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Journal articles on the topic 'Image memory'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Cieplak, Piotr A. "Image and Memory." French Cultural Studies 20, no. 2 (2009): 199–208. http://dx.doi.org/10.1177/0957155809102636.

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Avinash, Gopal B. "Image compression and data integrity in confocal microscopy." Proceedings, annual meeting, Electron Microscopy Society of America 51 (August 1, 1993): 206–7. http://dx.doi.org/10.1017/s0424820100146874.

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In confocal microscopy, one method of managing large data is to store the data in a compressed form using image compression algorithms. These algorithms can be either lossless or lossy. Lossless algorithms compress images without losing any information with modest compression ratios (memory for the original / memory for the compressed) which are usually between 1 and 2 for typical confocal 2-D images. However, lossy algorithms can provide higher compression ratios (3 to 8) at the expense of information content in the images. The main purpose of this study is to empirically demonstrate the use

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van der Linde, Ian, Umesh Rajashekar, Alan C. Bovik, and Lawrence K. Cormack. "Visual Memory for Fixated Regions of Natural Images Dissociates Attraction and Recognition." Perception 38, no. 8 (2009): 1152–71. http://dx.doi.org/10.1068/p6142.

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Recognition memory for fixated regions from briefly viewed full-screen natural images is examined. Low-level image statistics reveal that observers fixated, on average (pooled across images and observers), image regions that possessed greater visual saliency than non-fixated regions, a finding that is robust across multiple fixation indices. Recognition-memory performance indicates that, of the fixation loci tested, observers were adept at recognising those with a particular profile of image statistics; visual saliency was found to be attenuated for unrecognised loci, despite that all regions

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Bondestam, S., A. E. Lamminen, and H. J. Aronen. "Efficient Memory Management Strategy in Hypermedia with Interactive MR Images." Acta Radiologica 36, no. 4-6 (1995): 556–60. http://dx.doi.org/10.1177/028418519503600447.

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When composing hypermedia with interactive image sets the main problem is to allocate minimal memory of random access memory (RAM). The stand-alone product should be accessible to the public and not require too much memory to maintain interaction between the images and the text. We designed a new hypermedia application using SuperCard and image stacks in PICS format running on a Macintosh LC computer. Memory use was effective since the images were kept in external files outside the application. On browsing cards the images were requested from the image file and each image was displayed within

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Smalbrugge, Matthias, and Enrique A. Eguiarte B. "La imagen como modelo hermenéutico en el libro décimo de ‘Confesiones’." Augustinus 60, no. 236 (2015): 281–90. http://dx.doi.org/10.5840/augustinus201560236/23919.

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In this article, the notion of image, as it appears in Confessions X, is analysed. Literature on this theme is lacking, though the word imago appears up to a forty-five times. Augustine, in his analysis of memory, widely uses the notion of image. Memory, he states, confronts us with the unknown in ourselves. Firstly, we can’t grasp its dimensions; secondly, it also bears in it what has been forgotten. But in fact, what has been forgotten but still lingers on in memory, otherwise we couldn’t remember what has been forgotten. So it appears that all we know and all we remember is based on images.

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الاميلس, ام السعد محمد. "زيادة سعة إخفاء البيانات في الصورة الرقمية الرمادية باستخدام خوارزمية خانة الإشارة المعدلة MSD". International Science and Technology Journal 36, № 2 (2025): 1–15. https://doi.org/10.62341/amsz1803.

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In this paper, an experimental analysis and study of the Modified Signed-Digit (MSD) method was conducted, which aims to hide a quantity of secret data represented in a grayscale image of size 512 × 512 in a set of cover images. Some quality metrics such as signal-to-noise ratio (PSNR), mean squared error (MSE), and embedding capacity (BPP) were measured. As well as calculating, the optimal number of cover images, the time taken in the hiding process, and the consumed memory. The experiments were conducted two different sizes of cover images, where the number of cover images required for the s

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Rostam, Affendi Hamzah, Md Roslan Muttaqin, Fauzan bin Kadmin Ahmad, Fakhar bin Abd Gani Shamsul, and Azha A. Aziz Khairul. "JPG, PNG and BMP image compression using discrete cosine transform." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 19, no. 3 (2021): 1010–16. https://doi.org/10.12928/telkomnika.v19i3.14758.

