<span style="font-size: 9pt; font-family: &quot;Times New Roman&quot;; mso-fareast-font-family: 宋体; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">Focused on facial features localization on multi-view face arbitrarily rotated in plane, a novel detection algorithm based improved SVM is proposed. First, the face is located by the rotation invariant multi-view (RIMV) face detector and its pose in plane is corrected by rotation. After the searching ranges of the facial features are determined, the crossing detection method which uses the brow-eye and nose-mouth features and the improved SVM detectors trained by large scale multi-view facial features examples is adopted to find the candidate eye, nose and mouth regions,. Based on the fact that the window region with higher value in the SVM discriminant function is relatively closer to the object, and the same object tends to be repeatedly detected by near windows, the candidate eyes, nose and mouth regions are filtered and merged to refine their location on the multi-view face. Experiments show that the algorithm has very good accuracy and robustness to the facial features localization with expression and arbitrary face pose in complex background.</span>

The Editor-in-Chief has retracted this article [1], which was published as part of special issue "Multi-source Weak Data Management using Big Data", because its content has been duplicated from an unpublished manuscript authored by Laeeq Aslam, Muhammad Amir, Ijaz Mansoor Qureshi and Sharjeel Abid Butt without permission. In addition, there is evidence of figure duplication without appropriate permission, as well as evidence suggesting authorship manipulation and an attempt to subvert the peer review process. Author Chao Xiong agrees to this retraction. Authors Yuan Li, Xia Han, Ruxi Xiang, FengYou He, and Hongwei Du have not responded to correspondence about this retraction.

To improve the image quality and compensate deficiencies of haze removal, we presented a novel fusion method. By analyzing the darkness channel of each method, the effective darkness channel model that takes the correlation information of each darkness channel into account was constructed. This method was used to estimate the transmission map of the input image, and refined by the modified guided filter in order to further improve the image quality. Finally, the radiance image was restored by combining the monochrome atmospheric scattering model. Experimental results show that the proposed method not only effectively remove the haze of the image, but also outperform the other haze removal methods.

We proposed a new similarity measure method which could be used within the framework of probability trackers based on the particle filter to track the object. In the particle filter framework, the state transition model is chosen as the simple second-order auto-regressive mode, and the state measure is chosen as the BBRS (Blocks-Bin-Ratio-Similarity). The BBRS considers both the spatial information and the ratios between bin values of histograms. The simulation experiment was made to compare with the similarity measures based on color-histogram and the Bin-Ratio Similarity, and the tracking results in the videos showed that the BBRS-based similarity measure was more discriminative than the color histogram similarity measure in robustly tracking the object in challenging videos where the appearance and motion are drastically changing over time.

No abstract