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Journal articles on the topic 'Implicit image'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Lazor, K. P. "THE ROLE OF IMPLICIT LEADERSHIP THEORY IN BUILDING THE IMAGE OF A POLITICAL LEADER." PSYCHOLOGY AND SOCIAL WORK, no. 2(52) (December 15, 2020): 96–110. http://dx.doi.org/10.18524/2707-0409.2020.2(52).225432.

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An important role in building an effective, competitive image of a political leader is played by his perception by the population, especially during the election campaign. From today on, most Ukrainians have a tendency to personify political parties and factions with their political leader. When building the image of a political leader, it is important for image makers to take into account the implicit perception of images. After all, the image of a political leader, correctly formed by image makers, is a guarantee of victory and the achievement of certain goals set by a political leader. In t

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Chen, Yingxia, Yuqi Li, Tingting Wang, Yan Chen, and Faming Fang. "DPDU-Net: Double Prior Deep Unrolling Network for Pansharpening." Remote Sensing 16, no. 12 (2024): 2141. http://dx.doi.org/10.3390/rs16122141.

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The objective of the pansharpening task is to integrate multispectral (MS) images with low spatial resolution (LR) and to integrate panchromatic (PAN) images with high spatial resolution (HR) to generate HRMS images. Recently, deep learning-based pansharpening methods have been widely studied. However, traditional deep learning methods lack transparency while deep unrolling methods have limited performance when using one implicit prior for HRMS images. To address this issue, we incorporate one implicit prior with a semi-implicit prior and propose a double prior deep unrolling network (DPDU-Net

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Wang, Yuang, Siyeop Yoon, Pengfei Jin, et al. "Implicit Image-to-Image Schrödinger Bridge for image restoration." Pattern Recognition 165 (September 2025): 111627. https://doi.org/10.1016/j.patcog.2025.111627.

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Stucker, C., B. Ke, Y. Yue, S. Huang, I. Armeni, and K. Schindler. "IMPLICITY: CITY MODELING FROM SATELLITE IMAGES WITH DEEP IMPLICIT OCCUPANCY FIELDS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2022 (May 17, 2022): 193–201. http://dx.doi.org/10.5194/isprs-annals-v-2-2022-193-2022.

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Abstract. High-resolution optical satellite sensors, combined with dense stereo algorithms, have made it possible to reconstruct 3D city models from space. However, these models are, in practice, rather noisy and tend to miss small geometric features that are clearly visible in the images. We argue that one reason for the limited quality may be a too early, heuristic reduction of the triangulated 3D point cloud to an explicit height field or surface mesh. To make full use of the point cloud and the underlying images, we introduce IMPLICITY, a neural representation of the 3D scene as an implici

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Celis, L. Elisa, and Vijay Keswani. "Implicit Diversity in Image Summarization." Proceedings of the ACM on Human-Computer Interaction 4, CSCW2 (2020): 1–28. http://dx.doi.org/10.1145/3415210.

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Guo, Zhuen, and Li Lin. "Research on Image Acquisition Method Model Based on Implicit Association Test." E3S Web of Conferences 179 (2020): 02006. http://dx.doi.org/10.1051/e3sconf/202017902006.

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In the process of the traditional quantitative method is easily interfered with by subjective and external environment, and cannot reflect the real emotion of users. The implicit measurement method can better reflect the cognitive of users and has good reliability in perceptual evaluation. In this paper, the implicit cognitive processing process in users’ perceptual evaluation of products is quantitatively analyzed. The correlation between product image attribute values and implicit measurement data is obtained. Thus, an image extraction model based on implicit measurement data is obtained. Th

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Zhao, Zihe, Man Qin, Haosong Gou, Zhengyong Wang, and Chao Ren. "Real Image Deblurring Based on Implicit Degradation Representations and Reblur Estimation." Applied Sciences 13, no. 13 (2023): 7738. http://dx.doi.org/10.3390/app13137738.

