This paper describes a fusion of visual and thermal infrared (IR) images for robust face recognit... more This paper describes a fusion of visual and thermal infrared (IR) images for robust face recognition. Two types of fusion methods are discussed: data fusion and decision fusion. Data fusion produces an illumination-invariant face image by adaptively integrating registered visual and thermal face images. Decision fusion combines matching scores of individual face recognition modules. In the data fusion process, eyeglasses, which block thermal energy, are detected from thermal images and replaced with an eye template. Three fusion-based face recognition techniques are implemented and tested: Data fusion of visual and thermal images (Df), Decision fusion with highest matching score (Fh), and Decision fusion with average matching score (Fa). A commercial face recognition software FaceIt ® is used as an individual recognition module. Comparison results show that fusion-based face recognition techniques outperformed individual visual and thermal face recognizers under illumination variations and facial expressions.
IEEE Transactions on Systems, Man, and Cybernetics, 2010
Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ens... more Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera's field of view (FOV). However, visibility, which is a fundamental requirement of object tracking, is insufficient for automated persistent surveillance. In such applications, a continuous consistently labeled trajectory of the same object should be maintained across different camera views. Therefore, a sufficient uniform overlap between the cameras' FOVs should be secured so that camera handoff can successfully and automatically be executed before the object of interest becomes untraceable or unidentifiable. In this paper, we propose sensor-planning methods that improve existing algorithms by adding handoff rate analysis. Observation measures are designed for various types of cameras so that the proposed sensor-planning algorithm is general and applicable to scenarios with different types of cameras. The proposed sensor-planning algorithm preserves necessary uniform overlapped FOVs between adjacent cameras for an optimal balance between coverage and handoff success rate. In addition, special considerations such as resolution and frontal-view requirements are addressed using two approaches: 1) direct constraint and 2) adaptive weights. The resulting camera placement is compared with a reference algorithm published by Erdem and Sclaroff. Significantly improved handoff success rates and frontal-view percentages are illustrated via experiments using indoor and outdoor floor plans of various scales.
In size preserving video tracking, the camera's focal length (zoom) is adjusted automatically to ... more In size preserving video tracking, the camera's focal length (zoom) is adjusted automatically to compensate for the changes in the target's image size caused by the relative motion between the camera and the target. The accurate estimation of these changes is paramount to the system performance. Structure from motion (SFM) based on the weak perspective projection model has been applied to real time target scale estimation (B. Tordoff, et al., 2004). In this paper we design target scale estimation algorithms with linear solutions based on more advanced projection models: paraperspective and perspective. The performances of the algorithms using three projection models are examined and compared. Experimental results show that the proposed algorithm based on the paraperspective projection model produces the best performance with respect to accuracy, complexity, and robustness
Contrast enhancement has an important role in image processing applications. Conventional contras... more Contrast enhancement has an important role in image processing applications. Conventional contrast enhancement techniques either often fail to produce satisfactory results for a broad variety of low-contrast images, or cannot be automatically applied to different images, because their parameters must be specified manually to produce a satisfactory result for a given image. This paper describes a new automatic method for contrast enhancement. The basic procedure is to first group the histogram components of a low-contrast image into a proper number of bins according to a selected criterion, then redistribute these bins uniformly over the grayscale, and finally ungroup the previously grouped gray-levels. Accordingly, this new technique is named gray-level grouping (GLG). GLG not only produces results superior to conventional contrast enhancement techniques, but is also fully automatic in most circumstances, and is applicable to a broad variety of images. An extension of GLG, selective GLG (SGLG), and its variations will be discussed in Part II of this paper. SGLG selectively groups and ungroups histogram components to achieve specific application purposes, such as eliminating background noise, enhancing a specific segment of the histogram, and so on. The extension of GLG to color images will also be discussed in Part II.
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.
This paper summarizes the various components of face recognition research conducted at the IRIS L... more This paper summarizes the various components of face recognition research conducted at the IRIS Lab. First, fusion of visual and thermal infrared (IR) images for robust face recognition is discussed. Two techniques are implemented: data fusion and decision fusion. With the knowledge that eyeglasses block the emission of thermal energy, an algorithm is designed to detect and replace eyeglasses with an eye template in thermal images. A commercial face recognition software (FaceIt ® ) is used in the evaluation of the various fusion algorithms. Comparison results show that fusion-based face recognition outperforms individual visual or thermal face recognizers under illumination variations and facial expressions. Efforts in the 3D arena are also described. Results of high resolution stereo-based 3D reconstruction of faces are shown and analyzed, in a first approach, then in a second approach, a warping technique is applied to overlay color and thermal textures on 3D mannequin head models, obtained using a laser range scanner.
