Journal articles: 'Domain of images'

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Nuss, Martin C., and Rick L. Morrison. "Time-domain images." Optics Letters 20, no. 7 (April 1, 1995): 740. http://dx.doi.org/10.1364/ol.20.000740.

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Vasconcelos, Ivan, Paul Sava, and Huub Douma. "Nonlinear extended images via image-domain interferometry." GEOPHYSICS 75, no. 6 (November 2010): SA105—SA115. http://dx.doi.org/10.1190/1.3494083.

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Wave-equation, finite-frequency imaging and inversion still face many challenges in addressing the inversion of highly complex velocity models as well as in dealing with nonlinear imaging (e.g., migration of multiples, amplitude-preserving migration). Extended images (EIs) are particularly important for designing image-domain objective functions aimed at addressing standing issues in seismic imaging, such as two-way migration velocity inversion or imaging/inversion using multiples. General one- and two-way representations for scattered wavefields can describe and analyze EIs obtained in wave-equation imaging. We have developed a formulation that explicitly connects the wavefield correlations done in seismic imaging with the theory and practice of seismic interferometry. In light of this connection, we define EIs as locally scattered fields reconstructed by model-dependent, image-domain interferometry. Because they incorporate the same one- and two-way scattering representations usedfor seismic interferometry, the reciprocity-based EIs can in principle account for all possible nonlinear effects in the imaging process, i.e., migration of multiples and amplitude corrections. In this case, the practice of two-way imaging departs considerably from the one-way approach. We have studied the differences between these approaches in the context of nonlinear imaging, analyzing the differences in the wavefield extrapolation steps as well as in imposing the extended imaging conditions. When invoking single-scattering effects and ignoring amplitude effects in generating EIs, the one- and two-way approaches become essentially the same as those used in today’s migration practice, with the straightforward addition of space and time lags in the correlation-based imaging condition. Our formal description of the EIs and the insight that they are scattered fields in the image domain may be useful in further development of imaging and inversion methods in the context of linear, migration-based velocity inversion or in more sophisticated image-domain nonlinear inverse scattering approaches.

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Wang, Ximei, Liang Li, Weirui Ye, Mingsheng Long, and Jianmin Wang. "Transferable Attention for Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5345–52. http://dx.doi.org/10.1609/aaai.v33i01.33015345.

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Recent work in domain adaptation bridges different domains by adversarially learning a domain-invariant representation that cannot be distinguished by a domain discriminator. Existing methods of adversarial domain adaptation mainly align the global images across the source and target domains. However, it is obvious that not all regions of an image are transferable, while forcefully aligning the untransferable regions may lead to negative transfer. Furthermore, some of the images are significantly dissimilar across domains, resulting in weak image-level transferability. To this end, we present Transferable Attention for Domain Adaptation (TADA), focusing our adaptation model on transferable regions or images. We implement two types of complementary transferable attention: transferable local attention generated by multiple region-level domain discriminators to highlight transferable regions, and transferable global attention generated by single image-level domain discriminator to highlight transferable images. Extensive experiments validate that our proposed models exceed state of the art results on standard domain adaptation datasets.

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Divel, Sarah E., and Norbert J. Pelc. "Accurate Image Domain Noise Insertion in CT Images." IEEE Transactions on Medical Imaging 39, no. 6 (June 2020): 1906–16. http://dx.doi.org/10.1109/tmi.2019.2961837.

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Li, Linhao, Zhiqiang Zhou, Bo Wang, Lingjuan Miao, Zhe An, and Xiaowu Xiao. "Domain Adaptive Ship Detection in Optical Remote Sensing Images." Remote Sensing 13, no. 16 (August 10, 2021): 3168. http://dx.doi.org/10.3390/rs13163168.

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With the successful application of the convolutional neural network (CNN), significant progress has been made by CNN-based ship detection methods. However, they often face considerable difficulties when applied to a new domain where the imaging condition changes significantly. Although training with the two domains together can solve this problem to some extent, the large domain shift will lead to sub-optimal feature representations, and thus weaken the generalization ability on both domains. In this paper, a domain adaptive ship detection method is proposed to better detect ships between different domains. Specifically, the proposed method minimizes the domain discrepancies via both image-level adaption and instance-level adaption. In image-level adaption, we use multiple receptive field integration and channel domain attention to enhance the feature’s resistance to scale and environmental changes, respectively. Moreover, a novel boundary regression module is proposed in instance-level adaption to correct the localization deviation of the ship proposals caused by the domain shift. Compared with conventional regression approaches, the proposed boundary regression module is able to make more accurate predictions via the effective extreme point features. The two adaption components are implemented by learning the corresponding domain classifiers respectively in an adversarial training way, thereby obtaining a robust model suitable for both of the two domains. Experiments on both supervised and unsupervised domain adaption scenarios are conducted to verify the effectiveness of the proposed method.

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Hayder, Israa M., Hussain A. Younis, and Hameed Abdul-Kareem Younis. "Digital Image Enhancement Gray Scale Images In Frequency Domain." Journal of Physics: Conference Series 1279 (July 2019): 012072. http://dx.doi.org/10.1088/1742-6596/1279/1/012072.

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De, Kanjar, and V. Masilamani. "Image Sharpness Measure for Blurred Images in Frequency Domain." Procedia Engineering 64 (2013): 149–58. http://dx.doi.org/10.1016/j.proeng.2013.09.086.