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This paper proposes image compression using discrete cosine transform (DCT) for the format of joint photographic expert groups (JPEG) or JPG, portable network graphic (PNG) and bitmap (BMP). These three extensions are the most popular types used in current image processing storage. The purpose of image compression is to produce lower memory usage or to reduce memory file. This process removes redundant information of each pixel. The challenge for image compression process is to maintain the quality of images after the compression process. Hence, this article utilizes the DCT technique to susta

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Anku, Anku, and Susan Farnand. "The Effect of Texture on Perceived Memory Color Quality." Journal of Imaging Science and Technology 64, no. 5 (2020): 50407–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2020.64.5.050407.

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Abstract We learn the color of objects and scenes through our experience in everyday life. The colors of things that we see more frequently are defined as memory colors. These help us communicate, identify objects, detect crop ripeness or disease, evaluate the weather, and recognize emotions. Color quality has become a priority for the smartphone and camera industry. Color quality assessment (CQA) provides insight into user preference and can be put to use to improve cameras and display pipelines. The memory color of important content like human skin, food, etc. drives perceived color quality.

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Et.al, R. Amirtha Varshini. "A Reconfigurable Memory based Fast VLSI Architecture for Histogram Computation." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (2021): 244–52. http://dx.doi.org/10.17762/turcomat.v12i6.1359.

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Histogram computation is the crucial task used in processing so many image guided applications like pattern recognition, image segmentation etc. Image registration is one of the fundamental techniques for pre-processing of the images. Registration is the process of overlaying multiple images to geometrically align them. In medical Image processing, the improper registration can have negative impact on the analysis of the image which influences the final diagnosis. The accurate result of image registration is obtained by matching of multimodal images. Mutual Information is one of the commonly u

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Fei, Zhengcong. "Memory-Augmented Image Captioning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 1317–24. http://dx.doi.org/10.1609/aaai.v35i2.16220.

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Current deep learning-based image captioning systems have been proven to store practical knowledge with their parameters and achieve competitive performances in the public datasets. Nevertheless, their ability to access and precisely manipulate the mastered knowledge is still limited. Besides, providing evidence for decisions and updating memory information are also important yet under explored. Towards this goal, we introduce a memory-augmented method, which extends an existing image caption model by incorporating extra explicit knowledge from a memory bank. Adequate knowledge is recalled acc

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Shi, Xiangsheng, Xuefei Ning, Lidong Guo, et al. "Memory-Oriented Structural Pruning for Efficient Image Restoration." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 2245–53. http://dx.doi.org/10.1609/aaai.v37i2.25319.

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Deep learning (DL) based methods have significantly pushed forward the state-of-the-art for image restoration (IR) task. Nevertheless, DL-based IR models are highly computation- and memory-intensive. The surging demands for processing higher-resolution images and multi-task paralleling in practical mobile usage further add to their computation and memory burdens. In this paper, we reveal the overlooked memory redundancy of the IR models and propose a Memory-Oriented Structural Pruning (MOSP) method. To properly compress the long-range skip connections (a major source of the memory burden), we

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Žukauskienė, Odeta. "Vaizdas kaip atminties medija." Lietuvos kultūros tyrimai 10 (2018): 125–40. https://doi.org/10.53630/lkt.2018.9.

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Memory and visuality are two trajectories of the modern world that have led to a regular change of culture. From the second half of the 20th century nostalgia combines with repentance, memories with reminiscences, turning our gaze back to the past, collective and individual experience, but at the same time the supply of various images, expanded by creative engineering, increases. The processes of memorialisation are gradually overshadowed by the mediation and industrialisation of memory, supported by globally operating techno-liberalism. The overwhelmed culture of images also experiences signi

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Becker, Nina, Simone Kühn, and Andreas Olsson. "How emotional contexts modulate item memory in individuals with high and low negative affect and worry." Learning & Memory 31, no. 4 (2024): a053891. http://dx.doi.org/10.1101/lm.053891.123.