Face recognition is a rapidly growing research area due to increasing demands for security in com... more Face recognition is a rapidly growing research area due to increasing demands for security in commercial and law enforcement applications. This paper provides an up-to-date review of research efforts in face recognition techniques based on two-dimensional (2D) images in the visual and infrared (IR) spectra. Face recognition systems based on visual images have reached a significant level of maturity with some practical success. However, the performance of visual face recognition may degrade under poor illumination conditions or for subjects of various skin colors. IR imagery represents a viable alternative to visible imaging in the search for a robust and practical identification system. While visual face recognition systems perform relatively reliably under controlled illumination conditions, thermal IR face recognition systems are advantageous when there is no control over illumination or for detecting disguised faces. Face recognition using 3D images is another active area of face recognition, which provides robust face recognition with changes in pose. Recent research has also demonstrated that the fusion of different imaging modalities and spectral components can improve the overall performance of face recognition.
Very few image processing applications have dealt with x-ray luggage scenes in the past. Conceale... more Very few image processing applications have dealt with x-ray luggage scenes in the past. Concealed threats in general, and low-density items in particular, pose a major challenge to airport screeners. A simple enhancement method for data decluttering is introduced. Initially, the method is applied using manually selected thresholds to progressively generate decluttered slices. Further automation of the algorithm, using a novel metric based on the Radon transform, is conducted to determine the optimum number and values of thresholds and to generate a single optimum slice for screener interpretation. A comparison of the newly developed metric to other known metrics demonstrates the merits of the new approach. On-site quantitative and qualitative evaluations of the various decluttered images by airport screeners further establishes that the single slice from the image hashing algorithm outperforms traditional enhancement techniques with a noted increase of 58% in lowdensity threat detection rates.
Although sensor planning in computer vision has been a subject of research for over two decades, ... more Although sensor planning in computer vision has been a subject of research for over two decades, a vast majority of the research seems to concentrate on two particular applications in a rather limited context of laboratory and industrial workbenches, namely 3D object reconstruction and robotic arm manipulation. Recently, increasing interest is engaged in research to come up with solutions that provide wide-area autonomous surveillance systems for object characterization and situation awareness, which involves portable, wireless, and/or Internet connected radar, digital video, and/or infrared sensors. The prominent research problems associated with multisensor integration for wide-area surveillance are modality selection, sensor planning, data fusion, and data exchange (communication) among multiple sensors. Thus, the requirements and constraints to be addressed include far-field view, wide coverage, high resolution, cooperative sensors, adaptive sensing modalities, dynamic objects, and uncontrolled environments. This article summarizes a new survey and analysis conducted in light of these challenging requirements and constraints. It involves techniques and strategies from work done in the areas of sensor fusion, sensor networks, smart sensing, Geographic Information Systems (GIS), photogrammetry, and other intelligent systems where finding optimal solutions to the placement and deployment of multimodal sensors covering a wide area is important. While techniques covered in this survey are applicable to many wide-area environments such as traffic monitoring, airport terminal surveillance, parking lot surveillance, etc., our examples will be drawn mainly from such applications as harbor security and long-range face recognition.
Automatic tracking is essential for a 24 hours intruder-detection and, more generally, a surveill... more Automatic tracking is essential for a 24 hours intruder-detection and, more generally, a surveillance system. This paper presents an adaptive background generation and the corresponding moving region detection techniques for a Pan-Tilt-Zoom (PTZ) camera using a geometric transform-based mosaicing method. A complete system including adaptive background generation, moving regions extraction and tracking is evaluated using realistic experimental results. More specifically, experimental results include generated background images, a moving region, and input video with bounding boxes around moving objects. This experiment shows that the proposed system can be used to monitor moving targets in widely open areas by automatic panning and tilting in real-time.
Applications of digital imaging with extreme zoom are traditionally found in astronomy and wild l... more 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 as forest 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 systems 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.
