Guoliang Fan

Guoliang Fan
Oklahoma State University | Oklahoma State · School of Electrical and Computer Engineering

PhD

About

182
Publications
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3,310
Citations

Publications

Publications (182)
Article
Motion capture (Mocap) on the go based on a mobile platform is valuable for clinical studies and rehabilitation. For multi-view gait analysis, Mecanum wheeled robots offer advantages over traditional differential drive robots. However, control issues in multi-view human tracking using Mecanum robots remain unexplored and lack a suitable virtual env...
Article
For people living with dementia, information‐processing capabilities and previously acquired knowledge is generally limited. Daily tasks such as making coffee or teeth brushing may become too difficult to complete independently. People with normal cognitive functioning learn about the environment as they move through it with spatial information obt...
Article
In this paper, we survey the current research trends of enhancement and denoising of depth-based motion capture data (D-Mocap) and also discuss possible future research issues. We first present the commonly used problem formulation for human motion enhancement. We then review related work and cover a broad set of methodologies including filtering b...
Article
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To support indoor scene understanding, room layouts have been recently introduced that define a few typical space configurations according to junctions and boundary lines. In this paper, we study camera pose estimation from eight common room layouts with at least two boundary lines that is cast as a PnL (Perspective-n-Line) problem. Specifically, t...
Presentation
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This presentation describes preliminary National Institute of Health (NIH) funded user‐centered research into Cognitive Assistive Technology (CAT) for dementia homecare and the subsequent development of a lab for testing the CAT. Caregivers face on‐going significant risk throughout the ebb and flow of providing care, as individuals with dementia ma...
Article
Full-text available
We present a novel approach to enhance the quality of human motion data collected by low-cost depth sensors, namely D-Mocap, which suffers from low accuracy and poor stability due to occlusion, interference, and algorithmic limitations. Our approach takes advantage of a large set of high-quality and diverse Mocap data by learning a general motion m...
Article
Full-text available
In this paper, we propose a novel technique for human motion denoising by jointly optimizing kinematic and anthropometric constraints for a noisy skeleton data. Specifically, we are focused on depth-sensor-based motion capture (D-Mocap) data that are often prone to error, outliers and distortion. To capture human kinematics, we first propose a join...
Article
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UAV-enabled mobile edge computing (MEC) is a emerging technology to support resource-intensive yet delay-sensitive applications with edge clouds (ECs) deployed in the proximity to mobile users and UAVs served as computing base stations in the air. The formulated optimization problems therein are highly nonconvex and thus difficult to solve. To tack...
Article
5.5 million Americans have Alzheimer’s disease or some form of dementia and by mid-century, the number of people living with dementia in the United States is projected to grow to 13.8 million. About half of the persons with dementia currently live at home, 43% require occasional support, 47% daily support, and 10% continuous support; with most of t...
Article
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The central goal of few-shot scene classification is to learn a model that can generalize well to a novel scene category (UNSEEN) from only one or a few labeled examples. Recent works in the Remote Sensing (RS) community tackle this challenge by developing algorithms in a meta-learning manner. However, most prior approaches have either focused on r...
Preprint
The central goal of few-shot scene classification is to learn a model that can generalize well to a novel scene category (UNSEEN) from only one or a few labeled examples. Recent works in the remote sensing (RS) community tackle this challenge by developing algorithms in a meta-learning manner. However, most prior approaches have either focused on r...
Article
Full-text available
We propose a new method flow that utilizes pixel-level labeling information for instance-level object detection in indoor scenes from RGB-D data. Semantic labeling and instance segmentation are two different paradigms for indoor scene understanding that are usually accomplished separately and independently. We are interested in integrating the two...
Article
Full-text available
INTRODUCTION: Recently, low cost RGB-D depth sensors have emerged as a promising alternative for motion capture for clinical gait assessment. However, depth sensor based Mocap (D-Mocap) suffers from low accuracy and poor stability for 3D joint estimation due to noise, self-occlusion, interference, and other technical limitations, which prevents it...
Article
Full-text available
Rare-class objects in natural scene images that are usually small and less frequent often convey more important information for scene understanding than the common ones. However, they are often overlooked in scene labeling studies due to two main reasons, low occurrence frequency and limited spatial coverage. Many methods have been proposed to enha...
Article
We present a new framework for online dense 3D reconstruction of indoor scenes by using only depth sequences. This research is particularly useful in cases with a poor light condition or in a nearly featureless indoor environment. The lack of RGB information makes long-range camera pose estimation difficult in a large indoor environment. The key id...
Article
Full-text available
Image patch priors become a popular tool for image denoising. The Gaussian mixture model (GMM) is remarkably effective in modelling natural image patches. However, GMM prior learning using the expectation maximisation (EM) algorithm is sensitive to the initialisation, often leading to low convergence rate of parameter estimation. In this study, a n...
