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Xiao, Ruoxiu, Jian Yang, Mahima Goyal, Yue Liu, and Yongtian Wang. "Automatic Vasculature Identification in Coronary Angiograms by Adaptive Geometrical Tracking." Computational and Mathematical Methods in Medicine 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/796342.
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As the uneven distribution of contrast agents and the perspective projection principle of X-ray, the vasculatures in angiographic image are with low contrast and are generally superposed with other organic tissues; therefore, it is very difficult to identify the vasculature and quantitatively estimate the blood flow directly from angiographic images. In this paper, we propose a fully automatic algorithm named adaptive geometrical vessel tracking (AGVT) for coronary artery identification in X-ray angiograms. Initially, the ridge enhancement (RE) image is obtained utilizing multiscale Hessian information. Then, automatic initialization procedures including seed points detection, and initial directions determination are performed on the RE image. The extracted ridge points can be adjusted to the geometrical centerline points adaptively through diameter estimation. Bifurcations are identified by discriminating connecting relationship of the tracked ridge points. Finally, all the tracked centerlines are merged and smoothed by classifying the connecting components on the vascular structures. Synthetic angiographic images and clinical angiograms are used to evaluate the performance of the proposed algorithm. The proposed algorithm is compared with other two vascular tracking techniques in terms of the efficiency and accuracy, which demonstrate successful applications of the proposed segmentation and extraction scheme in vasculature identification.
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Moler, Emilce, Virginia Ballarin, Franco Pessana, Sebastian Torres, and Dario Olmo. "Fingerprint Identification Using Image Enhancement Techniques." Journal of Forensic Sciences 43, no. 3 (May 1, 1998): 16202J. http://dx.doi.org/10.1520/jfs16202j.
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Wang, Yifeng, Zhijiang Zhang, Ning Zhang, and Dan Zeng. "Attention Modulated Multiple Object Tracking with Motion Enhancement and Dual Correlation." Symmetry 13, no. 2 (February 4, 2021): 266. http://dx.doi.org/10.3390/sym13020266.
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The one-shot multiple object tracking (MOT) framework has drawn more and more attention in the MOT research community due to its advantage in inference speed. However, the tracking accuracy of current one-shot approaches could lead to an inferior performance compared with their two-stage counterparts. The reasons are two-fold: one is that motion information is often neglected due to the single-image input. The other is that detection and re-identification (ReID) are two different tasks with different focuses. Joining detection and re-identification at the training stage could lead to a suboptimal performance. To alleviate the above limitations, we propose a one-shot network named Motion and Correlation-Multiple Object Tracking (MAC-MOT). MAC-MOT introduces a motion enhance attention module (MEA) and a dual correlation attention module (DCA). MEA performs differences on adjacent feature maps which enhances the motion-related features while suppressing irrelevant information. The DCA module focuses on decoupling the detection task and re-identification task to strike a balance and reduce the competition between these two tasks. Moreover, symmetry is a core design idea in our proposed framework which is reflected in Siamese-based deep learning backbone networks, the input of dual stream images, as well as a dual correlation attention module. Our proposed approach is evaluated on the popular multiple object tracking benchmarks MOT16 and MOT17. We demonstrate that the proposed MAC-MOT can achieve a better performance than the baseline state of the arts (SOTAs).
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Gupta, S., J. M. Solomon, T. A. Tasciyan, M. M. Cao, R. D. Stone, J. L. Ostuni, J. M. Ohayon, et al. "Interferon-beta-1b effects on re-enhancing lesions in patients with multiple sclerosis." Multiple Sclerosis Journal 11, no. 6 (December 2005): 658–68. http://dx.doi.org/10.1191/1352458505ms1229oa.
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Interferon-beta (IFNβ) reduces the number and load of new contrast-enhancing lesions (CELs) in patients with multiple sclerosis (MS). However, the ability of IFNβ to reduce lesion sizes and re-enhancements of pre-existing CELs has not been examined extensively. Activity of contrast re-enhancing lesions (Re-CELs) and contrast single-enhancing lesions (S-CELs) were monitored in ten patients with relapsingremitting (RR) MS. These patients underwent monthly post-contrast magnetic resonance imaging (MRIs) for an 18-month natural history phase and an 18-month therapy phase with subcutaneous IFNβ-1b, totaling 37 images per patient. The activity was analysed using the first image as a baseline and registering subsequent active monthly images to the baseline. There was a 76.4% reduction in the number of CELs with IFNβ therapy. The decrease was greater (P=0.003) for S-CELs (82.3%) than for Re-CELs (57.4%). S-CELs showed no changes in durations of enhancement and maximal lesion sizes with treatment. Exclusively for Re-CELs, IFNβ-1b significantly decreased maximal lesion sizes, total number of enhancement periods and total months of enhancement. Thus, IFNβ appears to be effective in reducing the degree of severity of inflammation among Re-CELs, as reflected by their reduced maximal lesion sizes and durations of enhancement.
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Yan, Lingyu, Jiarun Fu, Chunzhi Wang, Zhiwei Ye, Hongwei Chen, and Hefei Ling. "Enhanced network optimized generative adversarial network for image enhancement." Multimedia Tools and Applications 80, no. 9 (January 23, 2021): 14363–81. http://dx.doi.org/10.1007/s11042-020-10310-z.
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AbstractWith the development of image recognition technology, face, body shape, and other factors have been widely used as identification labels, which provide a lot of convenience for our daily life. However, image recognition has much higher requirements for image conditions than traditional identification methods like a password. Therefore, image enhancement plays an important role in the process of image analysis for images with noise, among which the image of low-light is the top priority of our research. In this paper, a low-light image enhancement method based on the enhanced network module optimized Generative Adversarial Networks(GAN) is proposed. The proposed method first applied the enhancement network to input the image into the generator to generate a similar image in the new space, Then constructed a loss function and minimized it to train the discriminator, which is used to compare the image generated by the generator with the real image. We implemented the proposed method on two image datasets (DPED, LOL), and compared it with both the traditional image enhancement method and the deep learning approach. Experiments showed that our proposed network enhanced images have higher PNSR and SSIM, the overall perception of relatively good quality, demonstrating the effectiveness of the method in the aspect of low illumination image enhancement.
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DZULKIFLI, FAHMI AKMAL. "Identification of Suitable Contrast Enhancement Technique for Improving the Quality of Astrocytoma Histopathological Images." ELCVIA Electronic Letters on Computer Vision and Image Analysis 20, no. 1 (May 27, 2021): 84–98. http://dx.doi.org/10.5565/rev/elcvia.1256.
