As a global feature of fingerprints, the orientation field is very important for automatic fingerprint recognition. Many algorithms have been proposed for orientation field estimation, but their results are unsatisfactory, especially for poor quality fingerprint images. In this paper, a model-based method for the computation of orientation field is proposed. First a combination model is established for the representation of the orientation field by considering its smoothness except for several singular points, in which a polynomial model is used to describe the orientation field globally and a point-charge model is taken to improve the accuracy locally at each singular point. When the coarse field is computed by using the gradient-based algorithm, a further result can be gained by using the model for a weighted approximation. Due to the global approximation, this model-based orientation field estimation algorithm has a robust performance on different fingerprint images. A further experiment shows that the performance of a whole fingerprint recognition system can be improved by applying this algorithm instead of previous orientation estimation methods.

Dynamic monitoring of building environments is essential for observing rural land changes and socio-economic development, especially in agricultural countries, such as China. Rapid and accurate building extraction and floor area estimation at the village level are vital for the overall planning of rural development and intensive land use and the “beautiful countryside” construction policy in China. Traditional in situ field surveys are an effective way to collect building information but are time-consuming and labor-intensive. Moreover, rural buildings are usually covered by vegetation and trees, leading to incomplete boundaries. This paper proposes a comprehensive method to perform village-level homestead area estimation by combining unmanned aerial vehicle (UAV) photogrammetry and deep learning technology. First, to tackle the problem of complex surface feature scenes in remote sensing images, we proposed a novel Efficient Deep-wise Spatial Attention Network (EDSANet), which uses dual attention extraction and attention feature refinement to aggregate multi-level semantics and enhance the accuracy of building extraction, especially for high-spatial-resolution imagery. Qualitative and quantitative experiments were conducted with the newly built dataset (named the rural Weinan building dataset) with different deep learning networks to examine the performance of the EDSANet model in the task of rural building extraction. Then, the number of floors of each building was estimated using the normalized digital surface model (nDSM) generated from UAV oblique photogrammetry. The floor area of the entire village was rapidly calculated by multiplying the area of each building in the village by the number of floors. The case study was conducted in Helan village, Shannxi province, China. The results show that the overall accuracy of the building extraction from UAV images with the EDSANet model was 0.939 and that the precision reached 0.949. The buildings in Helan village primarily have two stories, and their total floor area is 3.1 × 10⁵ m². The field survey results verified that the accuracy of the nDSM model was 0.94; the RMSE was 0.243. The proposed workflow and experimental results highlight the potential of UAV oblique photogrammetry and deep learning for rapid and efficient village-level building extraction and floor area estimation in China, as well as worldwide.

Objective: This study aimed to investigate the relationship between prognostic and tumor parameters of cervical cancer patients, such as tumor size (TS), tumor volume (TV), and tumor volume reduction rate (TVRR) after external beam radiotherapy.Methods: A total of 217 patients with advanced cervical cancer, classified as Federation of Gynecology and Obstetrics (FIGO) IIa–IVa, were enrolled in the study. Pre- and mid-RT pelvic magnetic resonance imaging (MRI) were performed twice, during RT and just before brachytherapy.Results: The median follow-up time was 51 months (range, 7–111 months). The 5-year overall survival (OS), progression-free survival (PFS), and local failure-free survival (LFFS) rates were 81.3, 85.1, and 92.9%, respectively. Multivariate analysis revealed that tumor parameters including FIGO stage >II (Hazard Ratio, 2.377 and 95% confidence interval [CI], 1.091–5.182; P = 0.029), pre-RT TV >61.6 cm³ (HR, 0.417 and 95% CI, 0.188–0.926; P = 0.032), and mid-RT TV >11.38 cm³ (HR, 3.192 and 95% CI, 1.094–9.316; P = 0.034) were observably associated with OS. Univariate analysis showed that the tumor volume reduction rate (TVRR) was dramatically associated with overall survival (HR, 0.204 and 95% CI 0.033–1.282; P <0.001) and local failure-free survival (P = 0.050).Conclusions: In this retrospective study, TVRR and mid-radiotherapy tumor volume are independent and strong prognostic parameters for patients with local advanced cervical cancer receiving CCRT.