Removing specular reflections from images is critical for improving the performance of computer vision algorithms. Recently, state-of-the-art methods have demonstrated remarkably good performance at removing specular reflections from chromatic images. These methods are typically based on the chromatic pixels assumption; therefore, they are prone to failure in the achromatic regions. This paper presents a novel method that is applicable to natural images, because it is effective for both chromatic and achromatic regions. The proposed method is based on modeling the general properties of diffuse and specular reflections in a solid convex optimization framework. Considering the physical constraints, we determine the global optimal solution using the split Bregman method. Experimental results demonstrate the effectiveness of the proposed method, particularly for the achromatic regions, and its competence as a state-of-the-art method for removing specular reflections from the chromatic regions.

Abstract We present a novel method for enhancing details in a digital photograph, inspired by the principle of art photography. In contrast to the previous methods that primarily rely on tone scaling, our technique provides a flexible tone transform model that consists of two operators: shifting and scaling. This model permits shifting of the tonal range in each image region to enable significant detail boosting regardless of the original tone. We optimize these shift and scale factors in our constrained optimization framework to achieve extreme detail enhancement across the image in a piecewise smooth fashion, as in art photography. The experimental results show that the proposed method brings out a significantly large amount of details even from an ordinary low‐dynamic range image.

Stereoscopic 3D exploits the effects of stereopsis where the depth perception is triggered by binocular disparity, a difference in image location of an object by the left and right eyes. Despite the dissimilarity of stereo projections in terms of disparity and shape, the human visual system can find the matching stereo pair to fuse using their similarities in terms of color, size, shading, texture, shadows, that are normally present in photorealistic stereo imaging. Now if some of these elements (or depth cues) are missing, such as in non-photorealistic stereo imaging, how will it affect the depth perception and stereo fusion? In this paper, we investigate this issue by conducting a perceptual study on stereoscopic 3D line drawing, in which many of these cues have been abstracted away. We first evaluate the validity of using stereo images composed of lines only, and then compare its performance to stereo images of normal shading. We also examine the effect of changing line style as well as the prospect of using lines as an additional depth cue. Our study shows that stereo line drawing, when compared to stereo shading, does weaken the depth perception but only to a minor degree. On the other hand, modification of line style, and superimposition of stereo lines both have potentials to strengthen the perception of depth.

This paper discusses stereoscopic 3D imaging based on line drawing of 3D shapes. We describe the major issues and challenges in generating stereoscopic 3D effects using lines only, with a couple of relatively simple approaches called each-eye-based and center-eye-based. Each of these methods has its shortcomings, such as binocular rivalry and inaccurate lines. We explain why and how these problems occur, then describe the concept of stereo-coherent lines and an algorithm to extract them from 3D shapes. We also propose a simple method to stylize stereo lines that ensures the stereo coherence of stroke textures across binocular views. The proposed method provides viewers with unique visual experience of watching 2D drawings popping out of the screen like 3D.

Abstract In this paper, we introduce a novel framework that allows users to synthesize the expression of a 3D character by providing a intuitive set of parametric controls. Assuming that human face movements are formed by a set of basis actuation, we analyze a set of real expressions to extract this set together with skin deformation due to the actuation of face. To do this, we first decompose the movement of each marker into a set of distinctive movements. Independent component analysis technique is then adopted to find a independent set of actuations. Our simple and efficient skin deformation model are learned to reproduce the realistic movements of facial parts due to the actuations. In this framework, users can animate characters' faces by controlling the amount actuation or by directly manipulating the face geometry. In addition, the proposed method can be applied to expression transfer which reproduces one character's expression in another character's face. Experimental results demonstrate that our method can produce realistic expression efficiently.

Abstract This paper presents an automatic method for producing stipple renderings from photographs, following the style of professional hedcut illustrations. For effective depiction of image features, we introduce a novel dot placement algorithm which adapts stipple dots to the local shapes. The core idea is to guide the dot placement along ‘feature flow’ extracted from the feature lines, resulting in a dot distribution that conforms to feature shapes. The sizes of dots are adaptively determined from the input image for proper tone representation. Experimental results show that such feature‐guided stippling leads to the production of stylistic and feature‐emphasizing dot illustrations.