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Contrast enhancement plays an important part in image processing. In histology, the application of a contrast enhancement technique is necessary since it can help pathologists in diagnosing the sample slides by increasing the visibility of the morphological and features of cells in an image. Various techniques have been proposed to enhance the contrast of microscopic images. Thus, this paper aimed to study the effectiveness of contrast enhancement techniques in enhancing the Ki67 images of astrocytoma. Three contrast enhancement techniques consist of contrast stretching, histogram equalization, and CLAHE techniques were proposed to enhance the sample images. The performance of each technique was compared by computing seven quantitative measures. The CLAHE technique was preferred for enhancing the contrast of the astrocytoma images. This technique produces good results especially in contrast enhancement, edge conservation and enhancement, brightness preservation, and minimum distortions to the enhanced images.
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Aijing, Luo, and Yin Jin. "Research on an Improved Medical Image Enhancement Algorithm Based on P-M Model." Open Biomedical Engineering Journal 9, no. 1 (August 31, 2015): 209–13. http://dx.doi.org/10.2174/1874120701509010209.
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Image enhancement can improve the detail of the image to achieve the purpose of the identification of the image. At present, the image enhancement is widely used in medical images, which can help doctor’s diagnosis. IEABPM (Image Enhancement Algorithm Based on P-M Model) is one of the most common image enhancement algorithms. However, it may cause the loss of the texture details and other features. To solve the problems, this paper proposes an IIEABPM (Improved Image Enhancement Algorithm Based on P-M Model). The simulation demonstrates that IIEABPM can effectively solve the problems of IEABPM, and improve image clarity, image contrast, and image brightness.
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Stankevich, Sergey, Oleh Maslenko, and Vitalii Andronov. "Neural network technology adaptation to the small-size objects identification in satellite images of insufficient resolution within the graphic reference images database." Ukrainian journal of remote sensing, no. 27 (December 10, 2020): 13–17. http://dx.doi.org/10.36023/ujrs.2020.27.175.
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A novel flowchart for small-size objects identification in satellite images of insufficient resolution within the graphic reference images database using neural network technology based on compromise contradiction, i.e. simultaneously the resolution enhancement of the object segment of input image and the resolution reduction of the reference image to joint resolution through the simulation of the imaging system has been proposed. This is necessary due to a significant discrepancy between the resolutions of the input image and the graphic reference images used for identification. The required level of resolution enhancement for satellite images, as a rule, is unattainable, and a significant coarsening of reference images is undesirable because of identification errors. Therefore, a certain intermediate spatial resolution is used for identification, which, on the one hand, can be obtained, and on the other the loss of information contained in the reference image is still acceptable. The intermediate resolution is determined by simulating the process of image acquisition with satellite imaging system. To facilitate such simulation, it is advisable to perform it in the frequency domain, where the advanced Fourier analysis is available and, as a rule, all the necessary transfer properties of the links of image formation chain are known. Three main functional elements are engaged for identification: an artificial neural network for the resolution enhancement of input images, a module of frequency-domain simulating of the graphical reference satellite imaging and an artificial neural network for comparing the enhanced object segment with the reference model images. The feasibility of the described approach is demonstrated by the example of successful identification of the sea vessel image in the SPOT-7 satellite image. Currently, the works are under way to compare the performance of a neural network platforms variety for small-size objects identification in satellite images aa well as to assess achievable accuracy.
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AILISTO, HEIKKI, MIKKO LINDHOLM, and PAULI TIKKANEN. "A REVIEW OF FINGERPRINT IMAGE ENHANCEMENT METHODS." International Journal of Image and Graphics 03, no. 03 (July 2003): 401–24. http://dx.doi.org/10.1142/s0219467803001081.
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Automatic fingerprint identification methods have become the most widely used technology in rapidly growing bioidentification applications. In this paper, different image enhancement approaches presented in the scientific literature are reviewed. Fingerprint verification can be divided into image acquisition, enhancement, feature extraction and matching steps. The enhancement step is needed to improve image quality prior to feature extraction. By far the most common approach relies on the filtering of the fingerprint images with filters adapted to local ridge orientation, but alternative approaches based on Fourier domain processing, direct ridge following and global features also exist. Methods of comparing the performance of enhancement methods are discussed. An example of the performance of different methods is given. Conclusions are made regarding the importance of effective enhancement, especially for noisy or low quality images.
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Kim, Changi, Junghun Han, Giwon Yoon, Dongjin Kim, and Sejung Yang. "Novel Framework for Knee Arthroscopic Image Enhancement." Journal of Medical Imaging and Health Informatics 10, no. 6 (June 1, 2020): 1459–65. http://dx.doi.org/10.1166/jmihi.2020.3070.
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An arthroscope is a tool for allowing an endoscope to be inserted directly into the inside of a joint to observe its structure, in contrast to X-rays, computed tomography, and magnetic resonance imaging, which directly capture pictures of a joint. Therefore, it can effectively treat joint diseases by identifying causes of pain that are not found by, e.g., computed tomography and magnetic resonance imaging. However, joint endoscopy has a very high cost, is very burdensome for patients, and has problems in regards to infection when being re-used. Thus, we developed disposable joint endoscopic camera modules for preventing re-use and infection, and researched approaches to reducing patient waiting times and cost burdens. In that regard, it is necessary to improve the brightness and color of the images, as they are used for compacting and disposal of the camera modules. In addition, we studied methods for improving automatic images, as image colors may vary (owing to blood or other foreign substances) when observed using the arthroscope. The proposed framework is divided into two sequences. First, we perform a histogram modification algorithm as an image enhancement technique. This results in a brightness optimization effect on the arthroscopic image. Second, we conduct a high saturation color mapping before proceeding to the next step. In particular, one of the reference points for diagnosing a disease is color information; thus, the improvement of color saturation is considered first in the color mapping. The proposed method provides better brightness values while preserving color information.
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Olatubosun, Olabode, and Fasoranbaku O.A. "Fingerprint Image Enhancement Algorithms for Identification in an Electoral Process." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 14, no. 2 (December 13, 2014): 5453–63. http://dx.doi.org/10.24297/ijct.v14i2.2090.
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Electoral process in many developing nations is characterized with fraud and failure as a result of inefficient and effective system of voter’s register and voting system. This usually leads to crises. This paper presents enhancement algorithms of Denoising fingerprint images using Moving Average Filter, the Segmentation of Fingerprint using Chen algorithm, Gradient-Based Local Ridge Orientation, Binarization using Otsu Algorithm, Thinning of Fingerprint Image using the linear interactive approach and Minutiae Extraction using the minutiae location and minutiae angles for the purpose of verification and Identification in electoral process. The input to the system is a Gray-scale Fingerprint image of electorate and it detailed information while the Output is a Verified and enrolled fingerprint image with matching score and details of electorates’ information store as Template using the Matlab 2013a.
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Rasool Reddy, K., Dr K.NagaPrakash, Dr K.Prasanthi Jasmime, and M. Tulasidas. "Satellite Image Resolution Enhancement based on Dual-Domain Filtering." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 466. http://dx.doi.org/10.14419/ijet.v7i2.7.10864.
