The second association enhances Multi-Object Tracking (MOT) by reducing missed detections and trajectory fragmentation but is limited by the poor distinguishability of low-confidence detections. To address this, we introduce interactive features leveraging Graph Neural Networks (GNNs) to enhance object distinction. Unlike existing GNN-based trackers that compute interactive features for all objects, we selectively calculate interactive features based on directed reliable neighbor graphs for objects in the second stage. These graphs include two kinds of nodes: reliable nodes (already associated in the first association) and unreliable nodes (remaining objects). Bidirectional edges between reliable nodes indicate matches, while directed edges from reliable to unreliable nodes represent neighbor relationships. These graphs are forwarded to graph attention networks to obtain interactive features combined with motion features for the second association. Experimental results on the MOT17 (65.3 in HOTA, 80.6 in IDF1) and MOT20 (63.8 in HOTA, 78.0 in IDF1) benchmark datasets demonstrate the effectiveness of our proposed tracker, particularly in HOTA and IDF1.

Due to the vigorous development of artificial intelligence, robots has moved into our lives. In order to let the robot paint more closely to our ordinary people's paintings, this paper presents an accurate, fast and robust algorithm by using the Convolution Neural Networks for making facial line-drawings. We first improve the SFLG (Sketchy Facial Line-drawing Generation) networks, and then design a FLG (Facial Line-drawing Generation) networks. The FLG uses complete convolution and deconvolution that can generate images by end-to-end. Moreover, the FLG has no requirement to the size of the input image. Experimental results have illustrated the stableness and accuracy of proposed FLG networks. The accuracy rate of FLG networks is over 95% and it is 7% higher than SFLG networks.

Recreating the natural anatomy in ligament reconstruction is crucial to fully restore the knee joint function and reduce impingement on iatrogenic injury to adjacent structures, yet is subject to the difficulties in locating ligament and other associated soft tissues insertion sites intraoperatively and the high interperson morphological variability cross patients. In this study, we present a new quantitative analysis method capable of achieving personalized identification of cruciate ligament and soft tissue insertions. We craft patient-specific features of tibia outline that can be accurately and reliably measured from CT images. In addition, we propose a supervised structure learning and prediction model with special interdimensional and response structure regularization terms to capture relationship between the spatial arrangement of soft tissue insertions and the patient-specific features extracted from the tibia outlines. In the experiment, the proposed model outperforms baseline models and provides an accurate and accessible approach that can be used as the first and the most critical step to achieve personalized surgical planning in cruciate ligament reconstruction.

A maximum-amplitude-based coarse-to-fine algorithm is proposed with two novel ideas highlighted: adopting the maximum value of the fractional Fourier amplitude spectrum to measure the compactness of a signal, and using a coarse-to-fine strategy to speed up the searching process. The simulation results on synthetic and real signals show the validity of the proposed method.

The Hough transform (HT) is a commonly used technique for the identification of straight lines in an image. The Hough transform can be equivalently computed using the Radon transform (RT), by performing line detection in the frequency domain through use of central-slice theorem. In this research, an advanced Radon transform is developed using a multilayer fractional Fourier transform, a Cartesian-to-polar mapping, and 1-D inverse Fourier transforms, followed by peak detection in the sinogram. The multilayer fractional Fourier transform achieves a more accurate sampling in the frequency domain, and requires no zero padding at the stage of Cartesian-to-polar coordinate mapping. Our experiments were conducted on mix-shape images, noisy images, mixed-thickness lines and a large data set consisting of 751,000 handwritten Chinese characters. The experimental results have shown that our proposed method outperforms all known representative line detection methods based on the standard Hough transform or the Fourier transform.

Previous research shows that Wang-Smith chaotic simulated annealing, which employs a gradually decreasing time-step, has only a scaling effect to computational energy of the Hopfield model without changing its shape. This makes the net has sensitive dependence on the value of damping factor. Considering Chen-Aihara chaotic simulated annealing with decaying self-coupling has a shape effect to computational energy of the Hopfield model, a novel approach to improve Wang-Smith chaotic simulated annealing, which reaps the benefits of Wang-Smith model and Chen-Aihara model, is proposed in this paper. With the aid of this method the improved model can affect on computational energy of the Hopfield model from scaling and shape. By adjusting the time-step, the improved neural network can also pass from a chaotic to a non-chaotic state. From numerical simulation experiments, we know that the improved model can escape from local minima more efficiently than original Wang-Smith model.