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Fuchida, Takayasu, Sadayuki Murashima, and Hirofumi Nakamura. "Domain search using shrunken images for fractal image compression." Japan Journal of Industrial and Applied Mathematics 22, no. 2 (June 2005): 205–22. http://dx.doi.org/10.1007/bf03167438.

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Bernstein, Gary M., and Daniel Gruen. "Resampling Images in Fourier Domain." Publications of the Astronomical Society of the Pacific 126, no. 937 (March 2014): 287–95. http://dx.doi.org/10.1086/675812.

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Buzzelli, Marco. "Recent Advances in Saliency Estimation for Omnidirectional Images, Image Groups, and Video Sequences." Applied Sciences 10, no. 15 (July 27, 2020): 5143. http://dx.doi.org/10.3390/app10155143.

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We present a review of methods for automatic estimation of visual saliency: the perceptual property that makes specific elements in a scene stand out and grab the attention of the viewer. We focus on domains that are especially recent and relevant, as they make saliency estimation particularly useful and/or effective: omnidirectional images, image groups for co-saliency, and video sequences. For each domain, we perform a selection of recent methods, we highlight their commonalities and differences, and describe their unique approaches. We also report and analyze the datasets involved in the development of such methods, in order to reveal additional peculiarities of each domain, such as the representation used for the ground truth saliency information (scanpaths, saliency maps, or salient object regions). We define domain-specific evaluation measures, and provide quantitative comparisons on the basis of common datasets and evaluation criteria, highlighting the different impact of existing approaches on each domain. We conclude by synthesizing the emerging directions for research in the specialized literature, which include novel representations for omnidirectional images, inter- and intra- image saliency decomposition for co-saliency, and saliency shift for video saliency estimation.

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Kirilyuk, A., V. Kirilyuk, Th Rasing, V. V. Pavlov, and R. V. Pisarev. "DOMAIN AND DOMAIN WALL IMAGES BY SECOND HARMONIC GENERATION." Journal of the Magnetics Society of Japan 20, S_1_MORIS_96 (1996): S1_361–364. http://dx.doi.org/10.3379/jmsjmag.20.s1_361.

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Farquhar, T. H., G. Chinn, C. K. Hoh, S. C. Huang, and E. J. Hoffman. "A nonlinear, image domain filtering method for cardiac PET images." IEEE Transactions on Nuclear Science 45, no. 4 (1998): 2073–79. http://dx.doi.org/10.1109/23.708300.

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Kalra, G. S., R. Talwar, and H. Sadawarti. "Adaptive digital image watermarking for color images in frequency domain." Multimedia Tools and Applications 74, no. 17 (March 14, 2014): 6849–69. http://dx.doi.org/10.1007/s11042-014-1932-3.

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Qi, Yunliang, Xin He, Zhi Wang, and Jianxin Wang. "Frequency domain analysis of knock images." Measurement Science and Technology 25, no. 12 (October 20, 2014): 125001. http://dx.doi.org/10.1088/0957-0233/25/12/125001.

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Wenzel, L., J. McCord, K. Ramstock, and A. Hubert. "Simulation of magnetooptical domain boundary images." IEEE Transactions on Magnetics 33, no. 5 (September 1997): 3274–76. http://dx.doi.org/10.1109/20.617915.

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Chebil, F., M. K. Bel Hadj Miled, A. Islam, and Kai Willner. "Compressed domain editing of JPEG2000 images." IEEE Transactions on Consumer Electronics 51, no. 2 (May 2005): 710–17. http://dx.doi.org/10.1109/tce.2005.1468023.

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Kersting, Felipe Einsfeld, and Eduardo S. L. Gastal. "Domain transform for spherical geometry images." Computers & Graphics 93 (December 2020): 71–83. http://dx.doi.org/10.1016/j.cag.2020.09.016.

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Omura, Hajime, and Teruya Minamoto. "Image quality degradation assessment based on the dual-tree complex discrete wavelet transform for evaluating watermarked images." International Journal of Wavelets, Multiresolution and Information Processing 15, no. 05 (August 28, 2017): 1750046. http://dx.doi.org/10.1142/s0219691317500461.

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We propose a new image quality degradation assessment method based on the dual-tree complex discrete wavelet transform (DT-CDWT) for evaluating the image quality of watermarked images. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are widely used to evaluate image quality degradation resulting from embedding a digital watermark. The majority of digital image watermarking methods embed a digital watermark in the spatial or frequency domain of an original image. They evaluate image quality degradation using only the spatial domain in spite of the fact that the majority of digital image watermarking methods embed a digital watermark in the spatial or frequency domain. As a result, they do not always fairly evaluate the image quality degradation. Therefore, our method evaluates image quality degradation of the watermarked images using features in the spatial and frequency domains. To extract the features, we defined three indices: 1-norm estimation using bit-planes in the spatial domain, the sharpness, and 1-norm estimation based on the DT-CDWT domains. We describe our image quality assessment method in detail and present experimental results demonstrating that there is a strong positive correlation between the result obtained by our method and a subjective evaluation, in comparison with PSNR and SSIM.

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Gritzner, D., and J. Ostermann. "USING SEMANTICALLY PAIRED IMAGES TO IMPROVE DOMAIN ADAPTATION FOR THE SEMANTIC SEGMENTATION OF AERIAL IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 483–92. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-483-2020.