We present a novel scheme for automatically generating line drawings from 2D images, aiming to facilitate effective visual communication. In contrast to conventional edge detectors, our technique imitates the human line drawing process and consists of two parts: line extraction and line rendering. We propose a novel line extraction method based on likelihood-function estimation, which effectively finds the genuine shape boundaries. We consider the feature scale and the blurriness of lines with which the detail and the focus-level of lines are controlled in the rendering. We also employ stroke textures to provide a variety of illustration styles. Experimental results demonstrate that our technique generates various kinds of line drawings from 2D images enabled by the control over detail, focus, and style.

We describe a GPU-based algorithm for rendering a 3D model as a line drawing, based on the insight that a line drawing can be understood as an abstraction of a shaded image. We thus render lines along tone boundaries or thin dark areas in the shaded image. We extend this notion to the dual: we render highlight lines along thin bright areas and tone boundaries. We combine the lines with toon shading to capture broad regions of tone. The resulting line drawings effectively convey both shape and material cues. The lines produced by the method can include silhouettes. creases, and ridges, along with a generalization of suggestive contours that responds to lighting as well as viewing changes. The method supports automatic level of abstraction, where the size of depicted shape features adjusts appropriately as the camera zooms in or out. Animated models can be rendered in real time because costly mesh curvature calculations are not needed.

This paper proposes a general framework for overfitting control in surface reconstruction from noisy point data. The problem we deal with is how to create a model that will capture as much detail as possible and simultaneously avoid reproducing the noise of the input points. The proposed framework is based on extra-sample validation. It is fully automatic and can work in conjunction with any surface reconstruction algorithm. We test the framework with a Radial Basis Function algorithm, Multi-level Partition of Unity implicits, and the Power Crust algorithm.

Feature sensitive mesh segmentation is important for many computer graphics and geometric modeling applications. In this paper, we develop a mesh segmentation method, which is capable of producing high-quality shape partitioning. It respects fine shape features and works well on various types of shapes, including natural shapes and mechanical parts. The method combines a procedure for clustering mesh normals with a modification of the mesh clarification technique. For clustering of mesh normals, we adopt Mean Shift, a powerful general purpose technique for clustering scattered data. We demonstrate advantages of our method by comparing it with two state-of-the-art mesh segmentation techniques.

Mesh partitioning and parts extraction have become key ingredients for many mesh manipulation applications both manual and automatic. In this paper, we present an intelligent scissoring operator for meshes which supports both automatic segmentation and manual cutting. Instead of segmenting the mesh by clustering, our approach concentrates on finding and defining the contours for cutting. This approach is based on the minima rule, which states that human perception usually divides a surface into parts along the contours of concave discontinuity of the tangent plane. The technique uses feature extraction to find such candidate feature contours. Subsequently, such a contour can be selected either automatically or manually, or the user may draw a 2D line to start the scissoring process. The given open contour is completed to form a loop around a specific part of the mesh, and this loop is used as the initial position of a 3D geometric snake. The snake moves by relaxation until it settles to define the final scissoring position. This process uses several fundamental geometric mesh attributes, such as curvature and centricity, and enables both automatic segmentation and an easy-to-use intelligent-scissoring operator.

A surface light field is a function that gives the colors of each object point viewed from different directions. Object representation with a surface light field provides a nice structure for 3D photography. This paper presents a feature-based morphing technique for two objects equipped with surface light fields. The technique consists of geometry morphing and in-between light field mapping. Geometry morphing is accomplished by 3D mesh morphing, where we introduce a vertex merging technique to generate a simpler metamesh. In in-between light field mapping, an in-between object is rendered by extracting necessary fragments from input surface light fields. We also propose an acceleration technique for rendering an in-between object. Experimental results with real and synthetic data show natural and plausible morphing between objects with surface light fields. The proposed morphing technique can be used as an editing tool for 3D photography.

Feature detection is important in various mesh processing techniques, such as mesh editing, mesh morphing, mesh compression, and mesh signal processing. In spite of much research in computer vision, automatic feature detection even for images still remains a difficult problem. To avoid this difficulty, semi-automatic or interactive techniques for image feature detection have been investigated. In this paper, we propose a geometric snake as an interactive tool for feature detection on a 3D triangular mesh. A geometric snake is an extension of an image snake, which is an active contour model that slithers from its initial position specified by the user to a nearby feature while minimizing an energy functional. To constrain the movement of a geometric snake onto the surface of a mesh, we use the parameterization of the surrounding region of a geometric snake. Although the definition of a feature may vary among applications, we use the normal changes of faces to detect features on a mesh. Experimental results demonstrate that geometric snakes can successfully capture nearby features from user-specified initial positions.