- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Fluorescence Hyperspectral Imaging of Oil Samples and Its Quantitative Applications in Component Analysis and Thickness Estimation
摘要： The fast response and analysis of oil spill accidents is important but remains challenging. Here, a compact fluorescence hyperspectral system based on a grating-prism structure able to perform component analysis of oil as well as make a quantitative estimation of oil film thickness is developed. The spectrometer spectral range is 366–814 nm with a spectral resolution of 1 nm. The feasibility of the spectrometer system is demonstrated by determining the composition of three types of crude oil and various mixtures of them. The relationship between the oil film thickness and the fluorescent hyperspectral intensity is furthermore investigated and found to be linear, which demonstrates the feasibility of using the fluorescence data to quantitatively measure oil film thickness. Capable of oil identification, distribution analysis, and oil film thickness detection, the fluorescence hyperspectral imaging system presented is promising for use during oil spill accidents by mounting it on, e.g., an unmanned aerial vehicle.
关键词： K-means clustering,principal component analysis,fluorescence hyperspectral imaging,oil detection
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Krakow, Poland (2018.10.16-2018.10.18)] 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Colour Constancy for Image of Non-Uniformly Lit Scenes
摘要： This paper presents a colour constancy algorithm for images of scenes lit by non-uniform light sources. The proposed method determines number of colour regions within the image using a histogram-based algorithm. It then applies the K-means++ algorithm on the input image, dividing the image into its segments. The proposed algorithm computes the normalized average absolute difference (NAAD) for each segment’s coefficients and uses it as a measure to determine if the segment’s coefficients have sufficient colour variations. The initial colour constancy adjustment factors for each segment with sufficient colour variation is calculated based on the principle that the average values of colour components of the image are achromatic. The colour constancy adjustment weighting factors (CCAWF) for each pixel of image are determined by fusing the CCAWFs of the segments’ with sufficient colour variations, weighted by their normalized Euclidian distance of the pixel from the center of the segments. Experimental results were generated using both indoor and outdoor benchmark images from the scene illuminated by single or multiple illuminants. Results show that the proposed method outperforms the state of the art techniques subjectively and objectively.
关键词： multi-illuminants,fusion,k-means segmentation,colour constancy
更新于2025-09-23 15:22:29
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GWDWT-FCM: Change Detection in SAR Images Using Adaptive Discrete Wavelet Transform with Fuzzy C-Mean Clustering
摘要： Change detection in remote sensing images turns out to play a significant role for the preceding years. Change detection in synthetic aperture radar (SAR) images comprises certain complications owing to the reality that it endures from the existence of the speckle noise. Hence, to overcome this limitation, this paper intends to develop an improved model for detecting the changes in SAR image. In this model, two SAR images captivated at varied times will be considered as the input for the change detection process. Initially, discrete wavelet transform (DWT) is employed for image fusion, where the coefficients are optimized using improved grey wolf optimization (GWO) called adaptive GWO (AGWO) algorithm. Finally, the fused images after inverse transform are clustered using fuzzy C-means (FCM) clustering technique and a similarity measure is performed among the segmented image and ground truth image. With the use of all these technologies, the proposed model is termed as adaptive grey wolf-based DWT with FCM (AGWDWT-FCM). The similarity measures analyze the relevant performance measures such as accuracy, specificity and F1 score. Moreover, the performance of the AGWDWT-FCM in change detection model is compared to other conventional models, and the improvement is noted.
关键词： Filter coefficient,Adaptive discrete wavelet transform,Grey wolf optimization,Synthetic aperture radar,Fuzzy C-means clustering
更新于2025-09-23 15:21:21
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GiB: a Game theory Inspired Binarization technique for degraded document images
摘要： Document image binarization classi?es each pixel in an input document image as either foreground or background under the assumption that the document is pseudo binary in nature. However, noise introduced during acquisition or due to aging or handling of the document can make binarization a challenging task. This paper presents a novel game theory inspired binarization technique for degraded document images. A two-player, non-zero-sum, non-cooperative game is designed at the pixel level to extract the local information, which is then fed to a K-means algorithm to classify a pixel as foreground or background. We also present a preprocessing step that is performed to eliminate the intensity variation that often appears in the background and a post-processing step to re?ne the results. The method is tested on seven publicly available datasets, namely, DIBCO 2009-14 and 2016. The experimental results show that GiB (Game theory Inspired Binarization) outperforms competing state-of-the-art methods in most cases.
