An Efficient Approach to Correspondences between Multiple Non-Rigid Parts

Identifying multiple deformable parts on meshes and establishing dense correspondences between them are tasks of fundamental importance to computer graphics, with applications to e.g. geometric edit propagation and texture transfer. Much research has considered establishing correspondences between non-rigid surfaces, but little work can both identify similar multiple deformable parts and handle partial shape correspondences. This paper addresses two related problems, treating them as a whole: (i) identifying similar deformable parts on a mesh, related by a non-rigid transformation to a given query part, and (ii) establishing dense point correspondences automatically between such parts. We show that simple and efficient techniques can be developed if we make the assumption that these parts locally undergo isometric deformation. Our insight is that similar deformable parts are suggested by large clusters of point correspondences that are isometrically consistent. Once such parts are identified, dense point correspondences can be obtained by an iterative propagation process. Our techniques are applicable to models with arbitrary topology. Various examples demonstrate the effectiveness of our techniques

Non-photorealistic rendering with spot colour

Colour is an important aspect of art. Not only does it give richness to images, but it always provides a means to highlight certain objects. This idea of spot colour has been used extensively in both fine art and commercial illustrations. Many non-photorealistic rendering (NPR) algorithms produce grayscale or monochromatic images with low saturations. In this paper we introduce the idea of spot colour to NPR and propose a simple and automatic algorithm to add spot colour to these rendering styles. The hue is thresholded into colour layers and the most appropriate layer is automatically determined based on factors such as layer region shape and salience. We also consider using an edge-based criterion to colourise the background, which is an effective means of making the foreground stand out. We demonstrate the effectiveness of our approach by adding spot colour to a diverse set of NPR styles

Efficient circular thresholding

Otsu's algorithm for thresholding images is widely used, and the computational complexity of determining the threshold from the histogram is O(N) where N is the number of histogram bins. When the algorithm is adapted to circular rather than linear histograms then two thresholds are required for binary thresholding. We show that, surprisingly, it is still possible to determine the optimal threshold in O(N) time. The efficient optimal algorithm is over 300 times faster than traditional approaches for typical histograms and is thus particularly suitable for real-time applications. We further demonstrate the usefulness of circular thresholding using the adapted Otsu criterion for various applications, including analysis of optical flow data, indoor/outdoor image classification, and non-photorealistic rendering. In particular, by combining circular Otsu feature with other colour/texture features, a 96.9% correct rate is obtained for indoor/outdoor classification on the well known IITM-SCID2 data set, outperforming the state-of-the-art result by 4.3%

Non-photorealistic rendering with spot colour

Colour is an important aspect of art. Not only does it give richness to images, but it always provides a means to highlight certain objects. This idea of spot colour has been used extensively in both fine art and commercial illustrations. Many non-photorealistic rendering (NPR) algorithms produce grayscale or monochromatic images with low saturations. In this paper we introduce the idea of spot colour to NPR and propose a simple and automatic algorithm to add spot colour to these rendering styles. The hue is thresholded into colour layers and the most appropriate layer is automatically determined based on factors such as layer region shape and salience. We also consider using an edge-based criterion to colourise the background, which is an effective means of making the foreground stand out. We demonstrate the effectiveness of our approach by adding spot colour to a diverse set of NPR styles

Artistic rendering enhancing global structure

Non-photorealistic rendering techniques usu- ally produce abstracted images. Most existing methods consider local rendering primitives, and global struc- tures may be easily obscured. Inspired by artists, we propose a novel image abstraction method that con- siders preserving or even enhancing global structures in the input images. Linear structures are particularly considered due to their wide existence and the avail- ability of techniques for their reliable detection. Based on various computer vision techniques, the algorithm is fully automatic. As demonstrated in the paper, artistic looking results are obtained for various types of images. The technique is orthogonal to many non-photorealistic rendering techniques and can be combined with them

Example-based image colorization using locality consistent sparse representation

—Image colorization aims to produce a natural looking color image from a given grayscale image, which remains a challenging problem. In this paper, we propose a novel examplebased image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target grayscale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target grayscale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms state-ofthe-art methods, both visually and quantitatively using a user stud

