7385162
On the use of high-order ambiguity function for multi-component polynomial phase signals
Abstract Nonstationary signals appear often in real-life applications and many of them can be modeled as polynomial-phase signals (PPS). High-order ambiguity function (HAF) was first introduced to estimate the parameters of a single-component PPS, but has not been widely used for multi-component PPS because of its nonlinearity. Multi-component PPS arise for example, in Doppler radar applications when multiple objects are tracked simultaneously. In this paper, we show that HAF is virtually additive for multi-component PPS and suggest an algorithm to estimate their parameters. Numerical examples are presented to illustrate the theories.
117639554
Multidimensional two-scale dilation equations
Abstract . This work involves the study of multidimensional scaling functions. These functions satisfy a two-scale dilation equation, just as the 1-D scaling functions which are used to construct 1-D wavelets. Two frameworks are presented for studying these dilation equations. Fourier methods are used to analyze existence, uniqueness and regularity of solutions. Expansions with infinite matrix products are used to derive an iterated function system (IFS) algorithm for generating the scaling function. Application to subdivision schemes for surface generation is presented, as a genuine multidimensional example where scaling functions arise and the IFS algorithm can be applied.
527851
Efficient constrained local model fitting for non-rigid face alignment
Active appearance models (AAMs) have demonstrated great utility when being employed for non-rigid face alignment/tracking. The "simultaneous" algorithm for fitting an AAM achieves good non-rigid face registration performance, but has poor real time performance (2-3 fps). The "project-out" algorithm for fitting an AAM achieves faster than real time performance (> 200 fps) but suffers from poor generic alignment performance. In this paper we introduce an extension to a discriminative method for non-rigid face registration/tracking referred to as a constrained local model (CLM). Our proposed method is able to achieve superior performance to the "simultaneous" AAM algorithm along with real time fitting speeds (35 fps). We improve upon the canonical CLM formulation, to gain this performance, in a number of ways by employing: (i) linear SVMs as patch-experts, (ii) a simplified optimization criteria, and (iii) a composite rather than additive warp update step. Most notably, our simplified optimization criteria for fitting the CLM divides the problem of finding a single complex registration/warp displacement into that of finding N simple warp displacements. From these N simple warp displacements, a single complex warp displacement is estimated using a weighted least-squares constraint. Another major advantage of this simplified optimization lends from its ability to be parallelized, a step which we also theoretically explore in this paper. We refer to our approach for fitting the CLM as the "exhaustive local search" (ELS) algorithm. Experiments were conducted on the CMU Multi-PIE database.
11631224
An Algorithm for Finding Parameter Dependent: Connected Components of Gray Images
In a previous work we have introduced the concept of a parameter-dependent connected component of grayscale images that takes into account both the gray values of the pixels and the di erences of the gray values of the neighboring pixels. This concept is a convenient tool to analyze or understand images at a higher level than the pixel level. In this paper, we describe an algorithm for nding the parameter-dependent components for a given image. We discuss di erent strategies used in the algorithm and analyze their e ects through the experimental results. Since the proposed algorithm is independent of the formation of the images, it can be used for the analysis of many types of images. The experimental results show that for some appropriate values of the parameters, the objects of an image may be represented by its parameter-dependent components reasonably well. Thus, the proposed algorithm provides us with the posibility of analyzing images further at the component level.
38555873
Recognizing Human Action from Videos Using Histograms of Visual Words
We propose a new method for human action recognition from video sequences using histograms of visual words. Video sequences are represented by a novel "bag-of-words" representation, where each frame corresponds to a "word". The major difference between our model and previous "bag-of-words" models for recognition problems in computer vision is that, a "word" in our representation corresponds to a whole frame. The advantage of this representation is that the large-scale feature of a frame is better captured, which turns out to be important for recognition actions. We demonstrate our approach on two publicly available datasets. Our results are comparable to other state-of-the-art approaches for action recognition.
2310778
Boosting an associative classifier
Associative classification is a new classification approach integrating association mining and classification. It becomes a significant tool for knowledge discovery and data mining. However, high-order association mining is time consuming when the number of attributes becomes large. The recent development of the AdaBoost algorithm indicates that boosting simple rules could often achieve better classification results than the use of complex rules. In view of this, we apply the AdaBoost algorithm to an associative classification system for both learning time reduction and accuracy improvement. In addition to exploring many advantages of the boosted associative classification system, this paper also proposes a new weighting strategy for voting multiple classifiers.
3104032
Robot Navigation by Waypoints
In this paper we propose a novel waypoint-based robot navigation method that combines reactive and deliberative actions. The approach uses reactive exploration to generate waypoints that can then be used by a deliberative system to plan future movements through the same environment. The waypoints are used largely to provide the interface between reactive and deliberative navigation and a range of methods could be used for either type of navigation. In the current work, an incremental decision tree method is used to navigate the robot reactively from the specified initial position to its destination avoiding obstacles in its path and a genetic algorithm method is used to perform the deliberative navigation. The new method is shown to have a number of practical advantages. Firstly, in contrast with many deliberative approaches, complete knowledge of the environment is not required, nor is it necessary to make assumptions regarding the geometry of obstacles. Secondly, the presence of a reactive navigator means it is always possible to continue directed movements in unknown or changing environments or when time constraints become particularly demanding. Thirdly, the use of waypoints allows escape from certain obstacle configurations that would normally trap robots navigated under the control of purely reactive methods. In addition, the results presented in this paper from a number of realistic simulated environments show that the adoption of waypoints significantly reduces the time to calculate a deliberative path.