Showing papers in "Journal of Electronic Imaging in 2015"

HDR-VDP-2.2: a calibrated method for objective quality prediction of high-dynamic range and standard images

Abstract: With the emergence of high-dynamic range (HDR) imaging, the existing visual signal processing systems will need to deal with both HDR and standard dynamic range (SDR) signals. In such systems, computing the objective quality is an important aspect in various optimization processes (e.g., video encoding). To that end, we present a newly calibrated objective method that can tackle both HDR and SDR signals. As it is based on the previously proposed HDR-VDP-2 method, we refer to the newly calibrated metric as HDR-VDP-2.2. Our main contribution is toward improving the frequency-based pooling in HDR-VDP-2 to enhance its objective quality prediction accuracy. We achieve this by formulating and solving a constrained optimization problem and thereby finding the optimal pooling weights. We also carried out extensive cross-validation as well as verified the performance of the new method on independent databases. These indicate clear improvement in prediction accuracy as compared with the default pooling weights. The source codes for HDR-VDP-2.2 are publicly available online for free download and use.

Underwater image enhancement by dehazing and color correction

Abstract: Poor visibility due to the effects of light absorption and scattering is challenging for processing underwater images. We propose an approach based on dehazing and color correction algorithms for underwater image enhancement. First, a simple dehazing algorithm is applied to remove the effects of haze in the underwater image. Second, color compensation, histogram equalization, saturation, and intensity stretching are used to improve contrast, brightness, color, and visibility of the underwater image. Furthermore, bilateral filtering is utilized to address the problem of the noise caused by the physical properties of the medium and the histogram equalization algorithm. In order to evaluate the performance of the proposed approach, we compared our results with six existing methods using the subjective technique, objective technique, and color cast tests. The results show that the proposed approach outperforms the six existing methods. The enhanced images, as a result of implementing the proposed approach, are characterized by relatively genuine color, increased contrast and brightness, reduced noise level, and better visibility.

Vehicle license plate recognition using visual attention model and deep learning

Abstract: A vehicle’s license plate is the unique feature by which to identify each individual vehicle. As an important research area of an intelligent transportation system, the recognition of vehicle license plates has been investigated for some decades. An approach based on a visual attention model and deep learning is proposed to handle the problem of Chinese car license plate recognition for traffic videos. We first use a modified visual attention model to locate the license plate, and then the license plate is segmented into seven blocks using a projection method. Two classifiers, which combine the advantages of convolutional neural network-based feature learning and support vector machine for multichannel processing, are designed to recognize Chinese characters, numbers, and alphabet letters, respectively. Experimental results demonstrate that the presented method can achieve high recognition accuracy and works robustly even under the conditions of illumination change and noise contamination.

Three-dimensional visual comfort assessment via preference learning

Abstract: Three-dimensional (3-D) visual comfort assessment (VCA) is a particularly important and challenging topic, which involves automatically predicting the degree of visual comfort in line with human subjective judgment. State-of-the-art VCA models typically focus on minimizing the distance between predicted visual comfort scores and subjective mean opinion scores (MOSs) by training a regression model. However, obtaining precise MOSs is often expensive and time-consuming, which greatly constrains the extension of existing MOS-aware VCA models. This study is inspired by the fact that humans tend to conduct a preference judgment between two stereoscopic images in terms of visual comfort. We propose to train a robust VCA model on a set of preference labels instead of MOSs. The preference label, representing the relative visual comfort of preference stereoscopic image pairs (PSIPs), is generally precise and can be obtained at much lower cost compared with MOS. More specifically, some representative stereoscopic images are first selected to generate the PSIP training set. Then, we use a support vector machine to learn a preference classification model by taking a differential feature vector and the corresponding preference label of each PSIP as input. Finally, given a testing sample, by considering a full-round paired comparison with all the selected representative stereoscopic images, the visual comfort score can be estimated via a simple linear mapping strategy. Experimental results on our newly built 3-D image database demonstrate that the proposed method can achieve a better performance compared with the models trained on MOSs.

Automated exterior inspection of an aircraft with a pan-tilt-zoom camera mounted on a mobile robot

Abstract: This paper deals with an automated preflight aircraft inspection using a pan-tilt-zoom camera mounted on a mobile robot moving autonomously around the aircraft. The general topic is image processing framework for detection and exterior inspection of different types of items, such as closed or unlatched door, mechanical defect on the engine, the integrity of the empennage, or damage caused by impacts or cracks. The detection step allows to focus on the regions of interest and point the camera toward the item to be checked. It is based on the detection of regular shapes, such as rounded corner rectangles, circles, and ellipses. The inspection task relies on clues, such as uniformity of isolated image regions, convexity of segmented shapes, and periodicity of the image intensity signal. The approach is applied to the inspection of four items of Airbus A320: oxygen bay handle, air-inlet vent, static ports, and fan blades. The results are promising and demonstrate the feasibility of an automated exterior inspection.