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Emotional stimuli are usually remembered with high confidence. Yet, it remains unknown whether—in addition to memory for the emotional stimulus itself—memory for a neutral stimulus encountered just after an emotional one can be enhanced. Further, little is known about the interplay between emotion elicited by a stimulus and emotion relating to affective dispositions. To address these questions, we examined (1) how emotional valence and arousal of a context image preceding a neutral item image affect memory of the item, and (2) how such memory modulation is affected by two hallmark features of

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Xu, Wenjia, Guangluan Xu, Yang Wang, Xian Sun, Daoyu Lin, and Yirong Wu. "Deep Memory Connected Neural Network for Optical Remote Sensing Image Restoration." Remote Sensing 10, no. 12 (2018): 1893. http://dx.doi.org/10.3390/rs10121893.

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The spatial resolution and clarity of remote sensing images are crucial for many applications such as target detection and image classification. In the last several decades, tremendous image restoration tasks have shown great success in ordinary images. However, since remote sensing images are more complex and more blurry than ordinary images, most of the existing methods are not good enough for remote sensing image restoration. To address such problem, we propose a novel method named deep memory connected network (DMCN) based on the convolutional neural network to reconstruct high-quality ima

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Gong, Jia, Wenwei Yu, and Shao Ying Huang. "Image Quality Improvement and Memory-Saving in a Permanent-Magnet-Array-Based MRI System." Applied Sciences 10, no. 6 (2020): 2177. http://dx.doi.org/10.3390/app10062177.

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Point-of-care magnetic resonance imaging (MRI) requires clear images within a short scanning time, a small footprint of the scanner, and relatively low memory required for image reconstruction. A permanent magnet array (PMA)-based MRI system is a good candidate to supply a magnetic field due to its compactness and low power consumption. However, it has relatively inhomogeneous magnetic field and thus non-linear gradients, which results in location-dependent k-spaces (so called local k-spaces) and uneven signal point populations in the local k-spaces, compromising the image quality. Moreover, o

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Remazeilles, Anthony, and François Chaumette. "Image-based robot navigation from an image memory." Robotics and Autonomous Systems 55, no. 4 (2007): 345–56. http://dx.doi.org/10.1016/j.robot.2006.10.002.

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Ma, Jinlin, Yuetong Wan, and Ziping Ma. "Memory-Based Learning and Fusion Attention for Few-Shot Food Image Generation Method." Applied Sciences 14, no. 18 (2024): 8347. http://dx.doi.org/10.3390/app14188347.

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Generating food images aims to convert textual food ingredients into corresponding images for the visualization of color and shape adjustments, dietary guidance, and the creation of new dishes. It has a wide range of applications, including food recommendation, recipe development, and health management. However, existing food image generation models, predominantly based on GANs (Generative Adversarial Networks), face challenges in maintaining semantic consistency between image and text, as well as achieving visual realism in the generated images. These limitations are attributed to the constra

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Ito, T., Y. Kondo, S. Mori, T. Watabe, and Y. Arai. "Development of an Autofocus System in Tem." Proceedings, annual meeting, Electron Microscopy Society of America 48, no. 1 (1990): 158–59. http://dx.doi.org/10.1017/s0424820100179543.

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An autofocus system has been developed by combination of a high-sensitivity TV camera, a 32-bit CPU with a frame memory, and TEM image wobbler function. Center-crossing line profiles of images, instead of 2D images, have been used for autofocusing, thus ensuring high speed autofocusing within 8∽24 seconds for the medium magnification range, × 2000 ∽ 50000.The image wobbler is a well-known function for a standard commercial TEM, and widely used for biological specimens. For autofocusing by using an image wobbler, the 2D image matching technique is extremely important, and some papers have been

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Ustyantsev, Vladimir В., Svetlana M. Frolova, and Galina V. Yasakova. "The mythological image of Peter I in the context of social memory: From the historical image to the mythological one." Izvestiya of Saratov University. Philosophy. Psychology. Pedagogy 25, no. 1 (2025): 4–13. https://doi.org/10.18500/1819-7671-2025-25-1-4-13.

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Introduction. The authors of the article attempt to identify the mechanisms of formation of the mythological image of Peter I and the patterns of this process; the causes and patterns of manifestation of the relationship between the historical image of Peter I and his mythological images are investigated, as well as the analysis of interpretations of the image of Peter I is carried out, and the peculiarities of perception of mythological images of Peter I are determined. Theoretical analysis. In the work the method of social philosophy – reflection, the method of reconstruction, historical and

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Savić, Goran, Milan Prokin, Vladimir Rajović, and Dragana Prokin. "Digital Image Decoder for Efficient Hardware Implementation." Sensors 22, no. 23 (2022): 9393. http://dx.doi.org/10.3390/s22239393.

