Visible Spectrum Images Research Articles

Multi-Spectral Flash Imaging Under Low-Light Conditions Using an Optimization Method

Long-exposure shots and flash photography are normally used to acquire images under low-light conditions. However, flash photography often induces color distortion and creates a red-eye effect, while long-exposure shots are prone to motion blur due to camera shake or subject motion. Thus, multi-spectral flash imaging has recently been introduced to overcome the limitations of traditional low-light photography. Multi-spectral flash imaging combines invisible and visible spectrum images, but most multi-spectral flash approaches result in color distortion due to the lower accuracy of the invisible spectrum image. Accordingly, this article presents a multi-spectral flash imaging algorithm using optimization with a weight map to improve the color accuracy and brightness of the image. First, UV/IR and visible spectrum images are captured. To compensate the luminance values under low-light conditions, adaptive tone reproduction is performed using the Naka–Rushton equation. Next, to discriminate uniform regions from detail regions, a weight map is generated using a Canny operator. Finally, the optimization process takes account of the likelihood of the visible light image, the sparsity of the image gradients, and the spectral constraints of the IR and UV channels. The performance of the proposed method is subjectively evaluated using a z-score, and the resulting images are confirmed to have an improved color accuracy and lower noise when compared with the results of other methods. c © 2014 Society for Imaging Science and Technology. [DOI: 10.2352/J.ImagingSci.Technol.2014.58.5.050501] INTRODUCTION Cameras normally produce images by acquiring light in a controlled fashion: the camera shutter speed, aperture, and flash all play important roles in the image acquisition process. In particular, the most common solutions for low-light photography are either the use of a flash or a long exposure time. However, flash photography often introduces undesired artifacts or effects, such as red eyes, false shadows, highintensity specular reflections, and changes in the color of ambient light. Furthermore, the high intensity and suddenness of a flash can be very uncomfortable for the subject. A number of methods have already been proposed to reduce the artifacts produced by the use of a flash, for example, highlight and reflection removal by gradient coherency.1 Meanwhile, the use of long exposure times is particularly difficult due to possible subject or camera motion, which

Optical transmission in thin films of vanadium compounds

Thin films of five vanadium compounds/composite: (1) VO2(3-fl) (3-fl = 3-Hydroxyflavone), (2) VO(pbd)2 (pbd = 1-Phenyl-1, 3-butadione), (3) VO(dbm)2 (dbm = Dibenzoylmethane), (4) VPc (Vanadyl Phthalocyanine) and (5) V2O5-PEPC (V2O5- poly-N-epoxypropylcarbazole composite), were deposited by the drop-casting method from the solution in benzene. The transmittance—irradiance relationships were investigated and the transmission in the visible spectrum and optical images were obtained as well. It was found that the transmittance of the VO2(3-fl), VO(pbd)2, VO(dbm)2 and VPc, was practically independent of the irradiance; whereas the transmittance of V2O5-PEPC decreased by 4% for thin and 9% for thick films with an increase of the irradiance.

A novel phase-intensive local pattern for periocular recognition under visible spectrum

The article proposes a novel multi-scale local feature based on the periocular recognition technique which is capable of extracting high-dimensional subtle features existent in the iris region as well as low-dimensional gross features in the periphery skin region of the iris. A set of filter banks of different scales is employed to exploit the phase-intensive patterns in visible spectrum periocular image of a subject captured from a distance in partial non-cooperative scenario. The proposed technique is verified with experiments on near-infrared illumination databases like BATH and CASIA-IrisV3-Lamp. Experiments have been further extended to images from visible spectrum ocular databases like UBIRISv2 and low-resolution eye regions extracted from FERETv4 face database to establish that the proposed feature performs comparably better than existing local features. To find the robustness of the proposed approach, the low resolution visible spectrum images of mentioned databases are converted to grayscale images. The proposed approach yields unique patterns from these grayscale images. The ability to find coarse-to-fine features in multi-scale and different phases is accountable for the improved robustness of the proposed approach.

