2288578
Efficient algorithms for obtaining algebraic invariants from higher degree implicit polynomials for recognition of curved objects
Implicit polynomials can be used for describing a large variety of curved shapes. In most of the earlier works, the emphasis had been on the use of second degree implicit polynomials. To describe more complex curves and surfaces, it is necessary that higher degree implicit polynomials be used. In this paper, we have proposed a tensor based approach for obtaining affine and Euclidean invariants from the coefficients of higher degree implicit polynomials. Our approach is more general and computationally efficient than the matrix based approaches for obtaining invariants. For the Euclidean case, the algorithm can be used for both recognition and pose estimation. In this paper, we have demonstrated our approach for obtaining invariants of 2-D shapes using third and fourth degree implicit polynomials.
31566264
Cooperation-Based Behavior Design
Robot soccer explores such a research topic that multiple agents work together in a Real-time, Cooperative and Adversarial (RCA) environment to achieve specific objectives. It requires that each agent can not only deal with situations individually, but also present cooperation with its teammates. In this paper, we describe a robot architecture, which addresses "scaling cooperation" among robots, and meanwhile allows each robot to make decisions independently in real-time case. The architecture is based on "ideal cooperation" principle and implemented for Small Robot League in RoboCup. Experimental results prove its effectiveness and reveal several primary characteristics of behaviors in robot soccer.
35155949
Constrained nonlinear models of fingerprint orientations with prediction
In this paper, we developed an algorithm to model the fingerprint orientation field. The algorithm comprises two steps, orientation prediction and model computation. Orientation prediction is based on piece-wise first-order phase portrait model. It is used to estimate the orientation in areas where there is no ridge information in the input image or the coherence of the orientation field is low. In the model computation, a constrained nonlinear phase portrait algorithm is proposed, which aims to get an accurate mathematical model of the fingerprint orientation field. Compared to the prior works, this algorithm is able to predict orientation even in noisy regions and it integrates the global and local orientation description into a unified mathematical form. Experiments conducted on the first 500 images of the NIST-4 database showed that the proposed algorithm is able to model all the fingerprint orientation patterns except those with very poor quality images where the orientation information cannot be clearly extracted.
14477369
Markov random field modeled range image segmentation
In this paper, range image segmentation is studied in the framework of the maximum a posteriori estimation and Markov random field modeling. A novel range image segmentation model is proposed. The model serves as an evaluator for a small number of segmentation candidates obtained through a fast edge detection algorithm. A local method is employed to search for the optimal segmentation from the candidates. Experimental results show that such combination of heuristics and model-based evaluation leads to a fast and accurate segmentation.
28149553
Fingerprint Indexing Based On Symmetrical Measurement
Due to the small number of classes and uneven distribution, the Henry's fingerprint classification approach is not able to provide an efficient way to narrow down the search when identifying a person in a large database. In this paper, three symmetrical filters are used to measure different orientation structures. We proposed a new fingerprint indexing approach based on these three symmetrical measurements. Experiments conducted on the NIST database 4 show the effectiveness of the proposed approach
29758508
Corner Detection Based on Finding Edge Points Locally
12981432
A web-based tutoring system with styles-matching strategy for spatial geometric transformation
It has been a major objective for researchers to develop computer systems that can effectively deliver instruction to learners. Therefore, how to incorporate instructional strategies in computer-assisted learning systems in a systematic manner deserves further investigation. In this paper, a style-matching strategy that attempts to match learning materials' styles to learners' latent traits is proposed and realized in a web-based tutoring system, called CooTutor. The mechanism of adaptive material selection takes learners' different spatial ability and learning styles as an integral learning profile into account, and performs traits-based personalization of learning experience. This system is specifically designed to conquer the difficulty of tutoring the topic on fundamental spatial geometry in conventional curriculums. By conducting empirical evaluation with a small group of students, it is found that CooTutor is generally beneficial to learning the domain, but the effect of the styles-matching mechanism remains inconclusive. The work aims to contribute to the community of adaptive hypermedia in providing an explorative example adopting the concern of individual difference for personalization. The system design, a usage scenario, and an exploratory evaluation are presented in this paper as implications for further studies.
2318961
A Novel Robust Statistical Method for Background Initialization and Visual Surveillance
In many visual tracking and surveillance systems, it is important to initialize a background model using a training video sequence which may include foreground objects. In such a case, robust statistical methods are required to handle random occurrences of foreground objects (i.e., outliers), as well as general image noise. The robust statistical method Median has been employed for initializing the background model. However, the Median can tolerate up to only 50% outliers, which cannot satisfy the requirements of some complicated environments. In this paper, we propose a novel robust method for the background initialization. The proposed method can tolerate more than 50% of foreground pixels and noise. We give quantitative evaluations on a number of video sequences and compare our proposed method with five other methods. Experiments show that our method can achieve very promising results in background initialization: including applications in video segmentation, visual tracking and surveillance.
2366018
Constrained texture synthesis by scalable sub-patch algorithm
In this paper, we propose a novel scalable sub-patch algorithm for texture synthesis. Texture synthesis is an important research topic in computer graphics and image processing. We present an efficient algorithm which can paste a patch each time without boundary processing technique. In particular, the proposed sub-patch algorithm can be used in constrained texture synthesis to fill the large lost region. On the other hand, the searching neighborhood pixels can be changed due to different patch size in image reconstruction. The comparison of previous algorithms is also provided in this paper. We present a first algorithm used in constrained texture synthesis by regular synthesizing order
2396956
Location-based Spatial Queries with Data Sharing in Wireless Broadcast Environments
Location-based spatial queries (LBSQs) refer to spatial queries whose answers rely on the location of the inquirer. Efficient processing of LBSQs is of critical importance with the ever-increasing deployment and use of mobile technologies. We show that LBSQs have certain unique characteristics that traditional spatial query processing in centralized databases does not address. For example, a significant challenge is presented by wireless broadcasting environments, which often exhibit high-latency database access. In this paper, we present a novel query processing technique that, while maintaining high scalability and accuracy, manages to reduce the latency considerably in answering location-based spatial queries. Our approach is based on peer-to-peer sharing, which enables us to process queries without delay at a mobile host by using query results cached in its neighboring mobile peers. We illustrate the appeal of our technique through extensive simulation results.
15731582
Effective Appearance Model and Similarity Measure for Particle Filtering and Visual Tracking
In this paper, we adaptively model the appearance of objects based on Mixture of Gaussians in a joint spatial-color space (the approach is called SMOG). We propose a new SMOG-based similarity measure. SMOG captures richer information than the general color histogram because it incorporates spatial layout in addition to color. This appearance model and the similarity measure are used in a framework of Bayesian probability for tracking natural objects. In the second part of the paper, we propose an Integral Gaussian Mixture (IGM) technique, as a fast way to extract the parameters of SMOG for target candidate. With IGM, the parameters of SMOG can be computed efficiently by using only simple arithmetic operations (addition, subtraction, division) and thus the computation is reduced to linear complexity. Experiments show that our method can successfully track objects despite changes in foreground appearance, clutter, occlusion, etc.; and that it outperforms several color-histogram based methods.