关键词： Document image,Binarization,DIBCO,Game theory,K-means,Two-player game
更新于2025-09-23 15:21:21
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[Advances in Intelligent Systems and Computing] Recent Findings in Intelligent Computing Techniques Volume 709 (Proceedings of the 5th ICACNI 2017, Volume 3) || Detection and Analysis of Oil Spill in Ocean for Reduced Complexity in Extraction Using Image Processing
摘要： Oil spills occurring in oceans are difficult to detect and require sophisticated measures to obtain and analyze the images. In this chapter, both color image using high-resolution cameras and Synthetic Aperture Radar (SAR) images are analyzed and certain useful results are obtained to reduce the complexity in extracting the oil spills. The recognition and examination of the oil spill images are done using image processing technique. Furthermore, if the oil spill is scattered as patches, the algorithm classifies the patches into smaller patches and larger ones by using k-means clustering. Hence, the patches depending on the size or intensity can be extracted on a simpler basis.
关键词： Image processing,Synthetic aperture radar (SAR) images,Machine learning,K-means clustering
更新于2025-09-23 15:21:01
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Image-based relighting using image segmentation and bootstrap strategy
摘要： Image-based relighting technologies enable us to recover the illumination effects of modeled scenes under new light conditions without complicated geometrical information. However, most of them are troubled by specialized devices and tedious sampling work. In this study, we propose an efficient and accurate image-based relighting method for the estimation of the light transport matrix of modeled scene, starting from a small number of images acquired with a fixed viewpoint and with lighting sampled over a uniform 2D grid. Especially, the image space is segmented based on the position and average color value of each pixel using K-means. The local coherence among the pixels can be considered to associate with pixel position and pixels’ albedo. The pixels of each cluster can be trained by several neural networks and the training scene datasets can be chosen using the bootstrap strategy. These tricks improve the regression performance. We validate our method with light transport data of several scenes containing complex lighting effects. The obtained results show that the proposed method is useful for practical applications and we can get more plausible rendered images with fewer input images in comparison to related techniques.
关键词： Image-based relighting,Image segmentation,K-means,Neural network,Bootstrap strategy
更新于2025-09-23 15:21:01
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Decoupling mesoscale functional response in PLZT across the ferroelectric – relaxor phase transition with contact Kelvin probe force microscopy and machine learning
摘要： Relaxor ferroelectrics exhibit a range of interesting material behavior including high electromechanical response, polarization rotations as well as temperature and electric field-driven phase transitions. The origin of this unusual functional behavior remains elusive due to limited knowledge on polarization dynamics at the nanoscale. Piezoresponse force microscopy and associated switching spectroscopy provide access to local electromechanical properties on the micro- and nanoscale, which can help to address some of these gaps in our knowledge. However, these techniques are inherently prone to artefacts caused by signal contributions emanating from electrostatic interactions between tip and sample. Understanding functional behavior of complex, disordered systems like relaxor materials with unknown electromechanical properties therefore requires a technique that allows to distinguish between electromechanical and electrostatic response. Here, contact Kelvin probe force microscopy (cKPFM) is used to gain insight into the evolution of local electromechanical and capacitive properties of a representative relaxor material lead lanthanum zirconate across the phase transition from a ferroelectric to relaxor state. The obtained multidimensional data set was processed using an unsupervised machine learning algorithm to detect variations in functional response across the probed area and temperature range. Further analysis showed formation of two separate cKPFM response bands below 50°C, providing evidence for polarization switching. At higher temperatures only one band is observed, indicating an electrostatic origin of the measured response. In addition, from the cKPFM data qualitatively extracted junction potential difference, becomes independent of the temperature in the relaxor state. The combination of this multidimensional voltage spectroscopy technique and machine learning allows to identify the origin of the measured functional response and to decouple ferroelectric from electrostatic phenomena necessary to understand the functional behavior of complex, disordered systems like relaxor materials.