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Abstract. Modern machine learning, especially deep learning, which is used in a variety of applications, requires a lot of labelled data for model training. Having an insufficient amount of training examples leads to models which do not generalize well to new input instances. This is a particular significant problem for tasks involving aerial images: often training data is only available for a limited geographical area and a narrow time window, thus leading to models which perform poorly in different regions, at different times of day, or during different seasons. Domain adaptation can mitigate this issue by using labelled source domain training examples and unlabeled target domain images to train a model which performs well on both domains. Modern adversarial domain adaptation approaches use unpaired data. We propose using pairs of semantically similar images, i.e., whose segmentations are accurate predictions of each other, for improved model performance. In this paper we show that, as an upper limit based on ground truth, using semantically paired aerial images during training almost always increases model performance with an average improvement of 4.2% accuracy and .036 mean intersection-over-union (mIoU). Using a practical estimate of semantic similarity, we still achieve improvements in more than half of all cases, with average improvements of 2.5% accuracy and .017 mIoU in those cases.

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S. Garea, Alberto S., Dora B. Heras, and Francisco Argüello. "TCANet for Domain Adaptation of Hyperspectral Images." Remote Sensing 11, no. 19 (September 30, 2019): 2289. http://dx.doi.org/10.3390/rs11192289.

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The use of Convolutional Neural Networks (CNNs) to solve Domain Adaptation (DA) image classification problems in the context of remote sensing has proven to provide good results but at high computational cost. To avoid this problem, a deep learning network for DA in remote sensing hyperspectral images called TCANet is proposed. As a standard CNN, TCANet consists of several stages built based on convolutional filters that operate on patches of the hyperspectral image. Unlike the former, the coefficients of the filter are obtained through Transfer Component Analysis (TCA). This approach has two advantages: firstly, TCANet does not require training based on backpropagation, since TCA is itself a learning method that obtains the filter coefficients directly from the input data. Second, DA is performed on the fly since TCA, in addition to performing dimensional reduction, obtains components that minimize the difference in distributions of data in the different domains corresponding to the source and target images. To build an operating scheme, TCANet includes an initial stage that exploits the spatial information by providing patches around each sample as input data to the network. An output stage performing feature extraction that introduces sufficient invariance and robustness in the final features is also included. Since TCA is sensitive to normalization, to reduce the difference between source and target domains, a previous unsupervised domain shift minimization algorithm consisting of applying conditional correlation alignment (CCA) is conditionally applied. The results of a classification scheme based on CCA and TCANet show that the DA technique proposed outperforms other more complex DA techniques.

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Xu, Minghao, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, and Wenjun Zhang. "Adversarial Domain Adaptation with Domain Mixup." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6502–9. http://dx.doi.org/10.1609/aaai.v34i04.6123.

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Recent works on domain adaptation reveal the effectiveness of adversarial learning on filling the discrepancy between source and target domains. However, two common limitations exist in current adversarial-learning-based methods. First, samples from two domains alone are not sufficient to ensure domain-invariance at most part of latent space. Second, the domain discriminator involved in these methods can only judge real or fake with the guidance of hard label, while it is more reasonable to use soft scores to evaluate the generated images or features, i.e., to fully utilize the inter-domain information. In this paper, we present adversarial domain adaptation with domain mixup (DM-ADA), which guarantees domain-invariance in a more continuous latent space and guides the domain discriminator in judging samples' difference relative to source and target domains. Domain mixup is jointly conducted on pixel and feature level to improve the robustness of models. Extensive experiments prove that the proposed approach can achieve superior performance on tasks with various degrees of domain shift and data complexity.

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Sandouka, Soha B., Yakoub Bazi, Haikel Alhichri, and Naif Alajlan. "Unified Generative Adversarial Networks for Multidomain Fingerprint Presentation Attack Detection." Entropy 23, no. 8 (August 21, 2021): 1089. http://dx.doi.org/10.3390/e23081089.

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With the rapid growth of fingerprint-based biometric systems, it is essential to ensure the security and reliability of the deployed algorithms. Indeed, the security vulnerability of these systems has been widely recognized. Thus, it is critical to enhance the generalization ability of fingerprint presentation attack detection (PAD) cross-sensor and cross-material settings. In this work, we propose a novel solution for addressing the case of a single source domain (sensor) with large labeled real/fake fingerprint images and multiple target domains (sensors) with only few real images obtained from different sensors. Our aim is to build a model that leverages the limited sample issues in all target domains by transferring knowledge from the source domain. To this end, we train a unified generative adversarial network (UGAN) for multidomain conversion to learn several mappings between all domains. This allows us to generate additional synthetic images for the target domains from the source domain to reduce the distribution shift between fingerprint representations. Then, we train a scale compound network (EfficientNetV2) coupled with multiple head classifiers (one classifier for each domain) using the source domain and the translated images. The outputs of these classifiers are then aggregated using an additional fusion layer with learnable weights. In the experiments, we validate the proposed methodology on the public LivDet2015 dataset. The experimental results show that the proposed method improves the average classification accuracy over twelve classification scenarios from 67.80 to 80.44% after adaptation.

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Lin, Che-Tsung, Yen-Yi Wu, Po-Hao Hsu, and Shang-Hong Lai. "Multimodal Structure-Consistent Image-to-Image Translation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11490–98. http://dx.doi.org/10.1609/aaai.v34i07.6814.

