Journal articles: 'Self image'

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McCormack, W. J., and Joseph Th Leerssen. "Image and Self-Image." Books Ireland, no. 115 (1987): 150. http://dx.doi.org/10.2307/20625886.

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Langford, Rae W., and Vera Harmon. "Self-Image." AORN Journal 45, no. 4 (April 1987): 969–79. http://dx.doi.org/10.1016/s0001-2092(07)65875-x.

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Chon, Kye-Sung. "Self-image/destination image congruity." Annals of Tourism Research 19, no. 2 (January 1992): 360–63. http://dx.doi.org/10.1016/0160-7383(92)90090-c.

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Chang, Nam Fung. "Self-image and self-reflection." Babel. Revue internationale de la traduction / International Journal of Translation 63, no. 5 (December 31, 2017): 643–66. http://dx.doi.org/10.1075/babel.00002.cha.

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Abstract The futility of decades of government efforts to disseminate Chinese literature has triggered discussions among Chinese scholars on how to translate and who should be entrusted with this task. Some blame the failure on traditional concepts of translation that overemphasize faithfulness to the original to the point of disregarding target cultural conditions, but others insist that China should have control over its cultural export and that Sino-English should be used to internationalize English. Findings show that traditional concepts should not be blamed, as aggressively source-oriented strategies have been used in outbound translation only in recent years, and that this shift in translation norms in government-initiated outbound translation has spread to non-literary text types, and also to Hong Kong and Taiwan. The same kind of aggressiveness has recently been displayed in other forms of cultural export, triggering resistance in other cultures. All these changes may be attributable to a heightening of cultural self-image. What is needed to address the issue is cultural self-reflection, which will lead to the awareness that economic growth does not immediately bring cultural prestige, and that source-initiated cultural export efforts may make little difference in central cultures. Cultural awareness at a higher level can be achieved only through empathy.

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Lamprinou, Nefeli, Nikolaos Nikolikos, and Emmanouil Z. Psarakis. "Groupwise Image Alignment via Self Quotient Images." Sensors 20, no. 8 (April 19, 2020): 2325. http://dx.doi.org/10.3390/s20082325.

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Compared with pairwise registration, the groupwise one is capable of handling a large-scale population of images simultaneously in an unbiased way. In this work we improve upon the state-of-the-art pixel-level, Least-Squares (LS)-based groupwise image registration methods. Specifically, the registration technique is properly adapted by the use of Self Quotient Images (SQI) in order to become capable for solving the groupwise registration of photometrically distorted, partially occluded as well as unimodal and multimodal images. Moreover, the proposed groupwise technique is linear to the cardinality of the image set and thus it can be used for the successful solution of the problem on large image sets with low complexity. From the application of the proposed technique on a series of experiments for the groupwise registration of photometrically and geometrically distorted, partially occluded faces as well as unimodal and multimodal magnetic resonance image sets and its comparison with the Lucas–Kanade Entropy (LKE) algorithm, the obtained results look very promising, in terms of alignment quality, using as figures of merit the mean Peak Signal to Noise Ratio ( m P S N R ) and mean Structural Similarity ( m S S I M ), and computational cost.

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Bishop, John Douglas. "Is Self-Identity Image Advertising Ethical?" Business Ethics Quarterly 10, no. 2 (April 2000): 371–98. http://dx.doi.org/10.2307/3857882.

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Abstract:Discussions of the ethics of advertising have been based on a general distinction between informative and persuasive advertising without looking at specific techniques of persuasion. Self-identity image ads persuade by presenting an image of an idealized person-type such as a “beautiful” woman (Chanel) or a sexy teen (Calvin Klein). The product becomes a symbol of the ideal, and target consumers are invited to use the product to project the self-image to themselves and others. This paper argues that image ads are not false or misleading, and that whether or not they advocate false values is a matter for subjective reflection. Image ads can undermine a consumer’s self-esteem by collectively omitting images authentic for that sort of person (such as large women), and by combining impossible images with implied gaze. Image ads generally do not undermine autonomy of choice, internal autonomy, or social autonomy. It is concluded that image advertising is a basically ethical technique, but several recommendations are given on how use of image advertising can avoid specific harms.

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Dos Santos Ribeiro, Ivana, Itana Dos Santos Ribeiro, and Thaís Vieira Góis dos Santos. "SELF-ESTEEM, SELF-CONCEPT AND SELF-IMAGE." Health and Society 3, no. 02 (May 5, 2023): 92–111. http://dx.doi.org/10.51249/hs.v3i02.1289.

