Complementary Image Research Articles

Complementary Approach for Image Edge Detection Using Rough Mirror Mapping in Neural Network

In this paper, a complementary approach has applied to obtain the available edges in the image. The complementary image has obtained by subtracting the rough mirror mapped image from the input image. The universal approximation capability of feedforward neural network has applied to define the rough mirror mapping. Multilayer perceptron network and radial basis function network have considered obtaining the mapping. Effect of better learning has also explored in both network by applying adaptivenesss in their transform function available in the active nodes. Single image based training has given for few number of iterations in the development of mapping process. It is observed that proposed method has self adjusted content aware oriented edge detection where as many existing methods like Sobel, Prewitt have shown their limitations in observing the edges associated with contents having similar shade in the surroundings.

Intuitionistic fuzzy set approach to multi-objective evolutionary clustering with multiple spatial information for image segmentation

Abstract Intuitionistic fuzzy set is a useful tool to handle the uncertainty in data. In order to deal with the uncertainty in an image and meanwhile overcome the sensitivity to image noise, a multi-objective evolutionary intuitionistic fuzzy clustering algorithm with multiple image spatial information (MOEIFC-MSI) is proposed to perform image segmentation in this paper. The important innovations of the method are listed as follows: (1) an intuitionistic fuzzy set of the image is first constructed by using a generalized fuzzy complement function; (2) this intuitionistic fuzzy set of the image is then utilized to compute fitness functions and a fuzzy evaluation index for selecting the optimal solution; (3) two kinds of complementary image spatial information are also introduced into the fitness functions and the fuzzy evaluation index to make the proposed method robust to image noise; (4) a real-coded variable string length technique is utilized to encode the cluster centers to automatically determine the number of clusters. Experimental results on synthetic, Berkeley and magnetic resonance (MR) images show that the proposed method outperforms state-of-the-art methods in noise robustness and segmentation performance.

Toward a Blind Quality Metric for Temporally Distorted Streaming Video

With the rapid progress of mobile Internet, the streaming video service has boom over wireless networks in recent years. A smooth playback experience is crucial for the popularization of these services. However, limited by fluctuating bandwidth and various network impairments, the streaming video inevitably suffers kinds of stalling events, which significantly distorts its temporal structures and results in annoying jerky playback. In this paper, we propose an efficient quality metric to blindly evaluate the user experience for stalled streaming video without using its original sequence. Instead of requiring buffer or manifest information like existing methods, we only access to the decoded video and extract two complementary image features, i.e., global intensity and local texture, to estimate the stall number and duration. Then, by means of a straightforward and easy-to-use linear combination model, we can map the normalized stall number and duration information to a quantitative quality score. Experimental results on the publicly available LIVE-Avvasi mobile video database show that our predicted video quality is highly consistent with the user experience and outperforms many existing quality-of-experience models.

An Integrated Spatio-Spectral–Temporal Sparse Representation Method for Fusing Remote-Sensing Images With Different Resolutions

Different spectral, spatial, and temporal features have been widely used in the remote-sensing image analysis. The further development of multiple sensor remote-sensing technologies has made it necessary to explore new methods of remote-sensing image fusion using different optical image data sets which provide complementary image properties and a tradeoff among spatial, spectral, and temporal resolutions. However, due to problems in assessing correlations between different types of satellite data with different resolutions, a few efforts have been made to explore spatio-spectral–temporal features. For this purpose, we propose a novel sparse representation model to generate synthesized frequent high-spectral and high-spatial resolution data by blending multiple types: spatio-temporal data fusion, spectral–temporal data fusion, spatio-spectral data fusion, and spatio-spectral–temporal data fusion. The proposed method exploits high-spectral correlation across spectral domains and high self-similarity across spatial domains to learn the spatio-spectral fusion basis. Then, it associates temporal changes using a local constraint sparse representation. The integrated spatio-spectral–temporal sparse representation model based on the learned spectral–spatial and temporal change features strengthens the model’s ability to provide high-resolution data needed to address demanding work in real-world applications. Finally, the proposed method is not restricted to a certain type of data, but it can associate any type of remote-sensing data and be applied to dynamic changes in heterogeneous landscapes. The experimental results illustrate the effectiveness and efficiency of the proposed method.

