Hailing Zhou

Efficient Road Detection and Tracking for Unmanned Aerial Vehicle

An unmanned aerial vehicle (UAV) has many applications in a variety of fields. Detection and tracking of a specific road in UAV videos play an important role in automatic UAV navigation, traffic monitoring, and ground-vehicle tracking, and also is very helpful for constructing road networks for modeling and simulation. In this paper, an efficient road detection and tracking framework in UAV videos is proposed. In particular, a graph-cut-based detection approach is given to accurately extract a specified road region during the initialization stage and in the middle of tracking process, and a fast homography-based road-tracking scheme is developed to automatically track road areas. The high efficiency of our framework is attributed to two aspects: the road detection is performed only when it is necessary and most work in locating the road is rapidly done via very fast homography-based tracking. Experiments are conducted on UAV videos of real road scenes we captured and downloaded from the Internet. The promising results indicate the effectiveness of our proposed framework, with the precision of 98.4% and processing 34 frames per second for 1046 × 595 videos on average.

Recent Advances on Singlemodal and Multimodal Face Recognition: A Survey

High performance for face recognition systems occurs in controlled environments and degrades with variations in illumination, facial expression, and pose. Efforts have been made to explore alternate face modalities such as infrared (IR) and 3-D for face recognition. Studies also demonstrate that fusion of multiple face modalities improve performance as compared with singlemodal face recognition. This paper categorizes these algorithms into singlemodal and multimodal face recognition and evaluates methods within each category via detailed descriptions of representative work and summarizations in tables. Advantages and disadvantages of each modality for face recognition are analyzed. In addition, face databases and system evaluations are also covered.

Adaptive patch size determination for patch-based image completion

Patch-based image completion proceeds by iteratively filling the target (unknown) region by the best matching patches in the source image. Inmost existing such algorithms, the size of the patches is either fixed and specified by a default number or simply chosen to be inversely proportional to the spatial frequency. However, it is noted that the patch size affects how well the filled patch captures the local characteristics of the source image and thus the final completion accuracy. Thus in this paper we propose a new method to compute appropriate patch sizes for image completion to improve its performance. In particular, we formulate the patch size determination as an optimization problem that minimizes an objective function involving image gradients and distinct and homogenous features. Experimental results show that our method can provide a significant enhancement to patch-based image completion algorithms.

Cepstrum Based Unsupervised Spike Classification

In this research, we study the effect of feature selection in the spike detection and sorting accuracy. We introduce a new feature representation for neural spikes from multichannel recordings. The features selection plays a significant role in analyzing the response of brain neurons. The more precise selection of features leads to a more accurate spike sorting, which can group spikes more precisely into clusters based on the similarity of spikes. Proper spike sorting will enable the association between spikes and neurons. Different with other threshold-based methods, the cepstrum of spike signals is employed in our method to select the candidates of spike features. To choose the best features among different candidates, the Kolmogorov-Smirnov (KS) test is utilized. Then, we rely on the super paramagnetic method to cluster the neural spikes based on KS features. Simulation results demonstrate that the proposed method not only achieve more accurate clustering results but also reduce computational burden, which implies that it can be applied into real-time spike analysis.

Spike sorting using hidden markov models

In this paper, hidden Markov models (HMM) is studied for spike sorting. We notice that HMM state sequences have capability to represent spikes precisely and concisely. We build a HMM for spikes, where HMM states respect spike significant shape variations. Four shape variations are introduced: silence, going up, going down and peak. They constitute every spike with an underlying probabilistic dependence that is modelled by HMM. Based on this representation, spikes sorting becomes a classification problem of compact HMM state sequences. In addition, we enhance the method by defining HMM on extracted Cepstrum features, which improves the accuracy of spike sorting. Simulation results demonstrate the effectiveness of the proposed method as well as the efficiency.

Extending support to customised multi-point haptic devices in CHAI3D

CHAI3D is a widely accepted haptic SDK in the society because it is open-source and provides support to devices from different vendors. In many cases, CHAI3D and its related demos are used for benchmarking various haptic collision and rendering algorithms. However, CHAI3D is designed for off-the-shelf single-point haptic devices only, and it does not provide native support to customised multi-point haptic devices. In this paper, we aim to extend the existing CHAI3D framework and provide a standardized routine to support customised, single/multi-point haptic devices. Our extension aims at two issues: Intra-device communication and Inter-device communication. Therefore, our extension includes an HIP wrapper layer to concurrently handle multiple HIPs of a single device, and a communication layer to concurrently handle multiple position, orientation and force calculations of multiple haptic devices. Our extension runs on top of a custom-built 8-channel device controller, although other off-the shelf controllers can also be integrated easily. Our extension complies with the CHAI3D design framework and advanced provide inter-device communication capabilities for multi-device operations. With straightforward conversion routines, existing CHAI3D demos can be adapted to multi-point demos, supporting real-time parallel collision detection and force rendering.