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Self-esteem corresponds to a feeling of personal worth and self-respect, considering a positive or negative evaluation of oneself, while self-concept is attributed a descriptive and evaluative character, the first being related to self-image and the second to self-esteem. In order to build healthy self-esteem, the involvement of family members and school staff is strictly necessary, in order to provide a safe and motivating environment for children and adolescents. Thus, this article has the general objective of understanding the processes of building self-esteem, self-concept and self-image during child development, through the influences of the environment and affective relationships between the people with whom the child lives. The study is a review of narrative literature, through publications/articles in the following databases: Scientific Electronic Library (SCIELO), Latin American and Caribbean Literature on Health Sciences (LILACS) and Virtual Health Library (BVS). Studies have shown the importance of developing a positive self-concept to have a good quality of life. For that, the relationships that the individual establishes in the family, in the school, in his social environment are fundamental for the formation of the self-concept.

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Dyeson, Timothy B. "Burden Self-Image." Journal of Gerontological Social Work 33, no. 1 (June 13, 2000): 17–33. http://dx.doi.org/10.1300/j083v33n01_03.

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Rothrock, Jane C. "Professional Self-Image." AORN Journal 49, no. 5 (May 1989): 1419–25. http://dx.doi.org/10.1016/s0001-2092(07)70119-9.

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Fried, Ohad, Jennifer Jacobs, Adam Finkelstein, and Maneesh Agrawala. "Editing self-image." Communications of the ACM 63, no. 3 (February 24, 2020): 70–79. http://dx.doi.org/10.1145/3326601.

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Liu, Bingchen, Yizhe Zhu, Kunpeng Song, and Ahmed Elgammal. "Self-Supervised Sketch-to-Image Synthesis." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (May 18, 2021): 2073–81. http://dx.doi.org/10.1609/aaai.v35i3.16304.

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Imagining a colored realistic image from an arbitrary-drawn sketch is one of human capabilities that we eager machines to mimic. Unlike previous methods that either require the sketch-image pairs or utilize low-quantity detected edges as sketches, we study the exemplar-based sketch-to-image (s2i) synthesis task in a self-supervised learning manner, eliminating the necessity of the paired sketch data. To this end, we first propose an unsupervised method to efficiently synthesize line-sketches for general RGB-only datasets. With the synthetic paired-data, we then present a self-supervised Auto-Encoder (AE) to decouple the content/style features from sketches and RGB-images, and synthesize images both content-faithful to the sketches and style-consistent to the RGB-images. While prior works employ either the cycle-consistence loss or dedicated attentional modules to enforce the content/style fidelity, we show AE's superior performance with pure self-supervisions. To further improve the synthesis quality in high resolution, we also leverage an adversarial network to refine the details of synthetic images. Extensive experiments on $1024^2$ resolution demonstrate a new state-of-art-art performance of the proposed model on CelebA-HQ and Wiki-Art datasets. Moreover, with the proposed sketch generator, the model shows a promising performance on style mixing and style transfer, which the synthesized images are not only style-consistent but also semantically meaningful.

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Roy, Sangita, and Sheli Sinha Chaudhuri. "Fast Single Image Haze Removal Scheme Using Self-Adjusting." International Journal of Virtual and Augmented Reality 3, no. 1 (January 2019): 42–57. http://dx.doi.org/10.4018/ijvar.2019010103.

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At present the classical problem of visibility improvement is hot topic of research. An image formation optical model is presented where a clear day image has high contrast with respect to an image plagued with bad weather. A degraded daytime image has high intensity with minimum deviation among pixels in every channel. No reference digital image haze removal is a problem. The static haziness factor for all types of images cannot be applicable for effective haze removal. A minimum intensity channel of the three RGB channels is estimated as transmission of an image with a dynamic haziness factor to be a ratio of minimum to maximum pixel intensity of the hazy image. Adaptive contrast, extinction coefficient, the maximum visible distance of hazy images as well as dehazed images from each image are evaluated uniquely. The resulting high-quality haze free image with linear computational complexity O(n) is appropriate for real time applications. The effectiveness of the technique is validated by quantitative, and qualitative evaluations.

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Umiker, William. "Optimism, self-image, and self-esteem." Health Care Manager 12, no. 1 (September 1993): 23–28. http://dx.doi.org/10.1097/00126450-199309000-00005.

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Peng, Na Xin, and Yu Qiang Chen. "Improved Self-Adaptive Image Histogram Equalization Algorithm." Advanced Materials Research 760-762 (September 2013): 1495–500. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1495.

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Histogram equalization (HE) algorithm is wildly used method in image processing of contrast adjustment using images histogram. This method is useful in images with backgrounds and foreground that are both bright or both dark. But the performance of HE is not satisfactory to images with backgrounds and foregrounds that are both bright or both dark. To deal with the above problem, [ gives an improved histogram equalization algorithm named self-adaptive image histogram equalization (SIHE) algorithm. Its main idea is to extend the gray level of the image which firstly be processed by the classical histogram equalization algorithm. This paper gives detailed introduction to SIHE and analyzes the shortage of it, then give an improved version of SIHE named ISIHE, finally do experiments to show the performance of our algorithm.