Pore characteristics and dominant controlling factors of overmature shales: A case study of the Wangyinpu and Guanyintang Formations in the Jiangxi Xiuwu Basin

Because of the great potential for hydrocarbon generation, the Lower Cambrian Wangyinpu and Guanyintang Formations of the Jiangxi Xiuwu Basin have become the most important targets for shale-gas exploration in the Jiangxi province. We investigate the pore characteristics and main controlling factors of overmature shale using field emission-scanning electron microscopy, image-processing software (i.e., the Particles [Pores] and Crack Analysis System), X-ray diffraction, and gas-adsorption experiments. The results show that the shales have a high abundance of organic matter (OM), over maturity, and highly siliceous mineral content. The kerogen type is identified as type I. OM pores are the most developed, followed by interparticle (interP) pores and intraparticle (intraP) pores. We combine complementary image processing and gas-adsorption methods to reveal that micropores are mainly from OM pores; mesopores are from OM pores and interP pores; and macropores are from OM pores, interP pores, and intraP pores. Although the number of micropores is at a maximum, the total contribution of mesopores and macropores to the pore volume (PV) is larger than that of micropores. However, the specific surface area (SSA) is mainly from the micropores. OM content and maturity are the main controlling factors for the development of pore structures. Because of overmaturity, OM loses its potential for hydrocarbon generation and new pores cannot be produced. Gas loss leads to reservoir pressure drop, and the pores generated during the mature stage collapse and even disappear because they lack support. Therefore, PV, SSA, and porosity decrease when the OM content is more than 10%. When the OM content is less than 10%, most of the OM pores are preserved because they are protected by the skeleton particles.

Pterygoid process fracture during dental extraction – an unusual complication

INTRODUCTION: The extraction of the upper third molar is a simple and common procedure in Oral and Maxillofacial Surgery daily practice. Infection, facial swelling, trismus, wound dehiscence, root fracture, oroantral fistula, iatrogenic displacement, fracture of the maxillary tuberosity are some of the complications that can occur during third molar extraction.CASE DESCRIPTION: In this article, an unusual fracture of the pterygoid process during upper third molar extraction and a minimally invasive technique for treatment are described.CONCLUSION: It is important to recognize the fracture and not remove the fragment before a complementary image exam, it will guide the type of treatment that should be use.

On the Use of Radiosondes in Freezing Precipitation

AbstractRadiosonde observations are used the world over to provide critical upper-air observations of the lower atmosphere. These observations are susceptible to errors that must be mitigated or avoided when identified. One source of error not previously addressed is radiosonde icing in winter storms, which can affect forecasts, warning operations, and model initialization. Under certain conditions, ice can form on the radiosonde, leading to decreased response times and incorrect readings. Evidence of radiosonde icing is presented for a winter storm event in Norman, Oklahoma, on 24 November 2013. A special sounding that included a particle imager probe and a GoPro camera was flown into the system producing ice pellets. While the iced-over temperature sensor showed no evidence of an elevated melting layer (ML), complementary Particle Size, Image, and Velocity (PASIV) probe and polarimetric radar observations provide clear evidence that an ML was indeed present. Radiosonde icing can occur while passing through a layer of supercooled drops, such as frequently found in a subfreezing layer that often lies below the ML in winter storms. Events that have warmer/deeper MLs would likely melt any ice present off the radiosonde, minimizing radiosonde icing and allowing the ML to be detected. This paper discusses the hypothesis that the absence of an ML in the radiosonde data presented here is more likely to occur in winter storms that produce ice pellets, which tend to have cooler/shallower MLs. Where sounding data do appear to be compromised by icing, polarimetric radar data might be used to identify MLs for nowcasting purposes and numerical model initialization.

Fast Motion Object Detection Algorithm Using Complementary Depth Image on an RGB-D Camera

Stereo vision has become a popular topic in recent years, especially in-depth images from stereo vision. Depth information can be extracted either from a dual camera or RGB-D camera. In image processing, the realization of object detection is only based on the color information or depth images separately; however, both have advantages and disadvantages. Therefore, many researchers have combined them together to achieve better results. A new fast motion object-detection algorithm is presented based on the complementary depth images and color information, which is able to detect motion objects without background noise. The experiment results show that the proposed fast object detection algorithm can achieve 84.4% of the segmentation accuracy rate on average with a 45 FPS computation speed on an embedded platform.

