Huisi Wu

Resizing by symmetry-summarization

Image resizing can be achieved more effectively if we have a better understanding of the image semantics. In this paper, we analyze the translational symmetry, which exists in many real-world images. By detecting the symmetric lattice in an image, we can summarize, instead of only distorting or cropping, the image content. This opens a new space for image resizing that allows us to manipulate, not only image pixels, but also the semantic cells in the lattice. As a general image contains both symmetry & non-symmetry regions and their natures are different, we propose to resize symmetry regions by summarization and non-symmetry region by warping. The difference in resizing strategy induces discontinuity at their shared boundary. We demonstrate how to reduce the artifact. To achieve practical resizing applications for general images, we developed a fast symmetry detection method that can detect multiple disjoint symmetry regions, even when the lattices are curved and perspectively viewed. Comparisons to state-of-the-art resizing techniques and a user study were conducted to validate the proposed method. Convincing visual results are shown to demonstrate its effectiveness.

Effective Alternating Direction Optimization Methods for Sparsity-Constrained Blind Image Deblurring

Single-image blind deblurring for imaging sensors in the Internet of Things (IoT) is a challenging ill-conditioned inverse problem, which requires regularization techniques to stabilize the image restoration process. The purpose is to recover the underlying blur kernel and latent sharp image from only one blurred image. Under many degraded imaging conditions, the blur kernel could be considered not only spatially sparse, but also piecewise smooth with the support of a continuous curve. By taking advantage of the hybrid sparse properties of the blur kernel, a hybrid regularization method is proposed in this paper to robustly and accurately estimate the blur kernel. The effectiveness of the proposed blur kernel estimation method is enhanced by incorporating both the L1-norm of kernel intensity and the squared L2-norm of the intensity derivative. Once the accurate estimation of the blur kernel is obtained, the original blind deblurring can be simplified to the direct deconvolution of blurred images. To guarantee robust non-blind deconvolution, a variational image restoration model is presented based on the L1-norm data-fidelity term and the total generalized variation (TGV) regularizer of second-order. All non-smooth optimization problems related to blur kernel estimation and non-blind deconvolution are effectively handled by using the alternating direction method of multipliers (ADMM)-based numerical methods. Comprehensive experiments on both synthetic and realistic datasets have been implemented to compare the proposed method with several state-of-the-art methods. The experimental comparisons have illustrated the satisfactory imaging performance of the proposed method in terms of quantitative and qualitative evaluations.

Tensor Voting Guided Mesh Denoising

Mesh denoising is imperative for improving imperfect surfaces acquired by scanning devices. The main challenge is to faithfully retain geometric features and avoid introducing additional artifacts when removing noise. Unlike the existing mesh denoising techniques that focus only on either the first-order features or high-order differential properties, our approach exploits the synergy when facet normals and quadric surfaces are integrated to recover a piecewise smooth surface. In specific, we vote on surface normal tensors from robust statistics to guide the creation of consistent subneighborhoods subsequently used by moving least squares (MLS). This voting naturally leads to a conceptually simple way that gives a unified mesh-denoising framework for not only handling noise but also enabling the recovering of surfaces with both sharp and small-scale features. The effectiveness of our framework stems from: 1) the multiscale tensor voting that avoids the influence from noise; 2) the effective energy minimization strategy to searching the consistent subneighborhoods; and 3) the piecewise MLS that fully prevents the side effects from different subneighborhoods during surface fitting. Our framework is direct, practical, and easy to understand. Comparisons with the state-of-the-art methods demonstrate its outstanding performance on feature preservation and artifact suppression.

Midsagittal plane extraction from brain images based on 3D SIFT.

Midsagittal plane (MSP) extraction from 3D brain images is considered as a promising technique for human brain symmetry analysis. In this paper, we present a fast and robust MSP extraction method based on 3D scale-invariant feature transform (SIFT). Unlike the existing brain MSP extraction methods, which mainly rely on the gray similarity, 3D edge registration or parameterized surface matching to determine the fissure plane, our proposed method is based on distinctive 3D SIFT features, in which the fissure plane is determined by parallel 3D SIFT matching and iterative least-median of squares plane regression. By considering the relative scales, orientations and flipped descriptors between two 3D SIFT features, we propose a novel metric to measure the symmetry magnitude for 3D SIFT features. By clustering and indexing the extracted SIFT features using a k-dimensional tree (KD-tree) implemented on graphics processing units, we can match multiple pairs of 3D SIFT features in parallel and solve the optimal MSP on-the-fly. The proposed method is evaluated by synthetic and in vivo datasets, of normal and pathological cases, and validated by comparisons with the state-of-the-art methods. Experimental results demonstrated that our method has achieved a real-time performance with better accuracy yielding an average yaw angle error below 0.91° and an average roll angle error no more than 0.89°.

