Late fetal growth restriction (FGR) is a common complication of pregnancy characterized by chronic hypoxia. However, late FGR is in a dilemma of the high incidence but low detection rate. Depending on the non-invasiveness and convenient operation, the routine cardiotocography (CTG) allows continuous monitoring fetal heart rate (FHR) to assess fetal intrauterine stockpiling ability. In this paper, we aimed to explore the FHR pattern of late FGR in routine CTG. For analysis, the FHR features were acquired using routine CTG in a population of 160 healthy and 102 late FGR fetuses published in IEEE Dataport. First, we explored the relationships among FHR features and their importance on late FGR assessment by utilizing hypothesis testing, principal component analysis (PCA) and Spearman correlation analysis. Second, we presented a regression coefficient-based backward-stepwise-selection of association rules analysis (ARA) called backward-stepwise Max-R2Apriori ARA, to find the optimum itemset that helps diagnose late FGRs from healthy fetuses. The hypothesis testing, PCA and Spearman correlation analysis found eight FHR features were highly relevant to the late FGR. Moreover, the backward-stepwise Max-R2 Apriori ARA validated the correlation and interpretation about FHR features of late FGR. In conclusion, the analysis results are consistent with clinical knowledge on late FGR and help screen late FGR in antepartum fetal monitoring.

Human gaze is essential for various appealing applications. Aiming at more accurate gaze estimation, a series of recent works propose to utilize face and eye images simultaneously. Nevertheless, face and eye images only serve as independent or parallel feature sources in those works, the intrinsic correlation between their features is overlooked. In this paper we make the following contributions: 1) We propose a coarse-to-fine strategy which estimates a basic gaze direction from face image and refines it with corresponding residual predicted from eye images. 2) Guided by the proposed strategy, we design a framework which introduces a bi-gram model to bridge gaze residual and basic gaze direction, and an attention component to adaptively acquire suitable fine-grained feature. 3) Integrating the above innovations, we construct a coarse-to-fine adaptive network named CA-Net and achieve state-of-the-art performances on MPIIGaze and EyeDiap.

Camera calibration directly from image sequences of a pedestrian without using any calibration object is a really challenging task and should be well solved in computer vision, especially in visual surveillance. In this paper, we propose a novel camera calibration method based on recovering the three orthogonal vanishing points (TOVPs), just using an image sequence of a pedestrian walking in a straight line, without any assumption of scenes or motions, e.g., control points with known 3D coordinates, parallel or perpendicular lines, non-natural or pre-designed special human motions, as often necessary in previous methods. The traces of shoes of a pedestrian carry more rich and easily detectable metric information than all other body parts in the periodic motion of a pedestrian, but such information is usually overlooked by previous work. In this paper, we employ the images of the toes of the shoes on the ground plane to determine the vanishing point corresponding to the walking direction, and then utilize harmonic conjugate properties in projective geometry to recover the vanishing point corresponding to the perpendicular direction of the walking direction in the horizontal plane and the vanishing point corresponding to the vertical direction. After recovering all of the TOVPs, the intrinsic and extrinsic parameters of the camera can be determined. Experiments on various scenes and viewing angles prove the feasibility and accuracy of the proposed method.

Radial lens distortion is the most significant lens distortion in current cameras, and many models are proposed to describe it. Fitzgibbon presented the prestigious division distortion model with just a single parameter. Based on the fact that a line in 3D space may be projected onto a curve in the image plane due to radial distortion, Alemán et al. utilized the Hough transform with three parameters to automatically correct radial lens distortion, namely, one parameter coming from the division model, and the other two arising from the corrected image line. However, in some cases, especially for wide angle lenses, the corrected results are not very satisfactory. Someone may suggest that we can use the so-called extended division model with more than one parameter. Unfortunately, the problem will become very hard to solve, since the dimensions of the Hough parameter space become higher than three. In this paper, we propose a cascaded one-parameter division model to deal with the problem. In each stage the Hough parameter space is always three-dimensional. Enormous experiments on real images illustrate the ability of our method.

This paper presents a new efficient method to increase the accuracy and the robustness of ellipse fitting, by utilizing the 3L algorithm and semidefinite programming (SDP). The novelty lies on the combination of relaxed geometric distance constraints and semidefinite programming framework. Due to the relaxed 3L constraints, the proposed approach provides high robustness in the presence of noise. The accuracy of the final solution is prominently increased even if the data suffer from strong occlusions or noises. The proposed method represents significant advantages in both accuracy and robustness. Experimental results and comparisons with state-of-the-art fitting methods demonstrate the improvements in ellipse fitting.