2008 15th IEEE International Conference on Image Processing, 2008
ABSTRACT Multispectral imaging and fusion of multispectral images in visible spectrum have been e... more ABSTRACT Multispectral imaging and fusion of multispectral images in visible spectrum have been employed and proved to enhance the recognition performance over conventional broad-band images, especially when the illumination changes. In this paper, we further our study toward the recognition performance of a smaller number of spectral band images. Given the illumination, we propose an automated method that specifies the optimal spectral ranges to achieve an improved recognition performance compared with conventional broad-band images. Our discovery can be practically used for a new customized sensor design associated with given illuminations for an improved face recognition performance over conventional broad-band images.
Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004
A new criterion based on Gaussian fields is introduced and applied to the task of automatic rigid... more A new criterion based on Gaussian fields is introduced and applied to the task of automatic rigid registration of point-sets. The method defines a simple energy function, which is always differentiable and convex in a large neighborhood of the alignment parameters; allowing for the use of powerful standard optimization techniques. We show that the size of the region of convergence can be extended so that no close initialization is needed, thus overcoming local convergence problems of Iterative Closest Point algorithms. Furthermore, the Gaussian energy function can be evaluated with linear complexity using the Fast Gauss Transform, which permits efficient implementation of the registration algorithm. Analysis through several experimental results on real world datasets shows the practicality and points out the limits of the approach.
Color constancy is one of the important research areas with a wide range of applications in the f... more Color constancy is one of the important research areas with a wide range of applications in the fields of color image processing and computer vision. One such application is video tracking. Color is used as one of the salient features and its robustness to illumination variation is essential to the adaptability of video tracking algorithms. Color constancy can be applied to discount the influence of changing illuminations. In this paper, we present a review of established color constancy approaches. We also investigate whether these approaches in their present form of implementation can be applied to the video tracking problem. The approaches are grouped into two categories, namely, Pre-Calibrated and Data-driven approaches. The paper also talks about the ill-posedness of the color constancy problem, implementation assumptions of color constancy approaches, and problem statement for tracking. Publications on video tracking algorithms involving color correction or color compensation techniques are not included in this review.
Fourth IEEE International Conference on Computer Vision Systems (ICVS'06), 2006
Abstract Applications of digital imaging with extreme zoom are traditionally found in astronomy ,... more Abstract Applications of digital imaging with extreme zoom are traditionally found in astronomy ,and wild life monitoring. More recently, the need for such capabilities has extended to wide area monitoring such asforest fires, harbors, and waterways. This paper presents a number ,of sensor arrangements for the acquisition of high ,magnification images. It also describes user interfaces developed for the system’s remote control and data acquisition and processing. Hardware and software considerations are addressed, focusing on optical ,setups and image de-blurring techniques. An image restoration algorithm tailored to such systems is developed, where an adaptive sharpness measure is employed as a ,cost function to guide the search for an optimal point spread function (PSF) for image ,de-blur. Experimental ,results demonstrate a considerably enhanced robustness to noise and ability to select the optimum PSF, producing much superior restored images.
18th International Conference on Pattern Recognition (ICPR'06), 2006
A novel physics-based fusion of multispectral images within the visual spectra is proposed for th... more A novel physics-based fusion of multispectral images within the visual spectra is proposed for the purpose of improving face recognition under constant or varying illumination. Spectral images are fused according to the physics properties of the imaging system, including illumination, spectral response of the camera, and spectral reflectance of skin. The fused image is given as a probe to the recognition software FaceIt ® which compares it to a gallery of images. The identification performance of our physics-based fusion method is compared to the performance of Principle Component Analysis and average fusion methods. The results show that the proposed fusion yields a higher identification rate. A method of illumination adjustment is proposed when the probe and gallery images are acquired under different illumination conditions. The results show that the identification rate is higher than that of unadjusted gray-level images.