In this effort, we propose a new image fusion technique, utilizing Empirical Mode Decomposition (... more In this effort, we propose a new image fusion technique, utilizing Empirical Mode Decomposition (EMD), for improved face recognition. EMD is a non-parametric datadriven analysis tool that decomposes non-linear nonstationary signals into Intrinsic Mode Functions (IMFs). In this method, we decompose images from different imaging modalities into their IMFs. Fusion is performed at the decomposition level and the fused IMFs are reconstructed to form the fused image. The effect of fusion on face recognition is measured by obtaining the Cumulative Match Characteristics (CMCs) between galleries and probes. Apart from conducting face recognition tests on visible and infrared raw datasets, we use datasets fused by averaging, principal component (PCA) fusion, wavelet based fusion and our method, for comparison. The face recognition rate due to EMD fused images is higher than the face recognition rates due to raw visible, raw infrared and other fused images. Examples of the fused images and illustrative CMC comparison charts are shown.
In this paper, we describe a face video database, UTK-LRHM, acquired from long distances and with... more In this paper, we describe a face video database, UTK-LRHM, acquired from long distances and with high magnifications. Both indoor and outdoor sequences are collected under uncontrolled surveillance conditions. To our knowledge, it is the first database to provide face images from long distances (indoor: 10-16 m and outdoor: 50-300 m). The corresponding system magnifications range from 3· to 20· for indoor and up to 284· for outdoor. This database has applications in experimentations with human identification and authentication in long range surveillance and wide area monitoring. Deteriorations unique to long range and high magnification face images are investigated in terms of face recognition rates based on the UTK-LRHM database. Magnification blur is shown to be a major degradation source, the effect of which is quantified using a novel blur assessment measure and alleviated via adaptive deblurring algorithms. A comprehensive processing algorithm, including frame selection, enhancement, and super-resolution is introduced for long range and high magnification face images with a large variety of resolutions. Experimental results using face images of the UTK-LRHM database demonstrate a significant improvement in recognition rates after assessment and enhancement of degradations.
In size preserving video tracking, the cameras focal length (zoom) is adjusted automatically to ... more In size preserving video tracking, the cameras focal length (zoom) is adjusted automatically to compensate for the changes in the targets image size caused by the relative motion between the camera and the target. The accurate estimation of these changes is paramount to the system performance. The existing method of choice for real-time target scale estimation applies structure from motion
Active shape models can be applied to tracking non-rigid objects in video image sequences. Tradit... more Active shape models can be applied to tracking non-rigid objects in video image sequences. Traditionally these models do not include color information in their formulation. In this paper, we present a hierarchical realization of an enhanced active shape model for color video tracking and we study the performance of both hierarchical and nonhierarchical implementations in the RGB, YUV, and HSI color spaces.
This paper describes an imaging system that was developed to aid industrial bin-picking tasks. Th... more This paper describes an imaging system that was developed to aid industrial bin-picking tasks. The purpose of this system was to provide accurate 3D models of parts and objects in the bin, so that precise grasping operations could be performed. The t echnology described here is based on two types of sensors: range mapping scanners and video cameras. The geometry of bin contents was reconstructed from range maps and modeled using superquadric representations, providing location and parts surface informa tion that can be employed to guide the robotic arm. Video input served for tracking of the manipulator arm, allowing for path validation through pose estimation techniques. Texture was also provided by the video streams and applied to the recovered models. The system is expected to improve the accuracy and efficiency of bin sorting and represents a step toward full automation.
Dual camera systems (omnidirectional and slaved PTZ cameras) are widely used in public area monit... more Dual camera systems (omnidirectional and slaved PTZ cameras) are widely used in public area monitoring and target tracking. However, due to their low and non-uniform resolution, omnidirectional cameras are only able to provide moderate accuracy in both motion/target detection and tracking. To overcome this disadvantage, two methods are proposed: geometry mapping based on a polynomial imaging model and closed loop
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.
Tracking and recognizing non-rigid objects in video image sequences are complex tasks of increasi... more Tracking and recognizing non-rigid objects in video image sequences are complex tasks of increasing importance to many applications. In this paper, we present a hierarchical realization of an enhanced active shape model for color video tracking and we study the performance of hierarchical and non-hierarchical implementations in the RGB, YUV, and HSI color spaces.
