Sujung Kim

Automatic white balance based on adaptive feature selection with standard illuminants

Automatic white balance is a key function to remove undesirable color cast in digital cameras. Assumption-based methods (e.g. gray world method, white patch method) are widely used for the AWB process of digital cameras because of their low computational costs. However, they are highly dependent on the validity of their assumption. To solve this problem, we propose an adaptive feature extraction scheme for finding neutral color. The proposed method uses three features which are complementary and computationally effective. Moreover, we use the standard illuminants for evaluating an estimated illuminant not only to improve the accuracy of illuminant estimation, but also to provide the AWB skip mode. The experimental results show that the proposed method outperforms conventional assumption-based methods and arrives at reasonable color compensation over a wide range of scenes.

Fast computation of a visual hull

Two techniques for the fast computation of a visual hull without simplification are proposed. First, we tackle the most time consuming step for finding the intersections between projected rays and silhouette boundaries. We use the chain coding representation of silhouette boundaries for fast searching and computing with sub-pixel accuracy. Second, we analyze 3D-2D projection and back-projection relations and formulate them as 1D homographies. This formulation reduces computational cost and ambiguity that can be caused by measurement errors in the back-projection of 2D intersections to 3D. Furthermore, we show that the formulation is not limited to the projective space but also useful in the affine space. We generalize our techniques to an arbitrary 3D ray, so that the proposed method is directly applicable to both volume-based and surface-based visual hull methods. In our simulations, we compare the proposed algorithm with the state-of-the-art methods and show its advantages in terms of computational cost.

Dynamic range compression based on statistical analysis

In this paper, we propose a computationally efficient space-varying dynamic range compression method based on statistical analysis. The proposed method reveals fine details without affecting already well-balanced region. For this goal, we introduce an effective segmentation method for distinguishing a damaged region from a well-balanced region. The statistical analysis and indirect use of the characteristics in segmented regions are introduced. In addition, to automate the full chain of dynamic range compression, we propose an enhancement condition which determines whether a certain image needs to be processed or not.

Bi-DCT: DCT-based Local Binary Descriptor for Dense Stereo Matching

In this letter, we present a novel DCT-based local binary descriptor for the dense matching of multiple view stereo (MVS). Recently, although much progress has been made in the field of MVS, a key component of which, i.e., dense matching, is still a challenging task because it has two difficult issues: robust matching over non-salient regions (e.g., lines and textureless regions) and fast matching of a large number of pixels. To deal with these issues effectively, in the proposed dense descriptor, 2D DCT-based local features are utilized to achieve high discriminative power even for the non-salient regions. A binary representation is adopted to increase the matching performance as well as accelerate the matching speed via the Hamming distance. In addition, the discriminability of binarized vectors is further improved by a space-frequency pooling scheme. Through extensive experiments on the benchmark datasets for MVS, we demonstrate the superiority of the proposed descriptor over the state-of-the-art descriptors in terms of accuracy and efficiency.

Multiple View Stereo by Reflectance Modeling

Multiple view stereo is typically formulated as an optimization problem over a data term and a prior term. The data term is based on the consistency of images projected on a hypothesized surface. This consistency is based on a measure denoted a visual metric, e.g. normalized cross correlation. Here we argue that a visual metric based on a surface reflectance model should be founded on more observations than the degrees of freedom (dof) of the reflectance model. If (partly) specular surfaces are to be handled, this implies a model with at least two dof. In this paper, we propose to construct visual metrics of more than one dof using the DAISY methodology, which compares favorably to the state of the art in the experiments carried out. These experiments are based on a novel data set of eight scenes with diffuse and specular surfaces and accompanying ground truth. The performance of six different visual metrics based on the DAISY framework is investigated experimentally, addressing whether a visual metric should be aggregated from a set of minimal images, which dof is best, or whether a combination of one and two dof should be used. Which metric performs best is dependent on the viewed scene, although there are clear tendencies for the two dof minimal metric to be the preferred one.

Image-based object reconstruction using run-length representation

This paper presents an image-based object reconstruction with a low memory footprint using run-length representation. While conventional volume-based approaches, which utilize voxels as primitives, are intuitive and easy to manipulate 3D data, they require a large amount of memory and computation during the reconstruction process. To overcome these burdens, this paper uses 3D runs to represent a 3D object and reconstructs each 3D run from multi-view silhouettes with a small amount of memory. The proposed geometry reconstruction is also computationally inexpensive, as it processes multiple voxels simultaneously. And for the compatibility with the conventional data formats, generation of polygonal 3D meshes from the reconstructed 3D runs is proposed as well. Lastly, texture mapping is proposed to additionally reduce the amount of memory for object reconstruction. The proposed reconstruction scheme has been simulated using various types of multi-view datasets. The results show that the proposed method performs object reconstruction with a smaller amount of memory and computation than voxel-based approaches. An image-based object reconstruction using run-length representation is proposed.A fast geometry reconstruction by rectifying images is proposed.A 3D mesh generation algorithm from the reconstructed 3D runs is proposed.View dependent texture mapping algorithm using a color palette is proposed.

Visual hull-based prediction framework for 3D object coding

In this paper, we propose a visual hull-based predictive framework for compressing depth data of a 3D object. Given a 3D object represented by layered depth image, we obtain a visual hull as a model. Then we compute and encode prediction residuals between the visual hull and the original 3D object. We first explain the merits of a visual hull in terms of prediction accuracy, computational costs, and efficient encoding of a model, and then we show the overall process of the proposed method.

Subblock sum matching algorithm for block-based interframe coding

A fast block-matching algorithm for motion estimation is described for interframe image coding. Utilizing the fact that the motion is searched by a macroblock basis while the compression is performed by a block basis in many video compression standards, the proposed algorithm produces high PSNR and fast computation while keeping the bit-rate as low as those of conventional fast algorithms. The proposed algorithm uses the subblock sum as a matching parameter and the reversed square sum as a matching criterion. The new matching parameter and criterion reduce the total number of computations for block matching and also keep the quality of the decompressed image. The computational complexity and the compression performance of the proposed algorithm are compared with those of other block-matching algorithms.

Surface normal vector force driven 3D object reconstruction via Graph-cut

Multi-view 3D surface reconstruction is typically formulated as minimizing an appropriate energy functional, which tends to find the empty set, i.e. an undesirable result, especially when cooperating only with a boundary term. So generally a regional term is employed, which indicates inside or outside of the surface. It is, however, the challenge because inherently there is no straightforward way to assign interior or exterior of the surface since photo-consistency on the surface boundary is the main source of geometric information. In this paper, we address this problem by proposing a surface normal vector force driven regional term which is a data-aware ballooning term and cast it into a Graph-cut framework. In experiments, we verify the superiority of the proposed algorithm on various multiple view data sets.