Quality Assessment of Perceptual Crosstalk on Two-View Auto-Stereoscopic Displays.

Crosstalk is one of the most severe factors affecting the perceived quality of stereoscopic 3D images. It arises from a leakage of light intensity between multiple views, as in auto-stereoscopic displays. Well-known determinants of crosstalk include the co-location contrast and disparity of the left and right images, which have been dealt with in prior studies. However, when a natural stereo image that contains complex naturalistic spatial characteristics is viewed on an auto-stereoscopic display, other factors may also play an important role in the perception of crosstalk. Here, we describe a new way of predicting the perceived severity of crosstalk, which we call the Binocular Perceptual Crosstalk Predictor (BPCP). BPCP uses measurements of three complementary 3D image properties (texture, structural duplication, and binocular summation) in combination with two well-known factors (co-location contrast and disparity) to make predictions of crosstalk on two-view auto-stereoscopic displays. The new BPCP model includes two masking algorithms and a binocular pooling method. We explore a new masking phenomenon that we call duplicated structure masking, which arises from structural correlations between the original and distorted objects. We also utilize an advanced binocular summation model to develop a binocular pooling algorithm. Our experimental results indicate that BPCP achieves high correlations against subjective test results, improving upon those delivered by previous crosstalk prediction models.

A smartphone imaging-based label-free and dual-wavelength fluorescent biosensor with high sensitivity and accuracy

The accuracy of a bioassay based on smartphone-integrated fluorescent biosensors has been limited due to the occurrence of false signals from non-specific reactions as well as a high background and low signal-to-noise ratios for complementary metal oxide semiconductor image sensors. To overcome this problem, we demonstrate dual-wavelength fluorescent detection of biomolecules with high accuracy. Fluorescent intensity can be quantified using dual wavelengths simultaneously, where one decreases and the other increases, as the target analytes bind to the split capture and detection aptamer probes. To do this, we performed smartphone imaging-based fluorescence microscopy using a microarray platform on a substrate with metal-enhanced fluorescence (MEF) using Ag film and Al2O3 nano-spacer. The results showed that the sensitivity and specificity of the dual-wavelength fluorescent quantitative assay for the target biomolecule 17-β-estradiol in water were significantly increased through the elimination of false signals. The detection limit was 1pg/mL and the area under the receiver operating characteristic curve of the proposed assay (0.922) was comparable to that of an enzyme-linked immunosorbent assay (0.956) from statistical accuracy tests using spiked wastewater samples. This novel method has great potential as an accurate point-of-care testing technology based on mobile platforms for clinical diagnostics and environmental monitoring.

Spoof detection on face and palmprint biometrics

Spoofing attacks made by non-real images are a major concern to biometric systems. This paper presents a novel solution for distinguishing between live and forged identities using the fusion of texture-based methods and image quality assessment measures. In our approach, we used LBP and HOG texture descriptors to extract texture information of an image. Additionally, feature space of seven full-reference complementary image quality measures is considered including peak signal-to-noise ratio, structural similarity, mean-squared error, normalized cross-correlation, maximum difference, normalized absolute error and average difference. We built a palmprint spoof database made by printed palmprint photograph of PolyU palmprint database using camera. Experimental results on three public-domain face spoof databases (Idiap Print-Attack, Replay-Attack and MSU MFSD) and palmprint spoof database show that the proposed solution is effective in face and palmprint spoof detection.

Shopping online and off-line? Complementary service quality and image congruence

Abstract The purposes of this article are to examine the influence of complementary service quality on trust in the retailer, and use schema theory to explain how image congruence moderates the relationship between trust and the intention to make a recommendation. Structure equation modeling was employed to verify and validate the research model. The outcomes show the main influencing factors were identified as complementary service quality, trust, and image congruence. Complementary service quality has consistently been a major factor in measuring performance. The moderating effect of image congruence on customer trust in the retailer and intention recommend has been determined to be positive. The research results provide insight into how providing service quality enhances consumers' trust in the retailer. The retailer should consider service quality a priority to attract customers, and strive to create image congruence in their multi-channeled branding.

A platform-independent method to reduce CT truncation artifacts using discriminative dictionary representations.

