the understanding of data is highly relevant to how one senses and perceives them. the existing approaches for classification have been developed mainly based on exploring the intrinsic structure of dataset itself les...
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the understanding of data is highly relevant to how one senses and perceives them. the existing approaches for classification have been developed mainly based on exploring the intrinsic structure of dataset itself less or no emphasis paid on simulating human visual cognition. A new hyper surface classification method (HSC) has been studied since 2002. HSC is a universal classification method, in which a model of hyper surface is obtained by adoptively dividing the sample space and then the hyper surface is directly used to classify large database based on Jordan curve theorem in topology. In this paper we point out that HSC is a cognitive data visualization method. Simulation results show the effectiveness of the proposed method on large test data with complex distribution and high density. In particular, we show that HSC can very often bring a significant reduction of computation effort without loss of prediction capability.
Toward better understandings of emotions, we argue how emotions should be synthesized, from phenomenological, evolutionary-psychological, and embodied perspectives. In particular, based on the assumption that an emoti...
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Toward better understandings of emotions, we argue how emotions should be synthesized, from phenomenological, evolutionary-psychological, and embodied perspectives. In particular, based on the assumption that an emotion consist of appraisal process and reaction/expression process, we propose a novel model of primitive emotions, in which an appraisal mechanism exploits informations of "embodied dissonance" existing as conflicts among desired and actual states. the model consists of action modules representing motivation, and evaluation modules representing actual states, both of the modules are coordinated by a constraint satisfaction neural network which behaves by a minimization principle of its neural energy. By using a physically simulated bipedal creature, we show that the interplay of neural and body-environment dynamics leads to syncope/fainting-similar states which emerge as adaptive responses to embodied dissonance, that "drastic and random movements" coupled with dissonance leads to the transitions of stimulus-response patterns from approach to avoidance, and that the transitions let the creature to cope with high-order problems, the causal structure of which is not perceived nor recognized by the creature. Taking these results into account, we discuss three things; (1) a principle to discriminate emotional and non-emotional cold behaviors of artificial agents; (2) a prospective architecture of appraisal processes of animal's emotions; and (3) a prospective account for the difference among fear, anxiety, hysteria, anger, and sadness. this study may shed light on a link of emotions and intelligence.
Microarray data provides quantitative information about the transcription profile of cells. To analyze microarray datasets, methodology of machine learning has increasingly attracted bioinformatics researchers. Some a...
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ISBN:
(纸本)0769525210
Microarray data provides quantitative information about the transcription profile of cells. To analyze microarray datasets, methodology of machine learning has increasingly attracted bioinformatics researchers. Some approaches of machine learning are widely used to classify and mine biological datasets. However, many gene expression datasets are extremely high dimensionality, traditional machine learning methods can not be applied effectively and efficiently. this paper proposes a robust algorithm to find out rule groups to classify gene expression datasets. Unlike the most classification algorithms, which select dimensions (genes) heuristically to form rules groups to identify classes such as cancerous and normal tissues, our algorithm guarantees finding out best-k dimensions (genes), which are most discriminative to classify samples in different classes, to form rule groups for the classification of expression datasets. Our experiments show that the rule groups obtained by our algorithm have higher accuracy than that of other classification approaches.
Pixel force field (PFF) is a novel image representation where at each pixel a two-dimensional vector is defined for representing interaction of pixels. the vector is oriented to the center of the region composed of pi...
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ISBN:
(纸本)0769525210
Pixel force field (PFF) is a novel image representation where at each pixel a two-dimensional vector is defined for representing interaction of pixels. the vector is oriented to the center of the region composed of pixels having the same qualitative property, such as color and gray-scale level. Using the pixel force field and improved live-wire segmentation technique the task of interactive road extraction from remote sensing images is solved.
In this paper, the algorithm for thinning of grey-scale images is proposed that is based on a pseudo-distance map (PDM). the PDM is a simplified distance map of gray-scale image and uses only that features of image an...
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ISBN:
(纸本)0769525210
In this paper, the algorithm for thinning of grey-scale images is proposed that is based on a pseudo-distance map (PDM). the PDM is a simplified distance map of gray-scale image and uses only that features of image and objects that are necessary to build a skeleton. the algorithm works fast for large gray-scale images and allows constructing a high quality skeleton.
Various algorithms for the automatic extraction of features from micrographs of forged INCONEL 718 (TM) will be presented in this paper this includes the extraction of an optimized boundary image, the elimination of s...
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ISBN:
(纸本)0769525210
Various algorithms for the automatic extraction of features from micrographs of forged INCONEL 718 (TM) will be presented in this paper this includes the extraction of an optimized boundary image, the elimination of scratches and parallel lines ("twins") and from subsequent evaluation and the detection of delta phase particles. Requested features are grain size, the amount and distribution of 5 phase with respect to grain boundaries and anisotropic effects.
Protein fold recognition has been the focus of computational biologists for many years. In order to map a protein primary structure to its correct 3D fold, we introduce in this paper a machine learning paradigm that w...
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ISBN:
(纸本)0769525210
Protein fold recognition has been the focus of computational biologists for many years. In order to map a protein primary structure to its correct 3D fold, we introduce in this paper a machine learning paradigm that we entitled 11 structural hidden Markov model" (SHMM). We show how the concept of SHMM can efficiently use the protein secondary structure during the fold recognition task. Experimental results showed that the SHMM outperforms the SVM with a 6% improvement in the average accuracy. However, because in this application the two classifiers are not correlated, therefore their combination based on the highest rank criterion boosted the SHMM average accuracy with 10%.
In this paper we propose a new probabilistic approach to red eye detection and correction. It is based on stepwise refinement of a pixel-wise red eye probability map. Red eye detection starts with a fast non red eye r...
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ISBN:
(纸本)0769525210
In this paper we propose a new probabilistic approach to red eye detection and correction. It is based on stepwise refinement of a pixel-wise red eye probability map. Red eye detection starts with a fast non red eye region rejection step. A classification step then adjusts the probabilities attributed to the detected red eye candidates. the correction step finally applies a soft red eye correction based on the resulting probability map. the proposed approach is fast and allows achieving an excellent correction of strong red eyes while producing a still significant correction of weaker red eyes.
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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ISBN:
(纸本)0769525210
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.
this paper proposes a method of correcting intensity of a color image, which is used for the texture of a 3D model, using a range intensity image. A range intensity image has an effective characteristic that it is obt...
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ISBN:
(纸本)0769525210
this paper proposes a method of correcting intensity of a color image, which is used for the texture of a 3D model, using a range intensity image. A range intensity image has an effective characteristic that it is obtained under controlled illumination. this enables correction of the intensity of the color image without estimating illumination for the color image. Experiments show the effectiveness of the proposed method and a 3D model with proper color information generated by the method.
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