Article
Full-text available
In this article, the authors propose a concise corner detection algorithm, which is called CCDA. A cascade classifier concept is used to derive a corner detector, which can quickly discard the most non-corner pixels. The ruler of gradient direction is used to get the corner, which can avoid the influence of the light change. The method of second de...
Article
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Increased accuracy and affordability of depth sensors such as Kinect has created a great depth-data source for various 3D oriented applications. Specifically, 3D model retrieval is attracting attention in the field of computer vision and pattern recognition due to its numerous applications. A cross-domain retrieval approach such as depth image base...
Poster
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An autoencoder is a class of neural network that is trained to output an accurate reproduction of the input while learning key lower dimensional features, otherwise known as a manifold. A lower dimensional representation of the original input, referred to as the latent space, encodes the intrinsic data structure over the manifold. This paper propos...
Conference Paper
An autoencoder is a class of neural network that is trained to output an accurate reproduction of the input while learning key lower dimensional features, otherwise known as a manifold. A lower dimensional representation of the original input, referred to as the latent space, encodes the intrinsic data structure over the manifold. This paper propos...
Article
Full-text available
We propose a new topology-aware point set registration algorithm which can cope with multi-part articulated and non-rigid deformations. Point set registration is formulated as a maximum likelihood (ML) estimation problem where two topologically complementary constraints are jointly optimized in a probabilistic framework. The first is coherent point...
Article
Full-text available
Single image dehazing algorithms are recently attracting more and more attention from many researchers because of their flexibility and practicality. However, most existing algorithms have some challenges in dealing with images captured under complex weather conditions because the often used assumptions cannot always reflect true structural informa...
Article
Full-text available
We present a novel parametric 3D shape representation, Generalized sum of Gaussians (G-SoG), which is particularly suitable for pose estimation of articulated objects. Compared with the original sum-of-Gaussians (SoG), G-SoG can handle both isotropic and anisotropic Gaussians, leading to a more flexible and adaptable shape representation yet with m...
Article
Full-text available
Contrast enhancement which aims to increase the contrast of an image with low dynamic range, has been widely studied and exploited. In spite of the great success of many contrast enhancement algorithms, they still have difficulty in achieving both global and local contrast enhancement so that some over-enhancement, under-enhancement or even halo ar...
Conference Paper
In natural scene images, rare class objects have low occurrence frequencies and limited spatial coverage, and they may be easily ignored during scene labeling. However, rare class objects are often more important to semantic labeling and image understanding compared to background areas. In this work, we present a rare class-oriented scene labeling...
Conference Paper
Full-text available
3D surface registration of two or more range scans is an important step in building a complete 3D model of an object. When the overlaps between multi-view scans are insufficient, good initial alignment is necessary that usually requires some prior assumption such as pre-defined initial camera configuration or the use of landmarks. Specifically, thi...
Article
Single image dehazing has captured much attention due to increasing applications. However, state-of-the-art image dehazing algorithms often suffer from undesirable quantization artifacts and noises in heavily hazy regions or sky patches of hazy image where dense scattering often occurs, so that dehazed results may have poor image quality or even lo...
Article
Full-text available
We study two fundamental issues about video-based human walking estimation where the goal is to estimate 3D gait kinematics (i.e., joint positions) from 2D gait appearances (i.e., silhouettes). One is how to model gait kinematics from different walking styles, and the other is how to represent gait appearances captured under different views and fro...
Article
Full-text available
Social event detection in large photo collections is very challenging and multimodal clustering is an effective methodology to deal with the problem. Geographic information is important in event detection. This paper proposed a topic model based approach to estimate the missing geographic information for photos. The approach utilizes a supervised m...
Article
Full-text available
In this paper, we propose an articulated and generalized Gaussian kernel correlation (GKC)-based framework for human pose estimation. We first derive a unified GKC representation that generalizes previous sum of Gaussians (SoG)- based methods for the similarity measure between a template and an observation both of which are represented by various S...
Article
The key to feature fusion for classification is to take advantage of the discriminative and complementary information from different feature sets, which can be represented as internal (within each feature set) or external structures (across different feature sets). Traditional approaches tend to preserve either internal or external structures via c...
Article
Full-text available
Obstacle detection in urban traffic is a hot topic in intelligent visual surveillance systems. In this paper, a real-time automatic obstacle recognition method based on computer vision technology is presented. The proposed method aims at detecting and recognizing the road obstacles such as abandoned objects, accident vehicles and illegally parked v...
Article