Effects of cultural characteristics on building an emotion classifier through facial expression analysis

Abstract: Facial expressions are an important demonstration of humanity’s humors and emotions. Algorithms capable of recognizing facial expressions and associating them with emotions were developed and employed to compare the expressions that different cultural groups use to show their emotions. Static pictures of predominantly occidental and oriental subjects from public datasets were used to train machine learning algorithms, whereas local binary patterns, histogram of oriented gradients (HOGs), and Gabor filters were employed to describe the facial expressions for six different basic emotions. The most consistent combination, formed by the association of HOG filter and support vector machines, was then used to classify the other cultural group: there was a strong drop in accuracy, meaning that the subtle differences of facial expressions of each culture affected the classifier performance. Finally, a classifier was trained with images from both occidental and oriental subjects and its accuracy was higher on multicultural data, evidencing the need of a multicultural training set to build an efficient classifier.

No-reference video quality measurement: added value of machine learning

Abstract: Video quality measurement is an important component in the end-to-end video delivery chain. Video quality is, however, subjective, and thus, there will always be interobserver differences in the subjective opinion about the visual quality of the same video. Despite this, most existing works on objective quality measurement typically focus only on predicting a single score and evaluate their prediction accuracies based on how close it is to the mean opinion scores (or similar average based ratings). Clearly, such an approach ignores the underlying diversities in the subjective scoring process and, as a result, does not allow further analysis on how reliable the objective prediction is in terms of subjective variability. Consequently, the aim of this paper is to analyze this issue and present a machine-learning based solution to address it. We demonstrate the utility of our ideas by considering the practical scenario of video broadcast transmissions with focus on digital terrestrial television (DTT) and proposing a no-reference objective video quality estimator for such application. We conducted meaningful verification studies on different video content (including video clips recorded from real DTT broadcast transmissions) in order to verify the performance of the proposed solution.

Genetic feature selection for gait recognition

Abstract: Many research studies have demonstrated that gait can serve as a useful biometric modality for human identification at a distance. Traditional gait recognition systems, however, have mostly been evaluated without explicitly considering the most relevant gait features, which might have compromised performance. We investigate the problem of selecting a subset of the most relevant gait features for improving gait recognition performance. This is achieved by discarding redundant and irrelevant gait features while preserving the most informative ones. Motivated by our previous work on feature subset selection using genetic algorithms (GAs), we propose using GAs to select an optimal subset of gait features. First, features are extracted using kernel principal component analysis (KPCA) on spatiotemporal projections of gait silhouettes. Then, GA is applied to select a subset of eigenvectors in KPCA space that best represents a subject's identity. Each gait pattern is then rep- resented by projecting it only on the eigenvectors selected by the GA. To evaluate the effectiveness of the selected features, we have experimented with two different classifiers: k nearest-neighbor and Naive Bayes classifier. We report considerable gait recognition performance improvements on the Georgia Tech and CASIA databases. © 2015 SPIE and IS&T (DOI: 10.1117/1.JEI.24.1.013036)

Automated removal of quasiperiodic noise using frequency domain statistics

Abstract: Digital images may be impaired by periodic or quasiperiodic noise, which manifests itself by spurious long-range repetitive patterns. Most of the time, quasiperiodic noise is well localized in the Fourier domain; thus it can be attenuated by smoothing out the image spectrum with a well-designed notch filter. While existing algorithms require hand-tuned filter design or parameter setting, this paper presents an automated approach based on the expected power spectrum of a natural image. The resulting algorithm enables not only the elimination of simple periodic noise whose influence on the image spectrum is limited to a few Fourier coefficients, but also of quasiperiodic structured noise with a much more complex contribution to the spectrum. Various examples illustrate the efficiency of the proposed algorithm. A comparison with morphological component analysis, a blind source separation algorithm, is also provided. A MATLAB® implementation is available.

