A fast color image enhancement algorithm based on Max Intensity Channel

Functions of Traditional Chinese Medicine Combined with Recombinant Human Interferon α2b in Cervical Intraepithelial Neoplasias Patients

Cervical cancer is a common malignant neoplasm in women, and its incidence is increasing year by year. This study explored the effects of traditional Chinese medicine combined with recombinant human interferon α2b in cervical cancer patients. 178 cervical intraepithelial neoplasias (CIN) combined with high-risk HPV-positive patients from June 2017 to August 2020 were divided into the study group (n = 89 cases) and the control group (n = 89 cases) by the random number table method. Patients in the control group were treated with recombinant human interferon α2b, and the study group was treated with traditional Chinese medicine (TCM) on the basis of the control group. After treatment, the recurrence rate in the study group was significantly decreased while the human papillomavirus (HPV) negative conversion rate was significantly increased. 3 months after treatment, the TCM symptom scores in the study group were lower than in the control group. Moreover, serum levels of inflammatory factors decreased in both groups, and the decrease was more significant in the study group. After treatment, the ultrasound parameters were significantly decreased in the study group than in the control group. In conclusion, traditional Chinese medicine combined with recombinant human interferon α2b in cervical cancer patients could effectively improve the negative conversion rate of HPV infection, the level of inflammatory factors, reduce the degree of cervical erosion, and enhance the immunity of patients with high safety and significantly improve the quality of life.

An auxiliary gaze point estimation method based on facial normal

Considering the main disadvantage of the existing gaze point estimation methods which restrict user's head movement and have potential injury on eyes, we propose a gaze point estimation method based on facial normal and binocular vision. Firstly, we calibrate stereo cameras to determine the extrinsic and intrinsic parameters of the cameras; Secondly, face is quickly detected by Viola-Jones framework and the center position of the two irises can be located based on integro-differential operators; The two nostrils and mouth are detected based on the saturation difference and their 2D coordinates can be calculated; Thirdly, the 3D coordinates of these five points are obtained by stereo matching and 3D reconstruction; After that, a plane fitting algorithm based on least squares is adopted to get the approximate facial plane, then, the normal via the midpoint of the two pupils can be figured out; Finally, the point-of-gaze can be obtained by getting the intersection point of the facial normal and the computer screen. Experimental results confirm the accuracy and robustness of the proposed method.

Learning based particle filtering object tracking for visible-light systems

We propose a novel object tracking framework based on online learning scheme that can work robustly in challenging scenarios. Firstly, a learning-based particle filter is proposed with color and edge-based features. We train a. support vector machine (SVM) classifier with object and background information and map the outputs into probabilities, then the weight of particles in a particle filter can be calculated by the probabilistic outputs to estimate the state of the object. Secondly, the tracking loop starts with Lucas-Kanade (LK) affine template matching and follows by learning-based particle filter tracking. Lucas-Kanade method estimates errors and updates object template in the positive samples dataset, and learning-based particle filter tracker will start if the LK tracker loses the object. Finally, SVM classifier evaluates every tracked appearance to update the training set or restart the tracking loop if necessary. Experimental results show that our method is robust to challenging light, scale and pose changing, and test on eButton image sequence also achieves satisfactory tracking performance.

Fast single image haze removal via local atmospheric light veil estimation

In this study, a novel single-image based dehazing framework is proposed to remove haze artifacts from images through local atmospheric light estimation. We use a novel strategy based on a physical model where the extreme intensity of each RGB pixel is used to define an initial atmospheric veil (local atmospheric light veil). Across bilateral filter is applied to each veil to achieve both local smoothness and edge preservation. A transmission map and a reflection component of each RGB channel are constructed from the physical atmospheric scattering model. The proposed approach avoids adverse effects caused by the error in estimating the global atmospheric light. Experimental results on outdoor hazy images demonstrate that the proposed method produces image output with satisfactory visual quality and color fidelity. Our comparative study demonstrates a higher performance of our method over several state-of-the-art methods.