our algebraic approach not only provides an elegant and mathematically sound description of the space of octahedral frames but also suggests a generalization to frames whose three axes scale independently, better capturing the singular behavior we expect to see in volumetric frame fields. These new odeco frames , so called as they are represented by orthogonally decomposable tensors, also admit a semidefinite program–based projection operator. Our description of the spaces of octahedral and odeco frames suggests computing frame fields via manifold-based optimization algorithms; we show that these algorithms efficiently produce high-quality fields while maintaining stability and smoothness. [ABSTRACT FROM AUTHOR]

Parquetry is the art and craft of decorating a surface with a pattern of differently colored veneers of wood, stone, or other materials. Traditionally, the process of designing and making parquetry has been driven by color, using the texture found in real wood only for stylization or as a decorative effect. Here, we introduce a computational pipeline that draws from the rich natural structure of strongly textured real-world veneers as a source of detail to approximate a target image as faithfully as possible using a manageable number of parts. This challenge is closely related to the established problems of patch-based image synthesis and stylization in some ways, but fundamentally different in others. Most importantly, the limited availability of resources (any piece of wood can only be used once) turns the relatively simple problem of finding the right piece for the target location into the combinatorial problem of finding optimal parts while avoiding resource collisions. We introduce an algorithm that efficiently solves an approximation to the problem. It further addresses challenges like gamut mapping, feature characterization, and the search for fabricable cuts. We demonstrate the effectiveness of the system by fabricating a selection of pieces of parquetry from different kinds of unstained wood veneer. [ABSTRACT FROM AUTHOR]

Style transfer is a technique for combining two images based on the activations and feature statistics in a deep learning neural network architecture. This paper studies the analogous task in the audio domain and takes a critical look at the problems that arise when adapting the original vision-based framework to handle spectrogram representations. We conclude that CNN architectures with features based on 2D representations and convolutions are better suited for visual images than for time–frequency representations of audio. Despite the awkward fit, experiments show that the Gram matrix determined "style" for audio is more closely aligned with timbral signatures without temporal structure, whereas network layer activity determining audio "content" seems to capture more of the pitch and rhythmic structures. We shed insight on several reasons for the domain differences with illustrative examples. We motivate the use of several types of one-dimensional CNNs that generate results that are better aligned with intuitive notions of audio texture than those based on existing architectures built for images. These ideas also prompt an exploration of audio texture synthesis with architectural variants for extensions to infinite textures, multi-textures, parametric control of receptive fields and the constant-Q transform as an alternative frequency scaling for the spectrogram. [ABSTRACT FROM AUTHOR]

We present a technique to infer a model of the spatio-temporal statistics of a collection of images of dynamic scenes seen from a moving camera. We use a time-variant linear dynamical system to jointly model the statistics of the video signal and the moving vantage point. We propose three approaches to inference, the first based on the plenoptic function, the second based on interpolating linear dynamical models, the third based on approximating the scene as piecewise planar. For the last two approaches, we also illustrate the potential of the proposed techniques with a number of experiments. The resulting algorithms could be useful for video editing where the motion of the vantage point can be controlled interactively, as well as to perform stabilized synthetic generation of video sequences.

Transferring the style of an example image to a content image opens the door of artistic creation for end users. However, it is especially challenging for portrait photos since human vision system is sensitive to the slight artifacts on portraits. Previous methods use facial landmarks to densely align the content face with the style face to reduce the artifacts. However, they can only handle the facial region. As for the whole image, building the dense correspondence is difficult and may easily introduce errors. In this paper, we propose a robust approach for portrait style transfer that gets rid of dense correspondence. Our approach is based on the guided image synthesis framework. We propose three novel guidance maps for the synthesis process. Contrary to former methods, these maps do not require the dense correspondence between content image and style image, which allows our method to handle the whole portrait photo instead of facial region only. In comparison with recent neural style transfer methods, our method achieves more pleasing results and preserves more texture details. Extensive experiments demonstrate our advantage over former methods on portrait style transfer.