When the scan field of view (SFOV) of a CT system is not large enough to enclose the entire cross-section of the patient, or the patient needs to be positioned partially outside the SFOV for certain clinical applications, truncation artifacts often appear in the reconstructed CT images. Many truncation artifact correction methods perform extrapolations of the truncated projection data based on certain a priori assumptions. The purpose of this work was to develop a novel CT truncation artifact reduction method that directly operates on DICOM images. The blooming of pixel values associated with truncation was modeled using exponential decay functions, and based on this model, a discriminative dictionary was constructed to represent truncation artifacts and nonartifact image information in a mutually exclusive way. The discriminative dictionary consists of a truncation artifact subdictionary and a nonartifact subdictionary. The truncation artifact subdictionary contains 1000 atoms with different decay parameters, while the nonartifact subdictionary contains 1000 independent realizations of Gaussian white noise that are exclusive with the artifact features. By sparsely representing an artifact-contaminated CT image with this discriminative dictionary, the image was separated into a truncation artifact-dominated image and a complementary image with reduced truncation artifacts. The artifact-dominated image was then subtracted from the original image with an appropriate weighting coefficient to generate the final image with reduced artifacts. This proposed method was validated via physical phantom studies and retrospective human subject studies. Quantitative image evaluation metrics including the relative root-mean-square error (rRMSE) and the universal image quality index (UQI) were used to quantify the performance of the algorithm. For both phantom and human subject studies, truncation artifacts at the peripheral region of the SFOV were effectively reduced, revealing soft tissue and bony structure once buried in the truncation artifacts. For the phantom study, the proposed method reduced the relative RMSE from 15% (original images) to 11%, and improved the UQI from 0.34 to 0.80. A discriminative dictionary representation method was developed to mitigate CT truncation artifacts directly in the DICOM image domain. Both phantom and human subject studies demonstrated that the proposed method can effectively reduce truncation artifacts without access to projection data.

Ultralow power, high fill factor smart complementary metal oxide semiconductor image sensor with motion detection capability

Bandwidth saving, power consumption, and fill factor improvement are known as vitally important challenges image sensor designers face in order to accomplish high-performance imaging systems. This paper presents an ultralow power, high fill factor smart complementary metal oxide semiconductor (CMOS) image sensor with motion detection capability. In this efficient methodology, the amount of redundant data processed in unimportant frames has been reduced significantly, and therefore, the proposed imaging system consumes less power compared with counterpart imagers. Furthermore, a pixel structure is introduced that outputs two consecutive frame voltages in series, with the result that the pixel size is minimized and a higher fill factor is achieved. In order to simulate the image capturing procedure, a state-of-the-art approach based on MATLAB and HSPICE software is devised, which is another important achievement of this paper. The performance of this technique is demonstrated using a 64×64 pixel sensor designed in a 0.18-μm standard CMOS technology. The sensor chip consumes 0.2 mW of power while operating at 100 fps with a fill factor of 45%.

Robust Nanoparticles Detection From Noisy Background by Fusing Complementary Image Information.

This paper studies the problem of detecting the presence of nanoparticles in noisy transmission electron microscopic (TEM) images and then fitting each nanoparticle with an elliptic shape model. In order to achieve robustness while handling low contrast and high noise in the TEM images, we propose an approach to fuse two kinds of complementary image information, namely, the pixel intensity and the gradient (the first derivative in intensity). Our approach entails two main steps: 1) the first step is to, after necessary pre-processing, employ both intensity-based information and gradient-based information to process the same TEM image and produce two independent sets of results and 2) the subsequent step is to formulate a binary integer programming (BIP) problem for conflict resolution among the two sets of results. Solving the BIP problem determines the final nanoparticle identification. We apply our method to a set of TEM images taken under different microscopic resolutions and noise levels. The empirical results show the merit of the proposed method. It can process a TEM image of 1024×1024 pixels in a few minutes, and the processed outcomes appear rather robust.

Improved Spatiotemporal Noise Reduction for Very Low-Light Environments

In this brief, an improved spatiotemporal noise reduction algorithm for images that are acquired through a complementary metal-oxide semiconductor image sensor (CIS) in very low-light environments is presented. To reduce the strong noises amplified by an automatic gain controller in the dark, a motion detection process is used to determine how the pixels from a spatial noise filter and a temporal noise filter are adaptively blended. The proposed algorithm also amends fixed-pattern noise, which is caused by the allowable pixel deviation of the CIS before the image is fed to the noise reduction filters. After the low-light noise is reduced, the color distortion in the low-light environments is fixed in the hue, saturation, value color space. The proposed algorithm ultimately achieved more favorable objective and subjective image qualities than the existing algorithms, as shown in the experimental results.