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Satellite images place vital role in agriculture, Disaster mitigation and geosciences applications. Satellite images include both spatial and temporal resolution, in that spatial resolution is influence the accuracy of ground objects. The main idea of this work is to enhance the resolution of satellite images. In this work, a dual domain filtering based approach is introduced for resolution enhancement (RE). Initially, the source image is subdivided into approximation and detail coefficients by Stationary Wavelet transform (SWT). The detail coefficients are interpolated based on bi-linear interpolation. The interpolated detail coefficients are applied to Non-Local Means (NLM) filter to minimize the artifacts produced by SWT. The filtered detail and approximation coefficients of source image are fed to ISWT to attain high resolution (HR) image. The proposed system is superior to other existing strategies like DWT-NLM and DWT-SWT.
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P, Pakutharivu, and Srinath M. V. "Analysis of Fingerprint Image Enhancement using Gabor Filtering with Different Orientation Field Values." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 2 (February 1, 2017): 427. http://dx.doi.org/10.11591/ijeecs.v5.i2.pp427-432.
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<p>Fingerprint image enhancement is the key process in IAFIS systems. In order to reduce false identification ratio and to supply good fingerprint images to IAFIS systems for exact identification, fingerprint images are generally enhanced. A filtering process tries to filter out the noise from the input image, and emphasize on low, high and directional spatial frequency components of an image. This paper presents an experimental summary of enhancing fingerprint images using Gabor filters. Frequency, width and window domain filter ranges are fixed. The orientation angle alone is modified by 0 radians, , and radians. The experimental results show that Gabor filter enhances the fingerprint image in a better way than other filtering methods and extracts features. </p>
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Lazarov, A., and C. Minchev. "ISAR Image Recognition Algorithm and Neural Network Implementation." Cybernetics and Information Technologies 17, no. 4 (November 27, 2017): 183–99. http://dx.doi.org/10.1515/cait-2017-0048.
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AbstractThe image recognition and identification procedures are comparatively new in the scope of ISAR (Inverse Synthetic Aperture Radar) applications and based on specific defects in ISAR images, e.g., missing pixels and parts of the image induced by target’s aspect angles require preliminary image processing before identification. The present paper deals with ISAR image enhancement algorithms and neural network architecture for image recognition and target identification. First, stages of the image processing algorithms intended for image improving and contour line extraction are discussed. Second, an algorithm for target recognition is developed based on neural network architecture. Two Learning Vector Quantization (LVQ) neural networks are constructed in Matlab program environment. A training algorithm by teacher is applied. Final identification decision strategy is developed. Results of numerical experiments are presented.
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Wu, Yin Chao, Seong Jin Noh, and Suyun Ham. "Identification of Inundation Using Low-Resolution Images from Traffic-Monitoring Cameras: Bayes Shrink and Bayesian Segmentation." Water 12, no. 6 (June 17, 2020): 1725. http://dx.doi.org/10.3390/w12061725.
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This study presents a comparative assessment of image enhancement and segmentation techniques to automatically identify the flash flooding from the low-resolution images taken by traffic-monitoring cameras. Due to inaccurate equipment in severe weather conditions (e.g., raindrops or light refraction on camera lenses), low-resolution images are subject to noises that degrade the quality of information. De-noising procedures are carried out for the enhancement of images by removing different types of noises. For the comparative assessment of de-noising techniques, the Bayes shrink and three conventional methods are compared. After the de-noising, image segmentation is implemented to detect the inundation from the images automatically. For the comparative assessment of image segmentation techniques, k-means segmentation, Otsu segmentation, and Bayesian segmentation are compared. In addition, the detection of the inundation using the image segmentation with and without de-noising techniques are compared. The results indicate that among de-noising methods, the Bayes shrink with the thresholding discrete wavelet transform shows the most reliable result. For the image segmentation, the Bayesian segmentation is superior to the others. The results demonstrate that the proposed image enhancement and segmentation methods can be effectively used to identify the inundation from low-resolution images taken in severe weather conditions. By using the principle of the image processing presented in this paper, we can estimate the inundation from images and assess flooding risks in the vicinity of local flooding locations. Such information will allow traffic engineers to take preventive or proactive actions to improve the safety of drivers and protect and preserve the transportation infrastructure. This new observation with improved accuracy will enhance our understanding of dynamic urban flooding by filling an information gap in the locations where conventional observations have limitations.
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Sundaram, M., K. Ramar, N. Arumugam, and G. Prabin. "EFFICIENT EDGE EMPHASIZED MAMMOGRAM IMAGE ENHANCEMENT FOR DETECTION OF MICROCALCIFICATION." Biomedical Engineering: Applications, Basis and Communications 26, no. 05 (September 26, 2014): 1450056. http://dx.doi.org/10.4015/s1016237214500562.
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An efficient detection of microcalcification based on edge enhancement using discrete wavelet transform (DWT) is presented in this paper. The proposed method is implemented by separating the wavelet coefficients into weak and strong edge coefficients for effective detection of microcalcification. Identification of strong and weak edge locations corresponding to microcalcification is obtained by allowing the input image through appropriate filters before wavelet decomposition. Before reconstructing the output image, the strong and weak edge coefficients are modified based on the energy of the coefficients. The reconstructed image exhibits a better enhancement with the fine detail components of microcalcification than the original mammogram image. Standard Mias mammogram database images and clinical mammograms are used for testing and comparing subjective and objective measures of the mammogram images. A comparative study is made with the existing state-of-the-art edge enhancement and contrast enhancement methods and results are encouraging. The edge emphasizing ability of the proposed method is highly proficient in detection of microcalcification from the mammogram.
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Lin, Huei-Yung, Li-Qi Chen, and Min-Liang Wang. "Improving Discrimination in Color Vision Deficiency by Image Re-Coloring." Sensors 19, no. 10 (May 15, 2019): 2250. http://dx.doi.org/10.3390/s19102250.
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People with color vision deficiency (CVD) cannot observe the colorful world due to the damage of color reception nerves. In this work, we present an image enhancement approach to assist colorblind people to identify the colors they are not able to distinguish naturally. An image re-coloring algorithm based on eigenvector processing is proposed for robust color separation under color deficiency transformation. It is shown that the eigenvector of color vision deficiency is distorted by an angle in the λ , Y-B, R-G color space. The experimental results show that our approach is useful for the recognition and separation of the CVD confusing colors in natural scene images. Compared to the existing techniques, our results of natural images with CVD simulation work very well in terms of RMS, HDR-VDP-2 and an IRB-approved human test. Both the objective comparison with previous works and the subjective evaluation on human tests validate the effectiveness of the proposed method.
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Hu, Xiao Lang. "Study on Terminal Identification Enhancement Method of Track and Field Using Digital X-Ray Photography Images." Applied Mechanics and Materials 602-605 (August 2014): 2084–88. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2084.
