Lingfeng Wang

Edge-Directed Single-Image Super-Resolution Via Adaptive Gradient Magnitude Self-Interpolation

Super-resolution from a single image plays an important role in many computer vision systems. However, it is still a challenging task, especially in preserving local edge structures. To construct high-resolution images while preserving the sharp edges, an effective edge-directed super-resolution method is presented in this paper. An adaptive self-interpolation algorithm is first proposed to estimate a sharp high-resolution gradient field directly from the input low-resolution image. The obtained high-resolution gradient is then regarded as a gradient constraint or an edge-preserving constraint to reconstruct the high-resolution image. Extensive results have shown both qualitatively and quantitatively that the proposed method can produce convincing super-resolution images containing complex and sharp features, as compared with the other state-of-the-art super-resolution algorithms.

Robust level set image segmentation via a local correntropy-based K-means clustering

It is still a challenging task to segment real-world images, since they are often distorted by unknown noise and intensity inhomogeneity. To address these problems, we propose a novel segmentation algorithm via a local correntropy-based K-means (LCK) clustering. Due to the correntropy criterion, the clustering algorithm can decrease the weights of the samples that are away from their clusters. As a result, LCK based clustering algorithm can be robust to the outliers. The proposed LCK clustering algorithm is incorporated into the region-based level set segmentation framework. The iteratively re-weighted algorithm is used to solve the LCK based level set segmentation method. Extensive experiments on synthetic and real images are provided to evaluate our method, showing significant improvements on both noise sensitivity and segmentation accuracy, as compared with the state-of-the-art approaches.

Fast image upsampling via the displacement field.

In this paper, we present a fast image upsampling method within a two-scale framework to ensure the sharp construction of upsampled image for both large-scale edges and small-scale structures. In our approach, the low-frequency image is recovered via a novel sharpness preserving interpolation technique based on a well-constructed displacement field, which is estimated by a cross-resolution sharpness preserving model. Within this model, the distances of pixels on edges are preserved, which enables the recovery of sharp edges in the high-resolution result. Likewise, local high-frequency structures are reconstructed via a sharpness preserving reconstruction algorithm. Extensive experiments show that our method outperforms current state-of-the-art approaches, based on quantitative and qualitative evaluations, as well as perceptual evaluation by a user study. Moreover, our approach is very fast so as to be practical for real applications.

Discriminant Tensor Spectral–Spatial Feature Extraction for Hyperspectral Image Classification

We propose to integrate spectral-spatial feature extraction and tensor discriminant analysis for hyperspectral image classification. First, we apply remarkable spectral-spatial feature extraction approaches in the hyperspectral cube to extract a feature tensor for each pixel. Then, based on class label information, local tensor discriminant analysis is used to remove redundant information for subsequent classification procedure. The approach not only extracts sufficient spectral-spatial features from original hyperspectral images but also gets better feature representation owing to tensor framework. Comparative results on two benchmarks demonstrate the effectiveness of our method.

Visual Tracking Via Kernel Sparse Representation With Multikernel Fusion

It remains a challenging task to track an object robustly due to factors such as pose variation, illumination change, occlusion, and background clutter. In the past decades, a number of researchers have been attracted to tackling these difficulties, and they proposed many effective methods. Among them, sparse representation-based tracking method is a promising. While much success has been demonstrated, there are several issues that still need to be addressed. First, the introduction to trivial occlusion templates brings a high computational cost of this method. Second, the utilization of raw template object representation makes this method difficult to adopt sophisticated object features. To solve these problems, we consider the sparse representation problem in a kernel space and propose a kernel sparse representation (KSR)-based tracking algorithm. Under the kernel representation, it is not necessary to introduce trivial occlusion templates in order to reduce the computational cost. Furthermore, multikernel fusion allows our method to use multiple sophisticated object features, such as spatial color histogram and spatial gradient-orientation histogram, and let these features complement each other during the tracking process. Comparative experiments on challenging scenes demonstrate that our KSR-based tracking algorithm outperforms the state-of-the-art approaches in tracking accuracy.

Retargeted Least Squares Regression Algorithm

This brief presents a framework of retargeted least squares regression (ReLSR) for multicategory classification. The core idea is to directly learn the regression targets from data other than using the traditional zero-one matrix as regression targets. The learned target matrix can guarantee a large margin constraint for the requirement of correct classification for each data point. Compared with the traditional least squares regression (LSR) and a recently proposed discriminative LSR models, ReLSR is much more accurate in measuring the classification error of the regression model. Furthermore, ReLSR is a single and compact model, hence there is no need to train two-class (binary) machines that are independent of each other. The convex optimization problem of ReLSR is solved elegantly and efficiently with an alternating procedure including regression and retargeting as substeps. The experimental evaluation over a range of databases identifies the validity of our method.

Region-based image segmentation with local signed difference energy

Intensity inhomogeneity often causes considerable difficulties in image segmentation. To tackle this problem, we propose a new region-based level set method. The proposed method considers the local image information by describing it as a novel local signed difference (LSD) energy, which possesses both local separability and global consistency. The LSD energy term is integrated into an objective energy functional, which is minimized via a level set evolution process. Extensive experiments are performed to evaluate the proposed method, showing improvements in both accuracy and efficiency, as compared with the state-of-the-art approaches.

Manifold Regularized Local Sparse Representation for Face Recognition

Sparse representation-(or sparse coding)-based classification has been successfully applied to face recognition. However, it can become problematic in the presence of illumination variations or occlusions. In this paper, we propose a Manifold Regularized Local Sparse Representation (MRLSR) model to address such difficulties. The key idea behind the MRLSR method is that all coding vectors in sparse representation should be group sparse, which means holding the two properties of both individual sparsity and local similarity. As a consequence, the face recognition rate can be considerably improved. The MRLSR model is optimized by the modified homotopy algorithm, which keeps stable under different choices of the weighting parameter. Extensive experiments are performed on various face databases, which contain illumination variations and occlusions. We show that the proposed method outperforms the state-of-the-art approaches and provides the highest recognition rate.

Forward–Backward Mean-Shift for Visual Tracking With Local-Background-Weighted Histogram

Object tracking plays an important role in many intelligent transportation systems. Unfortunately, it remains a challenging task due to factors such as occlusion and target-appearance variation. In this paper, we present a new tracking algorithm to tackle the difficulties caused by these two factors. First, considering the target-appearance variation, we introduce the local-background-weighted histogram (LBWH) to describe the target. In our LBWH, the local background is treated as the context of the target representation. Compared with traditional descriptors, the LBWH is more robust to the variability or the clutter of the potential background. Second, to deal with the occlusion case, a new forward-backward mean-shift (FBMS) algorithm is proposed by incorporating a forward-backward evaluation scheme, in which the tracking result is evaluated by the forward-backward error. Extensive experiments on various scenarios have demonstrated that our tracking algorithm outperforms the state-of-the-art approaches in tracking accuracy.

MEAN-shift tracking algorithm with weight fusion strategy

In this paper, we propose a new Mean-shift algorithm to tackle some tracking difficulties, such as background clutter and partial occlusion. First, we compare all Mean-shift-like tracking algorithms, and indicate that the main difference among them is weight calculation. Then, a new fusion strategy is proposed to unify all weight calculation methods into a framework. Based on this framework, we propose a novel weight calculation method, which takes the candidate model into consideration as well as incorporates the local background. Extensive experiments are conducted to evaluate the proposed approach. Comparative experimental results indicate that the tracking accuracy is improved as compared with the state-of-the-arts.