Classification of basal cell carcinoma in human skin using machine learning and quantitative features captured by polarization sensitive optical coherence tomography.

We report the first fully automated detection of basal cell carcinoma (BCC), the most commonly occurring type of skin cancer, in human skin using polarization-sensitive optical coherence tomography (PS-OCT). Our proposed automated procedure entails building a machine-learning based classifier by extracting image features from the two complementary image contrasts offered by PS-OCT, intensity and phase retardation (PR), and selecting a subset of features that yields a classifier with the highest accuracy. Our classifier achieved 95.4% sensitivity and specificity, validated by leave-one-patient-out cross validation (LOPOCV), in detecting BCC in human skin samples collected from 42 patients. Moreover, we show the superiority of our classifier over the best possible classifier based on features extracted from intensity-only data, which demonstrates the significance of PR data in detecting BCC.

Hardware Development for Optical Tomography System based on Complementary Metal Oxide Semiconductor Linear Image Sensor

Nowadays, particle characterization such as size, count and shape, has been widely used in industrial processes to provide better understanding on their products. An optical tomography system has proven to be a useful approach in characterizing particles due to its ability to determine the content of a closed system without any physical contact. Thus, this paper presents the use of Complementary Metal Oxide Semiconductor linear image sensor in the development of optical tomography system, where the system is used for solid particle characterization. The developed system used the CMOS S10077 Linear Image Sensor as the optical detector, and Arduino Nano microcontroller to control the sensor’s operation and to provide a data acquisition system for the CMOS S10077. The Data Display Serial Port application is used to display data from the sensor in a numeric array to allow pattern plotting of its result. Particle characterization experiments based on count is conducted after the completion of the hardware and software setup. The experimental results are collected and analyzed. The verification of the experimental and actual results is determined based on the analysis of the particle counts.

Image Reconstruction for Optical Tomography System based on Complementary Metal Oxide Semiconductor Image Sensor

Quality of food products is important and highly demanded by customers in food industry. All physical attributions (size, shape, texture, concentration and color) of food products have to be considered. Monitoring the particle parameters can affect food flavor, texture, appearance, product size and shelf life. Most of the monitoring systems used in the food industry are based on the grading and sorting inspection such as sampling and Computer Vision technique. However, each of them has their limitations. Therefore, an optical tomography based on Complementary Metal Oxide Semiconductor (CMOS) area image sensor is developed to monitor these particle parameters by visualizing the particle non-invasively inside a flow pipe. The complete optical tomography system has been fabricated to produce a reliable tool to monitor particle characterization and to detect solid particles in flow pipe. The system offers reliable reconstructed images in monitoring particle concentration in air and liquid flows, particle shape and size. The optical tomography system consists of a lighting system, a sensing system that contains a measurement section and CMOS area image sensors, a Data Acquisition System (DAQ) that made up from a microcontroller module, and an image reconstruction system based on MATLAB software. The results obtained are sufficient to collect the qualitative and quantitative information of the particles used in the system to monitor concentration profile in air and liquid.

Automated quantification of one-dimensional nanostructure alignment on surfaces

A method for automated quantification of the alignment of one-dimensional (1D) nanostructures from microscopy imaging is presented. Nanostructure alignment metrics are formulated and shown to be able to rigorously quantify the orientational order of nanostructures within a two-dimensional domain (surface). A complementary image processing method is also presented which enables robust processing of microscopy images where overlapping nanostructures might be present. Scanning electron microscopy (SEM) images of nanowire-covered surfaces are analyzed using the presented methods and it is shown that past single parameter alignment metrics are insufficient for highly aligned domains. Through the use of multiple parameter alignment metrics, automated quantitative analysis of SEM images is shown to be possible and the alignment characteristics of different samples are able to be quantitatively compared using a similarity metric. The results of this work provide researchers in nanoscience and nanotechnology with a rigorous method for the determination of structure/property relationships, where alignment of 1D nanostructures is significant.