Boundary Detection Problem Research Articles

Boundary Detection Using Open Spline Curve Based on Mumford-Shah Model

Abstract Inspired by Cremers's work, this paper proposes a novel method for detecting open boundaries, such as coastline and skyline in an image. This method is based on B-spline function, curve evolution, and the cartoon model of Mumford-Shah functional (M-S model). Because the object to be detected is an open curve in the image domain, two constraint equations are introduced into the M-S model. Thus, the problem of open boundary detection becomes a minimal partition problem. With the partial differential equations (PDEs) of control points and constraint equations, the curve will stop on the desired boundary. The method can be used to detect automatically a curve that separates an image into two distinct regions and is not necessarily defined by gradient, even if the image is very noisy. In addition, with two open curves, our model can be extended to detect belt-like objects, such as rivers and roads.

Adaptive Edge-Oriented Shot Boundary Detection

We study the problem of video shot boundary detection using an adaptive edge-oriented framework. Our approach is distinct in its use of multiple multilevel features in the required processing. Adaptation is provided by a careful analysis of these multilevel features, based on shot variability. We consider three levels of adaptation: at the feature extraction stage using locally-adaptive edge maps, at the video sequence level, and at the individual shot level. We show how to provide adaptive parameters for the multilevel edge-based approach, and how to determine adaptive thresholds for the shot boundaries based on the characteristics of the particular shot being indexed. The result is a fast adaptive scheme that provides a slightly better performance in terms of robustness, and a five fold efficiency improvement in shot characterization and classification. The reported work has applications beyond direct video indexing, and could be used in real-time applications, such as in dynamic monitoring and modeling of video data traffic in multimedia communications, and in real-time video surveillance. Experimental results are included.

The relevance of alpha-hulls to the boundary detection problem in sensor networks

Intuitively, many wireless and sensing applications benefit from knowledge of network boundaries. Many virtual coordinate constructions rely on the farthest set of nodes as beacons. Network edges may also bound routing holes in the network or regions of failure due to environmental effects, or they may indicate the need for additional deployment. Solutions that are both manageable and accurate remain elusive, in part due to the challenge of finding localized solutions. In this paper the potential to solve the edge detection problem using a geometric structure called the alpha-shape (¿-shape) is explored. For a disc of radius 1/¿, the ¿-shape consists of nodes (and joining edges) that sit on the boundary of the discs that contain no other nodes in the network. In addition to geometry-related fields of study such as graphics and computational geometry, ¿-shapes have been used in disciplines such as molecular biology and particle physics. The relationship between the ¿-parameter and the radio communication range is explored, and it is shown that by setting the ¿-parameter appropriately, one can compute the network ¿-shape locally.

Computational analysis and learning for a biologically motivated model of boundary detection

In this work we address the problem of boundary detection by combining ideas and approaches from biological and computational vision. Initially, we propose a simple and efficient architecture that is inspired from models of biological vision. Subsequently, we interpret and learn the system using computer vision techniques: First, we present analogies between the system components and computer vision techniques and interpret the network as minimizing a cost functional, thereby establishing a link with variational techniques. Second, based on Mean Field Theory the equations describing the network behavior are interpreted statistically. Third, we build on this interpretation to develop an algorithm to learn the network weights from manually segmented natural images. Using a systematic evaluation on the Berkeley benchmark we show that when using the learned connection weights our network outperforms classical edge detection algorithms.

Distributed target tracking and boundary estimation in wireless sensor networks

We study the problem of target tracking and boundary detection of a substance diffusing from a mobile source using a wireless sensor network. We present a Prediction-based Mobility Adaptive Tracking (P-MAT algorithm to study the tradeoff among energy, accuracy of tracking, coverage and boundary estimation. P-MAT minimises overall energy consumption by incorporating adaptivity in two forms: (1) the size of the active region and (2) modulation of the sampling rate. It uses adaptive Kalman filtering to predict the target's future location and velocity. The predicted target location determines a set of sensors surrounding that location to be activated known as the active region. Sensors in the active region are responsible for target tracking and boundary detection. In this article, we include dynamic boundary estimation. Boundary estimation in many situations can be performed efficiently using a subset of nodes within the vicinity of the phenomenon. This subset of nodes in our algorithm is the set of nodes in the active region. As the substance spreads, sensors in the active region determine if additional sensors outside of the active region are needed to enclose the boundary. Results from simulation experiments show that P-MAT can perform both tracking and boundary.