Internal fingerprint zone detection in optical coherence tomography fingertip scans

Abstract: Optical coherence tomography (OCT) is a high-resolution imaging technology capable of capturing a three-dimensional (3-D) representation of fingertip skin. The papillary junction—a junction layer of skin containing the same topographical features as the surface fingerprint—is contained within this representation. The top edge of the papillary junction contains the topographical information pertinent to the internal fingerprint. Extracting the internal fingerprint from OCT fingertip scans has been shown to be possible. Currently, acquiring the internal fingerprint involves manually defining the region containing it. This manner of definition is inefficient. Perfect knowledge of the location of the papillary junction is hypothesized as achievable. This research details and tests a k-means clustering approach for papillary junction detection. All tested metrics are of a standard comparable to the measured human error. The technique presented in this research is highly successful in detection of the location of the papillary junction. Furthermore, high-quality internal fingerprints are acquired using the coordinates obtained.

Hyperspectral imaging applied to demolition waste recycling: innovative approach for product quality control

Abstract: Hyperspectral imaging (HSI) based sensing devices were utilized to develop nondestructive, rapid, and low cost analytical strategies finalized to detect and characterize materials constituting demolition waste. In detail, HSI was applied for quality control of high-grade recycled aggregates obtained from end-of-life concrete. The described HSI quality control approach is based on the utilization of a platform working in the near-infrared range (1000–1700 nm). The acquired hyperspectral images were analyzed by applying different chemometric methods: principal component analysis for data exploration and partial least-square-discriminant analysis to build classification models. Results showed that it is possible to recognize the recycled aggregates from different contaminants (e.g., brick, gypsum, plastic, wood, foam, and so on), allowing the quality control of the recycled flow stream.

Multifocus image fusion using phase congruency

Abstract: We address the problem of fusing multifocus images based on the phase congruency (PC). PC provides a sharpness feature of a natural image. The focus measure (FM) is identified as strong PC near a distinctive image feature evaluated by the complex Gabor wavelet. The PC is more robust against noise than other FMs. The fusion image is obtained by a new fusion rule (FR), and the focused region is selected by the FR from one of the input images. Experimental results show that the proposed fusion scheme achieves the fusion performance of the state-of-the-art methods in terms of visual quality and quantitative evaluations.

Box-constrained second-order total generalized variation minimization with a combined L1,2 data-fidelity term for image reconstruction

Abstract: Image reconstruction is a typical ill-posed inverse problem that has attracted increasing attention owing to its extensive use. To cope with the ill-posed nature of this problem, many regularizers have been presented to regularize the reconstruction process. One of the most popular regularizers in the literature is total variation (TV), known for its capability of preserving edges. However, TV-based reconstruction methods often tend to produce staircase-like artifacts since they favor piecewise constant solutions. To overcome this drawback, we propose to develop a second-order total generalized variation (TGVα2)-based image reconstruction model with a combined L1,2 data-fidelity term. The proposed model is applicable for restoration of blurred images with mixed Gaussian-impulse noise, and can be effectively used for undersampled magnetic resonance imaging. To further enhance the image reconstruction, a box constraint is incorporated into the proposed model by simply projecting all pixel values of the reconstructed image to lie in a certain interval (e.g., 0, 1 for normalized images and [0, 255] for 8-bit images). An optimization algorithm based on an alternating direction method of multipliers is developed to solve the proposed box-constrained image reconstruction model. Comprehensive numerical experiments have been conducted to compare our proposed method with some state-of-the-art reconstruction techniques. The experimental results have demonstrated its superior performance in terms of both quantitative evaluation and visual quality.

Fast single image dehazing based on image fusion

Abstract: Images captured in foggy weather conditions often fade the colors and reduce the contrast of the observed objects. An efficient image fusion method is proposed to remove haze from a single input image. First, the initial medium transmission is estimated based on the dark channel prior. Second, the method adopts an assumption that the degradation level affected by haze of each region is the same, which is similar to the Retinex theory, and uses a simple Gaussian filter to get the coarse medium transmission. Then, pixel-level fusion is achieved between the initial medium transmission and coarse medium transmission. The proposed method can recover a high-quality haze-free image based on the physical model, and the complexity of the proposed method is only a linear function of the number of input image pixels. Experimental results demonstrate that the proposed method can allow a very fast implementation and achieve better restoration for visibility and color fidelity compared to some state-of-the-art methods.