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Most existing image deblurring methods are based on the estimation of blur kernels and end-to-end learning of the mapping relationship between blurred and sharp images. However, since different real-world blurred images typically have completely different blurring patterns, the performance of these methods in real image deblurring tasks is limited without explicitly modeling blurring as degradation representations. In this paper, we propose IDR2ENet, which is the Implicit Degradation Representations and Reblur Estimation Network, for real image deblurring. IDR2ENet consists of a degradation es

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Tan, Zhaorui, Xi Yang, and Kaizhu Huang. "Semantic-Aware Data Augmentation for Text-to-Image Synthesis." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 6 (2024): 5098–107. http://dx.doi.org/10.1609/aaai.v38i6.28315.

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Data augmentation has been recently leveraged as an effective regularizer in various vision-language deep neural networks. However, in text-to-image synthesis (T2Isyn), current augmentation wisdom still suffers from the semantic mismatch between augmented paired data. Even worse, semantic collapse may occur when generated images are less semantically constrained. In this paper, we develop a novel Semantic-aware Data Augmentation (SADA) framework dedicated to T2Isyn. In particular, we propose to augment texts in the semantic space via an Implicit Textual Semantic Preserving Augmentation, in con

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Lou, Xuemei, Zhenhong Jia, Jie Yang, and Nikola Kasabov. "Change Detection in SAR Images Based on the ROF Model Semi-Implicit Denoising Method." Sensors 19, no. 5 (2019): 1179. http://dx.doi.org/10.3390/s19051179.

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The explicit solution of the traditional ROF model in image denoising has the disadvantages of unstable results and requiring many iterations. To solve the problem, a new method, ROF model semi-implicit denoising, is proposed in this paper and applied to change detections of synthetic aperture radar (SAR) images. All remote sensing images used in this article have been calibrated by ENVI software. First, the ROF model semi-implicit denoising method is used to denoise the remote sensing images. Second, for the denoised images, difference images are obtained by the logarithmic ratio and mean rat

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Ginger, Yiftach, Dov Danon, Hadar Averbuch-Elor, and Daniel Cohen-Or. "Implicit pairs for boosting unpaired image-to-image translation." Visual Informatics 4, no. 4 (2020): 50–58. http://dx.doi.org/10.1016/j.visinf.2020.10.001.

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Komarova, Aleksandra Vladimirovna, Tatyana Viktorovna Slotina, Valery L. Sitnikov, Elena Fedorovna Yashchenko, and Konstantin Pavlovich Zakharov. "Implicit notions of a happy person in elementary school students." SHS Web of Conferences 122 (2021): 04005. http://dx.doi.org/10.1051/shsconf/202112204005.

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The article is devoted to the substantiation and results of a study of implicit notions of a happy person in elementary school students. The methodological foundation is constructed by D.A. Leontiev’s two-level model of happiness, K. Riff’s concept of happiness as the basis of psychological well-being, and V.L. Sitnikov’s concept of the image of a person. The deployed research method is “SPI(H) — the Structure of a Person’s Image (Hierarchical)” (V.Sitnikov) including the verbal and non-verbal associative experiment with the subsequent content analysis. The novelty of the study lies in the com

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Hamidieh, Kimia, Haoran Zhang, Walter Gerych, Thomas Hartvigsen, and Marzyeh Ghassemi. "Identifying Implicit Social Biases in Vision-Language Models." Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 7 (October 16, 2024): 547–61. http://dx.doi.org/10.1609/aies.v7i1.31657.

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Vision-language models, like CLIP (Contrastive Language Image Pretraining), are becoming increasingly popular for a wide range of multimodal retrieval tasks. However, prior work has shown that large language and deep vision models can learn historical biases contained in their training sets, leading to perpetuation of stereotypes and potential downstream harm. In this work, we conduct a systematic analysis of the social biases that are present in CLIP, with a focus on the interaction between image and text modalities. We first propose a taxonomy of social biases called So-B-It, which contains

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Penketh, Harry, William L. Barnes, and Jacopo Bertolotti. "Implicit image processing with ghost imaging." Optics Express 30, no. 5 (2022): 7035. http://dx.doi.org/10.1364/oe.450191.

