Hai-Miao Hu

Naturalness Preserved Enhancement Algorithm for Non-Uniform Illumination Images

Image enhancement plays an important role in image processing and analysis. Among various enhancement algorithms, Retinex-based algorithms can efficiently enhance details and have been widely adopted. Since Retinex-based algorithms regard illumination removal as a default preference and fail to limit the range of reflectance, the naturalness of non-uniform illumination images cannot be effectively preserved. However, naturalness is essential for image enhancement to achieve pleasing perceptual quality. In order to preserve naturalness while enhancing details, we propose an enhancement algorithm for non-uniform illumination images. In general, this paper makes the following three major contributions. First, a lightness-order-error measure is proposed to access naturalness preservation objectively. Second, a bright-pass filter is proposed to decompose an image into reflectance and illumination, which, respectively, determine the details and the naturalness of the image. Third, we propose a bi-log transformation, which is utilized to map the illumination to make a balance between details and naturalness. Experimental results demonstrate that the proposed algorithm can not only enhance the details but also preserve the naturalness for non-uniform illumination images.

Region-Based Rate Control for H.264/AVC for Low Bit-Rate Applications

Rate control plays an important role in video coding. However, in the conventional rate control algorithms, the number and position of macroblocks (MBs) inside one basic unit for rate control is inflexible and predetermined. The different characteristics of the MBs are not fully considered. Also, there is no overall optimization of the coding of basic units. This paper proposes a new region-based rate control scheme for H.264/advanced video coding to improve the coding efficiency. The inter-frame information is explored to objectively divide one frame into multiple regions based on their rate-distortion (R-D) behaviors. The MBs with similar characteristics are classified into the same region, and the entire region, instead of a single MB or a group of contiguous MBs, is treated as a basic unit for rate control. A linear rate-quantization stepsize model and a linear distortion-quantization stepsize model are proposed to accurately describe the R-D characteristics for the region-based basic units. Moreover, based on the above linear models, an overall optimization model is proposed to obtain suitable quantization parameters for the region-based basic units. Experimental results demonstrate that the proposed region-based rate control approach can achieve both better subjective and objective quality by performing the rate control adaptively with the content, compared to the conventional rate control approaches.

A fast image dehazing algorithm based on negative correction

Dehazing is an important but difficult issue for image processing. Recently, many dehazing algorithms have been proposed based on the dark channel prior. However, these algorithms fail to achieve a good tradeoff between the dehazing performance and the computational complexity. Moreover, the perceptual quality of these algorithms can be further improved, especially for sky areas. Therefore, this paper firstly introduces the concept of negative correction inspired by the practical application of photographic developing and a fast image dehazing algorithm is accordingly proposed. Based on the observation of the photographic developing, we find that the contrast of images can be enlarged and their saturation can also be increased when their negative images (or reverse image) are rectified. Thus, instead of estimating the transmission map, the correction factor of negative is estimated and it is used to rectify the corresponding haze images. In order to suppress halos, a modified maximum-filter is proposed to limit the larger value of correction factor of local region. The experimental results demonstrate that the proposed algorithm can effectively remove hazes and maintain the naturalness of images. Moreover, the proposed algorithm can significantly reduce the computational complexity by 56.14% on average when compared with the state-of-the-art. We first introduce the concept of the negative correction of photographic developing into dehazing algorithm.Instead of estimating the transmission map, the correction factor of negative images is estimated and it is used to rectify the corresponding haze images.In order to suppress halos, a modified maximum-filter is proposed to limit the maximum value of modified correction factor of local region.The proposed algorithm can effectively remove hazes. It can not only maintain the naturalness of images, but also enhance the details of images. Moreover, it can significantly reduce the computational complexity.

A person re-identification algorithm by exploiting region-based feature salience

A region-based person re-identification algorithm is proposed to fully exploit the salience of features.Part-based feature extraction is proposed to adopt different features for different parts based on their characteristics.A salient color descriptor is proposed by considering color diversity between current region and surrounding regions.The proposed salient color descriptor can achieve a robust local color feature representation.Experimental results shows that our algorithm outperform the similar algorithms published in CVPR 2013, CVPR 2010, etc. Due to the changes of the pose and illumination, the appearances of the person captured in surveillance may have obvious variation. Different parts of persons will possess different characteristics. Applying the same feature extraction and description to all parts without differentiating their characteristics will result in poor re-identification performances. Therefore, a person re-identification algorithm is proposed to fully exploit region-based feature salience. Firstly, each person is divided into the upper part and the lower part. Correspondingly, a part-based feature extraction algorithm is proposed to adopt different features for different parts. Moreover, the features of every part are separately represented to retain their salience. Secondly, in order to accurately represent the color feature, the salient color descriptor is proposed by considering the color diversity between current region and its surrounding regions. The experimental results demonstrate that the proposed algorithm can improve the accuracy of person re-identification compared with the state-of-the-art algorithms.