This paper describes a fusion of visual and thermal infrared (IR) images for robust face recognit... more This paper describes a fusion of visual and thermal infrared (IR) images for robust face recognition. Two types of fusion methods are discussed: data fusion and decision fusion. Data fusion produces an illumination-invariant face image by adaptively integrating registered visual and thermal face images. Decision fusion combines matching scores of individual face recognition modules. In the data fusion process, eyeglasses, which block thermal energy, are detected from thermal images and replaced with an eye template. Three fusion-based face recognition techniques are implemented and tested: Data fusion of visual and thermal images (Df), Decision fusion with highest matching score (Fh), and Decision fusion with average matching score (Fa). A commercial face recognition software FaceIt ® is used as an individual recognition module. Comparison results show that fusion-based face recognition techniques outperformed individual visual and thermal face recognizers under illumination variations and facial expressions.
IEEE Transactions on Systems, Man, and Cybernetics, 2010
Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ens... more Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera's field of view (FOV). However, visibility, which is a fundamental requirement of object tracking, is insufficient for automated persistent surveillance. In such applications, a continuous consistently labeled trajectory of the same object should be maintained across different camera views. Therefore, a sufficient uniform overlap between the cameras' FOVs should be secured so that camera handoff can successfully and automatically be executed before the object of interest becomes untraceable or unidentifiable. In this paper, we propose sensor-planning methods that improve existing algorithms by adding handoff rate analysis. Observation measures are designed for various types of cameras so that the proposed sensor-planning algorithm is general and applicable to scenarios with different types of cameras. The proposed sensor-planning algorithm preserves necessary uniform overlapped FOVs between adjacent cameras for an optimal balance between coverage and handoff success rate. In addition, special considerations such as resolution and frontal-view requirements are addressed using two approaches: 1) direct constraint and 2) adaptive weights. The resulting camera placement is compared with a reference algorithm published by Erdem and Sclaroff. Significantly improved handoff success rates and frontal-view percentages are illustrated via experiments using indoor and outdoor floor plans of various scales.
In size preserving video tracking, the camera's focal length (zoom) is adjusted automatically to ... more In size preserving video tracking, the camera's focal length (zoom) is adjusted automatically to compensate for the changes in the target's image size caused by the relative motion between the camera and the target. The accurate estimation of these changes is paramount to the system performance. Structure from motion (SFM) based on the weak perspective projection model has been applied to real time target scale estimation (B. Tordoff, et al., 2004). In this paper we design target scale estimation algorithms with linear solutions based on more advanced projection models: paraperspective and perspective. The performances of the algorithms using three projection models are examined and compared. Experimental results show that the proposed algorithm based on the paraperspective projection model produces the best performance with respect to accuracy, complexity, and robustness
Contrast enhancement has an important role in image processing applications. Conventional contras... more Contrast enhancement has an important role in image processing applications. Conventional contrast enhancement techniques either often fail to produce satisfactory results for a broad variety of low-contrast images, or cannot be automatically applied to different images, because their parameters must be specified manually to produce a satisfactory result for a given image. This paper describes a new automatic method for contrast enhancement. The basic procedure is to first group the histogram components of a low-contrast image into a proper number of bins according to a selected criterion, then redistribute these bins uniformly over the grayscale, and finally ungroup the previously grouped gray-levels. Accordingly, this new technique is named gray-level grouping (GLG). GLG not only produces results superior to conventional contrast enhancement techniques, but is also fully automatic in most circumstances, and is applicable to a broad variety of images. An extension of GLG, selective GLG (SGLG), and its variations will be discussed in Part II of this paper. SGLG selectively groups and ungroups histogram components to achieve specific application purposes, such as eliminating background noise, enhancing a specific segment of the histogram, and so on. The extension of GLG to color images will also be discussed in Part II.
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.
This paper summarizes the various components of face recognition research conducted at the IRIS L... more This paper summarizes the various components of face recognition research conducted at the IRIS Lab. First, fusion of visual and thermal infrared (IR) images for robust face recognition is discussed. Two techniques are implemented: data fusion and decision fusion. With the knowledge that eyeglasses block the emission of thermal energy, an algorithm is designed to detect and replace eyeglasses with an eye template in thermal images. A commercial face recognition software (FaceIt ® ) is used in the evaluation of the various fusion algorithms. Comparison results show that fusion-based face recognition outperforms individual visual or thermal face recognizers under illumination variations and facial expressions. Efforts in the 3D arena are also described. Results of high resolution stereo-based 3D reconstruction of faces are shown and analyzed, in a first approach, then in a second approach, a warping technique is applied to overlay color and thermal textures on 3D mannequin head models, obtained using a laser range scanner.
