11895032
A Signal-Dependent Quadratic Time Frequency Distribution for Neural Source Estimation
A novel method for kernel design of a quadratic time frequency distribution (TFD) as the initial step for neural source estimation is proposed. The kernel is constructed based on the product ambiguity function (AF), which efficiently suppresses cross terms and noise in the ambiguity domain. In order to reduce the influence from the strong signal to the weak signal, an iterative approach is implemented. Simulation results validate the method and demonstrate suppression of cross terms and noise, and high resolution in the time frequency domain.
25531643
Multicomponent chirp signals analysis using product cubic phase function
This paper presents an algorithm for estimating the parameters of multicomponent chirp signals. The estimator is based on the cubic phase function (CPF), which is efficient to estimate the parameters of monocomponent first-, second-, and third-order polynomial phase signal. When the CPF is dealing with multicomponent chirp signals, the spurious peaks arise and hence the identifiability problem exists. A new approach based on the product cubic phase function (PCPF) is proposed to remove the identifiability problem. The occurrence probability of spurious peak, and the effect of noise to the estimator are statistically studied. The simulation examples are provided for validating the theoretical analysis.
15177304
A new method of color image segmentation based on intensity and hue clustering
A new method of color image segmentation is proposed. It is based on the K-means algorithm in HSI color space and has the advantage over those based on the RGB space. Both the hue and the intensity components are fully utilized. In the process of hue clustering, the special cyclic property of the hue component is taken into consideration. The paper gives the definition of the distance and the center in the hue space, based on which the hue-clustering algorithm is implemented. Utilised in medical image processing, the new method gives a good performance.
52809451
Improving Face Recognition by Online Image Alignment
Face recognition accuracy is affected by many factors. This paper studies one of the factors, and provides a reliable image alignment for face recognition. For this purpose, a performance metric is extracted from an analysis of face recognition similarity scores. The metric varies with face alignment, and has a relationship with the actual recognition accuracy. Our method adjusts face alignment online by selecting an alignment candidate corresponding to the largest performance metric. The experimental results show that the presented method can improve the accuracy and robustness of current face recognition systems
15572586
Complexity metrics for self-monitoring impact sensing networks
In this paper we describe novel metrics measuring complexity in self-organising networks. The metrics are investigated within the context of decentralised inspections, developed and implemented as part of the joint CSIRO-NASA Ageless Aerospace Vehicle (AAV) research project. The AAV Concept Demonstrator is a hardware multi-cellular sensing and communication network which is expected to detect and react to multiple impacts, without any centralised controllers. We present an extension of an ant colony optimisation algorithm, using an adaptive dead reckoning scheme and producing robust and reconfigurable minimum spanning trees connecting autonomous AAV cells. We then introduce a new metric detecting emergence through irregularities in the multi-agent communications, and contrast it with conventional macro-level ("global-view") graph-theoretic metrics.
38910166
Parallel Object Representation and Recognition
A parallel method for 3-dimensional (3d) object recognition is introduced, using the concept of coordinated graph, layered graph representation, and parallel matching techniques, and significantly reduces the time required for dealing with 3-dimensional image analysis problems. Their fundamental parallel properties and concept of finite representations are investigated and several interested examples including curved and disconnected images are illustrated. In addition to its importance in theoretical parallel study, it can also be applied to 3-d parallel object recognition, image processing and computer vision.
6309219
Contextual browsing for highlights in sports video
A novel highlight browsing approach for sports videos, based on contextual cue categorization, is proposed in this paper. Contextual cues are those salient audio and visual cues around highlight segments, and they are also informative and attractive to audiences. With further analysis and categorization of these contextual cues, the user could access highlights more flexibly and effectively. An experimental system is built for broadcasting diving videos in our current work, in which contextual cues such as slow-motion replays and game statistics information are extracted and categorized for highlight browsing. An efficient algorithm for slow-motion replay detection and categorization in diving videos is also proposed. Experimental results on 2 hours broadcasting diving videos have demonstrated the effectiveness and efficiency of the proposed approach.