Region-classification-based rate control for flicker suppression of I-frames in HEVC

In High Efficiency Video Coding (HEVC), the coding efficiency of I-frames is lower than P-frames and B-frames, which will cause the flicker artifact, especially in low bitrates applications. We propose a region-classification-based rate control for Coding Tree Units (CTUs) in I-frames to improve the reconstructed quality of I-frames to suppress the flicker artifact. The CTUs in I-frame are classified into three regions according to their motion vectors and complexity. When the bit budget of one I-frame is used up, the target bitrates for the remaining CTUs will be adjusted according to the regions they belong to, and the pixel-based unified rate-quantization (URQ) model is then used to calculate the QPs. Experimental results demonstrate that the proposed scheme can efficiently suppress the flicker artifacts and improve both the subjective and objective video quality when compared with the original scheme in HM9.0.

A hierarchal BoW for image retrieval by enhancing feature salience

Retrieving images with multiple features is an active research topic on boosting the performance of existing content-based image retrieval methods. The promising bags-of-words (BoW) models involve multiple features by applying feature fusion strategies in the early stage of image indexing. However, due to the different data forms of features, a simple joint may not guarantee a high retrieval performance. Moreover, a fused feature is not flexible enough to adapt to the variety of images. In order to avoid the submergence of feature salience, this letter proposes a hierarchal BoW to represent each feature in an individual codebook for obtaining the undisturbed ranks from each feature. Moreover, for feature salience enhancement, a query model based on ordinary-least-squared (OLS) regression is established for rank aggregation. The query model weighs each feature according to its retrieval performance and then selects the target images. The experimental results demonstrate that the proposed method improves the accuracy compared to the state-of-the-arts, meanwhile it maintains the stability.

Joint global-local information pedestrian detection algorithm for outdoor video surveillance

Our paper fully exploits global information to improve recognition performance.LBP extracted from image low-frequency part is used to suppress interference.Topology structure of human body is applied to LBP to make it discriminative.Our algorithm is suitable for small-scale pedestrian for outdoor surveillance. The pedestrian size is usually small in practical outdoor surveillances. The small-scale pedestrian detection for outdoor surveillances is an important but difficult issue due to the limited information and the background interference. According to human cognition, the global information is important for the pedestrian detection. Therefore, a joint global-local information pedestrian detection algorithm is proposed to fully exploit and utilize the global information. The LBP feature is explicitly extracted from the low-frequency component of original images, which are utilized as the global information to suppress the background interference and enrich the description of pedestrian. Moreover, a structure-LBP is proposed to apply the inherent topology structure of human body to LBP. The structure-LBP feature extracted from original images can achieve a more discriminative description of pedestrians compared with the original LBP. The experimental results demonstrate that the proposed algorithm can improve the overall recognition performance for the small-scale pedestrians.

A person re-identification algorithm by using region-based feature selection and feature fusion

In outdoor surveillance, person appearances captured by different cameras have obvious variations due to different poses and viewpoints, which affect the accuracy of person re-identification. In this paper, a person re-identification algorithm by using region-based feature selection and future fusion is proposed to divide one body into the upper region and the lower region. According to their different characteristics, each region adopts different kinds of features, which can efficiently reduce the negative impact from different poses and viewpoints. Moreover, since different features of one region may have different intrinsic meanings, during the feature fusion, different features of one region are separately represented instead of being comprehensively processed. The proposed feature fusion can make full use of the salience of different features. The experimental results demonstrate that the proposed algorithm improves the accuracy of person re-identification compared with the state of the art.

Perceptual hash-based feature description for person re-identification

Abstract Person re-identification is one of the most important and challenging problems in video surveillance systems. For person re-identification, feature description is a fundamental problem. While many approaches focus on exploiting low-level features to describe person images, most of them are not robust enough to illumination and viewpoint changes. In this paper, we propose a simple yet effective feature description method for person re-identification. Starting from low-level features, the proposed method uses perceptual hashing to binarize low-level feature maps and combines several feature channels for feature encoding. Then, an image pyramid is built, and three regional statistics are computed for hierarchical feature description. To some extent, the perceptual hash algorithm (PHA) can encode invariant macro structures of person images to make the representation robust to both illumination and viewpoint changes. On the other hand, while a rough hashing may be not discriminative enough, the combination of several different feature channels and regional statistics is able to exploit complementary information and enhance the discriminability. The proposed approach is evaluated on seven major person re-identification datasets. The results of comprehensive experiments show the effectiveness of the proposed method and notable improvements over the state-of-the-art approaches.

Pedestrian detection based on hierarchical co-occurrence model for occlusion handling

In pedestrian detection, occlusions are typically treated as an unstructured source of noise and explicit models have lagged behind those for object appearance, which will result in degradation of detection performance. In this paper, a hierarchical co-occurrence model is proposed to enhance the semantic representation of a pedestrian. In our proposed hierarchical model, a latent SVM structure is employed to model the spatial co-occurrence relations among the parent-child pairs of nodes as hidden variables for handling the partial occlusions. Moreover, the visibility statuses of the pedestrian can be generated by learning co-occurrence relations from the positive training data with large numbers of synthetically occluded instances. Finally, based on the proposed hierarchical co-occurrence model, a pedestrian detection algorithm is implemented to incorporate visibility statuses by means of a Random Forest ensemble. The experimental results on three public datasets demonstrate the log-average miss rate of the proposed algorithm has 5% improvement for pedestrians with partial occlusions compared with the state-of-the-arts.