Infrared face recognition: A comprehensive review of methodologies and databases

Automatic face recognition is an area with immense practical potential which includes a wide range of commercial and law enforcement applications. Hence it is unsurprising that it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in face recognition continues to improve, benefitting from advances in a range of different research fields such as image processing, pattern recognition, computer graphics, and physiology. Systems based on visible spectrum images, the most researched face recognition modality, have reached a significant level of maturity with some practical success. However, they continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease recognition accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject. Our key contributions are (i) a summary of the inherent properties of infrared imaging which makes this modality promising in the context of face recognition; (ii) a systematic review of the most influential approaches, with a focus on emerging common trends as well as key differences between alternative methodologies; (iii) a description of the main databases of infrared facial images available to the researcher; and lastly (iv) a discussion of the most promising avenues for future research.

Fusion of visible and thermal images for facial expression recognition

Most present research into facial expression recognition focuses on the visible spectrum, which is sensitive to illumination change. In this paper, we focus on integrating thermal infrared data with visible spectrum images for spontaneous facial expression recognition. First, the active appearance model AAM parameters and three defined head motion features are extracted from visible spectrum images, and several thermal statistical features are extracted from infrared (IR) images. Second, feature selection is performed using the F-test statistic. Third, Bayesian networks BNs and support vector machines SVMs are proposed for both decision-level and feature-level fusion. Experiments on the natural visible and infrared facial expression (NVIE) spontaneous database show the effectiveness of the proposed methods, and demonstrate thermal IR images’ supplementary role for visible facial expression recognition.

Multispectral Image Feature Points

This paper presents a novel feature point descriptor for the multispectral image caseFar-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both images through a SIFT-like based scale space representation. Then, these points are characterized using an Edge Oriented Histogram (EOH) descriptor. Finally, points of interest from multispectral images are matched by finding nearest couples using the information from the descriptor. The provided experimental results and comparisons with similar methods show both the validity of the proposed approach as well as the improvements it offers with respect to the current state-of-the-art.

Soft biometric classification using local appearance periocular region features

This paper investigates the effectiveness of local appearance features such as Local Binary Patterns, Histograms of Oriented Gradient, Discrete Cosine Transform, and Local Color Histograms extracted from periocular region images for soft classification on gender and ethnicity. These features are classified by Artificial Neural Network or Support Vector Machine. Experiments are performed on visible and near-IR spectrum images derived from FRGC and MBGC datasets. For 4232 FRGC images of 404 subjects, we obtain baseline gender and ethnicity classifications of 97.3% and 94%. For 350 MBGC images of 60 subjects, we obtain baseline gender and ethnicity results of 90% and 89%.

An introduction for machining researchers to measurement uncertainty sources in thermal images of metal cutting

Thermal imaging yields valuable information which improves and verifies the accuracy of machining models. There are three main steps involved when measuring temperature distribution of the tool, workpiece, or chip during metal cutting; acquiring a thermal image, converting imaged temperature to apparent temperature, and converting apparent temperature to true temperature. There are many error sources to consider. It is important to understand how these error sources affect measurement uncertainty. Some are familiar to anyone performing thermography measurements, such as uncertainties in camera calibration. However, metal cutting presents unique measurement challenges due to factors such as the high magnification required, high surface speeds, micro blackbody effects, and changing emissivity. This paper is an introduction, focusing on thermal images, though visible spectrum images are also shown. Some examples illustrate basic concepts, while others are more complex. For brevity, specifics of cutting experiments are not given, as this paper has a measurement focus.

Liquid-crystal micropolarimeter array for full Stokes polarization imaging in visible spectrum

In this paper, we describe the design, modeling, fabrication, and optical characterization of the first micropolarimeter array enabling full Stokes polarization imaging in visible spectrum. The proposed micropolarimeter is fabricated by patterning a liquid-crystal (LC) layer on top of a visible-regime metal-wire-grid polarizer (MWGP) using ultraviolet sensitive sulfonic-dye-1 as the LC photoalignment material. This arrangement enables the formation of either micrometer-scale LC polarization rotators, neutral density filters or quarter wavelength retarders. These elements are in turn exploited to acquire all components of the Stokes vector, which describes all possible polarization states of light. Reported major principal transmittance of 75% and extinction ratio of 1100 demonstrate that the MWGP's superior optical characteristics are retained. The proposed liquidcrystal micropolarimeter array can be integrated on top of a complementary metal-oxide-semiconductor (CMOS) image sensor for real-time full Stokes polarization imaging.