The calculation of the priority of the pixel to be repaired and the determination of the best matching block are based on two basic steps of the texture synthesis image inpainting method.The traditional method is not suitable because it is difficult to determine the confidence in the priority calculation,and it is difficult to search for the best matching block.An image inpainting method based on weighted priority and classification matching was proposed.In the priority model,the exponential function and the normalization function were introduced to optimize the confidence and data items separately,the further benefit of which was a more objective priority of calculation and a more reasonable repair order.Based on the proposed functions,the structure information was used as a measure factor of the search matching block,and the best matching block was selected by the classification and screening method.Experimental results show that the proposed method can shorten the repair time under the condition of obtaining good repair effect.

Abstract This paper surveys the state-of-the-art of research in patch-based synthesis. Patch-based methods synthesize output images by copying small regions from exemplar imagery. This line of research originated from an area called “texture synthesis”, which focused on creating regular or semi-regular textures from small exemplars. However, more recently, much research has focused on synthesis of larger and more diverse imagery, such as photos, photo collections, videos, and light fields. Additionally, recent research has focused on customizing the synthesis process for particular problem domains, such as synthesizing artistic or decorative brushes, synthesis of rich materials, and synthesis for 3D fabrication. This report investigates recent papers that follow these themes, with a particular emphasis on papers published since 2009, when the last survey in this area was published. This survey can serve as a tutorial for readers who are not yet familiar with these topics, as well as provide comparisons between these papers, and highlight some open problems in this area.

Facial aging simulation is one of the most challenging issues in automatic machine based face analysis, where the most essential requirements are (i) human identity should remain stable in texture synthesis and (ii) the texture synthesised is expected to accord with human cognitive perception in aging. In this study, the authors propose a tensor completion based method to transform the simulation task to a standard matrix completion one. To protect human dependent characteristics during texture synthesis, the proposed method processes the two major components, i.e. identity and age, in different channels. Furthermore, they incorporate prior information in such a process, assuming that the textures of different subjects in the same age group are similar and similar looking people tend to age in similar ways, and the metric learning technique is adopted to measure the similarity between identities so that the faces that have the highest similarities with the one in the test image are assigned bigger weights in texture generation. In addition, shape deformation is also considered to make the synthesised images more natural. Experimental results achieved on the FG‐NET database demonstrate the effectiveness of the proposed method.

• A new framework for regular texture synthesis is proposed. • A texture synthesis method based on long short-term memory model is proposed. • This method can generate arbitrary size texture without errors. Exemplar based texture synthesis is an important technique for image processing and computer graph in texture mapping. So far, great achievements have been made in this field. However, both traditional and modern methods based on deep learning are making errors in synthesizing patterned texture due to the failure for catching the regularity of texture. To obtain a better synthesized result, a new framework for regular texture synthesis is proposed in this paper. Besides, we use recurrent neural network (RNN) of long-shot term memory (LSTM) to produce a regular texture based on exemplar. Our method can generate at any size texture without errors, which is an improvement for texture synthesis with deep learning techniques.Compared with traditional method as well as deep learning method, our method is obviously better in synthesizing regular texture. [ABSTRACT FROM AUTHOR]

In order to improve the embedded capacity and anti-interference capability of the coverless steganography algorithm, this paper proposes a texture synthesis information hiding scheme based on LBP texture analysis. Firstly, the original small-size texture image is selected and divided into uniform pixel blocks, the LBP value of each pixel in the image blocks is calculated, and the LBP value with the largest LBP distribution is taken as the representive information of the image block. Secondly, when hiding the secret information, the pseudo-random sequence is generated with a specified key to determine the position of the texture candidate block placed on the white paper, then the candidate block is selected according to the value of the secret information and placed on the designated position on the white paper, and the remaining blank areas are filled by the texture synthesis method. Inversely, when extracting secret information, the position of the steganography image block is obtained according to the pseudo-random sequence generated by the key, then the LBP value of each image block with the largest distribution is calculated to obtain the secret information. Experimental results show that the steganography image generated by this method has good visual effect and further improves the embedded capacity and anti-interference ability. [ABSTRACT FROM AUTHOR]