Representing Images Using Curvilinear Feature Driven Subdivision Surfaces

This paper presents a subdivision-based vector graphics for image representation and creation. The graphics representation is a subdivision surface defined by a triangular mesh augmented with color attribute at vertices and feature attribute at edges. Special cubic B-splines are proposed to describe curvilinear features of an image. New subdivision rules are then designed accordingly, which are applied to the mesh and the color attribute to define the spatial distribution and piecewise-smoothly varying colors of the image. A sharpness factor is introduced to control the color transition across the curvilinear edges. In addition, an automatic algorithm is developed to convert a raster image into such a vector graphics representation. The algorithm first detects the curvilinear features of the image, then constructs a triangulation based on the curvilinear edges and feature attributes, and finally iteratively optimizes the vertex color attributes and updates the triangulation. Compared with existing vector-based image representations, the proposed representation and algorithm have the following advantages in addition to the common merits (such as editability and scalability): 1) they allow flexible mesh topology and handle images or objects with complicated boundaries or features effectively; 2) they are able to faithfully reconstruct curvilinear features, especially in modeling subtle shading effects around feature curves; and 3) they offer a simple way for the user to create images in a freehand style. The effectiveness of the proposed method has been demonstrated in experiments.

Fast road detection and tracking in aerial videos

We propose a fast approach for detecting and tracking a specific road in aerial videos. It combines adaptive Gaussian Mixture Models (GMMs) to describe road colour distributions, and homography based tracking to track road geometries, where an efficient technique is developed to estimate homography transformations between two frames. Experiments are conducted on videos captured by our unmanned aerial vehicles. All the results demonstrate the effectiveness of our proposed method. We test 1755 frames from 5 videos. Our approach can achieve 0.032 seconds per frame and 2.64% segmentation error for images with 908 × 513 resolutions, on average.

Integrating Kinect and haptics for interactive STEM education in local and distributed environments

Skill shortage is a realistic social problem that Australia is currently facing, especially in the fields of Science, Technology, Engineering and Mathematics (STEM). Various approaches have been proposed to soften this issue. By now the most successful approach is to attract pre-university youth and university freshmen into those fields before they make a decision on future subjects by introducing them with interactive, modifiable and inspiring virtual environments, which incorporates most essential knowledge of STEM. We propose to design a comprehensive virtual reality platform with immersive interactions, pluggable components and flexible configurations. It also involves haptics, motion capture and gesture recognition, and could be deployed in both local and distributed environments. The platform utilizes off the shelf low cost haptics and motion capture products, however the fidelity can be maintained at a good level. The proposed platform has been implemented with different configurations and has been tested on a group of users. Preliminary test results show that the interactivity, flexibility and fidelity of the platform are highly appreciated by users. User surveys also indicate that the proposed platform could help pre-university students and university freshmen build an overview of various aspects of STEM education. Besides, users are also positive on the fact that the platform enabled them to identify the challenges for higher education in STEM by providing them opportunities to interactively modify system configurations and instantly experience the corresponding results both visually and haptically.

Marine Object Detection Using Background Modelling and Blob Analysis

Monitoring marine object is important for understanding the marine ecosystem and evaluating impacts on different environmental changes. One prerequisite of monitoring is to identify targets of interest. Traditionally, the target objects are recognized by trained scientists through towed nets and human observation, which cause much cost and risk to operators and creatures. In comparison, a noninvasive way via setting up a camera and seeking objects in images is more promising. In this paper, a novel technique of object detection in images is presented, which is applicable to generic objects. A robust background modelling algorithm is proposed to extract foregrounds and then blob features are introduced to classify foregrounds. Particular marine objects, box jellyfish and sea snake, are successfully detected in our work. Experiments conducted on image datasets collected by the Australian Institute of Marine Science (AIMS) demonstrate the effectiveness of the proposed technique.