Asphalted Road Temperature Variations Due to Wind Turbine Cast Shadows

The contribution of this paper is a technique that in certain circumstances allows one to avoid the removal of dynamic shadows in the visible spectrum making use of images in the infrared spectrum. This technique emerged from a real problem concerning the autonomous navigation of a vehicle in a wind farm. In this environment, the dynamic shadows cast by the wind turbines' blades make it necessary to include a shadows removal stage in the preprocessing of the visible spectrum images in order to avoid the shadows being misclassified as obstacles. In the thermal images, dynamic shadows completely disappear, something that does not always occur in the visible spectrum, even when the preprocessing is executed. Thus, a fusion on thermal and visible bands is performed.

Face recognition in multi-sensor images based on a novel modular feature selection technique

In this paper we propose a novel multi-sensor information fusion based methodology for human face recognition. Recent studies have indicated that the fusion of complementary information obtained from multiple sensors provide better recognition accuracy compared to that from individual sensors. Images captured by visible and long wave infrared sensors are used in this research. The face recognition technique presented in this paper is based on a regional feature selection strategy. Phase congruency feature maps are used instead of intensity images to make the recognition procedure invariant to illumination and contrast in an image. A novel modularization procedure which takes the importance of the relationship among the pixels within the images into consideration is also presented. Smaller sub-regions from a predefined neighborhood within the phase congruency images of the training samples are merged to obtain a large set of features. As variations in face images are confined to local regions, it is helpful to consider additional pixel dependencies across various sub-regions to improve the classification accuracy. Experiments are conducted on the Aleix Martinez and Robert Benavente (AR) face database which contain visible spectrum images, to prove the efficiency of the proposed recognition technique. The proposed modular feature selection strategy outperformed the modular Gabor features based technique in terms of recognition accuracy. The recognition technique is implemented on both thermal and visible images obtained from the Equinox face database. The thermal face recognition accuracy is also boosted by the feature selection policy. A novel decision level fusion technique which incorporates the proposed feature selection strategy is developed for improved face recognition. In terms of recognition accuracy this technique outperforms all the individual image modalities as well as the data level fused images obtained using the Discrete Wavelet Transform (DWT) based fusion technique. We also discuss the effects of image registration, which is a critical aspect for image fusion techniques. Experiments are conducted on several image sets to study the effect of discrepancies in the image registration process. We observed that the performance of our proposed technique proves to be less affected by registration errors.

HYDRO-DAMS SECURITY ASSESSMENT BY VISIBLE AND INFRARED IMAGE FUSION

Hydro dams are very important economical and social structures that have a great impact on the population living in surrounding area. Surveillance of dam status consists of a complex process which involves data acquisition and analysis techniques, implying measurements from sensors and transducers and visual inspection. To enhance the visual inspection process made by experts, we propose a computer vision technique that will allow assessment and quantification of water infiltrations inside the dam wall, based on infrared and visible spectrum image analysis and data fusion techniques.

A 1/3'' VGA linear wide dynamic range CMOS image sensor implementing a predictive multiple sampling algorithm with overlapping integration intervals

Many tasks performed by machine vision systems involve processing of natural scenes with large intra-frame illumination ratios. Thus, wide dynamic range visible spectrum image sensors are required to achieve adequate processing performance and reliability. An image sensor implementing an algorithm that linearly increases the illumination dynamic range of solid-state pixels is presented. Optimal exposure is achieved with a predictive pixel saturation decision that allows for multiple integration intervals of different duration to run concurrently for different pixels while keeping the sensor frame rate constant. A proof-of-concept chip was fabricated in a 0.18-/spl mu/m CMOS process. Added functionality to standard imagers is mainly concentrated off-pixel so fill factor is not sacrificed. Measured data corroborates the algorithm functionality.