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Keller, Y., and A. Averbuch. "Multisensor image registration via implicit similarity." IEEE Transactions on Pattern Analysis and Machine Intelligence 28, no. 5 (2006): 794–801. http://dx.doi.org/10.1109/tpami.2006.100.

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Luo, Jinming, Lei Han, Xianjie Gao, Xiuping Liu, and Weiming Wang. "SR-FEINR: Continuous Remote Sensing Image Super-Resolution Using Feature-Enhanced Implicit Neural Representation." Sensors 23, no. 7 (2023): 3573. http://dx.doi.org/10.3390/s23073573.

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Remote sensing images often have limited resolution, which can hinder their effectiveness in various applications. Super-resolution techniques can enhance the resolution of remote sensing images, and arbitrary resolution super-resolution techniques provide additional flexibility in choosing appropriate image resolutions for different tasks. However, for subsequent processing, such as detection and classification, the resolution of the input image may vary greatly for different methods. In this paper, we propose a method for continuous remote sensing image super-resolution using feature-enhance

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Han, Dongshen, and Chaoning Zhang. "Residual-Based Implicit Neural Representation for Synthetic Aperture Radar Images." Remote Sensing 16, no. 23 (2024): 4471. http://dx.doi.org/10.3390/rs16234471.

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Implicit neural representations (INRs) are a new way to represent all kinds of signals ranging from 1D audio to 3D shape signals, among which 2D images are the most widely explored due to their ubiquitous presence. Image INRs utilize a neural network to learn a continuous function that takes pixel coordinates as input and outputs the corresponding pixel values. The continuous representation of synthetic aperture radar (SAR) images using INRs has not yet been explored. Existing INR frameworks developed on natural images show reasonable performance, but this performance suffers when capturing fi

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Alqaraleh, Saed, and Omar Ramadan. "Efficient Implicit Content-based Image Re-ranking Approach." Journal of Information & Knowledge Management 19, no. 01 (2020): 2040003. http://dx.doi.org/10.1142/s0219649220400031.

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This paper presents a new image re-ranking approach that can implicitly improve the retrieved images based on the file’s contents and some user-specific actions. In more detail, multiple descriptors are used to describe image files accurately and they do not require user intervention or tuned parameters. Furthermore, each of these descriptors has a weight, which affects the file rank. Unlike existing approaches, descriptor weight is assigned dynamically and changes from one file to another based on the percentage of differences found by the descriptor. Hence, the developed weight mechanism imp

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Liu, Yahui, Yajing Chem, Linchao Bao, Nicu Sebe, Bruno Lepri, and Nadai Marco De. "ISF-GAN: An Implicit Style Function for High Resolution Image-to-Image Translation." IEEE TRANSACTIONS ON MULTIMEDIA 25 (September 1, 2023): 3343–53. https://doi.org/10.1109/TMM.2022.3159115.

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Recently, there has been an increasing interest in image editing methods that employ pre-trained unconditional image generators (e.g., StyleGAN). However, applying these methods to translate images to multiple visual domains remains challenging. Existing works do not often preserve the domain-invariant part of the image (e.g., the identity in human face translations), or they do not usually handle multiple domains or allow for multi-modal translations. This work proposes an implicit style function (ISF) to straightforwardly achieve multi-modal and multi-domain image-to-image translation from p

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Belyaev, Alexander. "Implicit Image Differentiation and Filtering with Applications to Image Sharpening." SIAM Journal on Imaging Sciences 6, no. 1 (2013): 660–79. http://dx.doi.org/10.1137/12087092x.