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Due to the development of image segmentation and reconstruction technology, it provides a larger space for development of the image enhancement technology. Under the promoting of the image processing calculation, the capture and recognition function of digital X-ray photography technology are stronger, and the image processing precision is higher. Based on the variation principle, this paper uses the function approximation to improve the X-ray photography image processing technology, and obtains the new boundary value reconstruction condition of X-ray photography. In order to verify the effectiveness and reliability of the mathematical model and algorithm of the boundary value reconstruction, this paper uses MATLAB software and C language to debug the algorithm, and realizes the digital color rendering for the images at the terminal of track and field, obtains the image reconstruction algorithm under different boundary values. It provides a new computer method for the research on image enhancement technology.
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Priya Henry, Asha Gnana, and Anitha Jude. "Convolutional neural-network-based classification of retinal images with different combinations of filtering techniques." Open Computer Science 11, no. 1 (January 1, 2021): 480–90. http://dx.doi.org/10.1515/comp-2020-0177.
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Abstract Retinal image analysis is one of the important diagnosis methods in modern ophthalmology because eye information is present in the retina. The image acquisition process may have some effects and can affect the quality of the image. This can be improved by better image enhancement techniques combined with the computer-aided diagnosis system. Deep learning is one of the important computational application techniques used for a medical imaging application. The main aim of this article is to find the best enhancement techniques for the identification of diabetic retinopathy (DR) and are tested with the commonly used deep learning techniques, and the performances are measured. In this article, the input image is taken from the Indian-based database named as Indian Diabetic Retinopathy Image Dataset, and 13 filters are used including smoothing and sharpening filters for enhancing the images. Then, the quality of the enhancement techniques is compared using performance metrics and better results are obtained for Median, Gaussian, Bilateral, Wiener, and partial differential equation filters and are combined for improving the enhancement of images. The output images from all the enhanced filters are given as the convolutional neural network input and the results are compared to find the better enhancement method.
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Sardorbek, Numonov, Bong-Soo Sohn, and Byung-Woo Hong. "Coherence Enhancement Based on Recursive Anisotropic Scale-Space with Adaptive Kernels." Applied Sciences 10, no. 15 (July 23, 2020): 5079. http://dx.doi.org/10.3390/app10155079.
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The reduction of unnecessary details is important in a variety of imaging tasks. Image denoising can be generally formulated as a diffusion process that iteratively suppresses undesirable image features with high variance. We propose a recursive diffusion process that simultaneously computes the local geometrical property of the image features and determines the size and shape of the diffusion kernel, leading to an anisotropic scale-space. In the construction of the proposed anisotropic scale-space, image features due to undesirable noise are suppressed while significant geometrical image features such as edges and corners are preserved across the scale-space. The diffusion kernels are iteratively determined based on the local geometrical properties of the image features. We demonstrate the effectiveness and robustness of the proposed algorithm in the detection of curvilinear features using simple yet effective synthetic and real images. The algorithm is quantitatively evaluated based on the identification of fissures in lung CT imagery. The experimental results indicate that the proposed algorithm can be used for the detection of linear or curvilinear structures in a variety of images ranging from satellite to medical images.
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Saxena, Kumud. "Wavelets assisted fuzzy edge refinement." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 14, no. 2 (December 5, 2014): 5409–18. http://dx.doi.org/10.24297/ijct.v14i2.2063.
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Image enhancement is a crucial pre-processing step to be performed for various applications where object recognition, identification, verification is required. Among various image enhancement methods, edge enhancement has taken its importance as it is widely used for understanding features in an image. Several types of edge detectors are available for certain types of edges. If edges are enhanced and clear, the reliability for feature extraction increases. The Quality of edge detection can be measured from several criteria objectively. In this paper, a novel algorithm for edge enhancement has been proposed for multiple types of images. The features can be extracted clearly by using this method. For comparison purpose Roberts, Sobel, Prewitt, Canny, and Log edge operators are used and their results are displayed. Experimental results demonstrate the effectiveness of the proposed approach.
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Kim, Ji Seong, Doo Soo Chang, and Yong Suk Choi. "Enhancement of Multi-Target Tracking Performance via Image Restoration and Face Embedding in Dynamic Environments." Applied Sciences 11, no. 2 (January 11, 2021): 649. http://dx.doi.org/10.3390/app11020649.
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In this paper, we propose several methods to improve the performance of multiple object tracking (MOT), especially for humans, in dynamic environments such as robots and autonomous vehicles. The first method is to restore and re-detect unreliable results to improve the detection. The second is to restore noisy regions in the image before the tracking association to improve the identification. To implement the image restoration function used in these two methods, an image inference model based on SRGAN (super-resolution generative adversarial networks) is used. Finally, the third method includes an association method using face features to reduce failures in the tracking association. Three distance measurements are designed so that this method can be applied to various environments. In order to validate the effectiveness of our proposed methods, we select two baseline trackers for comparative experiments and construct a robotic environment that interacts with real people and provides services. Experimental results demonstrate that the proposed methods efficiently overcome dynamic situations and show favorable performance in general situations.
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Kim, Ji Seong, Doo Soo Chang, and Yong Suk Choi. "Enhancement of Multi-Target Tracking Performance via Image Restoration and Face Embedding in Dynamic Environments." Applied Sciences 11, no. 2 (January 11, 2021): 649. http://dx.doi.org/10.3390/app11020649.
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In this paper, we propose several methods to improve the performance of multiple object tracking (MOT), especially for humans, in dynamic environments such as robots and autonomous vehicles. The first method is to restore and re-detect unreliable results to improve the detection. The second is to restore noisy regions in the image before the tracking association to improve the identification. To implement the image restoration function used in these two methods, an image inference model based on SRGAN (super-resolution generative adversarial networks) is used. Finally, the third method includes an association method using face features to reduce failures in the tracking association. Three distance measurements are designed so that this method can be applied to various environments. In order to validate the effectiveness of our proposed methods, we select two baseline trackers for comparative experiments and construct a robotic environment that interacts with real people and provides services. Experimental results demonstrate that the proposed methods efficiently overcome dynamic situations and show favorable performance in general situations.
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Jain, Rajneesh, Sheelesh Kr Sharma, and Pankaj Agrawal. "Performance Analysis of Fingerprint Based Image Enhancement and Minutiae Extraction." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 5 (June 2, 2018): 43. http://dx.doi.org/10.23956/ijarcsse.v8i5.663.
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Extracting minutiae from fingerprint images is one of the most important steps in automatic fingerprint identification and classification. Minutiae are local discontinuities in the fingerprint pattern, mainly terminations and bifurcations. In this work we have propose a method for fingerprint image enhancement. Using histogram equalization over filtering and then minutia are calculated. The results achieved are compared with those obtained through some other methods. The Results show some improvement in the minutiae extraction in terms of quantity.
