Herein, a Harris corner detection algorithm is proposed based on the concepts of iterated threshold segmentation and adaptive iterative threshold (AIT–Harris), and a stepwise local stitching algorithm is used to obtain wide-field ultrasound (US) images. Cone-beam computer tomography (CBCT) and US images from 9 cervical cancer patients and 1 prostate cancer patient were examined. In the experiment, corner features were extracted based on the AIT–Harris, Harris, and Morave algorithms. Accordingly, wide-field ultrasonic images were obtained based on the extracted features after local stitching, and the corner matching rates of all tested algorithms were compared. The accuracies of the drawn contours of organs at risk (OARs) were compared based on the stitched ultrasonic images and CBCT. The corner matching rate of the Morave algorithm was compared with those obtained by the Harris and AIT–Harris algorithms, and paired sample t tests were conducted ( t = 6.142, t = 31.859, P < .05). The results showed that the differences were statistically significant. The average Dice similarity coefficient between the automatically delineated bladder region based on wide-field US images and the manually delineated bladder region based on ground truth CBCT images was 0.924, and the average Jaccard coefficient was 0.894. The proposed algorithm improved the accuracy of corner detection, and the stitched wide-field US image could modify the delineation range of OARs in the pelvic cavity.

Non-uniform temperature distributions in ovens affect the quality of baked goods and raise concerns regarding food safety. Traditional research on oven performance focuses on the heating mechanisms in simulated ovens and does not involve quantitative analysis of baked goods. This study proposes a model for calculating the uniformity of baked goods based on image processing technology, to quantitatively assess the distribution uniformity of different baked states and digitally express the internal temperature field distribution in the oven. First, the image of the baked goods is captured using a digital camera. Then, it is preprocessed to obtain an image containing only the region showing the baked goods. Subsequently, the simple linear iterative clustering segmentation algorithm is employed to extract the baked states. Finally, a meshing model is applied to calculate the distribution variance of each baked state, and the evaluation index describing the uniformity of the baked goods image is obtained by normalizing the variance in the distribution. The simple linear iterative clustering segmentation algorithm expresses the color features of acquired baked goods images in the form of superpixels. By determining the distribution and proportion of different baked states, the proposed method can qualitatively and quantitatively reflect the spatial distribution of the temperature fields inside the oven corresponding to the baked goods image. This provides a strong basis for further evaluation of the heat distribution field inside the oven. INDEX TERMS Image processing, meshing model, oven temperature field, simple linear iterative clustering, uniformity evaluation.

A novel flotation froth image segmentation based on threshold level set method is put forward in view of the problem of over-segmentation and under-segmentation which occurs when the existing method segmented the flotation froth images. Firstly, the proposed method adopts histogram equalization to improve the contrast of the image, and then chooses the upper threshold and lower threshold from grey value of histogram of the image equalization, and complete image segmentation using the level set method. In this paper, the model which integrates edge with region level set model is utilized, and the speed energy term is introduced to segment the target. Experimental results show that the proposed method has better segmentation results and higher segmentation efficiency on the images with under-segmentation and incorrect segmentation, and it is meaningful for ore dressing industrial.

Disruptive behavior by passengers delayed at airport terminals not only affects personal safety but also reduces civil aviation efficiency and passenger satisfaction. This study investigated the causal mechanisms of disruptive behavior by delayed passengers in three aspects: environmental, managerial, and personal. Data on flight delays at Shenzhen Airport in 2018 were collected and analyzed. The main factors leading to disruptive behavior by delayed passengers were identified, and an early warning model for disturbances was developed using multiple logistic regression and a back-propagation(BP) neural network. The results indicated that the proposed model and method were feasible. Compared to the logistic regression model, the BP neural network model had advantages in predicting disturbances by delayed passengers, showing higher prediction accuracy. The BP network weight analysis method was used to obtain the influence weight of each factor on behavior change of delayed passengers. The influence weight of different factors was obtained, providing an assistant decision-making method to address disruption from flight-delayed passengers.

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