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Lin, Huangxing, Yihong Zhuang, Xinghao Ding, et al. "Self-Supervised Image Denoising Using Implicit Deep Denoiser Prior." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 1586–94. http://dx.doi.org/10.1609/aaai.v37i2.25245.

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We devise a new regularization for denoising with self-supervised learning. The regularization uses a deep image prior learned by the network, rather than a traditional predefined prior. Specifically, we treat the output of the network as a ``prior'' that we again denoise after ``re-noising.'' The network is updated to minimize the discrepancy between the twice-denoised image and its prior. We demonstrate that this regularization enables the network to learn to denoise even if it has not seen any clean images. The effectiveness of our method is based on the fact that CNNs naturally tend to cap

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TAMAKI, TORU, TSUYOSHI YAMAMURA, and NOBORU OHNISHI. "CORRECTING DISTORTION OF IMAGE BY IMAGE REGISTRATION WITH THE IMPLICIT FUNCTION THEOREM." International Journal of Image and Graphics 02, no. 02 (2002): 309–29. http://dx.doi.org/10.1142/s0219467802000640.

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We propose a method for correcting image distortion due to camera lenses by calibrating intrinsic camera parameters. The proposed method is based on image registration and doesn't require point-to-point correspondence. Parameters of three successive transformations — view change, radial distortion and illumination change — are estimated using the Gauss–Newton method. Estimating all 19 unknowns simultaneously, we introduce the implicit function theorem for calculating the Jacobian. To avoid local minima, we first estimate parameters for view change and employ coarse-to-fine minimization. Experi

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Yang, Jie, Jiaxun He, and Yingkang Gu. "The implicit measurement of destination image: The application of Implicit Association Tests." Tourism Management 33, no. 1 (2012): 50–52. http://dx.doi.org/10.1016/j.tourman.2011.01.022.

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Meng, Ge, Jingjia Huang, Yingying Wang, Zhenqi Fu, Xinghao Ding, and Yue Huang. "Progressive High-Frequency Reconstruction for Pan-Sharpening with Implicit Neural Representation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 5 (2024): 4189–97. http://dx.doi.org/10.1609/aaai.v38i5.28214.

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Pan-sharpening aims to leverage the high-frequency signal of the panchromatic (PAN) image to enhance the resolution of its corresponding multi-spectral (MS) image. However, deep neural networks (DNNs) tend to prioritize learning the low-frequency components during the training process, which limits the restoration of high-frequency edge details in MS images. To overcome this limitation, we treat pan-sharpening as a coarse-to-fine high-frequency restoration problem and propose a novel method for achieving high-quality restoration of edge information in MS images. Specifically, to effectively ob

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Wang, Chao, Ana Serrano, Xingang Pan, et al. "An Implicit Neural Representation for the Image Stack: Depth, All in Focus, and High Dynamic Range." ACM Transactions on Graphics 42, no. 6 (2023): 1–11. http://dx.doi.org/10.1145/3618367.

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In everyday photography, physical limitations of camera sensors and lenses frequently lead to a variety of degradations in captured images such as saturation or defocus blur. A common approach to overcome these limitations is to resort to image stack fusion, which involves capturing multiple images with different focal distances or exposures. For instance, to obtain an all-in-focus image, a set of multi-focus images is captured. Similarly, capturing multiple exposures allows for the reconstruction of high dynamic range. In this paper, we present a novel approach that combines neural fields wit

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Sheela, Kalavakuri. "Content Based Image Retrieval Using Colour and Shape Features." International Journal for Research in Applied Science and Engineering Technology 13, no. 2 (2025): 731–35. https://doi.org/10.22214/ijraset.2025.66916.

