人工智能与机器视觉（AIMV）实验室成立于2017年，旨在进行人工智能相关的研究和应用，主要专注于计算机视觉、图像/视频处理、模式识别、机器人、计算智能和机器学习。 目前，AIMV实验室由6个学院、2个博士后、12名博士候选人和50多名硕士生组成，是一个包含数学、计算机工程、电子与控制工程和机械工程等多学科研究人员的交叉研究团队。 在IEEE Conference on Computer Vision and Pattern Recognition, IEEE International Conference on Computer Vision, IEEE Trans. Image Processing, IEEE Trans. Fuzzy System, IEEE Trans. Cybernetics，SIGGRAPH等国内外顶级期刊及会议发表论文400多篇， 申请发明专利40余项，其中授权美国专利5项，中国授权专利22项。
A Hybrid Method for Implicit Intention Inference Based on Punished-Weighted Naïve Bayes
Gaze-based implicit intention inference provides a new human-robot interaction for people with disabilities to accomplish activities of daily living independently. Existing gaze-based intention inference is mainly implemented by the data-driven method without prior object information in intention expression, which yields low inference accuracy. Aiming to improve the inference accuracy, we propose a gaze-based hybrid method by integrating model-driven and data-driven intention inference tailored to disability ...
Dual-Scale Single Image Dehazing via Neural Augmentation
Model-based single image dehazing algorithms restore haze-free images with sharp edges and rich details for real-world hazy images at the expense of low PSNR and SSIM values for synthetic hazy images. Data-driven ones restore haze-free images with high PSNR and SSIM values for synthetic hazy images but with low contrast, and even some remaining haze for realworld hazy images. In this paper, a novel single image dehazing algorithm is introduced by combining model-based and datadriven ...
Adaptive weighted guided image filtering for depth enhancement in shape-from-focus
Existing shape from focus (SFF) techniques cannot preserve depth edges and fine structural details from a sequence of multi-focus images. Moreover, noise in the sequence of multi-focus images affects the accuracy of the depth map. In this paper, a novel depth enhancement algorithm for the SFF based on an adaptive weighted guided image filtering (AWGIF) is proposed to address the above issues.The AWGIF is applied to decompose an initial depth map which is estimated by the traditional SFF into a base layer...
Water Column Detection Method at Impact Point Based on Improved YOLOv4 Algorithm
For a long time, the water column at the impact point of a naval gun firing at the sea has mainly depended on manual detection methods for locating, which has problems such as low accuracy, subjectivity and inefficiency. In order to solve the above problems, this paper proposes a water column detection method based on an improved you-only-look-once version 4 (YOLOv4) algorithm. Firstly, the method detects the sea antenna through the Hoffman line detection method...
Image noise level estimation via kurtosis test
Noise level estimation is a long-standing problem in image processing. The challenge arises from the fact 7 that the estimation can be easily affected by texture information. In this paper, a new noise level estimation method 8 via kurtosis test is proposed, which is a normalized fourth-order moment. The proposed method consists of two stages: 9 the first one is to determine the image patches with normality by using the kurtosis test, the noise level is then estimated from these selected normal patches in the second stage...
Pupil-Contour-Based Gaze Estimation With Real Pupil Axes for Head-Mounted Eye Tracking
Accurate gaze estimation that frees from glints and the slippage problem is challenging. Pupil-contour-based gaze estimation methods can meet this challenge, except that the gaze accuracy is low due to neglecting the pupil’s corneal refraction This article proposes a refraction-aware gaze estimation approach using the real pupil axis, which is calculated from the virtual pupil image based on the derived function between the real pupil ...
Adaptive rank estimate in robust principal component analysis
Robust principal component analysis (RPCA) and its variants have gained wide applications in computer vision. However, these methods either involve manual adjustment of some parameters, or require the rank of a low-rank matrix to be known a prior. In this paper, an adaptive rank estimate based RPCA (ARE-RPCA) is proposed, which adaptively assigns weights on different singular values via rank estimation. More specifically, we study the characteristics of the low-rank matrix, and develop an improved Gerschgorin disk theorem to ...
3D reconstruction of the specular surface using an iterative stereoscopic deflectometry method
Phase measuring deflectometry (PMD) is an effective technique for three-dimensional measurement of specular surfaces. However, the ambiguity of monoscopic PMD and the time-consuming searching process of stereoscopic PMD are challenges for specular surface reconstruction. To solve it, we propose an iterative reconstruction algorithm for the stereoscopic phase measuring deflectometry system free of the time-consuming searching process for each pixel. An arbitrary seed point on the specular surface ...