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Xiong, Jianbin, Dezheng Yu, Qi Wang, Lei Shu, Jian Cen, Qiong Liang, Huanyang Chen, and Baocheng Sun. "Application of Histogram Equalization for Image Enhancement in Corrosion Areas." Shock and Vibration 2021 (January 22, 2021): 1–13. http://dx.doi.org/10.1155/2021/8883571.
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In this paper, an image enhancement algorithm is presented for identification of corrosion areas and dealing with low contrast present in shadow areas of an image. This algorithm uses histogram equalization processing under the hue-saturation-intensity model. First of all, an etched image is transformed from red-green-blue color space to hue-saturation-intensity color space, and only the luminance component is enhanced. Then, part of the enhanced image is combined with the original tone component, followed by saturation and conversion to red-green-blue color space to obtain the enhanced corrosion image. Experimental results show that the proposed method significantly improves overall brightness, increases contrast details in shadow areas, and strengthens identification of corrosion areas in the image.
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Pandit, S. M., and G. A. Joshi. "Image Enhancement: A Data Dependent Systems Approach." Journal of Engineering for Industry 116, no. 2 (May 1, 1994): 247–52. http://dx.doi.org/10.1115/1.2901937.
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A new image enhancement scheme based on a mathematical model obtained by data dependent systems (DDS) approach is described in this paper. A separable 2-D AR model is fitted to the image. Analysis of this model leads to the identification of modes corresponding to dominant physical features. Other intrinsic modes, inherent to the image, are highly damped and constitute difficult-to-interpret local image behavior. Although exerting a minor influence on the image intensities, they hinder a clear perception of the image. In order to enhance the image, these modes must be filtered out. ARMA image enhancement filters are formed using the major inherent modes. Residuals, the part of the image not modeled by this ARMA filter, comprise the enhanced image. This approach can also be used to selectively enhance the desired image features. Examples illustrating clear enhancement of a real image, with natural degradations created by shadows and other artifacts as well as artificially added noise, are given.
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Zhan, Xiao Si, and Ya Yun Chu. "Two-Dimensional Sine Filter Fingerprint Enhancement Algorithm Based on Block Level." Applied Mechanics and Materials 88-89 (August 2011): 596–603. http://dx.doi.org/10.4028/www.scientific.net/amm.88-89.596.
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Enhancing low-quality fingerprint image is the effective method for improving the accuracy of minutia extraction and performance of the automatic fingerprint identification system. Fingerprint image is one kind of regular texture image in nature. To design two-dimensional sinusoidal surface model which accorded with the ridge gray distribution rule and propose the fingerprint image enhancement algorithm based on the two-dimensional sinusoidal surface model after analyzing the basic character of the fingerprint image. The experimental results indicate that the fingerprint image enhancement algorithm has better connecting ability for the broken ridges than the Gabor fingerprint enhancement algorithm. The algorithm can improve effectively the fingerprint image enhancement result and the accuracy of the minutiae extraction.
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Muijs, Remco, Johan O. Robertsson, and Klaus Holliger. "Prestack depth migration of primary and surface-related multiple reflections: Part II — Identification and removal of residual multiples." GEOPHYSICS 72, no. 2 (March 2007): S71—S76. http://dx.doi.org/10.1190/1.2424544.
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Depth imaging using primary and multiple reflections (DIPMR), as described in Part I of this study, allows subsurface information carried by multiple reflections to be utilized. In the presence of strong lateral heterogeneity, however, the migration results may be distorted by artifacts originating from reflections associated with layers above the imaging plane that are commonly referred to as crosstalk. We present an image enhancement procedure that allows such artifacts to be effectively suppressed by predicting the initial crosstalk in a second migration phase. This second migration uses reflections imaged at shallower depth levels and requires knowledge of the total and primary downgoing wavefield at the receiver level. The predicted crosstalk image it-self may assist the interpretational effort by indicating areas where artifacts may result in incorrect identification of geologic structures or may cause local distortions of amplitude informa-tion. Furthermore, a clean depth image can be obtained by adap-tively subtracting the predicted crosstalk-related noise from the original image. The proposed method is an extension of the DIPMR procedure outlined in Part I of this study. However, it is also applicable to seismic data imaged by using conventional mi gration techniques that are based exclusively on primary reflec-tions. In the latter case, the enhancement procedure allows the ef-fects of imperfect multiple removal during preprocessing to be revealed in the migrated sections. When applied to synthetic data simulated for a complex salt model, the proposed enhancement procedure proved to be valid and effective.
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Devi, R., and P. Sujatha. "Enhancement of fingerprint image using wiener filter." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 206. http://dx.doi.org/10.14419/ijet.v7i1.1.9456.
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A fingerprint is one of the most vital Biometric traits used for Personal Identification. To identify and match the fingerprint accurately, it has to be enhanced efficiently. In this paper, an efficient fingerprint enhancement technique is adopted and compared with the existing methods. The proposed methodology consists of three Phases. In the first phase, the fingerprint is subjected to the de-noising process. After adding noise such as salt and pepper, Gaussian and speckle noise, the image is blurred. In the second phase, the fingerprint is filtered with Wiener filter and then de-blurred. In the third, the filtered image is further enhanced for more clarity. The paper emphasizes, the fingerprint preprocessing followed with the enhancement produces better quality image. The performance of the proposed methodology is compared and evaluated using two performances measures namely Peak-Signal-Noise –Ratio and Mean Squared Error using Matlab R2013a.
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Wu, Li Ming, Shi Long Yang, Fu Jian Li, Xin Luo, and Bing Jing Li. "Adaptive Video Image Enhancement Algorithm Based on FPGA Design." Key Engineering Materials 620 (August 2014): 516–21. http://dx.doi.org/10.4028/www.scientific.net/kem.620.516.
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Aiming at the problem of image quality degradation due to scenes change rapidly, an image enhancement algorithm based on scene intelligent identification is proposed. The algorithm sharpen the image detail by using Laplace operator. Determines the change image scene according to the gray value, constructs different gray mapping function, and adjusts gray value range of image adaptively to improve the contrast ratio of image enhancement. By using parallel processing, the algorithm has high execution efficiency, so it can meet the real-time processing of HD video. Experimental result shows that the proposed algorithm has satisfying performance in the rapidly change scene.
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Zhang, Shu Wen, Xiao Ning Zhang, Zhi Yong Wu, and Li Wan Shi. "Research on Asphalt Mixture Injury Digital Image Based on Enhancement and Segmentation Processing Technology." Applied Mechanics and Materials 470 (December 2013): 832–37. http://dx.doi.org/10.4028/www.scientific.net/amm.470.832.