Face recognition is a rapidly growing research area due to increasing demands for security in com... more Face recognition is a rapidly growing research area due to increasing demands for security in commercial and law enforcement applications. This paper provides an up-to-date review of research efforts in face recognition techniques based on two-dimensional (2D) images in the visual and infrared (IR) spectra. Face recognition systems based on visual images have reached a significant level of maturity with some practical success. However, the performance of visual face recognition may degrade under poor illumination conditions or for subjects of various skin colors. IR imagery represents a viable alternative to visible imaging in the search for a robust and practical identification system. While visual face recognition systems perform relatively reliably under controlled illumination conditions, thermal IR face recognition systems are advantageous when there is no control over illumination or for detecting disguised faces. Face recognition using 3D images is another active area of face recognition, which provides robust face recognition with changes in pose. Recent research has also demonstrated that the fusion of different imaging modalities and spectral components can improve the overall performance of face recognition.
Very few image processing applications have dealt with x-ray luggage scenes in the past. Conceale... more Very few image processing applications have dealt with x-ray luggage scenes in the past. Concealed threats in general, and low-density items in particular, pose a major challenge to airport screeners. A simple enhancement method for data decluttering is introduced. Initially, the method is applied using manually selected thresholds to progressively generate decluttered slices. Further automation of the algorithm, using a novel metric based on the Radon transform, is conducted to determine the optimum number and values of thresholds and to generate a single optimum slice for screener interpretation. A comparison of the newly developed metric to other known metrics demonstrates the merits of the new approach. On-site quantitative and qualitative evaluations of the various decluttered images by airport screeners further establishes that the single slice from the image hashing algorithm outperforms traditional enhancement techniques with a noted increase of 58% in lowdensity threat detection rates.
Although sensor planning in computer vision has been a subject of research for over two decades, ... more Although sensor planning in computer vision has been a subject of research for over two decades, a vast majority of the research seems to concentrate on two particular applications in a rather limited context of laboratory and industrial workbenches, namely 3D object reconstruction and robotic arm manipulation. Recently, increasing interest is engaged in research to come up with solutions that provide wide-area autonomous surveillance systems for object characterization and situation awareness, which involves portable, wireless, and/or Internet connected radar, digital video, and/or infrared sensors. The prominent research problems associated with multisensor integration for wide-area surveillance are modality selection, sensor planning, data fusion, and data exchange (communication) among multiple sensors. Thus, the requirements and constraints to be addressed include far-field view, wide coverage, high resolution, cooperative sensors, adaptive sensing modalities, dynamic objects, and uncontrolled environments. This article summarizes a new survey and analysis conducted in light of these challenging requirements and constraints. It involves techniques and strategies from work done in the areas of sensor fusion, sensor networks, smart sensing, Geographic Information Systems (GIS), photogrammetry, and other intelligent systems where finding optimal solutions to the placement and deployment of multimodal sensors covering a wide area is important. While techniques covered in this survey are applicable to many wide-area environments such as traffic monitoring, airport terminal surveillance, parking lot surveillance, etc., our examples will be drawn mainly from such applications as harbor security and long-range face recognition.
Automatic tracking is essential for a 24 hours intruder-detection and, more generally, a surveill... more Automatic tracking is essential for a 24 hours intruder-detection and, more generally, a surveillance system. This paper presents an adaptive background generation and the corresponding moving region detection techniques for a Pan-Tilt-Zoom (PTZ) camera using a geometric transform-based mosaicing method. A complete system including adaptive background generation, moving regions extraction and tracking is evaluated using realistic experimental results. More specifically, experimental results include generated background images, a moving region, and input video with bounding boxes around moving objects. This experiment shows that the proposed system can be used to monitor moving targets in widely open areas by automatic panning and tilting in real-time.
Applications of digital imaging with extreme zoom are traditionally found in astronomy and wild l... more 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 as forest 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 systems 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.