Robust crack detection for unmanned aerial vehicles inspection in an a-contrario decision framework

Abstract: We are interested in the performance of currently available algorithms for the detection of cracks in the specific context of aerial inspection, which is characterized by image quality degradation. We focus on two widely used families of algorithms based on minimal cost path analysis and on image percolation, and we highlight their limitations in this context. Furthermore, we propose an improved strategy based on

Brightness preserving image enhancement based on a gradient and intensity histogram

Abstract: We present a straightforward brightness preserving image enhancement technique. The proposed method is based on an original gradient and intensity histogram (GIH) which contains both gradient and intensity information of the image. This character enables GIH to avoid high peaks in the traditional intensity histogram and, thus alleviate overenhancement in our enhancement method, i.e., gradient and intensity histogram equalization (GIHE). GIHE can also enhance the gradient strength of an image, which is good for improving the subjective quality since the human vision system is more sensitive to the gradient than the absolute intensity of image. Considering that brightness preservation and dynamic range compression are highly demanded in consumer electronics, we manipulate the intensity of the enhanced image appropriately by amplifying the small intensities and attenuating the large intensities, respectively, using a brightness preserving function (BPF). The BPF is straightforward and universal and can be used in other image enhancement techniques. We demonstrate that the proposed method can effectively improve the subjective quality as well as preserve the brightness of the input image.

Context adaptive binary arithmetic coding-based data hiding in partially encrypted H.264/AVC videos

Abstract: A scheme of data hiding directly in a partially encrypted version of H.264/AVC videos is proposed which includes three parts, i.e., selective encryption, data embedding and data extraction. Selective encryption is performed on context adaptive binary arithmetic coding (CABAC) bin-strings via stream ciphers. By careful selection of CABAC entropy coder syntax elements for selective encryption, the encrypted bitstream is format-compliant and has exactly the same bit rate. Then a data-hider embeds the additional data into partially encrypted H.264/AVC videos using a CABAC bin-string substitution technique without accessing the plaintext of the video content. Since bin-string substitution is carried out on those residual coefficients with approximately the same magnitude, the quality of the decrypted video is satisfactory. Video file size is strictly preserved even after data embedding. In order to adapt to different application scenarios, data extraction can be done either in the encrypted domain or in the decrypted domain. Experimental results have demonstrated the feasibility and efficiency of the proposed scheme.

License plate localization in complex scenes based on oriented FAST and rotated BRIEF feature

Abstract: Within intelligent transportation systems, fast and robust license plate localization (LPL) in complex scenes is still a challenging task. Real-world scenes introduce complexities such as variation in license plate size and orientation, uneven illumination, background clutter, and nonplate objects. These complexities lead to poor performance using traditional LPL features, such as color, edge, and texture. Recently, state-of-the-art performance in LPL has been achieved by applying the scale invariant feature transform (SIFT) descriptor to LPL for visual matching. However, for applications that require fast processing, such as mobile phones, SIFT does not meet the efficiency requirement due to its relatively slow computational speed. To address this problem, a new approach for LPL, which uses the oriented FAST and rotated BRIEF (ORB) feature detector, is proposed. The feature extraction in ORB is much more efficient than in SIFT and is invariant to scale and grayscale as well as rotation changes, and hence is able to provide superior performance for LPL. The potential regions of a license plate are detected by considering spatial and color information simultaneously, which is different from previous approaches. The experimental results on a challenging dataset demonstrate the effectiveness and efficiency of the proposed method.

Fingerprint image super-resolution via ridge orientation-based clustered coupled sparse dictionaries

Abstract: The process of image quality improvement through super-resolution methods is still a gray area in the field of biometric identification. This paper proposes a scheme for fingerprint super-resolution using ridge orientation-based clustered coupled sparse dictionaries. The training image patches are clustered into groups based on dominant orientation and corresponding coupled subdictionaries are learned for each low- and high-resolution patch groups. While reconstructing the image, the minimum residue error criterion is used for choosing a subdictionary for a particular patch. In the final step, back projection is applied to eliminate the discrepancy in the estimate due to noise or inaccuracy in sparse representation. The performance evaluation of the proposed method is accomplished in terms of peak signal-to-noise ratio and structural similarity index. A filter bank-based fingerprint matcher is used for evaluating the performance of the proposed method in terms of matching accuracy. Our experimental results show that the new method achieves better results in comparison with other methods and will establish itself for improving performances of fingerprint-identification systems.