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Yang, Kun, Haojie Zhang, Yufei Qiu, Tong Zhai, and Zhiguo Zhang. "Self-Supervised Joint Learning for pCLE Image Denoising." Sensors 24, no. 9 (April 30, 2024): 2853. http://dx.doi.org/10.3390/s24092853.

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Probe-based confocal laser endoscopy (pCLE) has emerged as a powerful tool for disease diagnosis, yet it faces challenges such as the formation of hexagonal patterns in images due to the inherent characteristics of fiber bundles. Recent advancements in deep learning offer promise in image denoising, but the acquisition of clean-noisy image pairs for training networks across all potential scenarios can be prohibitively costly. Few studies have explored training denoising networks on such pairs. Here, we propose an innovative self-supervised denoising method. Our approach integrates noise prediction networks, image quality assessment networks, and denoising networks in a collaborative, jointly trained manner. Compared to prior self-supervised denoising methods, our approach yields superior results on pCLE images and fluorescence microscopy images. In summary, our novel self-supervised denoising technique enhances image quality in pCLE diagnosis by leveraging the synergy of noise prediction, image quality assessment, and denoising networks, surpassing previous methods on both pCLE and fluorescence microscopy images.

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Indriasari, Ditya. "BODY IMAGE DAN SELF ESTEEM PADA REMAJA DITINJAU BERDASARKAN PERSPEKTIF GENDER." QUANTA: Jurnal Kajian Bimbingan dan Konseling dalam Pendidikan 7, no. 2 (May 22, 2023): 53–61. http://dx.doi.org/10.22460/quanta.v7i2.3915.

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AbstractÂ Late adolescence students have a negative body image, making them judge themselves as worthless. This study aims to test the influence of body image on self-esteem. The theory used wasÂ body image (Cash, 2002) and self-esteem (Rosenberg, 1965). This study was a quantitative research with a causality research design. The hypothesis in this study was that there is an influence of body image on self-esteem. The research subjects were 162 students who were active in the Psychology of UNJANI. The research instrument was the body image questionnaire of 26 items and a self-esteem questionnaire of 10 items. The body image instrument has a reliability value of 0.892 and the validity value ranges from 0.503 to 0.911, reliability value of self-esteem is 0.843 with a validity value ranging from 0.502 to 0.819.Â The writer used a simple linear regression test to determine the influence of body image. The results showed that the value of R Square = 0.180 and the significance of p = 0.000 means that there was a significant effect between body image and self-esteem by 18%. This means that the more positive the body image, the higher the self-esteem.Keywords:Â body image, self-esteem, late adolescenceÂ Â AbstrakÂ Mahasiswa remaja akhir memiliki citra tubuh negatif yang menyebabkan mereka menilai diri mereka tidak berharga. TujuanÂ dari penelitian ini untuk menguji pengaruh body imageÂ terhadap self esteem. Teori yang digunakan yaitu body imageÂ (Cash, 2002) dan self esteem (Rosenberg, 1965). Penelitian ini termasuk penelitian kuantitatif dengan desain penelitian kausalitas. Hipotesis dalam penelitian ini adalah terdapat pengaruh body imageÂ terhadap self esteem. Subjek penelitian, yaitu 162 orang mahasiswi aktif Â Psikologi UNJANI. Instrumen penelitian, yaitu kuesioner body image sebanyak 26 itemÂ dan kuesioner self esteemÂ sebanyak 10 item. Instrumen body imageÂ memiliki nilai reliabilitas sebesar 0,892Â dan nilai validitas berkisar dari 0,503 â€“ 0,911Â , self esteemÂ sebesar 0,843Â dengan nilai validitas berkisar 0,502 â€“ 0,819. Peneliti menggunakan uji regresi linier sederhana untuk mengetahui pengaruh dari body image. Hasil penelitian menunjukkan nilai R Square = 0,180 dan signifikansi p = 0,000, artinya terdapat pengaruh signifikan antara body imageÂ terhadap self esteemÂ sebesar 18%. Hal ini dapat diartikan bahwa semakin positif body image yang dimiliki, maka semakin tinggi self-esteem.Kata Kunci: body image, self-esteem, remaja akhir

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Kim, Dahyun, and Myung Hwan Na. "Rice yield prediction and self-attention visualization using Video Vision Transformer." Korean Data Analysis Society 25, no. 4 (August 31, 2023): 1249–59. http://dx.doi.org/10.37727/jkdas.2023.25.4.1249.