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Unpaired image-to-image translation is proven quite effective in boosting a CNN-based object detector for a different domain by means of data augmentation that can well preserve the image-objects in the translated images. Recently, multimodal GAN (Generative Adversarial Network) models have been proposed and were expected to further boost the detector accuracy by generating a diverse collection of images in the target domain, given only a single/labelled image in the source domain. However, images generated by multimodal GANs would achieve even worse detection accuracy than the ones by a unimodal GAN with better object preservation. In this work, we introduce cycle-structure consistency for generating diverse and structure-preserved translated images across complex domains, such as between day and night, for object detector training. Qualitative results show that our model, Multimodal AugGAN, can generate diverse and realistic images for the target domain. For quantitative comparisons, we evaluate other competing methods and ours by using the generated images to train YOLO, Faster R-CNN and FCN models and prove that our model achieves significant improvement and outperforms other methods on the detection accuracies and the FCN scores. Also, we demonstrate that our model could provide more diverse object appearances in the target domain through comparison on the perceptual distance metric.

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Zhao, Sicheng, Chuang Lin, Pengfei Xu, Sendong Zhao, Yuchen Guo, Ravi Krishna, Guiguang Ding, and Kurt Keutzer. "CycleEmotionGAN: Emotional Semantic Consistency Preserved CycleGAN for Adapting Image Emotions." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2620–27. http://dx.doi.org/10.1609/aaai.v33i01.33012620.

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Deep neural networks excel at learning from large-scale labeled training data, but cannot well generalize the learned knowledge to new domains or datasets. Domain adaptation studies how to transfer models trained on one labeled source domain to another sparsely labeled or unlabeled target domain. In this paper, we investigate the unsupervised domain adaptation (UDA) problem in image emotion classification. Specifically, we develop a novel cycle-consistent adversarial model, termed CycleEmotionGAN, by enforcing emotional semantic consistency while adapting images cycleconsistently. By alternately optimizing the CycleGAN loss, the emotional semantic consistency loss, and the target classification loss, CycleEmotionGAN can adapt source domain images to have similar distributions to the target domain without using aligned image pairs. Simultaneously, the annotation information of the source images is preserved. Extensive experiments are conducted on the ArtPhoto and FI datasets, and the results demonstrate that CycleEmotionGAN significantly outperforms the state-of-the-art UDA approaches.

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Choi, Jongwon, Youngjoon Choi, Jihoon Kim, Jinyeop Chang, Ilhwan Kwon, Youngjune Gwon, and Seungjai Min. "Visual Domain Adaptation by Consensus-Based Transfer to Intermediate Domain." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 10655–62. http://dx.doi.org/10.1609/aaai.v34i07.6692.

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We describe an unsupervised domain adaptation framework for images by a transform to an abstract intermediate domain and ensemble classifiers seeking a consensus. The intermediate domain can be thought as a latent domain where both the source and target domains can be transferred easily. The proposed framework aligns both domains to the intermediate domain, which greatly improves the adaptation performance when the source and target domains are notably dissimilar. In addition, we propose an ensemble model trained by confusing multiple classifiers and letting them make a consensus alternately to enhance the adaptation performance for ambiguous samples. To estimate the hidden intermediate domain and the unknown labels of the target domain simultaneously, we develop a training algorithm using a double-structured architecture. We validate the proposed framework in hard adaptation scenarios with real-world datasets from simple synthetic domains to complex real-world domains. The proposed algorithm outperforms the previous state-of-the-art algorithms on various environments.

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Shandilya, Vijaya. "Enhancement of Crop Images Using Image Fusion Method in Transform Domain." HELIX 8, no. 6 (October 31, 2018): 4353–57. http://dx.doi.org/10.29042/2018-4353-4357.

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Ji, Hui, Jia Li, Zuowei Shen, and Kang Wang. "Image deconvolution using a characterization of sharp images in wavelet domain." Applied and Computational Harmonic Analysis 32, no. 2 (March 2012): 295–304. http://dx.doi.org/10.1016/j.acha.2011.09.006.

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SPRING, B. Q., and R. M. CLEGG. "Image analysis for denoising full-field frequency-domain fluorescence lifetime images." Journal of Microscopy 235, no. 2 (August 2009): 221–37. http://dx.doi.org/10.1111/j.1365-2818.2009.03212.x.

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Liu, Yu Shu, and Ming Yan Jiang. "Despeckling of Ultrasound Images in Contourlet Domain." Advanced Materials Research 647 (January 2013): 283–87. http://dx.doi.org/10.4028/www.scientific.net/amr.647.283.

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Ultrasound images are the important foundation for disease diagnostics. Unfortunately, speckle noise is an inherent property of ultrasound images. So speckle reduction is an important pre-processing step in the ultrasound image feature extraction and analysis. This paper proposes a novel noise reduction algorithm for ultrasound images, which is based on edge detection of the images using the directional information of contourlet transform. The relative variance of the contourlet coefficients is used as a measure of edge detection. The adaptive threshold can be calculated using the probability density function of relative variance. It is shown that the proposed method outperforms several existing techniques in terms of the universal index, edge preservation and visual quality, and in addition, is able to maintain the significant details of ultrasound images.