The ability to produce high quality images and 3D reconstructions of objects leads to better defe... more The ability to produce high quality images and 3D reconstructions of objects leads to better defect detection and also a better understanding of the quantum effects that occur at the nano-level. This presentation reviews recent advances in the use of raw microscope data for the creation of 3D models representing real micro and nano-structures. The presentation is divided into five sections, each section dealing with a different set of algorithms and microscopy technology. The different technologies and algorithms include morphological reconstruction from atomic force microscopy (AFM), tomographic reconstruction methods used in transmission electrom microscopy (TEM), interferometry methods and optical microscopes, stereo imaging with scanning electron microscopy (SEM), and multifocus methods using confocal and general optical microscopes.
Novel image fusion approaches, including physics-based weighted fusion, illumination adjustment a... more Novel image fusion approaches, including physics-based weighted fusion, illumination adjustment and rank-based decision level fusion, for spectral face images are proposed for improving face recognition performance compared to conventional images. A new multispectral imaging system is briefly presented which can acquire continuous spectral face images for our concept proof with fine spectral resolution in the visible spectrum. Several experiments are designed and validated by calculating the cumulative match characteristics of probe sets via the well-known recognition engine-FaceIt . Experimental results demonstrate that proposed fusion methods outperform conventional images when gallery and probes are acquired under different illuminations and with different time lapses. In the case where probe images are acquired outdoors under different daylight situations, the fused images outperform conventional images by up to 78%.
This is Part II of the paper, "Gray-Level Grouping (GLG): an Automatic Method for Optimized Image... more This is Part II of the paper, "Gray-Level Grouping (GLG): an Automatic Method for Optimized Image Contrast Enhancement". Part I of this paper introduced a new automatic contrast enhancement technique: gray-level grouping (GLG). GLG is a general and powerful technique, which can be conveniently applied to a broad variety of low-contrast images and outperforms conventional contrast enhancement techniques. However, the basic GLG method still has limitations and cannot enhance certain classes of low-contrast images well, e.g., images with a noisy background. The basic GLG also cannot fulfill certain special application purposes, e.g., enhancing only part of an image which corresponds to a certain segment of the image histogram. In order to break through these limitations, this paper introduces an extension of the basic GLG algorithm, selective gray-level grouping (SGLG), which groups the histogram components in different segments of the grayscale using different criteria and, hence, is able to enhance different parts of the histogram to various extents. This paper also introduces two new preprocessing methods to eliminate background noise in noisy low-contrast images so that such images can be properly enhanced by the (S)GLG technique. The extension of (S)GLG to color images is also discussed in this paper. SGLG and its variations extend the capability of the basic GLG to a larger variety of low-contrast images, and can fulfill special application requirements. SGLG and its variations not only produce results superior to conventional contrast enhancement techniques, but are also fully automatic under most circumstances, and are applicable to a broad variety of images.
This is Part II of the paper, "Gray-Level Grouping (GLG): an Automatic Method for Optimized Image... more This is Part II of the paper, "Gray-Level Grouping (GLG): an Automatic Method for Optimized Image Contrast Enhancement". Part I of this paper introduced a new automatic contrast enhancement technique: gray-level grouping (GLG). GLG is a general and powerful technique, which can be conveniently applied to a broad variety of low-contrast images and outperforms conventional contrast enhancement techniques. However, the basic GLG method still has limitations and cannot enhance certain classes of low-contrast images well, e.g., images with a noisy background. The basic GLG also cannot fulfill certain special application purposes, e.g., enhancing only part of an image which corresponds to a certain segment of the image histogram. In order to break through these limitations, this paper introduces an extension of the basic GLG algorithm, selective gray-level grouping (SGLG), which groups the histogram components in different segments of the grayscale using different criteria and, hence, is able to enhance different parts of the histogram to various extents. This paper also introduces two new preprocessing methods to eliminate background noise in noisy low-contrast images so that such images can be properly enhanced by the (S)GLG technique. The extension of (S)GLG to color images is also discussed in this paper. SGLG and its variations extend the capability of the basic GLG to a larger variety of low-contrast images, and can fulfill special application requirements. SGLG and its variations not only produce results superior to conventional contrast enhancement techniques, but are also fully automatic under most circumstances, and are applicable to a broad variety of images.