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Increasing the resolution of digital images and the frame rate of video sequences leads to an increase in the amount of required logical and memory resources necessary for digital image and video decompression. Therefore, the development of new hardware architectures for digital image decoder with a reduced amount of utilized logical and memory resources become a necessity. In this paper, a digital image decoder for efficient hardware implementation, has been presented. Each block of the proposed digital image decoder has been described. Entropy decoder, decoding probability estimator, dequant

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Guo, Feng Ying. "The Hardware Design and Implement of an Image Information Collection System." Advanced Materials Research 804 (September 2013): 211–15. http://dx.doi.org/10.4028/www.scientific.net/amr.804.211.

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It introduces the hardware design and implement of the image information collection system which is one part of Fluorescent magnetic particle nondestructive detection system of Cannonball. The image information collection system includes Open collecting card model Initialize collection card modelApply for EMS memory modelCollecting image modelWrite bitmap information head model Show pictures modeClose collection card model. Through collecting image information to the EMS memory and then processing the image information, the magnetic images on the cannonball are showed clearly on computer, and

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Lin, Yiheng, Yihan Hu, Chenyi Zhang, et al. "Memory Efficient Matting with Adaptive Token Routing." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 5 (2025): 5298–306. https://doi.org/10.1609/aaai.v39i5.32563.

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Transformer-based models have recently achieved outstanding performance in image matting. However, their application to high-resolution images remains challenging due to the quadratic complexity of global self-attention. To address this issue, we propose MEMatte, a memory-efficient matting framework for processing high-resolution images. MEMatte incorporates a router before each global attention block, directing informative tokens to the global attention while routing other tokens to a Lightweight Token Refinement Module (LTRM). Specifically, the router employs a local-global strategy to predi

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Kim, Kwang Baek, and Doo Heon Song. "Colored facial image restoration by similarity enhanced implicative fuzzy association memory." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 1 (2019): 199. http://dx.doi.org/10.11591/ijeecs.v13.i1.pp199-204.

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Image restoration refers to the recovery of an underlying image from an observation that has been corrupted by various types of noise. In a digital forensic software, such image restoration process should be noise-tolerant, robust, fast, and scalable. In this paper, we apply implicative fuzzy association memory structure in colored facial image restoration with enhanced similarity measure involved in output computarion. The efficacy if the proposed fuzzy associative memory model is verified by the experiment in that it was 95% successful (with zero mean square error) out of 20 tested images.

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Kim, Kwang Baek, and Doo Heon Song. "Colored facial image restoration by similarity enhanced implicative fuzzy association memory." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 1 (2019): 199–204. https://doi.org/10.11591/ijeecs.v13.i1.pp199-204.

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Image restoration refers to the recovery of an underlying image from an observation that has been corrupted by various types of noise. In a digital forensic software, such image restoration process should be noise-tolerant, robust, fast, and scalable. In this paper, we apply implicative fuzzy association memory structure in colored facial image restoration with enhanced similarity measure involved in output computarion. The efficacy if the proposed fuzzy associative memory model is verified by the experiment in that it was 95% successful (with zero mean square error) out of 20 tested images.

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Nomura, S., S. Isakozawa, and S. Kamimura. "Functions Of TEM Incorporated With Personal Computer and Frame Memory." Proceedings, annual meeting, Electron Microscopy Society of America 43 (August 1985): 150–51. http://dx.doi.org/10.1017/s0424820100117753.

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It is expected that a computer aided TEM which incorporates a frame memory system and a TV camera, provides many useful functions which have not yet been made available. We constructed an experimental TEM, shown in Fig. 1, and made some experiments which are described below.Dual Magnification Display of Specimen Images; If low and high magnification images are displayed at the same time, field selection would be a lot easier. This method has been already established in SEM. In our TEM, this is performed by memorizing the low magnification image in a frame memory, and by controlling the specime

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Chai, Ruishuai. "Otsu’s Image Segmentation Algorithm with Memory-Based Fruit Fly Optimization Algorithm." Complexity 2021 (March 25, 2021): 1–11. http://dx.doi.org/10.1155/2021/5564690.