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In order to visually observe the damage evolution of the asphalt mixture, and analyze the damage evolution accurately and quantitatively, the CT image of asphalt mixture image was processed for image enhancing and segmenting, the entire or partial image features can be effectively improved. The dynamics gray-scale range of the image was adjusted by the histogram equalization and provision. Image gray-scale transformation is enhanced by using Matlab software. At the same time, when the algebraic operations and noise filtering is used to process, the mixture image segmentation and boundary identification are achieved. The results show that the contrast and gray scale dynamic range of the gray scale image can be effectively improved by provision and equalization of histogram. The gap, aggregates and binder can be extracted by segmentation technology from asphalt mixture CT images. The real microstructure obtained can accurately reflect the evolution of asphalt damage.
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Bernacki, Krzysztof, Tomasz Moroń, and Adam Popowicz. "Modified Distance Transformation for Image Enhancement in NIR Imaging of Finger Vein System." Sensors 20, no. 6 (March 16, 2020): 1644. http://dx.doi.org/10.3390/s20061644.
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Most of the current image processing methods used in the near-infrared imaging of finger vascular system concentrate on the extraction of internal structures (veins). In this paper, we propose a novel approach which allows to enhance both internal and external features of a finger. The method is based on the Distance Transformation and allows for selective extraction of physiological structures from an observed finger. We evaluate the impact of its parameters on the effectiveness of the already established processing pipeline used for biometric identification. The new method was compared with five state-of-the-art approaches to features extraction (position-gray-profile-curve—PGPGC, maximum curvature points in image profiles—MC, Niblack image adaptive thresholding—NAT, repeated dark line tracking—RDLT, and wide line detector—WD) on the GustoDB database of images obtained in a wide range of NIR wavelengths (730–950 nm). The results indicate a clear superiority of the proposed approach over the remaining alternatives. The method managed to reach over 90 % identification accuracy for all analyzed datasets.
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Pourasad, Yaghoub, and Fausto Cavallaro. "A Novel Image Processing Approach to Enhancement and Compression of X-ray Images." International Journal of Environmental Research and Public Health 18, no. 13 (June 22, 2021): 6724. http://dx.doi.org/10.3390/ijerph18136724.
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At present, there is an increase in the capacity of data generated and stored in the medical area. Thus, for the efficient handling of these extensive data, the compression methods need to be re-explored by considering the algorithm’s complexity. To reduce the redundancy of the contents of the image, thus increasing the ability to store or transfer information in optimal form, an image processing approach needs to be considered. So, in this study, two compression techniques, namely lossless compression and lossy compression, were applied for image compression, which preserves the image quality. Moreover, some enhancing techniques to increase the quality of a compressed image were employed. These methods were investigated, and several comparison results are demonstrated. Finally, the performance metrics were extracted and analyzed based on state-of-the-art methods. PSNR, MSE, and SSIM are three performance metrics that were used for the sample medical images. Detailed analysis of the measurement metrics demonstrates better efficiency than the other image processing techniques. This study helps to better understand these strategies and assists researchers in selecting a more appropriate technique for a given use case.
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Wang, Zhiyou, Xiaoqing Huang, and Zhiqiang Cheng. "Automatic Spot Identification Method for High Throughput Surface Plasmon Resonance Imaging Analysis." Biosensors 8, no. 3 (September 13, 2018): 85. http://dx.doi.org/10.3390/bios8030085.
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An automatic spot identification method is developed for high throughput surface plasmon resonance imaging (SPRi) analysis. As a combination of video accessing, image enhancement, image processing and parallel processing techniques, the method can identify the spots in SPRi images of the microarray from SPRi video data. In demonstrations of the method, SPRi video data of different protein microarrays were processed by the method. Results show that our method can locate spots in the microarray accurately regardless of the microarray pattern, spot-background contrast, light nonuniformity and spotting defects, but also can provide address information of the spots.
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Cho, Hyun, Sung Jin Song, and Hak Joon Kim. "Enhancement of Ultrasonic C-Scan Images for Inspection of Multi-Layered Composite Panels." Key Engineering Materials 326-328 (December 2006): 713–16. http://dx.doi.org/10.4028/www.scientific.net/kem.326-328.713.
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One of the serious problems that make the flaw identification in a multi-layered thick composite panel more difficult is the interference effect of the upper layer. To take care of such a problem, here we propose an image enhancement approach that can get rid of such an interference effect to ultrasonic C-scan images by a normalization of the acquired signals by a reference signals, and demonstrate its performance in the experiments. Specifically, three specimens with artificial flaws are prepared and ultrasonic C-scan images are acquired experimentally to eliminate the undesired interference effect. Great successes are observed in the present study demonstrating the high potential of the proposed algorithm as a practical image enhancement tool in many practical situations.
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Raicevic, Andjelija, and Brankica Popovic. "An effective and robust fingerprint enhancement by adaptive filtering in frequency domain." Facta universitatis - series: Electronics and Energetics 22, no. 1 (2009): 91–104. http://dx.doi.org/10.2298/fuee0901091r.
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Extensive research of automatic fingerprint identification system (AFIS), although started in the early 1960s, has not yet give the answer to reliable fingerprint recognition problem. A critical step for AFIS accuracy is reliable extraction of features (mostly minutiae) from the input fingerprint image. However, the effectiveness of a feature extraction relies heavily on the quality of the input fingerprint images. This leads to the incorporation of a fingerprint enhancement module in fingerprint recognition system to make the system robust with respect to the quality of input fingerprint images. In this paper we propose an adaptive filtering in frequency domain in order to enhance fingerprint image. Two different directional filters are proposed and results are compared. .
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Li, Mu, Tao Li, Lei Zheng, and Fen Xu. "Research on Infrared Temperature Measurement Image Enhancement Technology of Transmission Line." E3S Web of Conferences 257 (2021): 02048. http://dx.doi.org/10.1051/e3sconf/202125702048.
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It is one of the important means to ensure the safety of transmission line and its equipment to realize the rapid identification and accurate diagnosis of visual defect fault points of transmission line. In this paper, an adaptive infrared image enhancement algorithm is proposed based on the research of infrared image features and various image enhancement algorithms. The algorithm first filters the infrared image to remove the random noise and improve the signal-to-noise ratio of the image; then, based on the histogram analysis of the infrared image, adaptively selects the upper and lower thresholds of the gray histogram, and uses the histogram segmentation to divide the infrared image into three parts: target, target and background aliasing, and background. Finally, through the gray analysis of the three distributions, the algorithm can extract the gray image. Finally, infrared image background suppression and target enhancement are realized. By comparing the effect before and after image enhancement, it is proved that the algorithm has strong practicability.
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Mahalakshmi, T., and Alluri Sreenivas. "Adaptive Filter with Type-2 Fuzzy System and Optimization-Based Kernel Interpolation for Satellite Image Denoising." Computer Journal 63, no. 6 (February 18, 2020): 913–26. http://dx.doi.org/10.1093/comjnl/bxz168.