From angling smiles to duck faces, all kinds of facial expressions can be seen in selfies, portraits, and Internet pictures. These photos are taken from various camera types, and under a vast range of angles and lighting conditions. We present a deep learning framework that can fully normalize unconstrained face images, i.e., remove perspective distortions, relight to an evenly lit environment, and predict a frontal and neutral face. Our method can produce a high resolution image while preserving important facial details and the likeness of the subject, along with the original background. We divide this ill-posed problem into three consecutive normalization steps, each using a different generative adversarial network that acts as an image generator. Perspective distortion removal is performed using a dense flow field predictor. A uniformly illuminated face is obtained using a lighting translation network, and the facial expression is neutralized using a generalized facial expression synthesis framework combined with a regression network based on deep features for facial recognition. We introduce new data representations for conditional inference, as well as training methods for supervised learning to ensure that different expressions of the same person can yield to not only a plausible but also a similar neutral face. We demonstrate our results on a wide range of challenging images collected in the wild. Key applications of our method range from robust image-based 3D avatar creation, portrait manipulation, to facial enhancement and reconstruction tasks for crime investigation. We also found through an extensive user study, that our normalization results can be hardly distinguished from ground truth ones if the person is not familiar. [ABSTRACT FROM AUTHOR]

We introduce a method to stylize photographs with auxiliary textures, by means of the Laplacian pyramid. Laplacian pyramid coefficients from a synthetic texture are combined with the coefficients from the original image by means of a smooth maximum function. The final result is a stylized image which maintains the structural characteristics from the input, including edges, color, and existing texture, while enhancing the image with additional fine-scale details. Further, we extend patch-based texture synthesis to include a guidance channel so that texture structures are aligned with an orientation field, obtained through the image structure tensor. [ABSTRACT FROM AUTHOR]

In order to promote the technology research of image style transfer based on deep learning, and discussed the current major methods and representative work. Firstly,this paper reviewed the non-transfer, and introduced the basic principles and methods of image style transfer based on deep learning plication prospect of image style transfer technology in related fields. At last, this paper summarized tin future research directions of image style transfer based on deep learning. [ABSTRACT FROM AUTHOR]

We study discrete geodesic foliations of surfaces---foliations whose leaves are all approximately geodesic curves---and develop several new variational algorithms for computing such foliations. Our key insight is a relaxation of vector field integrability in the discrete setting, which allows us to optimize for curl-free unit vector fields that remain well-defined near singularities and robustly recover a scalar function whose gradient is well aligned to these fields. We then connect the physics governing surfaces woven out of thin ribbons to the geometry of geodesic foliations, and present a design and fabrication pipeline for approximating surfaces of arbitrary geometry and topology by triaxially-woven structures, where the ribbon layout is determined by a geodesic foliation on a sixfold branched cover of the input surface. We validate the effectiveness of our pipeline on a variety of simulated and fabricated woven designs, including an example for readers to try at home. [ABSTRACT FROM AUTHOR]

We tackle the problem of texture synthesis in the setting where many input images are given and a large-scale output is required. We build on recent generative adversarial networks and propose two extensions in this paper. First, we propose an algorithm to combine outputs of GANs trained on a smaller resolution to produce a large-scale plausible texture map with virtually no boundary artifacts. Second, we propose a user interface to enable artistic control. Our quantitative and qualitative results showcase the generation of synthesized high-resolution maps consisting of up to hundreds of megapixels as a case in point. [ABSTRACT FROM AUTHOR]

Procedural pattern synthesis is a fundamental tool of Computer Graphics, ubiquitous in games and special effects. By calling a single procedure in every pixel - or voxel - large quantities of details are generated at low cost, enhancing textures, producing complex structures within and along surfaces. Such procedures are typically implemented as pixel shaders. We propose a novel procedural pattern synthesis technique that exhibits desirable properties for modeling highly contrasted patterns, that are especially well suited to produce surface and microstructure details. In particular, our synthesizer affords for a precise control over the profile, orientation and distribution of the produced stochastic patterns, while allowing to grade all these parameters spatially. Our technique defines a stochastic smooth phase field - a phasor noise - that is then fed into a periodic function (e.g. a sine wave), producing an oscillating field with prescribed main frequencies and preserved contrast oscillations. In addition, the profile of each oscillation is directly controllable (e.g. sine wave, sawtooth, rectangular or any 1D profile). Our technique builds upon a reformulation of Gabor noise in terms of a phasor field that affords for a clear separation between local intensity and phase. Applications range from texturing to modeling surface displacements, as well as multi-material microstructures in the context of additive manufacturing. [ABSTRACT FROM AUTHOR]