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In this paper, the most common pepper noise in grayscale image noise is investigated in depth in the median filtering algorithm, and the improved median filtering algorithm, adaptive switching median filtering algorithm, and adaptive polar median filtering algorithm are applied to the OTSU algorithm. Two improved OTSU algorithms such as the adaptive switched median filter-based OTSU algorithm and the polar adaptive median filter-based OTSU algorithm are obtained. The experimental results show that the algorithm can better cope with grayscale images contaminated by pretzel noise, and the segmen

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Vypasniak, H. O. "TRAUMATIC MEMORY RESTORERS: IMAGE TYPOLOGY." Lviv Philological Journal, no. 6 (2019): 27–31. http://dx.doi.org/10.32447/2663-340x-2019-6-4.

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Lee, YoonSeok, and Sung-Eui Yoon. "Memory-Efficient NBNN Image Classification." Journal of Computing Science and Engineering 11, no. 1 (2017): 1–8. http://dx.doi.org/10.5626/jcse.2017.11.1.1.

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Orlov, Tanya, Daniel J. Amit, Volodya Yakovlev, Ehud Zohary, and Shaul Hochstein. "Memory of Ordinal Number Categories in Macaque Monkeys." Journal of Cognitive Neuroscience 18, no. 3 (2006): 399–417. http://dx.doi.org/10.1162/jocn.2006.18.3.399.

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What mechanism underlies serial order memory? Studying preverbal serial memory shows that macaque monkeys reproducing a sequence of items can acquire knowledge of item ordinal position. In our previous experiment, macaques were repeatedly presented with image lists (first shown sequentially and then simultaneously on a touch screen together with a distractor chosen randomly from other lists). The task was to touch list images in the correct order. The monkeys' natural tendency was to categorize images by their ordinal position or number because their most common error was touching the distract

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Kumar K, Kavin, Amirthavarshini S A, and Dhivya Dharshini T. "Optimization Of Memory Usage in High-Speed Cameras Using FPGA." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42409.

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High-speed cameras generate large amounts of data, making memory optimization difficult for real-time processing. This project minimizes data size by converting RGBA (Red, Green, Blue, Alpha) images to RGB, eliminating the Alpha channel to reduce memory usage. The captured images are provided as input to Verilog code in hexadecimal format, with the conversion done by MATLAB. Bilinear Interpolation is applied to reduce the potential quantization errors during the conversion, using the values of four surrounding pixels to smooth the image and maintain quality. After processing, the image is reco

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Chubala, Chrissy M., Tyler M. Ensor, Ian Neath, and Aimée M. Surprenant. "Dynamic Visual Noise Does Not Affect Memory for Fonts." Experimental Psychology 67, no. 3 (2020): 161–68. http://dx.doi.org/10.1027/1618-3169/a000491.

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Abstract. Dynamic visual noise (DVN) selectively impairs memory for some types of stimuli (e.g., colors, textures, concrete words), but not for others (e.g., matrices, Chinese characters, simple shapes). According to the image definition hypothesis, the key difference is whether the stimulus leads to images that are ill-defined or well-defined. The former will be affected because the addition of noise quickly reduces the usefulness of the image in supplying information about the item's identity. The image definition hypothesis predicts that fonts should lead to ill-defined images and therefore

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ISMAIL, I. A., A. A. ABDO, M. AMIN, and H. DIAB. "SELF-ADAPTIVE IMAGE ENCRYPTION BASED ON MEMORY CELLULAR AUTOMATA." International Journal of Information Acquisition 08, no. 03 (2011): 227–41. http://dx.doi.org/10.1142/s0219878911002434.

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Based on memory reversible cellular automata, a new self-adaptive image encryption is proposed in this paper. The self-adaptive encryption is realized by dividing the image data in to equal halves and then encrypting each half of image data using other mutually. We utilize cellular neural network chaotic system as a pseudo-random key sequence generation. Simulation results on some gray level images confirm that the proposed algorithm can be realized easily while guaranteeing high security and sensitivity levels, and other properties such as randomness.

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Ksheerasagar, Deepak R. "Image Description Generator using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 4244–48. http://dx.doi.org/10.22214/ijraset.2022.45988.