Multimodal image registration technique based on improved local feature descriptors

Abstract: Multimodal image registration has received significant research attention over the past decade, and the majority of the techniques are global in nature. Although local techniques are widely used for general image registration, there are only limited studies on them for multimodal image registration. Scale invariant feature transform (SIFT) is a well-known general image registration technique. However, SIFT descriptors are not invariant to multimodality. We propose a SIFT-based technique that is modality invariant and still retains the strengths of local techniques. Moreover, our proposed histogram weighting strategies also improve the accuracy of descriptor matching, which is an important image registration step. As a result, our proposed strategies can not only improve the multimodal registration accuracy but also have the potential to improve the performance of all SIFT-based applications, e.g., general image registration and object recognition.

Exploiting spatiospectral correlation for impulse denoising in hyperspectral images

Abstract: This paper proposes a technique for reducing impulse noise from corrupted hyperspectral images. We exploit the spatiospectral correlation present in hyperspectral images to sparsify the datacube. Since impulse noise is sparse, denoising is framed as an l1-norm regularized l1-norm data fidelity minimization problem. We derive an efficient split Bregman-based algorithm to solve the same. Experiments on real datasets show that our proposed technique, when compared with state-of-the-art denoising algorithms, yields better results.

Robust background subtraction to global illumination changes via multiple features-based online robust principal components analysis with Markov random field

Abstract: Background subtraction is an important task for various computer vision applications. The task becomes more critical when the background scene contains more variations, such as swaying trees and abruptly changing lighting conditions. Recently, robust principal component analysis (RPCA) has been shown to be a very efficient framework for moving-object detection. However, due to its batch optimization process, high-dimensional data need to be processed. As a result, computational complexity, lack of features, weak performance, real-time processing, and memory issues arise in traditional RPCA-based approaches. To handle these, a background subtraction algorithm robust against global illumination changes via online robust PCA (OR-PCA) using multiple features together with continuous constraints, such as Markov random field (MRF), is presented. OR-PCA with automatic parameter estimation using multiple features improves the background subtraction accuracy and computation time, making it attractive for real-time systems. Moreover, the application of MRF to the foreground mask exploits structural information to improve the segmentation results. In addition, global illumination changes in scenes are tackled by using sum of the difference of similarity measure among features, followed by a parameter update process using a low-rank, multiple features model. Evaluation using challenging datasets demonstrated that the proposed scheme is a top performer for a wide range of complex background scenes.

Face–iris multimodal biometric scheme based on feature level fusion

Abstract: Unlike score level fusion, feature level fusion demands all the features extracted from unimodal traits with high distinguishability, as well as homogeneity and compatibility, which is difficult to achieve. Therefore, most multimodal biometric research focuses on score level fusion, whereas few investigate feature level fusion. We propose a face-iris recognition method based on feature level fusion. We build a special two-dimensional- Gabor filter bank to extract local texture features from face and iris images, and then transform them by histo- gram statistics into an energy-orientation variance histogram feature with lower dimensions and higher distin- guishability. Finally, through a fusion-recognition strategy based on principal components analysis and support vector machine (FRSPS), feature level fusion and one-to-n identification are accomplished. The experimental results demonstrate that this method can not only effectively extract face and iris features but also provide higher recognition accuracy. Compared with some state-of-the-art fusion methods, the proposed method has a signifi- cant performance advantage. © 2015 SPIE and IS&T (DOI: 10.1117/1.JEI.24.6.063020)

Study on internal to surface fingerprint correlation using optical coherence tomography and internal fingerprint extraction

Abstract: Surface fingerprint scanners are limited to a two-dimensional representation of the fingerprint topography, and thus, are vulnerable to fingerprint damage, distortion, and counterfeiting. Optical coherence tomography (OCT) scanners are able to image (in three dimensions) the internal structure of the fingertip skin. Techniques for obtaining the internal fingerprint from OCT scans have since been developed. This research presents an internal fingerprint extraction algorithm designed to extract high-quality internal fingerprints from touchless OCT fingertip scans. Furthermore, it serves as a correlation study between surface and internal fingerprints. Provided the scanned region contains sufficient fingerprint information, correlation to the surface topography is shown to be good (74% have true matches). The cross-correlation of internal fingerprints (96% have true matches) is substantial that internal fingerprints can constitute a fingerprint database. The internal fingerprints’ performance was also compared to the performance of cropped surface counterparts, to eliminate bias owing to information level present, showing that the internal fingerprints’ performance is superior 63.6% of the time.

