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We propose an efficient algorithm for removing shadows of moving vehicles caused by non-uniform distributions of light reflections in the daytime. This paper presents a brand-new and complete structure in feature combination as well as analysis for orientating and labeling moving shadows so as to extract the defined objects in foregrounds more easily in each snapshot of the original files of videos which are acquired in the real traffic situations. Moreover, we make use of Gaussian Mixture Model (GMM) for background removal and detection of moving shadows in our tested images, and define two indices for characterizing non-shadowed regions where one indicates the characteristics of lines and the other index can be characterized by the information in gray scales of images which helps us to build a newly defined set of darkening ratios (modified darkening factors) based on Gaussian models. To prove the effectiveness of our moving shadow algorithm, we carry it out with a practical application of traffic flow detection in ITS (Intelligent Transportation System)-vehicle counting. Our algorithm shows the faster processing speed, 13.84 ms/frame, and can improve the accuracy rate in 4% ∼10% for our three tested videos in the experimental results of vehicle counting.

We present in this paper a modified independent component analysis (mICA) based on the conditional entropy to discriminate unsorted independent components. We make use of the conditional entropy to select an appropriate subset of the ICA features with superior capability in classification and apply support vector machine (SVM) to recognizing patterns of human and nonhuman. Moreover, we use the models of background images based on Gaussian mixture model (GMM) to handle images with complicated backgrounds. Also, the color-based shadow elimination and head models in ellipse shapes are combined to improve the performance of moving objects extraction and recognition in our system. Our proposed tracking mechanism monitors the movement of humans, animals, or vehicles within a surveillance area and keeps tracking the moving pedestrians by using the color information in HSV domain. Our tracking mechanism uses the Kalman filter to predict locations of moving objects for the conditions in lack of color information of detected objects. Finally, our experimental results show that our proposed approach can perform well for real-time applications in both indoor and outdoor environments.

In this paper, we develop a vision based obstacle detection system by utilizing our proposed fisheye lens inverse perspective mapping (FLIPM) method. The new mapping equations are derived to transform the images captured by the fisheye lens camera into the undistorted remapped ones under practical circumstances. In the obstacle detection, we make use of the features of vertical edges on objects from remapped images to indicate the relative positions of obstacles. The static information of remapped images in the current frame is referred to determining the features of source images in the searching stage from either the profile or temporal IPM difference image. The profile image can be acquired by several processes such as sharpening, edge detection, morphological operation, and modified thinning algorithms on the remapped image. The temporal IPM difference image can be obtained by a spatial shift on the remapped image in the previous frame. Moreover, the polar histogram and its post-processing procedures will be used to indicate the position and length of feature vectors and to remove noises as well. Our obstacle detection can give drivers the warning signals within a limited distance from nearby vehicles while the detected obstacles are even with the quasi-vertical edges.

BACKGROUND To date, a reliable, validated pain-level assessment approach to evaluate consciousness status has yet to be established. With appropriate algorithms, computers can learn to detect patterns and associations in large datasets. OBJECTIVE This study aimed to apply machine learning to electrocardiography (ECG) waveforms to create an algorithm to predict and monitor regular uterine contraction cycle-induced labor pain. METHODS Pregnant women undergoing natural spontaneous delivery (NSD) with regular uterine contraction pain were recruited from National Taiwan University Hospital for prospective data collection and cross-sectional analysis. Machine learning was used to fine-tune a nociception-related algorithm (NoP) based initially on the complex analysis of features in high-fidelity ECG waveform recordings in 4 pregnant women. The algorithm was then validated in another 12 pregnant women. Data on uterine contractions were collected as raw data from tocometry and ECG waveforms by re-cording simultaneously using the same computer. Every uterine contraction cycle with VAS pain score was also confirmed and recorded by an investigating nurse for further analysis. RESULTS The machine learning model that most accurately predicted pain was the XGBoost model, which exhibited the highest ROC (0.98), followed by the random forest and GBDT models with an ROC of 0.89. NoP threshold values and NoP indexes were the highest and most important features in the XGBoost models. CONCLUSIONS The newly developed algorithm supports obstetricians in clinical practice by providing early detection of alterations in ECG waveforms during uterine contraction cycles. The integration of ECG systems should be considered in artificial intelligence (AI) models for future parturient care.