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Abstract: To recognise the context of an image and describe it in a natural language like English, the fundamental task of creating image captions uses computer vision and natural language processing techniques. To create a natural language description from an input image, image caption generation is used. Convolutional Neural Network (CNN) model and Long Short-Term Memory (LSTM) model are the two parts of this Python project that are used to implement it. The CNN-LSTM architecture combines a Convolutional Neural Network (CNN), which creates features that describe the images, with a Long Short

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Hua, Hua, Xiaomin Yang, Binyu Yan, Kai Zhou, and Wei Lu. "An Efficient Image Enlargement Method for Image Sensors of Mobile in Embedded Systems." Journal of Sensors 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/6370845.

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Main challenges for image enlargement methods in embedded systems come from the requirements of good performance, low computational cost, and low memory usage. This paper proposes an efficient image enlargement method which can meet these requirements in embedded system. Firstly, to improve the performance of enlargement methods, this method extracts different kind of features for different morphologies with different approaches. Then, various dictionaries based on different kind of features are learned, which represent the image in a more efficient manner. Secondly, to accelerate the enlargem

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Muhammad Rizki, Erik Iman Heri Ujianto, and Rianto Rianto. "Digital Image Encryption Using Logistic Map." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 6 (2023): 1292–99. http://dx.doi.org/10.29207/resti.v7i6.5389.

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This study focuses on the application of the Logistic Map algorithm in the Python programming language for digital image encryption and decryption. It investigates the impact of image type, image size, and Logistic Map parameter values on computational speed, memory usage, encryption, and decryption results. Three image sizes (300px x 300px, 500px x 500px, and 1024px x 1024px) in TIFF, JPG, and PNG formats are considered. The Digital Image Encryption and Decryption process utilizes the Logistic Map algorithm implemented in Python. Various parameter values are tested for each image type and siz

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Yang, Jingwei. "Sports Video Athlete Detection Based on Associative Memory Neural Network." Computational Intelligence and Neuroscience 2022 (February 15, 2022): 1–9. http://dx.doi.org/10.1155/2022/6986831.

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Aiming at the detection of athletes in sports videos, an automatic detection method based on AMNN is proposed. The background image from the image sequence is obtained, the moving area is extracted, and the color information of pixels to extract the green stadium from the background image is used. In order to improve the accuracy of athletes’ detection, the texture similarity measurement method is used to eliminate the shadow in the movement area, the morphological method is used to eliminate the cracks in the area, and the noise outside the stadium is removed according to the stadium informat

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Haj-Ali, Ameer, Rotem Ben-Hur, Nimrod Wald, Ronny Ronen, and Shahar Kvatinsky. "IMAGING-In-Memory AlGorithms for Image processiNG." IEEE Transactions on Circuits and Systems I: Regular Papers 65, no. 12 (2018): 4258–71. https://doi.org/10.1109/TCSI.2018.2846699.

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Data-intensive applications such as image processing suffer from massive data movement between memory and processing units. The severe limitations on system performance and energy efficiency imposed by this data movement are further exacerbated with any increase in the distance the data must travel. This data transfer and its associated obstacles could be eliminated by the use of emerging non-volatile resistive memory technologies (memristors) that make it possible to both store and process data within the same memory cells. In this paper, we propose four in-memory algorithms for efficient exe

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Patil, Shivganga, and Lakshmi Patil. "DESIGN AND ANALYSIS ON IMAGE COMPRESSION USING NEURAL NETWORKS." ICTACT Journal on Image and Video Processing 14, no. 4 (2024): 3301–4. http://dx.doi.org/10.21917/ijivp.2024.0469.

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Due to the explosion of video based information proliferating in the world due to the ubiquitous usage of video cameras the amount of video based information that is currently being generated around the world is huge. And due to security purposes it is becoming imperative that these video data needs to be stored in computer memory for an extended period of time for referrals by security agencies. Because of the advancement of imaging technologies that is being used nowadays it is possible to capture extremely detailed high definition images. But it is not physically possible to store all these

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Zhou, Jing, Jun Jie Wu, and Yu Hua Tang. "Edge Detection of Binary Image Based on Memristors." Advanced Materials Research 791-793 (September 2013): 2066–70. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.2066.