A Formal Study of Shot Boundary Detection

This paper conducts a formal study of the shot boundary detection problem. First, a general formal framework of shot boundary detection techniques is proposed. Three critical techniques, i.e., the representation of visual content, the construction of continuity signal and the classification of continuity values, are identified and formulated in the perspective of pattern recognition. Meanwhile, the major challenges to the framework are identified. Second, a comprehensive review of the existing approaches is conducted. The representative approaches are categorized and compared according to their roles in the formal framework. Based on the comparison of the existing approaches, optimal criteria for each module of the framework are discussed, which will provide practical guide for developing novel methods. Third, with all the above issues considered, we present a unified shot boundary detection system based on graph partition model. Extensive experiments are carried out on the platform of TRECVID. The experiments not only verify the optimal criteria discussed above, but also show that the proposed approach is among the best in the evaluation of TRECVID 2005. Finally, we conclude the paper and present some further discussions on what shot boundary detection can learn from other related fields

모호성을 포함하고 있는 시계열 패턴인식을 위한 새로운 모델 RFAM과 그 응용

본 논문에서는 모호성을 포함하고 있는 시계열 패턴인식을 위한 새로운 인식모델인 순환퍼지기억장치를 제안한다. 순환퍼지기억장치는 기존의 퍼지기억장치에 순차적인 입력패턴를 처리하고 시간적 관련성을 표현할 수 있는 순환층을 추가함으로써 확장된 모델이다. 본 논문에서 제안하는 순환퍼지기억장치는 입력과 출력사이의 관련정도를 설정하기 위해 헤비안 방식의 학습알고리즘을 사용한다. 그리고 순환퍼지기억장치의 순환층에 필요한 가중치를 학습하기 위해서 오류역전파 알고리즘을 이용한다. 본 논문에서는 제안하는 모델을 음성신호의 경계를 추출하는 문제에 적용하여 성능을 평가한다. This paper proposes a novel recognition model, a recurrent fuzzy associative memory(RFAM), for recognizing time-series patterns contained an ambiguity. RFAM is basically extended from FAM(Fuzzy Associative memory) by adding a recurrent layer which can be used to deal with sequential input patterns and to characterize their temporal relations. RFAM provides a Hebbian-style learning method which establishes the degree of association between input and output. The error back-propagation algorithm is also adopted to train the weights of the recurrent layer of RFAM. To evaluate the performance of the proposed model, we applied it to a word boundary detection problem of speech signal.

Neural-networks-based edges selector for boundary extraction problems

In the present work, a neural-networks-based system is presented that makes it possible to reduce, when generating edge maps to be used in an object boundary detection problem, the number of edges that are not due to the object itself, but to the background. Starting from a conventional edge detection, the selection is carried out by a neural-networks-based classifier, which is trained using examples. As a test for the system, the application to bovine livestock images (from which we want to extract the boundary of the animal) is presented.

Differential strain measurement using multiplexed fiber Bragg grating sensors

We investigate differential measurement of strain using fiber Bragg gratings and spatial demultiplexing methods. Differential measurement is shown to improve strain resolution in the presence of noise by an average of 34% compared to absolute measurements of the Bragg wavelengths. Spatial demultiplexing of the gratings provides potentially high-resolution sensing without calculating the sensor wavelengths. The differential method is still temperature dependent. We apply the differential technique to the problem of oil-water boundary detection in production columns, with promising preliminary results.

Numerical solution of a boundary detection problem using genetic algorithms

In this paper we consider the identification of the geometric structure of the boundary of the solution domain for the two-dimensional Laplace equation. We investigate the determination of the location, size and shape of an unknown portion γ⊂ ∂Ω of the boundary ∂Ω of a solution domain Ω⊂R 2 from Cauchy data on the remaining portion of the boundary ∂Ω\\γ or on a subset Γ⊂ ∂Ω\\γ of this part of the boundary. This problem arises in the study of quantitative non-destructive evaluation of corrosion in materials in which boundary measurements of currents and voltages are used to determine the material loss caused by corrosion. The domain identification problem is considered as a variational problem to minimise a defect functional, which utilises some additional data on certain known parts of the boundary. A real coded genetic algorithm, combined with a function specification method, is used in order to minimise the objective functional. The Laplace equation is discretised using the boundary element method. Numerical results are presented and discussed for several test examples.

