Concurrent engineering (CE) is intertwined with the field of computer-aided engineering (CAE). The author presents a vision of future for CE and CAE where computational intelligence (CI) will play an increasingly sign...
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ISBN:
(纸本)9781586036515
Concurrent engineering (CE) is intertwined with the field of computer-aided engineering (CAE). The author presents a vision of future for CE and CAE where computational intelligence (CI) will play an increasingly significant role. Various disciplines within CE such as design, manufacturing, knowledge management, collaborative computing, Web processes and services, and distributed infrastructures must rely heavily on CI to achieve the increasing sophistication demand. The author has been advocating and advancing a multi-paradigm approach for solution of complicated and noisy computational intelligence problems. In 1995 he co-authored Machine Learning - Neural Networks, Genetic Algorithms, and Fuzzy Systems [1] the first authored book that presented and integrated the three principal soft computing and computational intelligence approaches. It was shown that such integration would provide a more powerful approach than any of the three approaches used individually. Since the publication of that ground-breaking book the author and his associates have demonstrated that chaos theory and wavelets can be used to further enhance computational intelligence especially for complicated and noisy patternrecognition problems. In this lecture it is shown how wavelets can be used as a powerful tool to complement and enhance other soft computing techniques such as neural networks and fuzzy logic as well as the chaos theory for solution of complicated and seemingly intractable Cl problems. Examples of research performed by the author and his research associates in the areas of intelligent transportation systems [2-4], vibrations control [5-8], and nonlinear system identification [9-10] are presented.
In visual-based robot navigation, panoramic vision emerges as a very attractive candidate for solving the localization task. Unfortunately, current systems rely on specific feature selection processes that do not cove...
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ISBN:
(纸本)0769525210
In visual-based robot navigation, panoramic vision emerges as a very attractive candidate for solving the localization task. Unfortunately, current systems rely on specific feature selection processes that do not cover the requirements of general purpose robots. In order to fulfil new requirements of robot versatility and robustness to environmental changes, we propose in this paper to perform the feature selection of a panoramic vision system by means of the saliency-based model of visual attention, a model known for its universality. The first part of the paper describes a localization system combining panoramic vision and visual attention. The second part presents a series of indoor localization experiments using panoramic vision and attention guided feature detection. The results show the feasibility of the approach and illustrate some of its capabilities
A new approach to mammographic mass detection is presented in this paper. Although different algorithms have been proposed for such a task, most of them are application dependent. In contrast, our approach makes use o...
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A new approach to mammographic mass detection is presented in this paper. Although different algorithms have been proposed for such a task, most of them are application dependent. In contrast, our approach makes use of a kindred topic in computervision adapted to our particular problem. In this sense, we translate the eigenfaces approach for face detection/classification problems to a mass detection. Two different databases were used to show the robustness of the approach. The first one consisted on a set of 160 regions of interest (RoIs) extracted from the MIAS database, being 40 of them with confirmed masses and the rest normal tissue. The second set of RoIs was extracted from the DDSM database, and contained 196 RoIs containing masses and 392 with normal, but suspicious regions. Initial results demonstrate the feasibility of using such approach with performances comparable to other algorithms, with the advantage of being a more general, simple and cost-effective approach
Henry Schneiderman at Carnegie Mellon University developed a face detection algorithm based upon a semi-naive Bayesian classifier and 5/3 linear phase wavelets. This paper explores the relative value of these wavelet ...
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Henry Schneiderman at Carnegie Mellon University developed a face detection algorithm based upon a semi-naive Bayesian classifier and 5/3 linear phase wavelets. This paper explores the relative value of these wavelet features compared to simpler pixel and edge features. Experiments suggest edge features are superior for highly controlled lighting, while pixel features are better and more stable for uncontrolled lighting. Tests use the Notre Dame face data collected in Fall 2003 and Spring 2004 and use over 400, 000 face and non-face test image chips
This work is about the communication system used by honeybees with the idea of designing a new intelligent approach for 3D reconstruction. A new framework is proposed to allow the communication between 3D points in or...
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ISBN:
(纸本)0769525210
This work is about the communication system used by honeybees with the idea of designing a new intelligent approach for 3D reconstruction. A new framework is proposed to allow the communication between 3D points in order to achieve an improved quasi-dense reconstruction. We have presented a novel bioinspired evolutionary algorithm based on the honeybee search behavior. The advantage of using the honeybee search algorithm is the robustness against outliers. Moreover, this work opens the avenue towards new intelligent reconstruction that we are planning to use as a sonar in a mobile robot
This paper presents a new algorithm for extracting the centerlines of 2D and 3D objects, based on clustering. The algorithm computes the centerline from all points of the object in order to remain faithful to the stru...
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This paper presents a new algorithm for extracting the centerlines of 2D and 3D objects, based on clustering. The algorithm computes the centerline from all points of the object in order to remain faithful to the structure of the shape. The idea is to cluster a data set constituted of the points composing the object and their relative distance transforms. The centerline is derived from the set of computed clusters. The proposed method is accurate and robust to noisy boundaries
It has been shown that integrating multiple cues will increase the reliability and robustness of a vision system in situations that no single cue is reliable. In this paper, we propose a method by fusing multiple cues...
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It has been shown that integrating multiple cues will increase the reliability and robustness of a vision system in situations that no single cue is reliable. In this paper, we propose a method by fusing multiple cues (i.e., the color cue and the edge cue). In contrast to previous work, we propose a novel shape similarity measure which includes the spatial distribution of the number of and the gradient intensity of the edge points. We integrate this shape similarity measure with our recently proposed SMOG-based color similarity measure in the framework of particle filter (PF). Experimental results demonstrate the high robustness and effectiveness of our method in handling appearance changes, cluttered background, moving camera, and occlusions
This paper presents a novel algorithm for unsupervised texture segmentation. The proposed algorithm incorporates the local binary pattern operator under a segmentation framework based on the active contour without edg...
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ISBN:
(纸本)0769525210
This paper presents a novel algorithm for unsupervised texture segmentation. The proposed algorithm incorporates the local binary pattern operator under a segmentation framework based on the active contour without edges model. The experiments performed, show that it can be used for fast segmentation of two-textured images, outperforming recent texture segmentation algorithms, with a segmentation quality that reaches 99% on average
Typically, algorithms for generating stereo disparity maps have been developed to minimise the energy equation of a single image. This paper proposes a method for implementing cross validation in a belief propagation ...
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Typically, algorithms for generating stereo disparity maps have been developed to minimise the energy equation of a single image. This paper proposes a method for implementing cross validation in a belief propagation optimisation. When tested using the Middlebury online stereo evaluation, the cross validation improves upon the results of standard belief propagation. Furthermore, it has been shown that regions of homogeneous colour within the images can be used for enforcing the so-called "segment constraint". Developing from this, segment support is introduced to boost belief between pixels of the same image region and improve propagation into textureless regions
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