In this effort, we propose a new image fusion technique, utilizing Empirical Mode Decomposition (... more In this effort, we propose a new image fusion technique, utilizing Empirical Mode Decomposition (EMD), for improved face recognition. EMD is a non-parametric datadriven analysis tool that decomposes non-linear nonstationary signals into Intrinsic Mode Functions (IMFs). In this method, we decompose images from different imaging modalities into their IMFs. Fusion is performed at the decomposition level and the fused IMFs are reconstructed to form the fused image. The effect of fusion on face recognition is measured by obtaining the Cumulative Match Characteristics (CMCs) between galleries and probes. Apart from conducting face recognition tests on visible and infrared raw datasets, we use datasets fused by averaging, principal component (PCA) fusion, wavelet based fusion and our method, for comparison. The face recognition rate due to EMD fused images is higher than the face recognition rates due to raw visible, raw infrared and other fused images. Examples of the fused images and illustrative CMC comparison charts are shown.
In this paper, we describe a face video database, UTK-LRHM, acquired from long distances and with... more In this paper, we describe a face video database, UTK-LRHM, acquired from long distances and with high magnifications. Both indoor and outdoor sequences are collected under uncontrolled surveillance conditions. To our knowledge, it is the first database to provide face images from long distances (indoor: 10-16 m and outdoor: 50-300 m). The corresponding system magnifications range from 3· to 20· for indoor and up to 284· for outdoor. This database has applications in experimentations with human identification and authentication in long range surveillance and wide area monitoring. Deteriorations unique to long range and high magnification face images are investigated in terms of face recognition rates based on the UTK-LRHM database. Magnification blur is shown to be a major degradation source, the effect of which is quantified using a novel blur assessment measure and alleviated via adaptive deblurring algorithms. A comprehensive processing algorithm, including frame selection, enhancement, and super-resolution is introduced for long range and high magnification face images with a large variety of resolutions. Experimental results using face images of the UTK-LRHM database demonstrate a significant improvement in recognition rates after assessment and enhancement of degradations.
In size preserving video tracking, the cameras focal length (zoom) is adjusted automatically to ... more In size preserving video tracking, the cameras focal length (zoom) is adjusted automatically to compensate for the changes in the targets image size caused by the relative motion between the camera and the target. The accurate estimation of these changes is paramount to the system performance. The existing method of choice for real-time target scale estimation applies structure from motion
Active shape models can be applied to tracking non-rigid objects in video image sequences. Tradit... more Active shape models can be applied to tracking non-rigid objects in video image sequences. Traditionally these models do not include color information in their formulation. In this paper, we present a hierarchical realization of an enhanced active shape model for color video tracking and we study the performance of both hierarchical and nonhierarchical implementations in the RGB, YUV, and HSI color spaces.
This paper describes an imaging system that was developed to aid industrial bin-picking tasks. Th... more This paper describes an imaging system that was developed to aid industrial bin-picking tasks. The purpose of this system was to provide accurate 3D models of parts and objects in the bin, so that precise grasping operations could be performed. The t echnology described here is based on two types of sensors: range mapping scanners and video cameras. The geometry of bin contents was reconstructed from range maps and modeled using superquadric representations, providing location and parts surface informa tion that can be employed to guide the robotic arm. Video input served for tracking of the manipulator arm, allowing for path validation through pose estimation techniques. Texture was also provided by the video streams and applied to the recovered models. The system is expected to improve the accuracy and efficiency of bin sorting and represents a step toward full automation.
Dual camera systems (omnidirectional and slaved PTZ cameras) are widely used in public area monit... more Dual camera systems (omnidirectional and slaved PTZ cameras) are widely used in public area monitoring and target tracking. However, due to their low and non-uniform resolution, omnidirectional cameras are only able to provide moderate accuracy in both motion/target detection and tracking. To overcome this disadvantage, two methods are proposed: geometry mapping based on a polynomial imaging model and closed loop
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.
Tracking and recognizing non-rigid objects in video image sequences are complex tasks of increasi... more Tracking and recognizing non-rigid objects in video image sequences are complex tasks of increasing importance to many applications. In this paper, we present a hierarchical realization of an enhanced active shape model for color video tracking and we study the performance of hierarchical and non-hierarchical implementations in the RGB, YUV, and HSI color spaces.
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