28 Result(s)
within
Chapter
**hu Li
Computer Science
Artificial Intelligence
Computer Graphics
Image Processing and Computer Vision
Pattern Recognition
Visualization
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Chapter and Conference Paper
Top-Down Cues for Event Recognition
How to fuse static and dynamic information is a key issue in event analysis. In this paper, we present a novel approach to combine appearance and motion information together through a top-down manner for event...
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Chapter and Conference Paper
Horror Image Recognition Based on Emotional Attention
Along with the ever-growing Web, people benefit more and more from sharing information. Meanwhile, the harmful and illegal content, such as pornography, violence, horror etc., permeates the Web. Horror images,...
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Chapter and Conference Paper
Occlusion Handling with ℓ1-Regularized Sparse Reconstruction
Tracking multi-object under occlusion is a challenging task. When occlusion happens, only the visible part of occluded object can provide reliable information for the matching. In conventional algorithms, the ...
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Chapter and Conference Paper
An Approximation Algorithm for Computing Minimum-Length Polygons in 3D Images
Length measurements in 3D images have raised interest in image geometry for a long time. This paper discusses the Euclidean shortest path (ESP) to be calculated in a loop of face-connected grid cubes in the 3D...
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Chapter and Conference Paper
Multi-illumination Face Recognition from a Single Training Image per Person with Sparse Representation
In real-world face recognition systems, traditional face recognition algorithms often fail in the case of insufficient training samples. Recently, the face recognition algorithms of sparse representation have ...
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Chapter and Conference Paper
Reconstructing Mass-Conserved Water Surfaces Using Shape from Shading and Optical Flow
This paper introduces a method for reconstructing water from real video footage. Using a single input video, the proposed method produces a more informative reconstruction from a wider range of possible scenes...
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Chapter and Conference Paper
Segmentation via NCuts and Lossy Minimum Description Length: A Unified Approach
We investigate a fundamental problem in computer vision: unsupervised image segmentation. During the last decade, the Normalized Cuts has become very popular for image segmentation. NCuts guarantees a globally...
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Chapter and Conference Paper
Gradual Sampling and Mutual Information Maximisation for Markerless Motion Capture
The major issue in markerless motion capture is finding the global optimum from the multimodal setting where distinctive gestures may have similar likelihood values. Instead of only focusing on effective searc...
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Chapter and Conference Paper
Towards Hypothesis Testing and Lossy Minimum Description Length: A Unified Segmentation Framework
We propose a novel algorithm for unsupervised segmentation of images based on statistical hypothesis testing. We model the distribution of the image texture features as a mixture of Gaussian distributions so t...
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Chapter and Conference Paper
Modeling Complex Scenes for Accurate Moving Objects Segmentation
In video surveillance, it is still a difficult task to segment moving object accurately in complex scenes, since most widely used algorithms are background subtraction. We propose an online and unsupervised te...
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Chapter and Conference Paper
Spatial-Temporal Affinity Propagation for Feature Clustering with Application to Traffic Video Analysis
In this paper, we propose STAP (Spatial-Temporal Affinity Propagation), an extension of the Affinity Propagation algorithm for feature points clustering, by incorporating temporal consistency of the clustering...
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Chapter and Conference Paper
Improving Gaussian Process Classification with Outlier Detection, with Applications in Image Classification
In many computer vision applications for recognition or classification, outlier detection plays an important role as it affects the accuracy and reliability of the result. We propose a novel approach for outli...
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Chapter and Conference Paper
Compressive Evaluation in Human Motion Tracking
The powerful theory of compressive sensing enables an efficient way to recover sparse or compressible signals from non-adaptive, sub-Nyquist-rate linear measurements. In particular, it has been shown that rand...
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Chapter and Conference Paper
Multiple Order Graph Matching
This paper addresses the problem of finding correspondences between two sets of features by using multiple order constraints all together. First, we build a high-order supersymmetric tensor, called multiple or...
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Chapter and Conference Paper
Cosine Similarity Metric Learning for Face Verification
Face verification is the task of deciding by analyzing face images, whether a person is who he/she claims to be. This is very challenging due to image variations in lighting, pose, facial expression, and age. ...
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Chapter and Conference Paper
Unsupervised Moving Object Detection with On-line Generalized Hough Transform
Generalized Hough Transform-based methods have been successfully applied to object detection. Such methods have the following disadvantages: (i) manual labeling of training data ; (ii) the off-line constructio...
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Chapter and Conference Paper
Real Time Myocardial Strain Analysis of Tagged MR Cines Using Element Space Non-rigid Registration
We develop a real time element-space non-rigid registration technique for cardiac motion tracking, enabling fast and automatic analysis of myocardial strain in tagged magnetic resonance (MR) cines. Non-rigid r...
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Chapter and Conference Paper
Multi-Target Tracking by Learning Class-Specific and Instance-Specific Cues
This paper proposes a novel particle filtering framework for multi-target tracking by using online learned class-specific and instance-specific cues, called Data-Driven Particle Filtering (DDPF). The learned cues
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Chapter and Conference Paper
Learning Rare Behaviours
We present a novel approach to detect and classify rare behaviours which are visually subtle and occur sparsely in the presence of overwhelming typical behaviours. We treat this as a weakly supervised classifi...
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Chapter and Conference Paper
Compressed Sensing for Robust Texture Classification
This paper presents a simple, novel, yet very powerful approach for texture classification based on compressed sensing. At the feature extraction stage, a small set of random features is extracted from local i...