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The government and farmers' organizations are paying much attention to the problem of predicting how much rice can be produced each year. However, it is difficult to accurately predict the yield of rice due to variable factors such as extreme climate change and various pests and diseases that change every year. In this study, images were collected several times during the growing season of rice through a multi-spectral sensor mounted on an unmanned aerial vehicle, and rice yield was predicted using a deep learning algorithm. Multispectral images can be viewed as a kind of image data taken several times at regular intervals, and rice yield was predicted using the Video Vision Transformer (ViViT) model, which applies the Transformer structure to image computer vision among deep learning algorithms. The ViViT model generates patches by dividing the input image into a certain size, and as a result of learning the model by setting the size of these patches differently, it was found that the smaller the patch size, the better the predictive power. In addition, as a result of comparing prediction performance with a 3D CNN model that receives an image as an input in a CNN (Convolutional Neural Network) structure used in the image processing field, it was found that the ViViT model using a small patch size performed better. As a result of visualizing the weight matrix of the ViViT model as a heat map, images taken in mid- to late August appear to be important in yield prediction, making it possible to predict yield about two months before rice harvest.

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Jung, Jaehee, and Sharron J. Lennon. "Body Image, Appearance Self-Schema, and Media Images." Family and Consumer Sciences Research Journal 32, no. 1 (September 1, 2003): 27–51. http://dx.doi.org/10.1177/1077727x03255900.

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Shin, Eun Jung, and Ae-ran Koh. "Korean Genderless Fashion Consumers' Self-image and Identification." Journal of the Korean Society of Clothing and Textiles 44, no. 3 (June 30, 2020): 400–412. http://dx.doi.org/10.5850/jksct.2020.44.3.400.

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Fisher, Allison, Mary Anne Lange, Virginia Young-Cureton, and Daryl Canham. "The Relationship Between Perceived and Ideal Body Size and Body Mass Index in 3rd-Grade Low Socioeconomic Hispanic Children." Journal of School Nursing 21, no. 4 (August 2005): 224–28. http://dx.doi.org/10.1177/10598405050210040701.

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Very little is known about body satisfaction among minority children. This study examined the relationship between perceived and actual body size and Body Mass Index among 43 low-socioeconomic Hispanic 3rd-graders. Researchers measured participants’ Body Mass Index; students self-reported Perceived Ideal Self Image and Perceived Actual Self Image using Collins’ (1991) pictorial instrument scale of seven child body images that illustrate body weight from very thin to obese. The majority of students chose body images from the scale that depicted a healthy weight for both their Perceived Ideal Self Image and Perceived Actual Self Image. More boys than girls chose underweight as their Perceived Ideal Self Image. Thirty percent of the students were found to be overweight and 30 percent were at risk for being overweight. A small positive correlation between Perceived Actual Self Image and Body Mass Index was found.

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Nguyen Chi Thanh. "Colonoscopy image classification using self-supervised visual feature learning." Journal of Military Science and Technology, CSCE5 (December 15, 2021): 3–13. http://dx.doi.org/10.54939/1859-1043.j.mst.csce5.2021.3-13.

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Colonoscopy image classification is an image classification task that predicts whether colonoscopy images contain polyps or not. It is an important task input for an automatic polyp detection system. Recently, deep neural networks have been widely used for colonoscopy image classification due to the automatic feature extraction with high accuracy. However, training these networks requires a large amount of manually annotated data, which is expensive to acquire and limited by the available resources of endoscopy specialists. We propose a novel method for training colonoscopy image classification networks by using self-supervised visual feature learning to overcome this challenge. We adapt image denoising as a pretext task for self-supervised visual feature learning from unlabeled colonoscopy image dataset, where noise is added to the image for input, and the original image serves as the label. We use an unlabeled colonoscopy image dataset containing 8,500 images collected from the PACS system of Hospital 103 to train the pretext network. The feature exactor of the pretext network trained in a self-supervised way is used for colonoscopy image classification. A small labeled dataset from the public colonoscopy image dataset Kvasir is used to fine-tune the classifier. Our experiments demonstrate that the proposed self-supervised learning method can achieve a high colonoscopy image classification accuracy better than the classifier trained from scratch, especially at a small training dataset. When a dataset with only annotated 200 images is used for training classifiers, the proposed method improves accuracy from 72,16% to 93,15% compared to the baseline classifier.

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Wu, Hao. "Image Self-Coding Algorithm Based on IoT Perception Layer." Mobile Information Systems 2022 (August 3, 2022): 1–11. http://dx.doi.org/10.1155/2022/9910655.