• Novel strategy for the measurement of mutual perceptual texture similarity. • Generation of continuous transitions between textures via texture interpolation. • User-based localization of intermediate interpolated specimen in the continuum. • Localization uncertainty is related with the perceived pairwise texture similarity. • Suitability for addressing fine-grained similarities. The perceptual similarity of textures has gained considerable attention in the computer vision and graphics communities. Here, we focus on the challenging task of estimating the mutual perceptual similarity between two textures from materials on a consistent scale. Unlike previous studies that more or less directly queried pairwise similarity from human subjects, we propose an indirect approach that is inspired by the notion of just-noticeable differences (JND). Similar metrics are common in imaging and color science, but so far have not been directly transferred to textures, since they require the generation of intermediate stimuli. Using patch-based statistical texture synthesis, we produce continuous transitions between pairs of textures. In a user experiment, participants are then asked to locate an interpolated specimen in the linear continuum. Our intuition is that the JND, defined as the uncertainty with which participants perform this task, is closely related with the perceived pairwise texture similarity. Using a dataset of fabric textures, we show that this metric is particularly suitable to address fine-grained similarities, produces approximately interval-scale measurements and is additionally convenient for crowdsourcing. We further validate our approach by comparing it with a well-established data collection technique using the same dataset. [ABSTRACT FROM AUTHOR]

Large scenes such as building facades and other architectural constructions often contain repeating elements such as identical windows and brick patterns. In this paper, we present a novel approach that improves the resolution and geometry of 3D meshes of large scenes with such repeating elements. By leveraging structure from motion reconstruction and an off-the-shelf depth sensor, our approach captures a small sample of the scene in high resolution and automatically extends that information to similar regions of the scene. Using RGB and SfM depth information as a guide and simple geometric primitives as canvas, our approach extends the high-resolution mesh by exploiting powerful, image-based texture synthesis approaches. The final results improve on standard SfM reconstruction with higher detail. Our approach benefits from reduced manual labor as opposed to full RGBD reconstruction, and can be done much more cheaply than with LiDAR-based solutions. [ABSTRACT FROM AUTHOR]

Texture synthesis methods based on patch sampling and pasting can generate realistic textures with a similar appearance to a small sample. However, the sample usually has to be used throughout the synthesis stage. In contrast, the learned representation of the textures is more compact and discriminative, and can also yield good synthesis results. In this paper, we introduce a learned approach for texture synthesis based on support vector machines (SVM). This approach benefits from the merit of SVM that the sample texture pattern is learned using a model, and the sample itself can be discarded during the synthesis stage; the approach is also used to synthesize three-dimensional surface textures. Experimental results show that our approach is particularly effective in modeling and synthesizing near-regular or regular textures, which are difficult to achieve using traditional parametric texture synthesis methods. We further apply the proposed approach to constrained texture synthesis, image extrapolation, and texture inpainting. For texture inpainting, we develop a new method for automatically detecting holes in textures without the requirement of human intervention. Our approach yields promising results for the three tasks. [ABSTRACT FROM AUTHOR]

Example-based texture synthesis aims to generate a new texture from an exemplar texture and has long been drawing attention in the fields of computer graphics, computer vision, and image processing. Nevertheless, synthesizing structured textures remains a challenging task. Most previous methods rely on additional guidance channels, which encode the structured features of textures. However, estimating the guidance channel is very difficult, and often fails when a texture has unpronounced features. In this paper, we propose a novel texture synthesis method, based on non-local operators, which captures the long-range structure of a texture without the additional guidance channel. The synthesized texture is generated by minimizing non-local texture energy through an expectation–maximization like optimization algorithm. A statistical constraint based on the Wasserstein distance is also proposed to ensure that the synthesized texture preserves the global statistics of the exemplar texture. Extensive experiments show that the proposed method can stably handle textures with different scale structures. [ABSTRACT FROM AUTHOR]