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Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Content based image retrieval (CBIR) deals with the extraction of implicit knowledge from the image database. Feature selection and extraction is the pre-processing step of CBIR. Obviously this is a critical step in the entire scenario of CBIR. Though there are various features available, the aim is to identify the best features and thereby extract relevant information from the images

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Zhou, Bin, and Li Lin. "Research on Cognitive Matching of Biological Morphological Features and Images for Profiling Design." E3S Web of Conferences 179 (2020): 01015. http://dx.doi.org/10.1051/e3sconf/202017901015.

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To obtain high-quality bionic design scheme of product form, this paper explores the matching relationship between users’ biological form features and their images from the level of implicit cognition, providing objective basis for effective selection of ideographic biological form features in bionic design of product form. The eye movement experiment was used to screen the biomorphic feature group that was focused on. Questionnaire survey and cluster analysis were used to obtain the main image phrases of the morphological feature group. The two collected materials were combined with implicit

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Feng, Yayuan, Yu Shi, and Dianjun Sun. "Blind Poissonian Image Deblurring Regularized by a Denoiser Constraint and Deep Image Prior." Mathematical Problems in Engineering 2020 (August 24, 2020): 1–15. http://dx.doi.org/10.1155/2020/9483521.

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The denoising and deblurring of Poisson images are opposite inverse problems. Single image deblurring methods are sensitive to image noise. A single noise filter can effectively remove noise in advance, but it also damages blurred information. To simultaneously solve the denoising and deblurring of Poissonian images better, we learn the implicit deep image prior from a single degraded image and use the denoiser as a regularization term to constrain the latent clear image. Combined with the explicit L0 regularization prior of the image, the denoising and deblurring model of the Poisson image is

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Yu, Xiaosheng, Yuanchen Qi, Ziwei Lu, and Nan Hu. "Implicit Active Contours Driven by Local and Global Image Fitting Energy for Image Segmentation and Target Localization." Journal of Sensors 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/713536.

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We propose a novel active contour model in a variational level set formulation for image segmentation and target localization. We combine a local image fitting term and a global image fitting term to drive the contour evolution. Our model can efficiently segment the images with intensity inhomogeneity with the contour starting anywhere in the image. In its numerical implementation, an efficient numerical schema is used to ensure sufficient numerical accuracy. We validated its effectiveness in numerous synthetic images and real images, and the promising experimental results show its advantages

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Jin, Dingjian, Mengqi Ji, Lan Xu, Gaochang Wu, Liejun Wang, and Lu Fang. "Boosting Single Image Super-Resolution Learnt From Implicit Multi-Image Prior." IEEE Transactions on Image Processing 30 (2021): 3240–51. http://dx.doi.org/10.1109/tip.2021.3059507.

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Trendel, Olivier, Marc Mazodier, and Kathleen D. Vohs. "Making Warnings about Misleading Advertising and Product Recalls more Effective: An Implicit Attitude Perspective." Journal of Marketing Research 55, no. 2 (2018): 265–76. http://dx.doi.org/10.1509/jmr.14.0305.

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The authors tested whether image-based information is more effective than text in changing implicit attitudes from positive to negative, even when both forms similarly change explicit attitudes. They studied corrective information (i.e., warnings about misleading advertising and product recall notices) because it is a common, important effort to change consumer attitudes. Corrective information in the form of pictures or imagery-evoking text, as well as direct instructions to imagine the scene, changed implicit attitudes more than plain, descriptive text, which is currently the most common war

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Nakahara, Yuta, and Toshiyasu Matsushima. "A Stochastic Model for Block Segmentation of Images Based on the Quadtree and the Bayes Code for It." Entropy 23, no. 8 (2021): 991. http://dx.doi.org/10.3390/e23080991.

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In information theory, lossless compression of general data is based on an explicit assumption of a stochastic generative model on target data. However, in lossless image compression, researchers have mainly focused on the coding procedure that outputs the coded sequence from the input image, and the assumption of the stochastic generative model is implicit. In these studies, there is a difficulty in discussing the difference between the expected code length and the entropy of the stochastic generative model. We solve this difficulty for a class of images, in which they have non-stationarity a

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Barlasov-Ioffe, Anna, and Shaul Hochstein. "Illusory-Contour Figures Prime Matching of Real Shapes." Perception 38, no. 8 (2009): 1118–31. http://dx.doi.org/10.1068/p6118.