A robust word boundary detection algorithm for variable noise-level environment in cars

This paper discusses the problem of automatic word boundary detection in the presence of variable-level background noise in cars. Commonly used robust word boundary detection algorithms always assume that the background noise level is fixed and sets fixed thresholds to find the boundary of word signal. In fact, the background noise level in cars varies in the procedure of recording due to speed change and moving environment, and some thresholds should be tuned according to the variation of background noise level. This is the major reason that most robust word boundary detection algorithms cannot work well in the condition of variable background noise level. To solve this problem, we propose a minimum mel-scale frequency band (MiMSB) parameter which can estimate the varying background noise level in cars by adaptively choosing one band with minimum energy from the mel-scale frequency bank. With the MiMSB parameter, some preset thresholds used to find the boundary of word signal are no longer fixed in all the recording intervals. These thresholds will be tuned according to the MiMSB parameter. We also propose an enhanced time frequency (ETF) parameter by extending the time-frequency (TF) parameter proposed by Junqua et al. from single band to multiband spectrum analysis, where the frequency bands help to make the distinction between speech signal and noise. The ETF parameter can extract useful frequency information by choosing some bands of the mel-scale frequency bank. Based on the MiMSB and ETF parameters, we finally propose a new robust algorithm for word boundary detection in variable noise-level environment. The new algorithm has been tested over a variety of noise conditions in cars and has been found to perform well not only under variable background noise level condition, but also under fixed background noise level condition. The new robust algorithm using the MiMSB and ETF parameters achieved higher recognition rate than the TF-based robust algorithm, which has been shown to outperform several commonly used algorithms, by about 5% in variable background noise level condition. It also reduced the recognition error rate due to endpoint detection to 25%, compared to an average of 34% obtained with the TF-based robust algorithm.

A recurrent neural fuzzy network for word boundary detection in variable noise-level environments

This paper discusses the problem of automatic word boundary detection in the presence of variable-level background noise. Commonly used robust word boundary detection algorithms always assume that the background noise level is fixed. In fact, the background noise level may vary during the procedure of recording. This is the major reason that most robust word boundary detection algorithms cannot work well in the condition of variable background noise level. In order to solve this problem, we first propose a refined time-frequency (RTF) parameter for extracting both the time and frequency features of noisy speech signals. The RTF parameter extends the (time-frequency) TF parameter proposed by Junqua et al. from single band to multiband spectrum analysis, where the frequency bands help to make the distinction between speech signal and noise clear. The RTF parameter can extract useful frequency information. Based on this RTF parameter, we further propose a new word boundary detection algorithm by using a recurrent self-organizing neural fuzzy inference network (RSONFIN). Since RSONPIN can process the temporal relations, the proposed RTF-based RSONFIN algorithm can find the variation of the background noise level and detect correct word boundaries in the condition of variable background noise level. As compared to normal neural networks, the RSONFIN can always find itself an economic network size with high-learning speed. Due to the self-learning ability of RSONFIN, this RTF-based RSONFIN algorithm avoids the need for empirically determining ambiguous decision rules in normal word boundary detection algorithms. Experimental results show that this new algorithm achieves higher recognition rate than the TF-based algorithm which has been shown to outperform several commonly used word boundary detection algorithms by about 12% in variable background noise level condition, It also reduces the recognition error rate due to endpoint detection to about 23%, compared to an average of 47% obtained by the TF-based algorithm in the same condition.

2D+T acoustic boundary detection in echocardiography

In this paper we address the problem of spatio-temporal acoustic boundary detection in echocardiography. We propose a phase-based feature detection method to be used as the front end to higher-level 2D+T/3D+T reconstruction algorithms. We develop a 2D+T version of this algorithm and illustrate its performance on some typical echocardiogram sequences. We show how our temporal-based algorithm helps to reduce the number of spurious feature responses due to speckle and provides feature velocity estimates. Further, our approach is intensity-amplitude invariant. This makes it particularly attractive for echocardiographic segmentation, where choosing a single global intensity-based edge threshold is problematic.