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We propose new techniques for joint recognition, segmentation and pose estimation of infrared (IR) targets. The problem is formulated in a probabilistic level set framework where a shape constrained generative model is used to provide a multi-class and multi-view shape prior and where the shape model involves a couplet of view and identity manifold...
Article
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We introduce an SIR particle filter for tracking civilian targets including vehicles and pedestrians in dual-band midwave/longwave infrared imagery as well as a novel dualband track consistency check for triggering appearance model updates. Because of the paucity of available dual-band data, we constructed a custom sensor to acquire the test sequen...
Article
Full-text available
This paper presents an improved local ternary pattern (LTP) for automatic target recognition (ATR) in infrared imagery. Firstly, a robust LTP (RLTP) scheme is proposed to overcome the limitation of the original LTP for achieving the invariance with respect to the illumination transformation. Then, a soft concave-convex partition (SCCP) is introduce...
Conference Paper
We propose a new non-rigid articulated point set registration framework for human pose estimation that aims at improving two recent registration techniques and filling the gap between the two. One is Coherent Point Drift (CPD) that is a powerful Gaussian Mixture Model (GMM) -based non-rigid registration method, but may not be suitable for articulat...
Conference Paper
Full-text available
We propose a generalized Sum-of-Gaussians (G-SoG) model for statistical 3D shape modeling that is applied to human pose tracking from a single depth sensor. G-SoG generalizes the original SoG model by involving much fewer anisotropic Gaussians yet with better flexibility and adapt-ability. Both SoG and G-SoG are involved for pose tracking with diff...
Conference Paper
Full-text available
We propose a generative framework for 3D human pose estimation that is able to operate on both individual point sets and sequential depth data. We formulate human pose estimation as a point set registration problem, where we propose three new approaches to address several major technical challenges in this research. First, we integrate two registra...
Conference Paper
Full-text available
We introduce a simple yet effective 3D human pose tracking from a single depth sensor by using the Sum of Gaussians (SoG) models. Both the human body model and the point cloud converted from a depth map are represented by two different SoG models, which allow us to compute and optimize their similarity analytically. We have two main contributions i...
Article
We study automatic target recognition (ATR) in infrared (IR) imagery by applying two recent computer vision techniques, Histogram of Oriented Gradients (HOG) and Bag-of-Words (BoW). We propose the idea of dense HOG features which are extracted from a set of high-overlapped local patches in a small IR chip and we apply a vocabulary tree that is lear...
Conference Paper
Full-text available
We propose a new point set registration method, Global-Local Topology Preservation (GLTP), which can cope with complex non-rigid transformations including highly articulated deformation. The registration is formulated as a Maximum Likelihood (ML) estimation problem with two topologically complementary constraints. The first is the previous Coherent...
Article
Full-text available
We propose a new integrated target tracking, recognition and segmentation algorithm, called ATR-Seg, for infrared imagery. ATR-Seg is formulated in a probabilistic shape-aware level set framework that incorporates a joint view-identity manifold (JVIM) for target shape modeling. As a shape generative model, JVIM features a unified manifold structure...
Article
Full-text available
This paper presents a novel feature extraction algorithm based on the local binary features for automatic target recognition (ATR) in infrared imagery. Since the inception of the local binary pattern (LBP) and local ternary pattern (LTP) features, many extensions have been proposed to improve their robustness and performance in a variety of applica...
Article
Full-text available
We propose a new joint view-identity manifold (JVIM) for multi-view and multi-target shape modeling that is well-suited for automated target tracking and recognition (ATR) in infrared imagery. As a shape generative model, JVIM features a novel manifold structure that imposes a conditional dependency between the two shape-related factors, view and i...
Conference Paper
Full-text available
We propose a simultaneous target recognition, segmentation and pose estimation algorithm for the infrared ATR task. A probabilistic framework of level set segmentation is extended by incorporating a shape generative model that provides a multi-class and multiview shape prior. This generative model involves a couplet of a view manifold and an identi...
Conference Paper
Full-text available
A new method, called local edge binary patterns (LEBP), is introduced in the paper, which takes the advantages of local binary patterns and local edges into account. Furthermore, several extensions to LEBP are also discussed in detail. Center-symmetric local binary pattern (CS-LBP) and direction local binary pattern (D-LBP) are chosen as examples t...
Conference Paper
We present a directional shock diffusion approach to single image super-resolution(SR) that is featured by two special treatments to image primitives including edges, corners and junctions. The first is to incorporate an adaptively weighted shock filter to recover the edge sharpness along with the structure-sensitive diffusion terms, and the second...
Conference Paper
Full-text available
A new probabilistic model called ATR-Seg for automated target tracking, recognition and segmentation is proposed that incorporates a shape constrained level set with a shape generative model along with motion model. The shape model involves a view-independent identity manifold and infinite identity-dependent view manifolds for multi-view and multi-...
Conference Paper
Full-text available
We present a multi-layer joint gait-pose manifold (multi-layer JGPM) for human motion modeling to enhance the representative capability of the original JGPM that represents gait kinematics by two variables. One is the pose to denote a series of stages in a walking cycle and the o