Visual tracking with multifeature joint sparse representation

Abstract: We present a visual tracking method with feature fusion via joint sparse presentation. The proposed method describes each target candidate by combining different features and joint sparse representation for robustness in coefficient estimation. Then, we build a probabilistic observation model based on the approximation error between the recovered candidate image and the observed sample. Finally, this observation model is integrated with a stochastic affine motion model to form a particle filter framework for visual tracking. Furthermore, a dynamic and robust template update strategy is applied to adapt the appearance variations of the target and reduce the possibility of drifting. Quantitative evaluations on challenging benchmark video sequences demonstrate that the proposed method is effective and can perform favorably compared to several state-of-the-art methods.

Image watermarking via fractional polar harmonic transforms

Abstract: Invariant harmonic transforms based on the fractional Fourier transform are proposed in this paper. The so-called fractional polar harmonic transforms (FrPHTs) with the order parameter α are first defined, which are generalizations of the PHTs. Second, a watermarking scheme is presented and discussed in detail associated with the newly defined FrPHTs. Finally, the simulations are clearly performed to verify the well capabilities of the transforms on image watermarking, which show that the proposed transforms with suitable parameters outperform the traditional PHTs. In addition, the experimental results also demonstrate that the order parameter α has an effect on the performance of FrPHTs in the image watermarking robustness and can improve the watermarking safety.

Document forgery detection using distortion mutation of geometric parameters in characters

Abstract: Tampering related to document forgeries is often accomplished by copy-pasting or add-printing. These tampering methods introduce character distortion mutation in documents. We present a method of exposing document forgeries using distortion mutation of geometric parameters. We estimate distortion parameters, which consist of translation and rotation distortions, through image matching for each character. Detection of tampered characters with distortion mutation occurs based on a distortion probability, which is calculated from character distortion parameters. The introduction of a visualized probability map describes the degree of distortion mutation for a full page. The proposed method exposes the forgeries based on individual characters and applies to English and Chinese document examinations. Experimental results demonstrate the effectiveness of our method on low JPEG compression quality and low resolution.

Lean histogram of oriented gradients features for effective eye detection

Abstract: Reliable object detection is very important in computer vision and robotics applications. The histogram of oriented gradients (HOG) is established as one of the most popular hand-crafted features, which along with support vector machine (SVM) classification provides excellent performance for object recognition. We investigate dimensionality deduction on HOG features in combination with SVM classifiers to obtain efficient feature representation and improved classification performance. In addition to lean HOG features, we explore descriptors resulting from dimensionality reduction on histograms of binary descriptors. We consider three-dimensionality reduction techniques: standard principal component analysis, random projections, a computationally efficient linear mapping that is data independent, and locality preserving projections (LPP), which learns the manifold structure of the data. Our methods focus on the application of eye detection and were tested on an eye database created using the BioID and FERET face databases. Our results indicate that manifold learning is beneficial to classification utilizing HOG features. To demonstrate the broader usefulness of lean HOG features for object class recognition, we evaluated our system’s classification performance on the CalTech-101 dataset with favorable outcomes.

End-to-end system of license plate localization and recognition

Abstract: An end-to-end license plate recognition system is proposed. It is composed of preprocessing, detection, segmentation, and character recognition to find and recognize plates from camera-based still images. The system utilizes connected component (CC) properties to quickly extract the license plate region. A two-stage CC filtering is utilized to address both shape and spatial relationship information to produce high precision and to recall values for detection. Floating peak and valleys of projection profiles are used to cut the license plates into individual characters. A turning function-based method is proposed to quickly and accurately recognize each character. It is further accelerated using curvature histogram-based support vector machine. The INFTY dataset is used to train the recognition system, and MediaLab license plate dataset is used for testing. The proposed system achieved 89.45% F-measure for detection and 87.33% accuracy for overall recognition rate which is comparable to current state-of-the-art systems.

Adaptive sigmoid function bihistogram equalization for image contrast enhancement

Abstract: Contrast enhancement plays a key role in a wide range of applications including consumer electronic applications, such as video surveillance, digital cameras, and televisions. The main goal of contrast enhancement is to increase the quality of images. However, most state-of-the-art methods induce different types of distortion such as intensity shift, wash-out, noise, intensity burn-out, and intensity saturation. In addition, in consumer electronics, simple and fast methods are required in order to be implemented in real time. A bihistogram equalization method based on adaptive sigmoid functions is proposed. It consists of splitting the image histogram into two parts that are equalized independently by using adaptive sigmoid functions. In order to preserve the mean brightness of the input image, the parameter of the sigmoid functions is chosen to minimize the absolute mean brightness metric. Experiments on the Berkeley database have shown that the proposed method improves the quality of images and preserves their mean brightness. An application to improve the colorfulness of images is also presented.