2008 15th IEEE International Conference on Image Processing, 2008
ABSTRACT Multispectral imaging and fusion of multispectral images in visible spectrum have been e... more ABSTRACT Multispectral imaging and fusion of multispectral images in visible spectrum have been employed and proved to enhance the recognition performance over conventional broad-band images, especially when the illumination changes. In this paper, we further our study toward the recognition performance of a smaller number of spectral band images. Given the illumination, we propose an automated method that specifies the optimal spectral ranges to achieve an improved recognition performance compared with conventional broad-band images. Our discovery can be practically used for a new customized sensor design associated with given illuminations for an improved face recognition performance over conventional broad-band images.
Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004
A new criterion based on Gaussian fields is introduced and applied to the task of automatic rigid... more A new criterion based on Gaussian fields is introduced and applied to the task of automatic rigid registration of point-sets. The method defines a simple energy function, which is always differentiable and convex in a large neighborhood of the alignment parameters; allowing for the use of powerful standard optimization techniques. We show that the size of the region of convergence can be extended so that no close initialization is needed, thus overcoming local convergence problems of Iterative Closest Point algorithms. Furthermore, the Gaussian energy function can be evaluated with linear complexity using the Fast Gauss Transform, which permits efficient implementation of the registration algorithm. Analysis through several experimental results on real world datasets shows the practicality and points out the limits of the approach.
Color constancy is one of the important research areas with a wide range of applications in the f... more Color constancy is one of the important research areas with a wide range of applications in the fields of color image processing and computer vision. One such application is video tracking. Color is used as one of the salient features and its robustness to illumination variation is essential to the adaptability of video tracking algorithms. Color constancy can be applied to discount the influence of changing illuminations. In this paper, we present a review of established color constancy approaches. We also investigate whether these approaches in their present form of implementation can be applied to the video tracking problem. The approaches are grouped into two categories, namely, Pre-Calibrated and Data-driven approaches. The paper also talks about the ill-posedness of the color constancy problem, implementation assumptions of color constancy approaches, and problem statement for tracking. Publications on video tracking algorithms involving color correction or color compensation techniques are not included in this review.
Fourth IEEE International Conference on Computer Vision Systems (ICVS'06), 2006
Abstract Applications of digital imaging with extreme zoom are traditionally found in astronomy ,... more Abstract Applications of digital imaging with extreme zoom are traditionally found in astronomy ,and wild life monitoring. More recently, the need for such capabilities has extended to wide area monitoring such asforest fires, harbors, and waterways. This paper presents a number ,of sensor arrangements for the acquisition of high ,magnification images. It also describes user interfaces developed for the system’s remote control and data acquisition and processing. Hardware and software considerations are addressed, focusing on optical ,setups and image de-blurring techniques. An image restoration algorithm tailored to such systems is developed, where an adaptive sharpness measure is employed as a ,cost function to guide the search for an optimal point spread function (PSF) for image ,de-blur. Experimental ,results demonstrate a considerably enhanced robustness to noise and ability to select the optimum PSF, producing much superior restored images.
18th International Conference on Pattern Recognition (ICPR'06), 2006
A novel physics-based fusion of multispectral images within the visual spectra is proposed for th... more A novel physics-based fusion of multispectral images within the visual spectra is proposed for the purpose of improving face recognition under constant or varying illumination. Spectral images are fused according to the physics properties of the imaging system, including illumination, spectral response of the camera, and spectral reflectance of skin. The fused image is given as a probe to the recognition software FaceIt ® which compares it to a gallery of images. The identification performance of our physics-based fusion method is compared to the performance of Principle Component Analysis and average fusion methods. The results show that the proposed fusion yields a higher identification rate. A method of illumination adjustment is proposed when the probe and gallery images are acquired under different illumination conditions. The results show that the identification rate is higher than that of unadjusted gray-level images.
