Robust face recognition under illumination variations is an important and challenging task in a face recognition system, particularly for face recognition in the wild. In this paper, a face image preprocessing approach, called spatial adaptive shadow compensation (SASC), is proposed to eliminate shadows in the face image due to different lighting directions. First, spatial adaptive histogram equalization (SAHE), which uses face intensity prior model, is proposed to enhance the contrast of each local face region without generating visible noises in smooth face areas. Adaptive shadow compensation (ASC), which performs shadow compensation in each local image block, is then used to produce a wellcompensated face image appropriate for face feature extraction and recognition. Finally, null-space linear discriminant analysis (NLDA) is employed to extract discriminant features from SASC compensated images. Experiments performed on the Yale B, Yale B extended, and CMU PIE face databases have shown that the proposed SASC always yields the best face recognition accuracy. That is, SASC is more robust to face recognition under illumination variations than other shadow compensation approaches.
In this paper, a nearest feature line (NFL) embedding transformation is proposed for dimension reduction of
hyperspectral image (HSI). Eigenspace projection approaches are generally used for feature extraction of HSI in remote
sensing image classification. In order to improve the classification accuracy, the feature vectors of high dimensions are
reduced to the low dimensionalities by the effective projection transformation. Similarly, the proposed NFL
measurement is embedded into the transformation during the discriminant analysis stage instead of the matching stage.
The class separability, neighborhood structure preservation, and NFL measurement are also simultaneously considered to
find the effective and discriminating transformation in eigenspaces for image classification. The nearest neighbor
classifier is used to show the discriminative performance. The proposed NFL embedding transformation is compared
with several conventional state-of-the-art algorithms. It was evaluated by the AVIRIS data sets of Northwest Tippecanoe
County. Experimental results have demonstrated that NFL embedding method is an effective transformation for
dimension reduction in land cover classification of earth remote sensing.
KEYWORDS: Video, Video compression, Chromium, Head, Optical engineering, Detection and tracking algorithms, Feature extraction, Video surveillance, Motion analysis, Algorithm development
A new approach is proposed for human behavior classification from MPEG compressed videos. Moving objects are first detected by subtracting the dc values in I frames from those in the background. In addition, the dc values of the background are also adapted to avoid noise and illumination change. The tracking process is then performed using the consecutive frames. Motion vectors extracted from P frames are used to predict the next position of moving objects. An overlapping table is constructed to determine relationships between moving objects, and the number of moving objects is updated. For analyzing human behavior, motion vectors and velocities of moving objects from P and B frames are extracted. These features are clustered to codewords using a codebook generated by vector quantization (VQ) for the input of discrete hidden Markov models (HMMs). By applying the HMM, four kinds of human behaviors are successfully identified from the human behavior sequences. The proposed approach is, furthermore, more accurate than the previous method based on conventional features.
KEYWORDS: Video surveillance, Video, Digital video recorders, Feature extraction, Cameras, Databases, Control systems, Biometrics, Surveillance systems, Intelligence systems
In this paper, a novel on-line signature verification approach
is proposed for personal authentication in video surveillance
systems. As we know, digit password-based authentication is the
most popular manner in many network-based applications. However,
if the passwords were leaked, the monitoring data are easily
falsified. Biometric-based authentication using signature features
is a natural and friendly approach to remedy this problem. In this
study, a signature-based authentication is proposed to identify
the individuals by using the template matching strategy. Some
experimental results were conducted to show the effectiveness of
our proposed methods.
A novel technique is proposed for data fusion of earth remote sensing. The method is developed for land cover classification based on fusion of remote sensing images of the same scene collected from multiple sources. It presents a framework for fusion of multisource remote sensing images, which consists of two algorithms, referred to as the greedy modular eigenspace (GME) and the feature scale uniformity transformation (FSUT). The GME method is designed to extract features by a simple and efficient GME feature module, while the FSUT is performed to fuse most correlated features from different data sources. Finally, an optimal positive Boolean function based multiclass classifier is further developed for classification. It utilizes the positive and negative sample learning ability of the minimum classification error criteria to improve classification accuracy. The performance of the proposed method is evaluated by fusing MODIS/ASTER airborne simulator (MASTER) images and the airborne synthetic aperture radar (SAR) images for land cover classification during the PacRim II campaign. Experimental results demonstrate that the proposed fusion approach is an effective method for land cover classification in earth remote sensing, and improves the precision of image classification significantly compared to conventional single source classification.
High-dimensional spectral imageries obtained from multispectral, hyperspectral or even ultraspectral bands generally provide complementary characteristics and analyzable information. Synthesis of these data sets into a composite image containing such complementary attributes in accurate registration and congruence would provide truly connected information about land covers for the remote sensing community. In this paper, a novel feature selection algorithm applied to the greedy modular eigenspaces (GME) is proposed to explore a multi-class classification technique using data fused from data gathered by the MODIS/ASTER airborne simulator (MASTER) and the Airborne Synthetic Aperture Radar (AIRSAR) during the Pacrim II campaign. The proposed approach, based on a synergistic use of these fused data, represents an effective and flexible utility for land cover classifications in earth remote sensing. An optimal positive Boolean function (PBF) based multi-classifier is built by using the labeled samples of these data as the classifier parameters in a supervised training stage. It utilizes the positive and negative sample learning ability of minimum classification error criteria to improve the classification accuracy. It is proved that the proposed method improves the precision of image classification significantly.
In this paper, a novel filter-based greedy modular subspace (GMS)technique is proposed to improve the accuracy of high-dimensional remote sensing image supervisor classification. The approach initially divides the whole set of high-dimensional features into several arbitrary number of highly correlated subgroup by performing a greedy correlation matrix reordering transformation for each class. These GMS can be regarded as a unique feature for each distinguishable class in high-dimensional data sets. The similarity measures are next calculated by projecting the samples into different modular feature subspaces. Finally, a supervised multi-class classifer which is implemented based on positive Boolean function (PBF) schemes is adopted to build a non-linear optimal classifer. A PBF is exactly one sum-of-product form without any negative components. The PBF possesses the well-known threshold decomposition and stacking properties. The classification errors can be calculated from the summation of the absolute errors incurred at each level. The optimal PBF are found and designed as a classifer by minimize the classification error rate among the training samples. Experimental results demonstrate that the proposed GMS feature extraction method suits the PBF classifer best as a classification preprocess. It signifcantly improves the precision of image classification compared with conventional feature extraction schemes. Moreover, a practicable and convenient "vague" boundary sampling property of PBF is introduced to visually select training samples from high-dimensional data sets more effciently.
In this paper, a greedy and A*-based searching algorithm is proposed to find the optimal morphological filter on binary images. According to the Matheron representation, the estimator for mean square error (MSE) is defined as the union of multiple erosions. Unfortunately, finding the optimal solution is a long search and time consuming procedure because we have to compute the MSE values over all possible structuring element combinations and make comparisons among them. In this presented paper, the search for the solution is reduced to the problem of obtaining a path with the minimal cost from the root node to one vertex on error code graph. Two graph searching techniques, greedy and A* algorithms are applied to avoid the search on the extremely large number of search space. Experimental results are illustrated to show the efficiency and performances of our proposed method.
We present a parallel mechanism for detecting contours embedded in a binary image. The proposed algorithm can detect contours in parallel in sequential machines with less computational effort. The hardware architecture of the algorithm is also proposed. Experiments with a wide variety of binary images show that the speed of this new technique is much faster than that of other contour detection metho
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