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Dorafshan, Thomas, and Maguire. "Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures." Infrastructures 4, no. 2 (April 30, 2019): 19. http://dx.doi.org/10.3390/infrastructures4020019.

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This paper summarizes the results of traditional image processing algorithms for detection of defects in concrete using images taken by Unmanned Aerial Systems (UASs). Such algorithms are useful for improving the accuracy of crack detection during autonomous inspection of bridges and other structures, and they have yet to be compared and evaluated on a dataset of concrete images taken by UAS. The authors created a generic image processing algorithm for crack detection, which included the major steps of filter design, edge detection, image enhancement, and segmentation, designed to uniformly compare different edge detectors. Edge detection was carried out by six filters in the spatial (Roberts, Prewitt, Sobel, and Laplacian of Gaussian) and frequency (Butterworth and Gaussian) domains. These algorithms were applied to fifty images each of defected and sound concrete. Performances of the six filters were compared in terms of accuracy, precision, minimum detectable crack width, computational time, and noise-to-signal ratio. In general, frequency domain techniques were slower than spatial domain methods because of the computational intensity of the Fourier and inverse Fourier transformations used to move between spatial and frequency domains. Frequency domain methods also produced noisier images than spatial domain methods. Crack detection in the spatial domain using the Laplacian of Gaussian filter proved to be the fastest, most accurate, and most precise method, and it resulted in the finest detectable crack width. The Laplacian of Gaussian filter in spatial domain is recommended for future applications of real-time crack detection using UAS.

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Wittich, D. "DEEP DOMAIN ADAPTATION BY WEIGHTED ENTROPY MINIMIZATION FOR THE CLASSIFICATION OF AERIAL IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 591–98. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-591-2020.

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Abstract. Fully convolutional neural networks (FCN) are successfully used for the automated pixel-wise classification of aerial images and possibly additional data. However, they require many labelled training samples to perform well. One approach addressing this issue is semi-supervised domain adaptation (SSDA). Here, labelled training samples from a source domain and unlabelled samples from a target domain are used jointly to obtain a target domain classifier, without requiring any labelled samples from the target domain. In this paper, a two-step approach for SSDA is proposed. The first step corresponds to a supervised training on the source domain, making use of strong data augmentation to increase the initial performance on the target domain. Secondly, the model is adapted by entropy minimization using a novel weighting strategy. The approach is evaluated on the basis of five domains, corresponding to five cities. Several training variants and adaptation scenarios are tested, indicating that proper data augmentation can already improve the initial target domain performance significantly resulting in an average overall accuracy of 77.5%. The weighted entropy minimization improves the overall accuracy on the target domains in 19 out of 20 scenarios on average by 1.8%. In all experiments a novel FCN architecture is used that yields results comparable to those of the best-performing models on the ISPRS labelling challenge while having an order of magnitude fewer parameters than commonly used FCNs.

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Yin, Xu, Yan Li, and Byeong-Seok Shin. "DAGAN: A Domain-Aware Method for Image-to-Image Translations." Complexity 2020 (March 28, 2020): 1–15. http://dx.doi.org/10.1155/2020/9341907.

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The image-to-image translation method aims to learn inter-domain mappings from paired/unpaired data. Although this technique has been widely used for visual predication tasks—such as classification and image segmentation—and achieved great results, we still failed to perform flexible translations when attempting to learn different mappings, especially for images containing multiple instances. To tackle this problem, we propose a generative framework DAGAN (Domain-aware Generative Adversarial etwork) that enables domains to learn diverse mapping relationships. We assumed that an image is composed with background and instance domain and then fed them into different translation networks. Lastly, we integrated the translated domains into a complete image with smoothed labels to maintain realism. We examined the instance-aware framework on datasets generated by YOLO and confirmed that this is capable of generating images of equal or better diversity compared to current translation models.

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Bertram, F., Daniel Forster, J. Christen, N. Oleynik, Armin Dadgar, and A. Krost. "Microscopic Spatial Distribution of Bound Excitons in High-Quality ZnO." Materials Science Forum 483-485 (May 2005): 1065–0. http://dx.doi.org/10.4028/www.scientific.net/msf.483-485.1065.

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The surface morphology of the ZnO layers is dominated by a distinct hexagonal domain structure. While the laterally integrated cathodoluminescence spectrum shows intense and narrow I8 luminescence, a distinct emission line at spectral position of I0/I1 emerges in the local spectra taken at domain boundaries. In contrast, no I0/I1 emission is found inside the domains. Monochromatic images further evidence the selective incorporation of impurities at the grain boundaries of domains. Monochromatic images of the I8 peak wavelength directly visualize the strain relaxation across the domains towards their very center, where a drop in quantum efficiency indicates enhanced defect concentration.

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Liu, Ji, and Lei Zhang. "Optimal Projection Guided Transfer Hashing for Image Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8754–61. http://dx.doi.org/10.1609/aaai.v33i01.33018754.