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We investigated explicit and implicit properties of the internal representation of illusory-contour figures by studying potential priming effects of this representation. Using a primed matching paradigm (Beller 1971, Journal of Experimental Psychology87 176–182), we found that illusory ‘Kanizsa’ squares and triangles prime later matching of the same shapes, respectively, and not of the alternative shape. This priming effect is present despite the use of an illusory figure as a prime and real shapes as tests. To determine whether implicit processing mechanisms sufficiently induce a representati

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Liu, Jinhua, Jiayun Wu, Mulian Xu, and Yuanyuan Huang. "Plug-and-Play-Based Algorithm for Mixed Noise Removal with the Logarithm Norm Approximation Model." Mathematics 10, no. 20 (2022): 3810. http://dx.doi.org/10.3390/math10203810.

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During imaging and transmission, images are easily affected by several factors, including sensors, camera motion, and transmission channels. In practice, images are commonly corrupted by a mixture of Gaussian and impulse noises, further complicating the denoising problem. Therefore, in this work, we propose a novel mixed noise removal model that combines a deterministic low-rankness prior and an implicit regularization scheme. In the optimization model, we apply the matrix logarithm norm approximation model to characterize the global low-rankness of the original image. We further adopt the plu

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Imran, Sheik, and Pradeep N. "A Review on Ensemble Machine and Deep Learning Techniques Used in the Classification of Computed Tomography Medical Images." International Journal of Health Sciences and Research 14, no. 1 (2024): 201–13. http://dx.doi.org/10.52403/ijhsr.20240124.

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Ensemble learning combines multiple base models to enhance predictive performance and generalize better on unseen data. In the context of Computed Tomography (CT) image processing, ensemble techniques often leverage diverse machine learning or deep learning architectures to achieve the best results. Ensemble machine learning and deep learning techniques have revolutionized the field of CT image processing by significantly improving accuracy, robustness, and efficiency in various medical imaging tasks. These methods have been instrumental in tasks such as image reconstruction, segmentation, cla

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Chen, Meikang, Kurban Ubul, Xuebin Xu, Alimjan Aysa, and Mahpirat Muhammat. "Connecting Text Classification with Image Classification: A New Preprocessing Method for Implicit Sentiment Text Classification." Sensors 22, no. 5 (2022): 1899. http://dx.doi.org/10.3390/s22051899.

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As a research hotspot in the field of natural language processing (NLP), sentiment analysis can be roughly divided into explicit sentiment analysis and implicit sentiment analysis. However, due to the lack of obvious emotion words in the implicit sentiment analysis task and because the sentiment polarity contained in implicit sentiment words is not easily accurately identified by existing text-processing methods, the implicit sentiment analysis task is one of the most difficult tasks in sentiment analysis. This paper proposes a new preprocessing method for implicit sentiment text classificatio

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Yu, Muzhou, Shuyun Lin, Hongwei Yan, and Kaisheng Ma. "MindPainter: Efficient Brain-Conditioned Painting of Natural Images via Cross-Modal Self-Supervised Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 13 (2025): 14468–76. https://doi.org/10.1609/aaai.v39i13.33585.

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Despite significant advancements in image and text conditional image editing, the exploration of using brain signals, which are more direct and personalized to reflect user intentions, remains limited. An intuitive method is to convert implicit brain signals into explicit representations such as images, which can then serve as prompts for editing. However, such two-stage method suffers from low inference efficiency, inaccurate brain interpretation, and unnatural editing results. In this paper, we apply brain signals of visual perception as prompts and propose a cross-modal self-supervised lear

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Zhao, Jiang Kun, Yu Zhu, and Jian Feng Yu. "Segmentation by Local Binary Fitting Active Contour Model for Activated Carbon Fibers Material Microscopic Images." Advanced Materials Research 811 (September 2013): 370–74. http://dx.doi.org/10.4028/www.scientific.net/amr.811.370.