Predictive Multiple Sampling Algorithm With Overlapping Integration Intervals for Linear Wide Dynamic Range Integrating Image Sensors

Many tasks performed by intelligent transportation systems involve processing of natural scenes. Wide dynamic range visible spectrum image sensors are required to achieve optimal processing performance. Recently several approaches that rely on the multiple sampling technique to achieve wide dynamic range have been presented. The behavior of a novel predictive variant of these algorithms is analyzed together with its key assumptions and limits to its performance and flexibility.

Dilational fault slip and pit chain formation on Mars

Pit crater chains that are parallel or collinear with normal faults are common on Mars, but only rarely observed on Earth. We explore the origin of pit crater chains using recent high-resolution imagery from Mars, laboratory simulation of pit chains, investigation of recent pit chains formed in response to seismic fault slip in Iceland, and assessment of rock failure criteria and the Martian crustal stress field to develop a deeper understanding of the relationship between pit chain formation and fault activity. Based on these analyses, we conclude that pit chains form in response to dilational fault slip. Because gravitational acceleration on Earth (9.81 m/s2) is higher than on Mars (3.72 m/s2), stress within Earth is greater than that of Mars for any given depth. Consequently, steep fault segments are likely to extend to about 5 km depth on Mars compared with about 2 km depth on Earth. Dilation of these steep segments associated with fault slip on Mars could result in large volume increase in the uppermost crust, influencing groundwater flow and discharge and mineralization. Based on crosscutting relationships, pristine pit morphologies, and lack of evidence of sediment accumulation in the bottoms of pits, we interpret that some pit craters may be actively forming in response to dilational fault slip. Figure 1. (A) Composite Viking imagery of normal fault scarps defining horsts and grabens in the southeast part of Alba Patera, Mars. Fault segmentation and en echelon arrangement of faults and grabens are common. Scale bar is 50 km. (B) THEMIS visible spectrum image shows detail of normal faults and pit chain (Phlegethon Catena) shown in A. Scale bar is 5 km. (C) Mars Orbital Camera image shows clear association of pit chains with normal fault scarps within graben on the southeast flank of Alba Patera. Scale bar is 1 km.

Mapping Pigmentation in Human Skin from a Multi-Channel Visible Spectrum Image by Inverse Optical Scattering Technique

Mapping Pigmentation in Human Skin from a Multi-Channel Visible Spectrum Image by Inverse Optical Scattering Technique

Magnetic resonance axonography of the rat spinal cord.

In spite of dramatic advancement in biomedical imaging technologies, non-invasive visualization of anatomic detail of the spinal cord has remained a major challenge. Here, a novel color-coded contrast method for magnetic resonance imaging (MRI) which provides superb resolution of the spinal cord in live animals, comparable to that of histological preparations, is described. The method, referred to here as three dimensional anisotropy (3DAC) contrast MRI, displays cross-sectional images in the full visible color spectrum, encoding directional information regarding intravoxel anisotropic water motion in space. Since neuronal fibers, especially axons, possess significantly higher intravoxel anisotropic water motion compared with other elements in the nervous system, 3DAC is highly sensitive to axonal direction and density. Axonography of the spinal cord of rats obtained using this technique showed anatomic detail at a resolution hitherto unobtainable in live animals.

Turbidity Related to Surface Temperature in Oxidation Ponds: Studies toward Development of a Remote Sensing Method

Recent results obtained from a pilot plant study showed that the rate of morning temperature increase in surface water is significantly correlated with the degree of water pollution expressed as turbidity. The aim of the present study was to validate the above findings under field conditions. Two oxidation ponds differing in their effluent quality were investigated during the summer. In clear weather and moderate winds the ponds were thermally stratified. Continuous records of subsurface temperature and parallel measurements of turbidity provided data for statistical analysis. The variables tested appeared significantly correlated and the more polluted pond exhibited consistently a higher rate of morning temperature increase. Temperature measurements can be carried out remotely using airborn IR radiometric equipment. The thermal method should be applied together with visible spectrum imaging, which can identify pollution components according to the specific waveband of the reflected light.