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Recently, learning to hash has been widely studied for image retrieval thanks to the computation and storage efficiency of binary codes. For most existing learning to hash methods, sufficient training images are required and used to learn precise hashing codes. However, in some real-world applications, there are not always sufficient training images in the domain of interest. In addition, some existing supervised approaches need a amount of labeled data, which is an expensive process in terms of time, labor and human expertise. To handle such problems, inspired by transfer learning, we propose a simple yet effective unsupervised hashing method named Optimal Projection Guided Transfer Hashing (GTH) where we borrow the images of other different but related domain i.e., source domain to help learn precise hashing codes for the domain of interest i.e., target domain. Besides, we propose to seek for the maximum likelihood estimation (MLE) solution of the hashing functions of target and source domains due to the domain gap. Furthermore, an alternating optimization method is adopted to obtain the two projections of target and source domains such that the domain hashing disparity is reduced gradually. Extensive experiments on various benchmark databases verify that our method outperforms many state-of-the-art learning to hash methods. The implementation details are available at https://github.com/liuji93/GTH.

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sandhu, Tajman. "Domain Specific CBIR for Highly Textured Images." Computer Science & Engineering: An International Journal 3, no. 2 (April 30, 2013): 33–39. http://dx.doi.org/10.5121/cseij.2013.3203.

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CHEN, YEN-LUN, YUAN F. ZHENG, and YI LIU. "MARGIN AND DOMAIN INTEGRATED CLASSIFICATION FOR IMAGES." International Journal of Information Acquisition 08, no. 01 (March 2011): 1–16. http://dx.doi.org/10.1142/s0219878911002343.

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Multi-category classification is an ongoing research topic in image acquisition and processing for numerous applications. In this paper, a novel approach called margin and domain integrated classifier (MDIC) is addressed. It merges the conventional support vector machine (SVM) and support vector domain description (SVDD) classifiers, and handles multi-class problems as a combination of several target classes plus outliers. The basic idea behind the proposed approach is that target classes possess structured characteristics while outliers scatter around in the feature space. In our approach, the domain description and large-margin discrimination are adjustable and therefore yield higher classification accuracy which leads to better performance than conventional classifiers. The properties of MDIC are analyzed and the performance comparisons using synthetic and real data are presented.

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Das, M., R. Manmatha, and E. M. Riseman. "Indexing flower patent images using domain knowledge." IEEE Intelligent Systems 14, no. 5 (September 1999): 24–33. http://dx.doi.org/10.1109/5254.796084.

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Baba, Sami E. I., Lala Z. Krikor, Thawar Arif, and Zyad Shaaban. "Watermarking of digital images in frequency domain." International Journal of Automation and Computing 7, no. 1 (February 2010): 17–22. http://dx.doi.org/10.1007/s11633-010-0017-7.

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Ferdeghini, E. M., L. Landini, D. Rovai, M. Lombardi, G. De Pieri, A. L'Abbate, and A. Benassi. "Frequency domain analysis of contrast echocardiographic images." Journal of Biomedical Engineering 11, no. 2 (March 1989): 90–95. http://dx.doi.org/10.1016/0141-5425(89)90114-3.

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Costa, Mirian Cristina Gomes, Isabela Maria de Lima Cunha, Lúcio André de Castro Jorge, and Isabel Cristina da Silva Araújo. "Public-domain software for root image analysis." Revista Brasileira de Ciência do Solo 38, no. 5 (October 2014): 1359–66. http://dx.doi.org/10.1590/s0100-06832014000500001.

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In the search for high efficiency in root studies, computational systems have been developed to analyze digital images. ImageJ and Safira are public-domain systems that may be used for image analysis of washed roots. However, differences in root properties measured using ImageJ and Safira are supposed. This study compared values of root length and surface area obtained with public-domain systems with values obtained by a reference method. Root samples were collected in a banana plantation in an area of a shallower Typic Carbonatic Haplic Cambisol (CXk), and an area of a deeper Typic Haplic Ta Eutrophic Cambisol (CXve), at six depths in five replications. Root images were digitized and the systems ImageJ and Safira used to determine root length and surface area. The line-intersect method modified by Tennant was used as reference; values of root length and surface area measured with the different systems were analyzed by Pearson's correlation coefficient and compared by the confidence interval and t-test. Both systems ImageJ and Safira had positive correlation coefficients with the reference method for root length and surface area data in CXk and CXve. The correlation coefficient ranged from 0.54 to 0.80, with lowest value observed for ImageJ in the measurement of surface area of roots sampled in CXve. The IC (95 %) revealed that root length measurements with Safira did not differ from that with the reference method in CXk (-77.3 to 244.0 mm). Regarding surface area measurements, Safira did not differ from the reference method for samples collected in CXk (-530.6 to 565.8 mm²) as well as in CXve (-4231 to 612.1 mm²). However, measurements with ImageJ were different from those obtained by the reference method, underestimating length and surface area in samples collected in CXk and CXve. Both ImageJ and Safira allow an identification of increases or decreases in root length and surface area. However, Safira results for root length and surface area are closer to the results obtained with the reference method.

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Bao, Paul, and Dan Xu. "Multiresolution View Morphing in the Wavelet Domain." International Journal of Virtual Reality 4, no. 3 (January 1, 2000): 21–32. http://dx.doi.org/10.20870/ijvr.2000.4.3.2646.

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This paper presents a new view-synthesis technique using the 2D discrete wavelet-based view morphing. The view morphing is completely based on pairwise images without the camera calibration and depth information of images. First a Fundamental Matrix related to any pair of images is estimated. Then using the fundamental matrix, the pair of image planes is rectified to be parallel with their corresponding points lying on the same scanline, giving an opportunity to generate new views with linear interpolation techniques. The pre-warped images are then decomposed into a hierarchical structure with the wavelet transform. Corresponding coefficients between two decomposed images are linearly interpolated to form the multiresolution representation of an intermediate view. Quantization techniques [10,11] can be embedded here to compress the coefficients for the purpose of reducing the morphing complexity. Finally, during the display, compressed images are decoded and an inverse wavelet transform is applied. A postwarping procedure is employed to transform the interpolated views to its desired position.