Automatic Leaf Recognition from a Big Hierarchical Image Database

Automatic plant recognition has become a research focus and received more and more attentions recently. However, existing methods usually only focused on leaf recognition from small databases that usually only contain no more than hundreds of species, and none of them reported a stable performance in either recognition accuracy or recognition speed when compared with a big image database. In this paper, we present a novel method for leaf recognition from a big hierarchical image database. Unlike the existing approaches, our method combines the textural gradient histogram with the shape context to form a more distinctive feature for leaf recognition. To achieve efficient leaf image retrieval, we divided the big database into a set of subsets based on mean‐shift clustering on the extracted features and build hierarchical k‐dimensional trees (KD‐trees) to index each cluster in parallel. Finally, the proposed parallel indexing and searching schemes are implemented with MapReduce architectures. Our method is evaluated with extensive experiments on different databases with different sizes. Comparisons to state‐of‐the‐art techniques were also conducted to validate the proposed method. Both visual results and statistical results are shown to demonstrate its effectiveness.

Image Retrieval Based on Saliency Attention

The feature extraction is the most critical step in image retrieval. Among various local feature extraction methods, scale-invariant feature transform (SIFT) has been proven to be the most robust local invariant feature descriptor, which is widely used in the field of image matching and retrieval. However, the SIFT algorithm has a disadvantage that the algorithm will produce a large number of feature points and is not suited for widely using in the field of image retrieval. Firstly, a novel significant measure algorithm is proposed in this paper, and the regions of interest in images are obtained. Then, SIFT features are extracted from salient regions, reducing the number of SIFT features. Our algorithm also abstracts color features from salient regions, and this method overcomes SIFT algorithm’s drawback that could not reflect image’s color information. The experiments demonstrate that the integrated visual saliency analysis-based feature selection algorithm provides significant benefits both in retrieval accuracy and in speed.

Cartoon image segmentation based on improved SLIC superpixels and adaptive region propagation merging

This paper present a novel algorithm for cartoon image segmentation based on the simple linear iterative clustering (SLIC) superpixels and adaptive region propagation merging. To break the limitation of the original SLIC algorithm in confirming to image boundaries, this paper proposed to improve the quality of the superpixels generation based on the connectivity constraint. To achieve efficient segmentation from the superpixels, this paper employed an adaptive region propagation merging algorithm to obtain independent segmented object. Compared with the pixel-based segmentation algorithms and other superpixel-based segmentation methods, the method proposed in this paper is more effective and more efficient by determining the propagation center adaptively. Experiments on abundant cartoon images showed that our algorithm outperforms classical segmentation algorithms with the boundary-based and region-based criteria. Furthermore, the final cartoon image segmentation results are also well consistent with the human visual perception.

Fast and Robust Leaf Recognition Based on Rotation Invariant Shape Context

As leaf images can be captured more and more conveniently, automatic leaf recognition has been the key to help us identify different kinds of plant. However, fast and robust leaf recognition is still an unsolved problem, because the leaf images can be collected among different growing stages and with different shapes and colors. In this paper, we present a fast and robust method for leaf recognition by identifying leaves based on rotation invariant shape context (RISC) and summed squared differences (SSD) color matching. Unlike the existing shape context, which is only scale and translational invariant, our proposed method can recognize the leaves with different rotational angles, namely rotation invariant. To distinguish plants having the same shape context but with different colors, we use SSD color matching to measure the similarity of different leaves. The combination of RISC and SSD makes our leaf recognition method faster and much more robust than conventional shape context method. In our experiment, we obtained convincing results to demonstrate its effectiveness.

Cardiac motion recovery using an incompressible B-solid model.

B-spline based deformable model is commonly used in recovering three-dimensional (3D) cardiac motion from tagged MRI due to its compact description, localized continuity and control flexibility. However, existing approaches usually ignore an important well-known fact that myocardial tissue is incompressible. In this paper, we propose to reconstruct 3D cardiac motion from tagged MRI using an incompressible B-solid model. We demonstrate that cardiac motion recovery can be achieved more with greater accuracy by considering both smoothness and incompressibility of the myocardium. Specifically, our incompressible B-solid model is formulated as a 3D tensor product of B-splines, where each piece of B-spline represents a smooth and divergence-free displacement field of myocardium with respect to radial, longitudinal and circumferential direction, respectively. We further formulate the fitting of the incompressible B-solid model as an optimization problem and solve it with a two-stage algorithm. Finally, the 3D myocardium strains are obtained from the reconstructed incompressible displacement fields and visualized in a comprehensive way. The proposed method is evaluated on both synthetic and in vivo human datasets. Comparisons with state-of-the-art methods are also conducted to validate the proposed method. Experimental results demonstrate that our method has a higher accuracy and more stable volume-preserving ability than previous methods, yielding an average displacement error of 0.21 mm and a Jacobian determinant mean of 1.029.

An Analog Gamma Correction Scheme for High Dynamic Range CMOS Logarithmic Image Sensors

In this paper, a novel analog gamma correction scheme with a logarithmic image sensor dedicated to minimize the quantization noise of the high dynamic applications is presented. The proposed implementation exploits a non-linear voltage-controlled-oscillator (VCO) based analog-to-digital converter (ADC) to perform the gamma correction during the analog-to-digital conversion. As a result, the quantization noise does not increase while the same high dynamic range of logarithmic image sensor is preserved. Moreover, by combining the gamma correction with the analog-to-digital conversion, the silicon area and overall power consumption can be greatly reduced. The proposed gamma correction scheme is validated by the reported simulation results and the experimental results measured for our designed test structure, which is fabricated with 0.35 μm standard complementary-metal-oxide-semiconductor (CMOS) process.