Simultaneous detection of lane and pavement boundaries using model-based multisensor fusion

Treats a problem arising in the design of intelligent vehicles: automated detection of lane and pavement boundaries using forward-looking optical and radar imaging sensors mounted on an automobile. In previous work, lane and pavement boundaries have always been located separately. This separate detection strategy is problematic in situations when either the optical or the radar image is too noisy. We propose a Bayesian multisensor image fusion method to solve our boundary detection problem. This method makes use of a deformable template model to globally describe the boundaries of interest. The optical and radar imaging processes are described with random field likelihoods. The multisensor fusion boundary detection problem is reformulated as a joint MAP estimation problem. However, the joint MAP estimate is intractable, as it involves the computation of a notoriously difficult normalization constant, also known as the partition function. Therefore, we settle for the so-called empirical MAP estimate, as an approximation to the true MAP estimate. Several experimental results are provided to demonstrate the efficacy of the empirical MAP estimation method in simultaneously detecting lane and pavement boundaries. Fusion of multi-modal images is not only of interest to the intelligent vehicles community, but to others as well, such as biomedicine, remote sensing, target recognition. The method presented in the paper is also applicable to image fusion problems in these other areas.

Word boundary detection with mel-scale frequency bank in noisy environment

This paper addresses the problem of automatic word boundary detection in the presence of noise. We first propose an adaptive time-frequency (ATF) parameter for extracting both the time and frequency features of noisy speech signals. The ATF parameter extends the TF parameter proposed by Junqua et al. (1994) from single band to multiband spectrum analysis, where the frequency bands help to make the distinction of speech and noise signals clear. The ATF parameter can extract useful frequency information by adaptively choosing proper bands of the mel-scale frequency bank. The ATF parameter increased the recognition rate by about 3% of a TF-based robust algorithm which has been shown to outperform several commonly used algorithms for word boundary detection in the presence of noise. The ATF parameter also reduced the recognition error rate due to endpoint detection to about 20%. Based on the ATF parameter, we further propose a new word boundary detection algorithm by using a neural fuzzy network (called SONFIN) for identifying islands of word signals in a noisy environment. Due to the self-learning ability of SONFIN, the proposed algorithm avoids the need of empirically determining thresholds and ambiguous rules in normal word boundary detection algorithms. As compared to normal neural networks, the SONFIN can always find itself an economic network size in high learning speed. Our results also showed that the SONFIN's performance is not significantly affected by the size of training set. The ATF-based SONFIN achieved higher recognition rate than the TF-based robust algorithm by about 5%. It also reduced the recognition error rate due to endpoint detection to about 10%, compared to an average of approximately 30% obtained with the TF-based robust algorithm, and 50% obtained with the modified version of the Lamel et al. (1981) algorithm.

Optimal boundary detection on grey-tone image

The paper is devoted to the problem of optimal boundary detection for a two-object image. The objects have different average brightnesses under conditions of stationary noise. There is no a priori information about the average brightness of the objects, the type and parameters of noise density distribution functions, the shapes of the objects or the boundary length. However, the location of an initial point belonging to an object contour is presumed to be known. It is shown that an unknown contour line can only be found among level lines. A graph is constructed from a group of level lines passing through the initial point. The problem of finding the optimal path on the graph is solved according to a pre-defined quality criterion. The investigation included previously known criteria (maximization of gradient sums, the average gradient maximization) as well as some proposed by the authors (average risk minimization, as applied to a segmentation task, and alternative choice). The efficiency of the criteria is tested on a set of image models with different signal-to-noise ratios. An effective suboptimal tracking algorithm is developed for practical tasks of grey-tone image segmentation.

A robust algorithm for word boundary detection in the presence of noise

The authors address the problem of automatic word boundary detection in quiet and in the presence of noise. Attention has been given to automatic word boundary detection for both additive noise and noise-induced changes in the talker's speech production (Lombard reflex). After a comparison of several automatic word boundary detection algorithms in different noisy-Lombard conditions, they propose a new algorithm that is robust in the presence of noise. This new algorithm identifies islands of reliability (essentially the portion of speech contained between the first and the last vowel) using time and frequency-based features and then, after a noise classification, applies a noise adaptive procedure to refine the boundaries. It is shown that this new algorithm outperforms the commonly used algorithm developed by Lamel (1981) et al. and several other recently developed methods. They evaluated the average recognition error rate due to word boundary detection in an HMM-based recognition system across several signal-to-noise ratios and noise conditions. The recognition error rate decreased to about 20% compared to an average of approximately 50% obtained with a modified version of the Lamel et al. algorithm.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>