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In fact, with the quick growth of IoT-related industries in recent years, multimedia contents such as digital image videos have also shown explosive growth. In the sensing layer of the three-layer IoT architecture, sensors are the most critical part, which mainly sense the state of the environment. In this paper, an image self-coding algorithm based on the IoT perception layer is proposed. There is no specific encoding algorithm for the pictures collected by the current Internet of Things network perception layer. This results in poor search results for the network images collected by the sensor at the perception layer. A deep convolutional neural network image self-coding algorithm based on the IoT perception layer combines prior knowledge with deep involutional aural networks to increase the discriminative ability of images while preserving the message of the images themselves and improving the goal of image search accuracy. The experimental results show that the block search algorithm is used for image registration to reduce energy consumption, the absolute difference sum algorithm is improved to improve the accuracy of image registration, and the progressively out weighted average algorithm is used to stitch the images. After image stitching, the communication volume with the base station is reduced, which can effectively reduce the network load by 60%.

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Liu, Yangyang, Minghua Tian, Chang Xu, and Lixiang Zhao. "Neural network feature learning based on image self-encoding." International Journal of Advanced Robotic Systems 17, no. 2 (March 1, 2020): 172988142092165. http://dx.doi.org/10.1177/1729881420921653.

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With the rapid development of information technology and the arrival of the era of big data, people’s access to information is increasingly relying on information such as images. Today, image data are showing an increasing trend in the form of an index. How to use deep learning models to extract valuable information from massive data is very important. In the face of such a situation, people cannot accurately and timely find out the information they need. Therefore, the research on image retrieval technology is very important. Image retrieval is an important technology in the field of computer vision image processing. It realizes fast and accurate query of similar images in image database. The excellent feature representation not only can represent the category information of the image but also capture the relevant semantic information of the image. If the neural network feature learning expression is combined with the image retrieval field, it will definitely improve the application of image retrieval technology. To solve the above problems, this article studies the problems encountered in deep learning neural network feature learning based on image self-encoding and discusses its feature expression in the field of image retrieval. By adding the spatial relationship information obtained by image self-encoding in the neural network training process, the feature expression ability of the selected neural network is improved, and the neural network feature learning based on image coding is successfully applied to the popular field of image retrieval.

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Mu, Shao-Shuo, Ye Zhang, Ping Jia, Xun Yang, and Xiao-Feng Qiu. "Multiframe Superresolution Reconstruction Based on Self-Learning Method." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/181864.

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One category of the superresolution algorithms widely used in practical applications is dictionary-based superresolution algorithms, which constructs a single high-resolution (HR) and high-clarity image from multiple low-resolution (LR) images. Despite the fact that general dictionary-based superresolution algorithms obtain redundant dictionaries from numerous HR-LR images, HR image distortion is unavoidable. To solve this problem, this paper proposes a multiframe superresolution reconstruction based on self-learning methods. First, multiple images from the same scene are selected to be both input and training images, and larger-scale images, which are also involved in the training set, are constructed from the learning dictionary. Then, different larger-scale images are constructed via repetition of the first step and the initial HR sets whose scale closely approximates that of the target HR image are finally obtained. Lastly, initial HR images are fused into one target HR image under the NLM idea, while the IBP idea is adopted to meet the global constraint. The simulation results demonstrate that the proposed algorithm produces more accurate reconstructions than those produced by other general superresolution algorithms, while, in real scene experiments, the proposed algorithm can run well and create clearer HR images from input images captured by cameras.

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Kersten, Saskia, and Netaya Lotze. "Creating a Self-Image." Journal für Medienlinguistik 2, no. 2 (July 3, 2020): 123–56. http://dx.doi.org/10.21248/jfml.2019.20.

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In this article, we build on research arguing that linguistic self-representation on social media can be viewed as a form of face-work and that the strategies employed by users are influenced by both a desire to connect with others and a need to preserve privacy. Drawing on our own analyses of usernames as well as that of others which were conducted as part of a large-scale project investigating usernames in 14 languages (Schlobinski/T. Siever 2018a), we argue that these conflicting goals of wanting to be recognised as an authentic member of an in-group while retaining a degree of anonymity are also observable in the choice of username. Online self-naming can thus be viewed as a key practice in the debate of face-work on social media platforms, because names and naming strategies can be studied more readily than broader and more complex aspects, such as stylistic variation or text-image interdependence, while at the same time forming part of these.

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Kersten, Saskia, and Netaya Lotze. "Creating a Self-Image." Journal für Medienlinguistik 2, no. 2 (July 3, 2020): 123–56. http://dx.doi.org/10.21248/jfml.2019.20.

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In this article, we build on research arguing that linguistic self-representation on social media can be viewed as a form of face-work and that the strategies employed by users are influenced by both a desire to connect with others and a need to preserve privacy. Drawing on our own analyses of usernames as well as that of others which were conducted as part of a large-scale project investigating usernames in 14 languages (Schlobinski/T. Siever 2018a), we argue that these conflicting goals of wanting to be recognised as an authentic member of an in-group while retaining a degree of anonymity are also observable in the choice of username. Online self-naming can thus be viewed as a key practice in the debate of face-work on social media platforms, because names and naming strategies can be studied more readily than broader and more complex aspects, such as stylistic variation or text-image interdependence, while at the same time forming part of these.