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Guo, Yunhui, Yandong Li, Liqiang Wang, and Tajana Rosing. "Depthwise Convolution Is All You Need for Learning Multiple Visual Domains." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8368–75. http://dx.doi.org/10.1609/aaai.v33i01.33018368.

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There is a growing interest in designing models that can deal with images from different visual domains. If there exists a universal structure in different visual domains that can be captured via a common parameterization, then we can use a single model for all domains rather than one model per domain. A model aware of the relationships between different domains can also be trained to work on new domains with less resources. However, to identify the reusable structure in a model is not easy. In this paper, we propose a multi-domain learning architecture based on depthwise separable convolution. The proposed approach is based on the assumption that images from different domains share cross-channel correlations but have domain-specific spatial correlations. The proposed model is compact and has minimal overhead when being applied to new domains. Additionally, we introduce a gating mechanism to promote soft sharing between different domains. We evaluate our approach on Visual Decathlon Challenge, a benchmark for testing the ability of multi-domain models. The experiments show that our approach can achieve the highest score while only requiring 50% of the parameters compared with the state-of-the-art approaches.

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Wittig, J. E. "Magnetic domain wall movement in iron silicon." Proceedings, annual meeting, Electron Microscopy Society of America 51 (August 1, 1993): 1044–45. http://dx.doi.org/10.1017/s0424820100151052.

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Lorentz microscopy in the transmission electron microscope directly images magnetic domains. By changing the magnetic field of the electromagnetic lenses relative to the specimen plane, the movement of the magnetic domain walls and their interaction with microstructural features can be observed in situ. This type of experiment has successfully analyzed the microstructure-domain wall interactions in spinel ferrites and iron-rare-earth-boron magnetic materials. The domain wall motion reveals the qualitative pinning potential of grain boundaries, precipitates, inclusions, stacking faults, and cracks. In addition, these in situ experiments display the dynamics of magnetic domain nucleation. The current study investigates the magnetic domain wall movement in iron silicon alloys. Since magnetic properties such as intrinsic coercivity and permeability are structure sensitive, the influence of microstructure on domain wall movement dictates the soft magnetic behavior.Thin foils of iron-6.5 wt% silicon were prepared by electropolishing ribbons produced by melt spinning techniques. The magnetic domain walls were imaged in the defocused (Fresnel) mode with a Philips CM20T operated at 200 kV.

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Xu, Xiaowe, Jiawei Zhang, Jinglan Liu, Yukun Ding, Tianchen Wang, Hailong Qiu, Haiyun Yuan, et al. "Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images." ACM Journal on Emerging Technologies in Computing Systems 17, no. 4 (July 19, 2021): 1–16. http://dx.doi.org/10.1145/3462328.

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As one of the most commonly ordered imaging tests, the computed tomography (CT) scan comes with inevitable radiation exposure that increases cancer risk to patients. However, CT image quality is directly related to radiation dose, and thus it is desirable to obtain high-quality CT images with as little dose as possible. CT image denoising tries to obtain high-dose-like high-quality CT images (domain Y ) from low dose low-quality CT images (domain X ), which can be treated as an image-to-image translation task where the goal is to learn the transform between a source domain X (noisy images) and a target domain Y (clean images). Recently, the cycle-consistent adversarial denoising network (CCADN) has achieved state-of-the-art results by enforcing cycle-consistent loss without the need of paired training data, since the paired data is hard to collect due to patients’ interests and cardiac motion. However, out of concerns on patients’ privacy and data security, protocols typically require clinics to perform medical image processing tasks including CT image denoising locally (i.e., edge denoising). Therefore, the network models need to achieve high performance under various computation resource constraints including memory and performance. Our detailed analysis of CCADN raises a number of interesting questions that point to potential ways to further improve its performance using the same or even fewer computation resources. For example, if the noise is large leading to a significant difference between domain X and domain Y , can we bridge X and Y with a intermediate domain Z such that both the denoising process between X and Z and that between Z and Y are easier to learn? As such intermediate domains lead to multiple cycles, how do we best enforce cycle- consistency? Driven by these questions, we propose a multi-cycle-consistent adversarial network (MCCAN) that builds intermediate domains and enforces both local and global cycle-consistency for edge denoising of CT images. The global cycle-consistency couples all generators together to model the whole denoising process, whereas the local cycle-consistency imposes effective supervision on the process between adjacent domains. Experiments show that both local and global cycle-consistency are important for the success of MCCAN, which outperforms CCADN in terms of denoising quality with slightly less computation resource consumption.

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Zhang, Kaihao, Wenhan Luo, Lin Ma, and Hongdong Li. "Cousin Network Guided Sketch Recognition via Latent Attribute Warehouse." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9203–10. http://dx.doi.org/10.1609/aaai.v33i01.33019203.