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Abstract Satellite image denoising is a recent trend in image processing, but faces many challenges due to the environmental factors. Previous works have developed many filters for denoising the hyperspectral satellite images. Accordingly, this work utilizes an adaptive filter with the type 2 fuzzy system and the optimization-based kernel interpolation for the satellite image denoising. Here, the image denoising has been done through three steps, namely noise identification, noise correction and image enhancement. Initially, the type 2 fuzzy system identifies the noisy pixels in the satellite image and converts the image into a binary image, which is passed through the adaptive nonlocal mean filter (ANLMF) for the noise correction. Finally, the kernel-based interpolation scheme carries out the image enhancement, which is done through the proposed chronological Jaya optimization algorithm (chronological JOA) that is developed by modifying Jaya optimization algorithm (JOA) with the chronological idea. The performance of the proposed denoising scheme is analyzed by considering the satellite images from two standard databases, namely Indian pines database and NRSC/ISRO satellite database. Also, the comparative analysis is performed with the state-of-the-art denoising methods using the evaluation metrics, peak signal to noise ratio (PSNR), structural similarity index (SSIM) and second derivative-like measure of enhancement (SDME). From the results, it is exposed that the proposed adaptive filter with the chronological JOA has the improved performance with the PSNR of 22.0408 dB, SDME of 244.133 dB and SSIM of 0.872.
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Jacobsen, K., H. Topan, A. Cam, M. Özendi, and M. Oruc. "Radiometric and geometric characteristics of Pleiades images." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1 (November 7, 2014): 173–77. http://dx.doi.org/10.5194/isprsarchives-xl-1-173-2014.
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Pleiades images are distributed with 50 cm ground sampling distance (GSD) even if the physical resolution for nadir images is just 70 cm. By theory this should influence the effective GSD determined by means of point spread function at image edges. Nevertheless by edge enhancement the effective GSD can be improved, but this should cause enlarged image noise. Again image noise can be reduced by image restoration. Finally even optimized image restoration cannot improve the image information from 70 cm to 50 cm without loss of details, requiring a comparison of Pleiades image details with other very high resolution space images. The image noise has been determined by analysis of the whole images for any sub-area with 5 pixels times 5 pixels. Based on the standard deviation of grey values in the small sub-areas the image noise has been determined by frequency analysis. This leads to realistic results, checked by test targets. On the other hand the visual determination of image noise based on apparently homogenous sub-areas results in too high values because the human eye is not able to identify small grey value differences – it is limited to just approximately 40 grey value steps over the available gray value range, so small difference in grey values cannot be seen, enlarging results of a manual noise determination. <br><br> A tri-stereo combination of Pleiades 1A in a mountainous, but partially urban, area has been analyzed and compared with images of the same area from WorldView-1, QuickBird and IKONOS. The image restoration of the Pleiades images is very good, so the effective image resolution resulted in a factor 1.0, meaning that the effective resolution corresponds to the nominal resolution of 50 cm. This does not correspond to the physical resolution of 70 cm, but by edge enhancement the steepness of the grey value profile across the edge can be enlarged, reducing the width of the point spread function. Without additional filtering edge enhancement enlarges the image noise, but the average image noise of approximately 1.0 grey values related to 8 bit images is very small, not indicating the edge enhancement and the down sampling of the GSD from 70 cm to 50 cm. So the direct comparison with the other images has to give the answer if the image quality of Pleiades images is on similar level as corresponding to the nominal resolution. As expected with the image geometry there is no problem. This is the case for all used space images in the test area, where the point identification limits the accuracy of the scene orientation.
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Song, Chenyong, Dongwei Wang, Haoran Bai, and Weihao Sun. "Apple Disease Recognition Based on Small-scale Data Sets." Applied Engineering in Agriculture 37, no. 3 (2021): 481–90. http://dx.doi.org/10.13031/aea.14187.
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HighlightsThe proposed data enhancement method can be used for small-scale data sets with rich sample image features.The accuracy of the new model reaches 98.5%, which is better than the traditional CNN method.Abstract: GoogLeNet offers far better performance in identifying apple disease compared to traditional methods. However, the complexity of GoogLeNet is relatively high. For small volumes of data, GoogLeNet does not achieve the same performance as it does with large-scale data. We propose a new apple disease identification model using GoogLeNet’s inception module. The model adopts a variety of methods to optimize its generalization ability. First, geometric transformation and image modification of data enhancement methods (including rotation, scaling, noise interference, random elimination, color space enhancement) and random probability and appropriate combination of strategies are used to amplify the data set. Second, we employ a deep convolution generative adversarial network (DCGAN) to enhance the richness of generated images by increasing the diversity of the noise distribution of the generator. Finally, we optimize the GoogLeNet model structure to reduce model complexity and model parameters, making it more suitable for identifying apple tree diseases. The experimental results show that our approach quickly detects and classifies apple diseases including rust, spotted leaf disease, and anthrax. It outperforms the original GoogLeNet in recognition accuracy and model size, with identification accuracy reaching 98.5%, making it a feasible method for apple disease classification. Keywords: Apple disease identification, Data enhancement, DCGAN, GoogLeNet.
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Men, Hong, Xin Su, Peng Chen, and Jia Xue Yu. "Comparison of Infrared Image Pre-Processing Technique on Electronic Power Equipment." Advanced Materials Research 680 (April 2013): 339–44. http://dx.doi.org/10.4028/www.scientific.net/amr.680.339.
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The disadvantages of infrared image are low resolution, bad stereoscopic sense, fuzzy image and low SNR, according to the application of infrared image in fault diagnosis of electronic power equipment, in this paper ,we make a comparative research on pre-processing technique of image de-noising and enhancement, and propose an infrared image enhancement algorithm based on platform histogram equalization combined with enhanced high-pass filtering, the algorithm can effectively improve the contrast by comparison, it is obvious to the noise effect, highlighting the objectives and details, and makes a good foundation for the subsequent target identification and fault diagnosis.
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Potente, Stefan, Frank Ramsthaler, Mattias Kettner, Tomoya Ikeda, and Peter Schmidt. "Application of the “bubbling” procedure to dead body portraits in forensic identification." International Journal of Legal Medicine 135, no. 4 (February 5, 2021): 1655–59. http://dx.doi.org/10.1007/s00414-021-02515-0.
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Abstract Purpose A procedure is needed for bodies with disfiguring injuries to the face and the use of their portrait for visual identification. Method We present the application of a simple image processing procedure, otherwise known as ”bubbling,” which is based on the concept of ”perceptual filling-in,” to images for visual identification in the forensic context. The method is straight forward and can be performed using readily available software and hardware.. Results The method is demonstrated and examples are shown. The visual recognition of known persons using “bubbled” images was successfully tested. Conclusion The “bubbling” procedure for visual identification enhancement is quick and straightforward and may be attempted before escalating to more involved identification methods and procedures.