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Many bubbles and pores are appeared on Activated Carbon Fibers (ACFs) material microscopic images. The morphology of ACFs surface image is complicated. Some widely used traditional methods are difficult to segment the object correctly. In this paper, an implicit active contour driven by local binary fitting energy is used to segment the objects for ACFs micro-images. This method is based on local image edge information to obtain optimal level set active contour model. Experimental results show that this active contour model is flexible for analyzing images with complex porous structure.

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E, Shanmuga Priya, Velvizhy P, and Deepa Arul. "3D Human Reconstruction from A Single Image Using Parametric Model-Conditioned Implicit Representation." Indian Journal of Science and Technology 16, no. 44 (2023): 4054–62. https://doi.org/10.17485/IJST/v16i44.1618.

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Abstract <strong>Background:</strong> PaMIR is a novel approach for image-based human reconstruction that utilizes a parametric model-conditioned implicit representation. This method enables the generation of a complete 3D mesh of a human body from a single input image. It uses a neural network that is conditioned on a parametric model of the human body to produce an implicit representation of the 3D surface. <strong>Objectives:</strong> To develop a novel approach for image based human reconstruction by training neural network and to generate high quality images. <strong>M

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Jun, Sun, Pan Zhenkuan, Wei Weibo, and Zhang Haitao. "High-order image diffusion model on implicit surfaces." Journal of Image and Graphics 15, no. 10 (2010): 1449. http://dx.doi.org/10.11834/jig.20101011.

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Palfai, Tibor P., Carl K. Kantner, and Kelli D. Tahaney. "The image-based alcohol-action implicit association test." Journal of Behavior Therapy and Experimental Psychiatry 50 (March 2016): 135–38. http://dx.doi.org/10.1016/j.jbtep.2015.07.002.

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Corsaro, S., K. Mikula, A. Sarti, and F. Sgallari. "Semi‐Implicit Covolume Method in 3D Image Segmentation." SIAM Journal on Scientific Computing 28, no. 6 (2006): 2248–65. http://dx.doi.org/10.1137/060651203.

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Longo, Matthew R., and Patrick Haggard. "Implicit body representations and the conscious body image." Acta Psychologica 141, no. 2 (2012): 164–68. http://dx.doi.org/10.1016/j.actpsy.2012.07.015.

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Smith, Gavin, Chris Brien, and Helen Ashman. "Evaluating implicit judgments from image search clickthrough data." Journal of the American Society for Information Science and Technology 63, no. 12 (2012): 2451–62. http://dx.doi.org/10.1002/asi.22742.

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Priya, E. Shanmuga, P. Velvizhy, and Arul Deepa. "3D Human Reconstruction from A Single Image Using Parametric Model-Conditioned Implicit Representation." Indian Journal Of Science And Technology 16, no. 44 (2023): 4054–62. http://dx.doi.org/10.17485/ijst/v16i44.1618.

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Li, Yu Jie, Hui Min Lu, Li Feng Zhang, Shi Yuan Yang та Serikawa Seiichi. "A New Image Segmentation Method Based on Improved Fast Implicit Level Set Scheme in X/γ-Ray Inspection System". Applied Mechanics and Materials 103 (вересень 2011): 705–10. http://dx.doi.org/10.4028/www.scientific.net/amm.103.705.