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Image processing is a type of memory-access-intensive application which puts a great pressure on memory access speed and bandwidth. Memristors has the ability of both memory and computing. With this property, we have designed and implemented image logic operations in memristor memory system, which could save a large amount of memory access at relatively low memory space price. Based on this work, we use image logic operations with memristors into edge detection of binary image. The main idea is as follows. First, we use XOR operation of neighboring pixels to determine whether there is an edge

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Yang, Jucheng, Feng Wei, Yaxin Bai, Meiran Zuo, Xiao Sun, and Yarui Chen. "An Effective Multi-Task Two-Stage Network with the Cross-Scale Training Strategy for Multi-Scale Image Super Resolution." Electronics 10, no. 19 (2021): 2434. http://dx.doi.org/10.3390/electronics10192434.

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Convolutional neural networks and the per-pixel loss function have shown their potential to be the best combination for super-resolving severely degraded images. However, there are still challenges, such as the massive number of parameters requiring prohibitive memory and vast computing and storage resources as well as time-consuming training and testing. What is more, the per-pixel loss measured by L2 and the Peak Signal-to-Noise Ratio do not correlate well with human perception of image quality, since L2 simply does not capture the intricate characteristics of human visual systems. To addres

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Garrouri, Sihem. "Mythologizing the Memory of Gloriana." Anafora 8, no. 1 (2021): 81–98. http://dx.doi.org/10.29162/anafora.v8i1.5.

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Consideration of Anne Bradstreet’s poem “In Honour of That High and Mighty Princess, Queen Elizabeth, of Most Happy Memory” (1643) draws our attention to the paramount significance of mythical imagery in shaping Elizabeth I’s posthumous reputation. The examination of this poem illustrates the ways in which Elizabeth’s memory is glorified and discusses the elegiac mythical reconstruction of her image by what Schweitzer aptly labelled a “gendered poetic voice” (307). This project shows that the poet makes good use of myth to write Elizabeth’s afterlife image. It scrutinizes Bradstreet’s mytholog

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Li, Jiangtong, Li Niu, and Liqing Zhang. "Action-Aware Embedding Enhancement for Image-Text Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 1323–31. http://dx.doi.org/10.1609/aaai.v36i2.20020.

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Image-text retrieval plays a central role in bridging vision and language, which aims to reduce the semantic discrepancy between images and texts. Most of existing works rely on refined words and objects representation through the data-oriented method to capture the word-object cooccurrence. Such approaches are prone to ignore the asymmetric action relation between images and texts, that is, the text has explicit action representation (i.e., verb phrase) while the image only contains implicit action information. In this paper, we propose Action-aware Memory-Enhanced embedding (AME) method for

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Fu, Kun, Yang Li, Wenkai Zhang, Hongfeng Yu, and Xian Sun. "Boosting Memory with a Persistent Memory Mechanism for Remote Sensing Image Captioning." Remote Sensing 12, no. 11 (2020): 1874. http://dx.doi.org/10.3390/rs12111874.

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The encoder–decoder framework has been widely used in the remote sensing image captioning task. When we need to extract remote sensing images containing specific characteristics from the described sentences for research, rich sentences can improve the final extraction results. However, the Long Short-Term Memory (LSTM) network used in decoders still loses some information in the picture over time when the generated caption is long. In this paper, we present a new model component named the Persistent Memory Mechanism (PMM), which can expand the information storage capacity of LSTM with an exter

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Герасимов, I. Gerasimov, Яшин, and A. Yashin. "Ion-Molecular Memory Model. Retrieving Information and the Temporary Memory." Journal of New Medical Technologies 22, no. 3 (2015): 171–77. http://dx.doi.org/10.12737/13322.

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The article is devoted to the issues of retrieving information and the temporary memory in frame of formation of the ion-molecular memory model. It is shown that the retrieving information, i.e., in everyday life language -remembering, in essence functioning close to the process of memorizing discussed in the previous article in the series. Memorizing and remembering are the essence of &#34;antagonists&#34; (plus or minus) with an almost identical mechanism of action. Here the main task memory to recall what exactly you need to remember. This isn&#180;t a tautology, but the essence

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ITOH, MAKOTO, and LEON O. CHUA. "AUTOASSOCIATIVE MEMORY CELLULAR NEURAL NETWORKS." International Journal of Bifurcation and Chaos 20, no. 10 (2010): 3225–66. http://dx.doi.org/10.1142/s0218127410027647.