Image completion techniques are often used to fill in damaged images. In this study, a novel method is proposed that can handle large hole regions surrounded by different types of texture. This method is based on reconstructing damaged texture structure and texture synthesizing. The images are segmented into several regions and similar regions touching the hole region are linked, thereby resulting in new regions. It is found that the new regions provide sufficient information to generate a guidance map, which serves as a soft constraint in the inpainting process. With the guidance map, the hole is filled by using a global optimization texture-synthesis method. The experimental results illustrate that this novel method is a potential tool for image completion. [ABSTRACT FROM AUTHOR]

Nonlocal texture similarity and local intensity smoothness are both essential for solving most image inpainting problems. In this paper, we propose a novel image inpainting algorithm that is capable of reproducing the underlying textural details using a nonlocal texture measure and also smoothing pixel intensity seamlessly in order to achieve natural-looking inpainted images. For matching texture, we propose a Gaussian-weighted nonlocal texture similarity measure to obtain multiple candidate patches for each target patch. To compute the pixel intensity, we apply the $\alpha $ -trimmed mean filter to the candidate patches to inpaint the target patch pixel-by-pixel. The proposed algorithm is compared with four current image inpainting algorithms under different scenarios, including object removal, texture synthesis, and error concealment. Experimental results show that the proposed algorithm outperforms the existing algorithms when inpainting large missing regions in images with texture and geometric structures. [ABSTRACT FROM AUTHOR]

This paper addresses the example-based stylization of videos. Style transfer aims at editing an image so that it matches the style of an example. This topic has been recently investigated by several researchers, both in the industry and in academia. The difficulty lies in how to capture the style of an image and correctly transferring it to a video. In this paper, we build on our previous work "Split and Match" for still pictures, based on adaptive patch synthesis. We address the issue of extending that particular technique to video, ensuring that the solution is spatially and temporally consistent. Results show that our video style transfer is visually plausible, while being very competitive regarding computation time and memory when compared to neural network approaches. [ABSTRACT FROM AUTHOR]

Abstract This paper presents side scan sonar (SSS) image segmentation and synthesis methods based on extreme learning machine (ELM). As an algorithm derived from single-hidden layer feedforward neural networks (SLFNs), ELM has superior performance and fast learning speed with randomly generated hidden layer parameters. The SSS image segmentation uses ELM as a classifier with features generated by convolutional neural network (CNN) of multiple pathways. The CNN of multiple pathways can learn local and global features from SSS images adaptively. Taking these features as input, ELM assigns the central pixel of each input image patch of CNN to one class. Moreover, the presented SSS image synthesis method utilizes ELM as a regression algorithm, in which the non-parametric sampling algorithm is used first to synthesize coarse SSS images according to segmentation maps and sample images for each class. Then ELM trained with the coarse synthesis images and their ground truth maps (the Gaussian-filtered SSS images) synthesizes fine SSS images. Furthermore, peak signal to noise ratio (PSNR) of the synthetic SSS images with the Gaussian-filtered SSS images as ref is used as one evaluation metric for segmentation performance. Experimental results demonstrate that the SSS image segmentation method combining convolutional features with ELM outperforms typical CNN and support vector machine (SVM), and the presented SSS image synthesis method and the evaluation metric are applicable. [ABSTRACT FROM AUTHOR]

A discontinuously transitional color appears in the connection part between different planes when 2D digital camouflage pattern is applied on a 3D target surface. To solve this problem, a digital camouflage stitching method is proposed based on texture synthesis and best seam line algorithm. The tex¬ture synthesis technology is used to create naturally transitional extended digital camouflage pattern, thus constructing a fake overlap region for digital camouflage pattern, and searching the best seam line in the overlap region. And then the patterns to be stitiched are tailored and stitched together to achieve the seamless stitching of digital camouflage pattern. The qualitative and quantitative methods are used to eva¬luate the stitching result. Experimental results show that the proposed method can effectively eliminate the significant gaps in stitched camouflage pattern, and make it more natural. [ABSTRACT FROM AUTHOR]