The ability to produce high quality images and 3D reconstructions of objects leads to better defe... more The ability to produce high quality images and 3D reconstructions of objects leads to better defect detection and also a better understanding of the quantum effects that occur at the nano-level. This presentation reviews recent advances in the use of raw microscope data for the creation of 3D models representing real micro and nano-structures. The presentation is divided into five sections, each section dealing with a different set of algorithms and microscopy technology. The different technologies and algorithms include morphological reconstruction from atomic force microscopy (AFM), tomographic reconstruction methods used in transmission electrom microscopy (TEM), interferometry methods and optical microscopes, stereo imaging with scanning electron microscopy (SEM), and multifocus methods using confocal and general optical microscopes.
Novel image fusion approaches, including physics-based weighted fusion, illumination adjustment a... more Novel image fusion approaches, including physics-based weighted fusion, illumination adjustment and rank-based decision level fusion, for spectral face images are proposed for improving face recognition performance compared to conventional images. A new multispectral imaging system is briefly presented which can acquire continuous spectral face images for our concept proof with fine spectral resolution in the visible spectrum. Several experiments are designed and validated by calculating the cumulative match characteristics of probe sets via the well-known recognition engine-FaceIt . Experimental results demonstrate that proposed fusion methods outperform conventional images when gallery and probes are acquired under different illuminations and with different time lapses. In the case where probe images are acquired outdoors under different daylight situations, the fused images outperform conventional images by up to 78%.
This is Part II of the paper, "Gray-Level Grouping (GLG): an Automatic Method for Optimized Image... more This is Part II of the paper, "Gray-Level Grouping (GLG): an Automatic Method for Optimized Image Contrast Enhancement". Part I of this paper introduced a new automatic contrast enhancement technique: gray-level grouping (GLG). GLG is a general and powerful technique, which can be conveniently applied to a broad variety of low-contrast images and outperforms conventional contrast enhancement techniques. However, the basic GLG method still has limitations and cannot enhance certain classes of low-contrast images well, e.g., images with a noisy background. The basic GLG also cannot fulfill certain special application purposes, e.g., enhancing only part of an image which corresponds to a certain segment of the image histogram. In order to break through these limitations, this paper introduces an extension of the basic GLG algorithm, selective gray-level grouping (SGLG), which groups the histogram components in different segments of the grayscale using different criteria and, hence, is able to enhance different parts of the histogram to various extents. This paper also introduces two new preprocessing methods to eliminate background noise in noisy low-contrast images so that such images can be properly enhanced by the (S)GLG technique. The extension of (S)GLG to color images is also discussed in this paper. SGLG and its variations extend the capability of the basic GLG to a larger variety of low-contrast images, and can fulfill special application requirements. SGLG and its variations not only produce results superior to conventional contrast enhancement techniques, but are also fully automatic under most circumstances, and are applicable to a broad variety of images.
This is Part II of the paper, "Gray-Level Grouping (GLG): an Automatic Method for Optimized Image... more This is Part II of the paper, "Gray-Level Grouping (GLG): an Automatic Method for Optimized Image Contrast Enhancement". Part I of this paper introduced a new automatic contrast enhancement technique: gray-level grouping (GLG). GLG is a general and powerful technique, which can be conveniently applied to a broad variety of low-contrast images and outperforms conventional contrast enhancement techniques. However, the basic GLG method still has limitations and cannot enhance certain classes of low-contrast images well, e.g., images with a noisy background. The basic GLG also cannot fulfill certain special application purposes, e.g., enhancing only part of an image which corresponds to a certain segment of the image histogram. In order to break through these limitations, this paper introduces an extension of the basic GLG algorithm, selective gray-level grouping (SGLG), which groups the histogram components in different segments of the grayscale using different criteria and, hence, is able to enhance different parts of the histogram to various extents. This paper also introduces two new preprocessing methods to eliminate background noise in noisy low-contrast images so that such images can be properly enhanced by the (S)GLG technique. The extension of (S)GLG to color images is also discussed in this paper. SGLG and its variations extend the capability of the basic GLG to a larger variety of low-contrast images, and can fulfill special application requirements. SGLG and its variations not only produce results superior to conventional contrast enhancement techniques, but are also fully automatic under most circumstances, and are applicable to a broad variety of images.
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