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Hirooka, Yoshiki. "Structure of self-image." Journal of Medical Ultrasonics 49, no. 2 (April 2022): 315. http://dx.doi.org/10.1007/s10396-022-01211-9.

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Bertrand, Marie-Andrée. "Self-Image and Delinquency." Acta Criminologica 2, no. 1 (January 19, 2006): 71–144. http://dx.doi.org/10.7202/017007ar.

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RésuméIMAGE DE SOI ET CRIMINALITECet article represente la seconde partie d'une etude en deux tranches du phenomene de la delinquance et de la criminalite feminines au Canada, aux Etats-Unis, en France et en Belgique, intitulee: Self-Image and Social Representations of Female Offenders, du meme auteur.La premiere partie s'attache a la valeur de « representation sociale » de la criminalite. On y etudie le volume relatif de la criminalite des femmes, la nature specifique des delits et crimes qu'elles commettent et pour lesquels elles sont arretees et inculpees, le traitement qu'on leur fait subir, compare aux dispositions prises a l'endroit des criminels de sexe masculin trouves coupablesdes memes mefaits, les dispositions particulieres des codes criminels qui, en plusieurs cas, prevoient des delits limites aux femmes mais aussi les excluent comme auteurs possibles de plusieurs crimes. Ce sont la des indices des roles assignes aux femmes dans une societe donnee.Les representations sociales ainsi analysees nous ont suggere que non seulement les lois et les sanctions prevues, mais aussi le choix des penalites imposees au moment du prononce de la sentence offrent la meilleure explication de ce taux comparativement tres bas et relativement constant de la criminalite feminine a travers le monde. Ces representations sociales sont des renforcements de roles precedemment prescrits a la femme. Ainsi, la theorie du role (role theory) nous semble la meilleure base d'explication de cet ecart entre la criminalite masculine et la criminalite feminine.La seconde partie, dont le present article est tire, resume une recherche empirique qui a dure pres d'une annee (aout 1966, juin 1967).InstrumentPour mesurer la perception de soi, nous avons utilise un questionnaire bref et direct compose essentiellement de quatre parties. La premiere partie fait appel, chez le repondant, a des donnees conscientes, en l'amenant a decrire la decision la plus importante qu'il juge avoir prise au cours des quelques dernieres annees et les motifs qui ont inspire cette decision. La deuxieme et la troisieme parties referent a du materiel psychologique (intra-psychique) preconscient ou inconscient, par mode de projection, c'est-a-dire que le repondant choisit de nommer les « grandes figures » de bienfaiteur (personnel ou non personnel) et de malfaiteur, resume les «grands gestes» qu'il leur attribue et donne sa perception de leur motivation. La derniere partie est constituee par une fiche bio-socio-psycho-educationnelle ou petite histoire de cas en resume.Rationnel de l'instrument: cet instrument d'analyse est base sur une polarite bien decrite dans l'oeuvre du psychologue et psychanalyste Erikson (1964). Il s'agit d'un continuum allant de la notion d'agent a celle de patient: « agens vs patiens ». Cette polarite est reprise dans les travaux de R.R. Korn (1966) dans les termes suivants: «agent-acteur vs object-spectateur ».Quelle est la signification precise des categories ainsi nommees ? XJagent est, pour Erikson et pour Korn, celui qui se percoit comme capable d influencer le monde, les evenements, les personnes. Il a une prise sur sa vie. Il ne se sent pas « brise » dans ses elans (« unbroken in initiative »). L'objet est celui a qui les choses arrivent («to whom things happen»), celui qui se sent pousse par des forces, internes ou externes, a poser des gestes qui lui paraissent inevitables.Hypotheses: nous avons choisi cet instrument a cause de nos deux grandes hypotheses de depart, l'une etant la condition sociale faite a la femme dans les societes dominees par l'homme (condition d'instrument, d'objet), l'autre etant la position sociale de la femme criminelle et de la jeune fille delinquante dans les societes structurees, position determinee par les codes penaux et par l'organisation repressive, mais aussi par la culture qui privilegie certaines valeurs et fait de la femme leur gardienne (position d'instrument mais aussi de victime). La condition sociale de la femme normale et la position sociale de la femme criminelle sont des « miroirs » (« looking-glass self »), selon la theorie de G.H. Mead, « miroirs » dans lesquels la femme normale et la delinquante trouvent une image d'elles-memes.ResultatsNos resultats peuvent se resumer comme suit: Premiere hypothese: « Les femmes adultes normales d'une societe donnee se percoivent moins que les hommes de la meme couche socio-economique et du meme groupe d'age, comme des agents. » Cette hypothese ne s'est pas verifiee en ce qui touche les Canadiennes francaises. La difference entre hommes et femmes n'est pas significative dans ce groupe. Notre hypothese s'est verifiee chez les Canadiens anglais mais a un niveau de signification peu eleve (x2:0.20). Seconde hypothese: «Les femmes adultes criminelles se percoivent plus comme desobjets et des victimes que les non criminelles d'une part et que les hommes criminels d'autre part. » Cette hypothese s'est verifiee statistiquement et la difference est tres significative dans le premier cas (0.01) et un peu moins dans le second (0.10).Il ressort que si la femme non delinquante, suivant le test « agent-objet », ne se percoit pas de facon sensiblement differente de l'homme non delinquant, par contre la femme criminelle, elle, se percoit nettement comme un objet-spectateur, comme une victime, plus que l'homme criminel et beaucoup plus que la femme non delinquante.