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An autoassociative memory is a device which accepts an input pattern and generates an output as the stored pattern which is most closely associated with the input. In this paper, we propose an autoassociative memory cellular neural network, which consists of one-dimensional cells with spatial derivative inputs, thresholds and memories. Computer simulations show that it exhibits good performance in face recognition: The network can retrieve the whole from a part of a face image, and can reproduce a clear version of a face image from a noisy one. For human memory, research on "visual illusions"

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Khan, Tareq Hasan, and Khan A. Wahid. "Lossless and Low-Power Image Compressor for Wireless Capsule Endoscopy." VLSI Design 2011 (June 9, 2011): 1–12. http://dx.doi.org/10.1155/2011/343787.

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We present a lossless and low-complexity image compression algorithm for endoscopic images. The algorithm consists of a static prediction scheme and a combination of golomb-rice and unary encoding. It does not require any buffer memory and is suitable to work with any commercial low-power image sensors that output image pixels in raster-scan fashion. The proposed lossless algorithm has compression ratio of approximately 73% for endoscopic images. Compared to the existing lossless compression standard such as JPEG-LS, the proposed scheme has better compression ratio, lower computational complex

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Mishra, Vikas Kumar, Shobhit Kumar, and Neeraj Shukla. "Image Acquisition and Techniques to Perform Image Acquisition." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 9, no. 01 (2017): 21–24. http://dx.doi.org/10.18090/samriddhi.v9i01.8333.

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In the present scenario images have become the most suitable way to keep our past alive. Nowadays people are very busy in earning their livelihood and day to day life, even though they want to keep their golden moment alive for the rest of life. Images have touched almost all the fields like medical, sports, social networking and many more. It is the need of time to know how the images are being captured and stored into memory. To deal with images and before analyzing them the most important thing is to capture the image. This is called as Image Acquisition. Image Acquisition is achieved by su

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Pinto, Rooney F., Isabel Maria Freitas Valente, and Maria João Guia. "The object of memory and the memory of the object: refugee crisis in the news on September 2nd 2015." Debater a Europa, no. 16 (July 3, 2017): 221–31. http://dx.doi.org/10.14195/1647-6336_16_10.

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This article aims to contribute on reflecting about the strict relation between an object (an image) and the memory, particularly regarding the memory in the news on September 2nd 2015 about the refugee crisis. Every year, Porto Editora (a Portuguese press company) holds a survey with ten words in order to elect the word of the year, and, for 2015, the elected one was “Refugees” (Palavra do Ano, 2015); this would be one more evidence of the impact of this issue in the news. The photo of a dead Syrian child on a beach in Turkey has become one of the most striking images of the refugee crisis in

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Reddy, V. Varshith, Y. Shiva Krishna, U. Varun Kumar Reddy, and Shubhangi Mahule. "Gray Scale Image Captioning Using CNN and LSTM." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 1566–71. http://dx.doi.org/10.22214/ijraset.2022.41589.

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Abstract: The objective of the project is to generate caption of an image. The process of generating a description of an image is called image captioning. It requires recognizing the important objects, their attributes, and the relationships among the objects in an image. With the advancement in Deep learning techniques and availability of huge datasets and computer power, we can build models that can generate captions for an image. This is what we have implemented in this Python based project where we have used the deep learning techniques of CNN (Convolutional Neural Networks) and LSTM (Long

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Kramer, Robin S. S., Chloe J. Berry, Alex L. Jones, and Georgina Gous. "Face Familiarity and Image-Specific Memory." Perception 49, no. 9 (2020): 978–87. http://dx.doi.org/10.1177/0301006620946265.

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Face familiarity produces advantages for both memory and matching. By developing an internal representation through repeated experience, viewers extract identity-specific information that aids subsequent recognition. However, researchers have recently argued that this process may also result in a familiarity disadvantage, whereby specific instances of the face are more difficult to remember, perhaps due to this process of prioritising identity- over image-specific information. Although previous experiments found no evidence of this disadvantage in working memory, initial research has demonstra

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