Author name cluster

Yue Du

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

Adaptive Morph-Patch Transformer for Aortic Vessel Segmentation

Zhenxi Zhang
Fuchen Zheng
Adnan Iltaf
Yifei Han
Zhenyu Cheng
Yue Du
Bin Li
Tianyong Liu

Accurate segmentation of aortic vascular structures is critical for diagnosing and treating cardiovascular diseases. Traditional Transformer-based models have shown promise in this domain by capturing long-range dependencies between vascular features. However, their reliance on fixed-size rectangular patches often influences the integrity of complex vascular structures, leading to suboptimal segmentation accuracy. To address this challenge, we propose the adaptive Morph-Patch Transformer (MPT), a novel architecture specifically designed for aortic vascular segmentation. Specifically, MPT introduces an adaptive patch partitioning strategy that dynamically generates morphology-aware patches aligned with complex vascular structures. This strategy can preserve semantic integrity of complex vascular structures within individual patches. Moreover, a Semantic Clustering Attention (SCA) method is proposed to dynamically aggregate features from various patches with similar semantic characteristics. This method enhances the model's capability to segment vessels of varying sizes, preserving the integrity of vascular structures. Extensive experiments on three open-source datasets (AVT, AortaSeg24 and TBAD) demonstrate that MPT achieves state-of-the-art performance, with improvements in segmenting intricate vascular structures.

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IROS Conference 2022 Conference Paper

Simultaneous Depth Estimation and Localization for Cell Manipulation Based on Deep Learning

Zengshuo Wang
Huiying Gong
Ke Li 0026
Bin Yang
Yue Du
Yaowei Liu
Xin Zhao 0010
Mingzhu Sun

Visual localization, which is a key technology to realize the automation of cell manipulation, has been widely studied. Since the depth of field of the microscope is narrow, the planar localization and depth estimation are usually coupled together. At present, most methods adopt the serial working mode of focusing first and then planar localization, but they usually do not have good real-time performance and stability. In this paper, a simultaneous depth estimation and localization network was developed for cell manipulation. The network takes a focused image and a defocus-offset image as inputs, and outputs the defocus in the depth direction and the offset in the plane at the same time after going through defocus-offset information extraction, defocus classification mapping and offset regression mapping. To train and test our network, we also create two datasets: An Adherent Cell dataset and an Injection Micropipette dataset. The experimental results demonstrated that the proposed method achieves the detection of all test samples with a frame rate of more than 40Hz, and the maximum errors of depth estimation and localization are $\boldsymbol{2. 44\mu m}$ and $\boldsymbol{0. 49\mu m}$, respectively. The proposed method has good stability, which is mainly reflected in its strong generalization ability and anti-noise ability.

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ICRA Conference 1997 Conference Paper

Real-time vehicle following through a novel symmetry-based approach

Yue Du
Nikolaos P. Papanikolopoulos

This paper describes a novel approach to real-time vehicle following. A scheme, called "symmetry axis detection and filtering based on symmetry constraints", is proposed and has been implemented. This scheme incorporates many elements of our research efforts in sensor-based control of mobile robots and manipulators. The proposed algorithm detects and tracks the rear portion of the exterior of a leading vehicle via a camera mounted on a vehicle that belongs to a platoon. Our scheme uses the symmetry property of the shape of most vehicles. In particular, our technique takes advantage of the stable contour symmetry instead of the intensity symmetry. An algorithm that employs a voting technique is proposed in order to detect the vertical symmetry axis of the leading vehicle. A filtering method based on symmetry constraints is performed to eliminate the pixels which do not contribute to the leading vehicle's contour. As a result of the filtering, a distinct symmetric contour is obtained. Robustness and real-time performance of the symmetry-based scheme are greatly enhanced by using an adaptive processing window, the local symmetry properties, and a filtering procedure.

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IROS Conference 1995 Conference Paper

A color projection for fast generic target tracking

Yue Du
Jill D. Crisman

We present a piecewise linear projection of the 3D color space that greatly reduces the computations required for using color information for robot vision tasks which we call categorical color. This 24-bit to 6-bit projection is inspired by the way humans name colors. This projection is developed to provide generic target tracking in real-time. A generic target in our system is defined by a user selecting a distinctive object in the window of a color image. The system has no a priori models of object shapes or colors. Therefore, the generic target tracking must perform robustly, in real time, using only the initial example appearance of the target object. To evaluate the performance of our piecewise linear projection on the task of generic target tracking, we compare similar RGB, intensity, and categorical color algorithms. Rather than simply observing the located target, we have developed a quantitative method for evaluating generic target tracking algorithms. By using this procedure, we show that categorical color is a better feature for generic tracking than RGB and gray-level.

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