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29

Astrachan, Owen, and Peter Denning. "Innovating our self image." ACM SIGCSE Bulletin 40, no. 1 (February 29, 2008): 178–79. http://dx.doi.org/10.1145/1352322.1352195.

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30

Barber, Jenny. "A positive self-image." 5 to 7 Educator 2010, no. 64 (April 2010): 34–35. http://dx.doi.org/10.12968/ftse.2010.9.4.46950.

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Benziman, Yotam. "Integrity and Self Image." Philosophy in the Contemporary World 24, no. 1 (2017): 29–39. http://dx.doi.org/10.5840/pcw20172413.

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Negrin, Llewellyn. "The Self as Image." Theory, Culture & Society 16, no. 3 (June 1999): 99–118. http://dx.doi.org/10.1177/02632769922050638.

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Atwell, C. L., D. Rigassio Radler, J. Ziegler, R. Touger-Decker, and H. Khan. "Body Image Self-Perception." Journal of the American Dietetic Association 109, no. 9 (September 2009): A76. http://dx.doi.org/10.1016/j.jada.2009.06.246.

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Øyhus, Arne Olav. "The Entrepreneurial Self-Image." Journal of Entrepreneurship 12, no. 2 (September 2003): 201–23. http://dx.doi.org/10.1177/097135570301200203.

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35

Nangle, Julian. "Fear and Self-Image." Self & Society 25, no. 1 (March 1997): 34–35. http://dx.doi.org/10.1080/03060497.1997.11085719.

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36

Afsaruddin, Asma. "The Qur'an's Self-Image." American Journal of Islam and Society 19, no. 2 (April 1, 2002): 129–31. http://dx.doi.org/10.35632/ajis.v19i2.1950.

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This is a well-researched and carefully thought out book on the highly complex issue of the Qur' an 's self-referential terms to its own status as Scripture. Particularly illuminating are the author, Daniel Madigan's, clear and profound engagements with the semantic content of key Qur'anic words like kitab, mushaf, qur'an, dhikr, tanzil. and wahy, and his discussion of the inter-relatedness of these tenns. Madigan successfully problematizes particularly the key terms kitab and Qur'an since, as he shows, their meanings can be fairly fluid and their essence cannot be easily and crudely reduced to a rigid demarcation between orality and "writtenness" alone. A central focus of his book is indeed the tension between the orality and the written nature of Islam's sacred scripture, already suggested in the name given to it, al-Qur'an, which itself may be translated as ''the Recitation," and "the Reading." Madigan stresses the primacy of the oral nature of the Qur'an; in his (rather brief) discussion of the terms kalam Allah (the speech of God) and kitabAl/ah (the book of God), he states, ... the focus on the ontological status of the Qur'an [as represented in the usage of the term kalam Allah] may be not merely the result of speculation but rather an attempt to recover something that was lost when the concepts of kitab Allah and Qur'an were collapsed into the content of the mushaf. Chapters 2 - 4 provide a fine and nuanced exposition of the Qur'anic conception of kitab, which, as Madigan persuasively suggests, has to do with divine, timeless authority becoming manifest in the human, timebound world. The difference between Qur'an and kitab is therefore, not merely a question of display or storage, through the medium of the human voice in the fonner and through written composition in the latter, but has to do rather with the Qur'an's origin, that is, "its author and the source of its composition." ...

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37

Kirkcaldy, Bruce. "Self-image and personality." Personality and Individual Differences 11, no. 4 (1990): 321–26. http://dx.doi.org/10.1016/0191-8869(90)90213-b.

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GEORGANDA, EUGENIA T. "The Impact of Thalassemia on Body Image, Self-Image, and Self-Esteem." Annals of the New York Academy of Sciences 612, no. 1 Sixth Cooley' (December 1990): 466–72. http://dx.doi.org/10.1111/j.1749-6632.1990.tb24334.x.