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Digital X/γ-ray imaging technology has been widely used to help people deliver effective and reliable security in airports, train stations, and public buildings. Nowadays, luggage inspection system with digital radiographic/computed tomography (DR/CT) represents a most advanced nondestructive inspection technology in aviation system, which is capable of automatically discerning interesting regions in the luggage objects with CT subsystem. In this paper, we propose a new model for active contours to detect luggage objects in the system, in order to facilitate people to identify the things in lu

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Baklanova, Irina I. "THE IDENTITY OF THE AUTHOR OF A SCIENTIFIC TEXT FROM THE PERSPECTIVE OF THE SENDER AND RECIPIENT." RSUH/RGGU Bulletin. "Literary Theory. Linguistics. Cultural Studies" Series, no. 8 (2022): 253–65. http://dx.doi.org/10.28995/2686-7249-2022-8-253-265.

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The subject of the article is the linguistic means of reflecting in a scientific text the personality of its author in terms of, firstly, the communicative intentions of the author, and secondly, the perception of the recipient of this text. The article also considers the degree of objectivity in the construction of a scientific text. It is shown that the image of the author of a scientific text, revealed in terms of the communicative intentions of the sender of the text, and the image of the author, perceived by its recipient, may not coincide. As a theoretical basis of the study, the observa

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Padulo, Caterina, Luca Tommasi, and Alfredo Brancucci. "Implicit Association Effects Between Sound and Food Images." Multisensory Research 31, no. 8 (2018): 779–91. http://dx.doi.org/10.1163/22134808-20181308.

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Abstract A growing body of empirical research documents the existence of several interesting crossmodal correspondences between auditory and gustatory/flavor stimuli, demonstrating that people can match specific acoustic and musical parameters with different tastes and flavors. In this context, a number of researchers and musicians arranged their own soundtracks so as to match specific tastes and used them for research purposes, revealing explicit crossmodal effects on judgments of taste comparative intensity or of taste/sound accordance. However, only few studies have examined implicit associ

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Kulkarni, Prerna Shirish, Simran Raju Mulani, Shruti Shrikant Wagaj, and G. B. Birajadar. "Multi-Level Pixel-Aligned Implicit Function for High- Resolution 3D Human Digitization." Dec 2023-Jan 2024, no. 41 (December 20, 2023): 39–49. http://dx.doi.org/10.55529/jipirs.41.39.49.

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Current strides of image dependent 3 dimension human outline estimation have progressed due to remarkable strides in depiction capabilities facilitated by deep NN. Despite the strides made in real-world applications, existing methods still fall short in generating reconstructions that match the intricate details often found in the original images. We posit that this deficiency primarily arises from the clash between two competing demands: accurate predictions necessitate extensive contextual information, while precise predictions hinge on higher resolutions. Owing to the limitations in current

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Wu, Yulun, Han Huang, Wenyuan Zhang, et al. "Sparis: Neural Implicit Surface Reconstruction of Indoor Scenes from Sparse Views." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 8 (2025): 8514–22. https://doi.org/10.1609/aaai.v39i8.32919.

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In recent years, reconstructing indoor scene geometry from multi-view images has achieved encouraging accomplishments. Current methods incorporate monocular priors into neural implicit surface models to achieve high-quality reconstructions. However, these methods require hundreds of images for scene reconstruction. When only a limited number of views are available as input, the performance of monocular priors deteriorates due to scale ambiguity, leading to the collapse of the reconstructed scene geometry. In this paper, we propose a new method, named Sparis, for indoor surface reconstruction f

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Wang, Zijian, Lulu Xu, Wen Rong, et al. "Semantic-Guided Iterative Detail Fusion Network for Single-Image Deraining." Electronics 13, no. 18 (2024): 3634. http://dx.doi.org/10.3390/electronics13183634.

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Existing approaches for image deraining often rely on synthetic or unpaired real-world rainy datasets, leading to sub-optimal generalization ability when processing the complex and diverse real-world rain degradation. To address these challenges, we propose a novel iterative semantic-guided detail fusion model with implicit neural representations (INR-ISDF). This approach addresses the challenges of complex solution domain variations, reducing the usual negative impacts found in these situations. Firstly, the input rainy images are processed through implicit neural representations (INRs) to ob

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