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We study the problem of sketch image recognition. This problem is plagued with two major challenges: 1) sketch images are often scarce in contrast to the abundance of natural images, rendering the training task difficult, and 2) the significant domain gap between sketch image and its natural image counterpart makes the task of bridging the two domains challenging. In order to overcome these challenges, in this paper we propose to transfer the knowledge of a network learned from natural images to a sketch network - a new deep net architecture which we term as cousin network. This network guides a sketch-recognition network to extract more relevant features that are close to those of natural images, via adversarial training. Moreover, to enhance the transfer ability of the classification model, a sketch-to-image attribute warehouse is constructed to approximate the transformation between the sketch domain and the real image domain. Extensive experiments conducted on the TU-Berlin dataset show that the proposed model is able to efficiently distill knowledge from natural images and achieves superior performance than the current state of the art.

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Li, Jinjiang, Genji Yuan, and Hui Fan. "Multifocus Image Fusion Using Wavelet-Domain-Based Deep CNN." Computational Intelligence and Neuroscience 2019 (February 20, 2019): 1–23. http://dx.doi.org/10.1155/2019/4179397.

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Multifocus image fusion is the merging of images of the same scene and having multiple different foci into one all-focus image. Most existing fusion algorithms extract high-frequency information by designing local filters and then adopt different fusion rules to obtain the fused images. In this paper, a wavelet is used for multiscale decomposition of the source and fusion images to obtain high-frequency and low-frequency images. To obtain clearer and complete fusion images, this paper uses a deep convolutional neural network to learn the direct mapping between the high-frequency and low-frequency images of the source and fusion images. In this paper, high-frequency and low-frequency images are used to train two convolutional networks to encode the high-frequency and low-frequency images of the source and fusion images. The experimental results show that the method proposed in this paper can obtain a satisfactory fusion image, which is superior to that obtained by some advanced image fusion algorithms in terms of both visual and objective evaluations.

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Taroni, P., L. Spinelli, A. Torricelli, A. Pifferi, G. M. Danesini, and R. Cubeddu. "Multi-wavelength Time Domain Optical Mammography." Technology in Cancer Research & Treatment 4, no. 5 (October 2005): 527–37. http://dx.doi.org/10.1177/153303460500400506.

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A time-resolved optical mammograph operating at 7 wavelengths (637, 683, 785, 832, 905, 916, and 975 nm) in compressed breast geometry was developed. Its clinical application was started on patients bearing malignant and benign lesions. Late gated intensity images are used to obtain information on the spatial distribution of the absorption properties of breast. Scattering images derived from the diffusion theory are also applied for lesion detection and characterization. Cancers are identified in intensity images at short wavelengths, due to the high blood content, while cysts are typically characterized by low scattering at all wavelengths. The increase (from 4 to 7) in the number of wavelengths as compared to the previous versions of the instrument aims at improving the robustness of the fitting procedures for a better estimate of tissue composition and structure and of physiological parameters. Moreover, the new wavelengths contribute to the qualitatively identify tissue composition from intensity images, and could assist lesion detection.

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Joshi, Madhusudan, Chandrashakher, and Kehar Singh. "Color image encryption and decryption for twin images in fractional Fourier domain." Optics Communications 281, no. 23 (December 2008): 5713–20. http://dx.doi.org/10.1016/j.optcom.2008.08.024.

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de Campos, Marcos Flavio, and Fernando José Gomes Landgraf. "Determination of Intrinsic Magnetic Parameters of SmCo₅ Phase in Sintered Samples." Materials Science Forum 498-499 (November 2005): 129–33. http://dx.doi.org/10.4028/www.scientific.net/msf.498-499.129.

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SmCo5 magnets are usually produced by powder metallurgy route, including milling, compaction and orientation under magnetic field, sintering and heat treatment. The samples produced by powder metallurgy, with grain size around 10 μm, are ideal for determination of intrinsic parameters. The first step for determination of intrinsic magnetic parameters is obtaining images of domain structure in demagnetized samples. In the present study, the domain images were produced by means of Kerr effect, in a optical microscope. After the test of several etchings, Nital appears as the most appropriate for observation of magnetic domains by Kerr effect. Applying Stereology and Domain Theory, several intrinsic parameters of SmCo5 phase were determined: domain wall energy 120 erg/cm2, critical diameter for single domain particle size 2 μm and domain wall thickness 60 Å. In the case of SmCo5, and also other phases with high magnetocrystalline anisotropy, Domain Theory presents several advantages when compared with Micromagnetics.

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Chantara, Wisarut, and Moongu Jeon. "All-in-Focused Image Combination in the Frequency Domain Using Light Field Images." Applied Sciences 9, no. 18 (September 8, 2019): 3752. http://dx.doi.org/10.3390/app9183752.

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All-in-focused image combination is a fusion technique used to acquire related data from a set of focused images at different depth levels, which suggests that one can determine objects in the foreground and background regions. When attempting to reconstruct an all-in-focused image, we need to identify in-focused regions from multiple input images captured with different focal lengths. This paper presents a new method to find and fuse the in-focused regions of the different focal stack images. After we apply the two-dimensional discrete cosine transform (DCT) to transform the focal stack images into the frequency domain, we utilize the sum of the updated modified Laplacian (SUML), enhancement of the SUML, and harmonic mean (HM) for calculating in-focused regions of the stack images. After fusing all the in-focused information, we transform the result back by using the inverse DCT. Hence, the out-focused parts are removed. Finally, we combine all the in-focused image regions and reconstruct the all-in-focused image.

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Journal articles on the topic 'Domain of images'

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