Artistic minimal rendering with lines and blocks

Many non-photorealistic rendering techniques exist to produce artistic effects from given images. Inspired by various artists, interesting effects can be produced by using a minimal rendering, where the minimum refers to the number of tones as well as the number and complexity of the primitives used for rendering. Our method is based on various computer vision techniques, and uses a combination of refined lines and blocks (potentially simplified), as well as a small number of tones, to produce abstracted artistic rendering with sufficient elements from the original image. We also considered a variety of methods to produce different artistic styles, such as colour and 2-tone drawings, and use semantic information to improve renderings for faces. By changing some intuitive parameters a wide range of visually pleasing results can be produced. Our method is fully automatic. We demonstrate the effectiveness of our method with extensive experiments and a user study

Example-based image colorization via automatic feature selection and fusion

Image colorization is an important and difficult problem in image processing with various applications including image stylization and heritage restoration. Most existing image colorization methods utilize feature matching between the reference color image and the target grayscale image. The effectiveness of features is often significantly affected by the characteristics of the local image region. Traditional methods usually combine multiple features to improve the matching performance. However, the same set of features is still applied to the whole images. In this paper, based on the observation that local regions have different characteristics and hence different features may work more effectively, we propose a novel image colorization method using automatic feature selection with the results fused via a Markov Random Field (MRF) model for improved consistency. More specifically, the proposed algorithm automatically classifies image regions as either uniform or non-uniform, and selects a suitable feature vector for each local patch of the target image to determine the colorization results. For this purpose, a descriptor based on luminance deviation is used to estimate the probability of each patch being uniform or non-uniform, and the same descriptor is also used for calculating the label cost of the MRF model to determine which feature vector should be selected for each patch. In addition, the similarity between the luminance of the neighborhood is used as the smoothness cost for the MRF model which enhances the local consistency of the colorization results. Experimental results on a variety of images show that our method outperforms several state-of-the-art algorithms, both visually and quantitatively using standard measures and a user study

WCGAN: Robust portrait watercolorization with adaptive hierarchical localized constraints

Deep learning has enabled image style transfer to make great strides forward. However, unlike many other styles, transferring the watercolor style to portraits is significantly challenging in image synthesis and style transfer. Pixel-correlation-based methods do not produce satisfactory watercolors. This is because portrait watercolors exhibit the sophisticated fusion of various painting techniques in local areas, which poses a problem for convolutional neural networks to accurately handle fine-grained features. Moreover, the common but problematic way of coping with multiple scales greatly impedes the performance of existing style transfer methods with fixed receptive fields. Although it is possible to develop an image processing pipeline mimicking various watercolor effects, such algorithms are slow and fragile, especially for inputs of different scales. As a remedy, this paper proposes WCGAN, a generative adversarial network (GAN) architecture dedicated to watercolorization of portraits. Specifically, a novel localized style loss suitable for watercolorization is proposed to deal with local details. To handle portraits of different scales and improve robustness, a novel discriminator architecture with three parallel branches of varying sizes of receptive fields is introduced. In addition, the application of WCGAN is expanded to video style transfer where a novel kind of video training data based on random crops is developed to efficiently capture temporal consistency. Extensive experimental results from qualitative and quantitative analyses demonstrate that WCGAN generates state-of-the-art, high quality watercolors from portraits

Non-rigid registration under anisotropic deformations

Non-rigid registration of deformed 3D shapes is a challenging and fundamental task in geometric processing, which aims to non-rigidly deform a source shape into alignment with a target shape. Current state-of-the-art methods assume deformations to be near-isometric. This assumption does not reflect real-world conditions, for example in large-scale deformation, where moderate anisotropic deformations (e.g., stretches) are common. In this paper we propose two significant changes to a typical registration pipeline to address such challenging deformations. First, we introduce a method to estimate anisotropic non-isometric deformations and incorporate this into an iterative non-rigid registration pipeline. Second, we compute additional correspondences in non-isometrically deforming regions using reliable correspondences as landmarks and prune inconsistent correspondences. We compare the performance of our proposed algorithm to several state-of-the-art methods using existing benchmarks. Experimental results show that our method outperforms existing methods