关键词： phase transition,machine learning,Relaxor ferroelectric,lead lanthanum zirconium titanate,piezoresponse force microscopy,k-means clustering,contact Kelvin probe force microscopy
更新于2025-09-23 15:21:01
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Non-Local Means Image Denoising using Shapiro-Wilk Similarity Measure
摘要： Most of the real-time image acquisitions produce noisy measurements of the unknown true images. Image denoising is the post-acquisition technique to improve the signal-to-noise ratio (SNR) of the acquired images. Denoising is an essential pre-processing step for different image processing applications such as image segmentation, feature extraction, registration and other quantitative measurements. Among different denoising methods proposed in the literature, the non-local means method is a preferred choice for images corrupted with additive Gaussian noise. Conventional non-local means ?lter (CNLM) suppresses noise in a given image with minimum loss of structural information. In this paper, we propose modi?cations to CNLM algorithm where the samples are selected statistically using Shapiro-Wilk test. The experiments on standard test images demonstrate the effectiveness of the proposed method.
关键词： Shapiro-Wilk test,Noise,Denoising,Gaussian,Non-Local Means
更新于2025-09-23 15:21:01
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Simple Fusion Approach of Chlorophyll Images and Sea Surface Temperature Images for Improving the Detection of Moroccan Coastal Upwelling
摘要： In order to improve the decision-making on the Moroccan upwelling region detection, we present in this paper a simple and reliable fusion approach. In this context, we started by applying Fuzzy C-means algorithm on each 46 Sea Surface Chlorophyll images and on each 46 Sea Surface Temperature images during the year of 2014. After that, we implement post classification fusion by using logical AND operator set to combine FCM result of the both types and consequently having single image more informative and suitable for visual perception. The oceanographer validation indicate that the proposed methodology detect automatically and effectively the different Moroccan coastal upwelling scenarios of our database.
关键词： Moroccan Coastal Upwelling,Fuzzy C-means,Sea Surface Temperature Image,Sea Surface Chlorophyll Image,Post Classification Fusion
更新于2025-09-23 15:21:01
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Parallel K-Means Clustering for Brain Cancer Detection Using Hyperspectral Images
摘要： The precise delineation of brain cancer is a crucial task during surgery. There are several techniques employed during surgical procedures to guide neurosurgeons in the tumor resection. However, hyperspectral imaging (HSI) is a promising non-invasive and non-ionizing imaging technique that could improve and complement the currently used methods. The HypErspectraL Imaging Cancer Detection (HELICoiD) European project has addressed the development of a methodology for tumor tissue detection and delineation exploiting HSI techniques. In this approach, the K-means algorithm emerged in the delimitation of tumor borders, which is of crucial importance. The main drawback is the computational complexity of this algorithm. This paper describes the development of the K-means clustering algorithm on different parallel architectures, in order to provide real-time processing during surgical procedures. This algorithm will generate an unsupervised segmentation map that, combined with a supervised classification map, will offer guidance to the neurosurgeon during the tumor resection task. We present parallel K-means clustering based on OpenMP, CUDA and OpenCL paradigms. These algorithms have been validated through an in-vivo hyperspectral human brain image database. Experimental results show that the CUDA version can achieve a speed-up of ~150× with respect to a sequential processing. The remarkable result obtained in this paper makes possible the development of a real-time classification system.
关键词： unsupervised clustering,brain cancer detection,Graphics Processing Units (GPUs),OpenCL,CUDA,K-means,OpenMP,hyperspectral imaging
更新于2025-09-23 15:21:01