Since PCA-based teeth-image personal identification method (K. Prajuabklang, et al., 2004) is not robust against reflection and orientation, registered persons in database are rejected around 7%. This paper proposes a...
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Since PCA-based teeth-image personal identification method (K. Prajuabklang, et al., 2004) is not robust against reflection and orientation, registered persons in database are rejected around 7%. This paper proposes a method to improve the PCA-based teeth-image personal identification method. In this method, the teeth image failed from the matching in the PCA-based system is reconsidered by feeding back the image to eliminate the reflection and the rotation problems. The enhanced teeth image is fed back to PCA process in order to rescue misclassified teeth-image. In the experiments, 25 teeth images are tested with 20-teeth database. The results revealed that of the 7% errors caused by the two problems, 5% are correctly identified because of the proposed method
Support vector machines (SVM) are state-of-the-art learning machines and have found a great deal of success in a wide range of applications. In the framework of SVM, each sample belongs to either one class or the othe...
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Support vector machines (SVM) are state-of-the-art learning machines and have found a great deal of success in a wide range of applications. In the framework of SVM, each sample belongs to either one class or the other. This requirement, however, makes it difficult to apply SVM to the applications where the data exhibit partial or unclear class memberships. To address this problem, this paper reformulates the standard SVM to be a new learning machine that is capable of dealing with binary (or hard) as well as real-valued (or soft) class memberships. The new machine, which is named soft SVM (S-SVM), has been integrated into a classification-based video object extraction approach, and the experimental results demonstrate the effectiveness of the new approach.
An image enhancement algorithm based on illuminance-reflectance model is proposed for improving the visual quality of digital images captured under insufficient and/or non-uniform lighting conditions. The paper presen...
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An image enhancement algorithm based on illuminance-reflectance model is proposed for improving the visual quality of digital images captured under insufficient and/or non-uniform lighting conditions. The paper presents computational methods for estimation of scene illuminance and reflectance, adaptive dynamic range compression of illuminance, and adaptive enhancement for mid-tone frequency components. The images are processed in a similar way as human eyes sensing a scene. The algorithm demonstrates high quality of enhanced images, robust performance and fast processing speed. Compared with Retinex and multi-scale retinex with color restoration (MSRCR), the proposed method shows a better balance between luminance enhancement and contrast enhancement as well as a more consistent and reliable color rendition without introducing incorrect colors. This is an effective technique for image enhancement with simple computational procedures, which makes real-time application successfully realized. The application of this image enhancement technique to the FRGC images yields improved face recognition results.
The independent component analysis (ICA) has been a well studied subject in recent years. Its implementation may employ neural networks or other techniques with the objective of deriving component signals or images th...
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ISBN:
(纸本)078039013X
The independent component analysis (ICA) has been a well studied subject in recent years. Its implementation may employ neural networks or other techniques with the objective of deriving component signals or images that are as independent as possible. ICA has been used in remotesensing, medical signal analysis, speech and seismic signal processing, wireless communications, nondestructive testing, and other areas. All of such applications involve knowledge intensive systems that employ a large amount of data. ICA may be considered as a mechanism for knowledge decomposition and integration, as well as multiresolution representation and generalization.
3D face recognition has lately been attracting ever increasing attention. In this paper we review the full spectrum of 3D face processing technology, from sensing to recognition. The review covers 3D face modelling, 3...
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3D face recognition has lately been attracting ever increasing attention. In this paper we review the full spectrum of 3D face processing technology, from sensing to recognition. The review covers 3D face modelling, 3D to 3D and 3D to 2D registration, 3D based recognition and 3D assisted 2D based recognition. The fusion of 2D and 3D modalities is also addressed. The paper complements other reviews in the face biometrics area by focusing on the sensor technology, and by detailing the efforts in 3D face modelling and 3D assisted 2D face matching.
One of the most interesting aspects of the world is that it can be considered made up of patterns. In the most patternrecognition problem pattern have a dynamic nature and non-adaptive algorithms (instruction sets) w...
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One of the most interesting aspects of the world is that it can be considered made up of patterns. In the most patternrecognition problem pattern have a dynamic nature and non-adaptive algorithms (instruction sets) will fail to give a realistic solution to model them. In these cases, adaptive algorithms are used and among them, neural networks have the greatest hit. For example, the defense applications very frequently need to record, detect, identify and classify images of objects or signals coming from various directions and from various sources - static or dynamic. There are many applications in remotesensing where study of dynamic data is needed such as deforestation, effects of natural and man made disasters, migration in the paths of rivers due to the dynamic nature of Earth's plates. Artificial Neural Networks (ANN) can play a role in modeling such applications because of their capability to model nonlinear processes and to identify unknown patterns and images based on their learning model, or to forecast certain outcomes by extrapolation. In this study we present results on classifying the images using SOFM classification and detect temporal changes in patterns.
The spectral radiance measured by an airborne sensor is dependent on the spectral reflectance of the ground material, the orientation of the material surface, and the atmospheric and illumination conditions. We presen...
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The spectral radiance measured by an airborne sensor is dependent on the spectral reflectance of the ground material, the orientation of the material surface, and the atmospheric and illumination conditions. We present a nonlinear algorithm for estimating surface spectral reflectance of a surface on the ground from the spectral radiance measured by an airborne sensor. The nonlinear separation algorithm uses a low-dimensional subspace model for the reflectance spectra. The algorithm also considers the inter-dependence of the path radiance and illumination spectra by using a coupled subspace model. We have applied the algorithm to a large set of 0.4-1.74 micron sensor radiance spectra. We have examined the use of the recovered reflectance vectors for material identification over a database of materials from the US geological Survey (USGS) library. We also extend the nonlinear algorithm to estimate the reflectance spectra of materials having varying orientations. We have applied the algorithm to 0.42-1.74 micron sensor radiance spectra in digital imaging and remotesensingimage generation (DIRSIG) model scenes that contain 3D objects.
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