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Liu, Jian Guo, Jun Luo, and Xi Li. "A Research of an Improved Method for Lane Detection in a High Light Condition." Advanced Materials Research 694-697 (May 2013): 1914–18. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.1914.
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Most of the methods, which being used to the normal light, are easy to make a mistake in the high light condition. An improved method is proposed in this paper to solve these problems. It includes a series of pretreatment for road image, like white balance, contrast enhancement, edge enhancement. Then the method makes an image segmentation to increase the efficiency of the identification. Use an improved Hough transformation to recognize the parameters of lane line. Finally, establish a trapezoidal interested region to achieve a real-time dynamic extraction of lane lines parameters from the continuous image. The results of identification show that the improved method for the high light condition makes a better work and its more accurate and efficient to acquire the parameters.
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Sharma, Bhubneshwar, and Jyoti Dadwal. "Design of image processing technique in digital enhancement application." International Journal of Advances in Scientific Research 1, no. 8 (October 4, 2015): 340. http://dx.doi.org/10.7439/ijasr.v1i8.2463.
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This paper describes the basic technological aspects of Digital Image Processing with special reference to satellite image processing. Basically, all satellite image-processing operations can be grouped into three categories: Image Rectification and Restoration, Enhancement and Information Extraction. The former deals with initial processing of raw image data to correct for geometric distortion, to calibrate the data radio metrically and to eliminate noise present in the data. The enhancement procedures are applied to image data in order to effectively display the data for subsequent visual interpretation. It involves techniques for increasing the visual distinction between features in a scene. The objective of the information extraction operations is to replace visual analysis of the image data with quantitative techniques for automating the identification of features in a scene. This involves the analysis of multispectral image data and the application of statistically based decision rules for determining the land cover identity of each pixel in an image. The intent of classification process is to categorize all pixels in a digital image into one of several land cover classes or themes. This classified data may be used to produce thematic maps of the land cover present in an image.
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Dong, Heng, Ying Jiang, Yaping Fan, Yu Wang, and Guan Gui. "Secondary segmentation extracted algorithm based on image enhancement for intelligent identification systems." International Journal of Distributed Sensor Networks 14, no. 12 (December 2018): 155014771881873. http://dx.doi.org/10.1177/1550147718818737.
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Shahidi, SH, SHMomeni Danaei, and M. Oshagh. "Effects of image enhancement on reliability of landmark identification in digital cephalometry." Indian Journal of Dental Research 24, no. 1 (2013): 98. http://dx.doi.org/10.4103/0970-9290.114958.
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Abdulhamid, Mohanad, and Gitonga Muthomi. "Study of Feature Extraction of Retinal Scans." Scientific Bulletin 24, no. 1 (June 1, 2019): 5–13. http://dx.doi.org/10.2478/bsaft-2019-0001.
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Abstract In this paper, the retina is discussed as part of the feature of extraction of retinal scans for use in security systems as a means of identification. The design system contains a method of image acquisition and processing of the image. A computer system is also incorporated for matching and verifying the image captured to an already present representation of unique features of the retina that are stored as templates for matching and identification. It should then either allow or deny the user depending on the results of the matching process. This paper shows the development of the step undertaken to process the image to the extraction of the features. The high resolution images are taken through a series of image enhancement process before feature extraction technics are applied and before templates are created for future referencing. The main limitation of this process is attributed to capturing the image from the retina. The image obtained may be of poor quality thus making the unique features of the retina unclear.
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Rahim, Robbi. "Identification of Hemorrhages in Iris Using Hybrid Morphological Method." International Journal on Recent and Innovation Trends in Computing and Communication 8, no. 3 (March 31, 2020): 01–05. http://dx.doi.org/10.17762/ijritcc.v8i3.5422.
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In the field of ophthalmology, hemorrhage is the term used more often because of increasing diabetic patients. It’s a challenge amidst the ophthalmologist to distinguish the hemorrhage from the blood vessels, these lands in various problems. In the past various techniques were employed for the detection of the hemorrhage but they were not so accurate and often encountered misclassification between hemorrhage and blood vessels. Precise detection and classification of hemorrhage and blood vessel is very important in the diagnosis of many problems. This paper depicts a mechanized procedure for recognizing hemorrhages in fundus pictures. The acknowledgment of hemorrhages is one of the critical factors in the early finish of diabetic retinopathy. The algorithm proceeds through several steps such as image enhancement, image subtraction, morphological operations such as image thresholding, image strengthening, image thinning, erosion, morphological closing, image complement to suppress blood vessels and to highlight the hemorrhages
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Agapiou, Athos. "Enhancement of Archaeological Proxies at Non-Homogenous Environments in Remotely Sensed Imagery." Sustainability 11, no. 12 (June 17, 2019): 3339. http://dx.doi.org/10.3390/su11123339.
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Optical remote sensing has been widely used for the identification of archaeological proxies. Such proxies, known as crop or soil marks, can be detected in multispectral images due to their spectral signatures and the distinct contrast that they provide in relation to the surrounding area. The current availability of high-resolution satellite datasets has enabled researchers to provide new methodologies and algorithms that can further enhance archaeological proxies supporting thus image-interpretation. However, a critical point that remains unsolved is the detection of crop and soil marks in non-homogenous environments. In these areas, interpretation is problematic even after the application of sophisticated image enhancement analysis techniques due to the mixed landscape and spectral confusion produced from the high-resolution datasets. To overcome this problem, we propose an image-based methodology in which the vegetation is suppressed following the “forced invariance” method and then we apply a linear orthogonal transformation to the suppressed spectral bands. The new Red–Green–Blue (RGB) image corresponds to a new three-band spectral space where the three axes are linked with the crop mark, vegetation, and soil components. The study evaluates the proposed approach in the archaeological site of “Nea Paphos” in Cyprus using a WorldView-2 multispectral image aiming to overcome the limitations of the mixed environments.
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Brisken, Stefan, Dietmar Matthes, Torsten Mathy, and Josef Worms. "Spatially Diverse ISAR Imaging for Classification Performance Enhancement." International Journal of Electronics and Telecommunications 57, no. 1 (March 1, 2011): 15–21. http://dx.doi.org/10.2478/v10177-011-0002-2.
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Spatially Diverse ISAR Imaging for Classification Performance Enhancement One popular approach to the problem of Non-Cooperative Target Identification is the use of 2D Inverse SAR images. Methods to successfully identify a target include the comparison of a set of scattering centers in the ISAR image to a database or the estimation of target dimensions. While working well in theory, these techniques face major difficulties in practice. In the conventional case of a monostatic radar, visibility of scattering centers varies with the target aspect angle due to fading. In this paper we examine that the visibility of scattering centers can be improved by incoherent addition of images from spatially distributed radars. The main focus lies in the description and results of a multistatic ISAR experiment carried out at Fraunhofer FHR. We display theoretically derived bistatic synchronization errors in a practical system and formulate additional multistatic synchronization requirements, necessary to add up the images.