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Chen, Liwei, Tieshen Wang, and Haifeng Zhu. "Subpixel Mapping Algorithms Based on Block Structural Self-Similarity Learning." Mathematical Problems in Engineering 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/5254024.

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Subpixel mapping (SPM) algorithms effectively estimate the spatial distribution of different land cover classes within mixed pixels. This paper proposed a new subpixel mapping method based on image structural self-similarity learning. Image structure self-similarity refers to similar structures within the same scale or different scales in image itself or its downsampled image, which widely exists in remote sensing images. Based on the similarity of image block structure, the proposed method estimates higher spatial distribution of coarse-resolution fraction images and realizes subpixel mapping. The experimental results show that the proposed method is more accurate than existing fast subpixel mapping algorithms.

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40

Dear, David. "Chinese Yangsheng: Self-help and Self-image." Asian Medicine 7, no. 1 (January 1, 2012): 1–33. http://dx.doi.org/10.1163/15734218-12341242.

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Tai, Wei-Liang, and Zi-Jun Liao. "Image self-recovery with watermark self-embedding." Signal Processing: Image Communication 65 (July 2018): 11–25. http://dx.doi.org/10.1016/j.image.2018.03.011.

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Yuan, Wei, Han Liu, Lili Liang, and Wenqing Wang. "Learning the Hybrid Nonlocal Self-Similarity Prior for Image Restoration." Mathematics 12, no. 9 (May 6, 2024): 1412. http://dx.doi.org/10.3390/math12091412.

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As an immensely important characteristic of natural images, the nonlocal self-similarity (NSS) prior has demonstrated great promise in a variety of inverse problems. Unfortunately, most current methods utilize either the internal or the external NSS prior learned from the degraded image or training images. The former is inevitably disturbed by degradation, while the latter is not adapted to the image to be restored. To mitigate such problems, this work proposes to learn a hybrid NSS prior from both internal images and external training images and employs it in image restoration tasks. To achieve our aims, we first learn internal and external NSS priors from the measured image and high-quality image sets, respectively. Then, with the learned priors, an efficient method, involving only singular value decomposition (SVD) and a simple weighting method, is developed to learn the HNSS prior for patch groups. Subsequently, taking the learned HNSS prior as the dictionary, we formulate a structural sparse representation model with adaptive regularization parameters called HNSS-SSR for image restoration, and a general and efficient image restoration algorithm is developed via an alternating minimization strategy. The experimental results indicate that the proposed HNSS-SSR-based restoration method exceeds many existing competition algorithms in PSNR and SSIM values.

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Litt, Iris F. "Self-graffiti?, self-image?, self-destruction?: Tattoos and adolescents." Journal of Adolescent Health 15, no. 3 (May 1994): 198. http://dx.doi.org/10.1016/1054-139x(94)90503-7.

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Chen, Hong, Li Xu, Bing Luo, Shuai Du, Kunshu Xiao, and Siwei Li. "Image Super-Resolution Based on Additional Self-Loop Supervision." Journal of Physics: Conference Series 2025, no. 1 (September 1, 2021): 012060. http://dx.doi.org/10.1088/1742-6596/2025/1/012060.

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Abstract Relying on the prevalence of deep neural networks, only one image super-resolution (SR) research has taken a big step forward. Many network models use the difference between the real image and the generated super-resolution image as a loss function, ignoring the difference between the small-scale image and the super-resolution generated image. We adopt an end-to-end network model with two-way supervision, which can ensure that the image content is similar in both large and small scales. Only real natural images are used for supervision. As the scale of the image becomes larger, the difference becomes larger, and the effect of correcting image details becomes smaller. Adding the loss between the input image and the degraded image of the generated image can not only keep the content of the image similar on a small scale, but also ensure the image details. Compared with several state-of-the-arts, our method obtains the best results.

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Vivekanand, M., and S. R. Kulkarni. "Self-noise in interferometers." International Astronomical Union Colloquium 131 (1991): 1–5. http://dx.doi.org/10.1017/s0252921100012975.

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AbstractWe study the distribution of noise in images synthesized by radio interferometers, focusing particularly on the noise due to source power fluctuations. For a total power interferometer, the r.m.s. fluctuation in the image consists of a term that is constant all over the map, which is the uncorrelated system noise divided by the number of telescopes in the interferometer, and a term that is the image itself. For a correlation interferometer the expression is not so simple but is qualitatively similar. Our results are consistent with intuitive expectations in various observing situations. We find similarities in self-noise at optical and radio wavelengths.

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Yang, Jinhai, Mengxi Guo, Shijie Zhao, Junlin Li, and Li Zhang. "Self-Asymmetric Invertible Network for Compression-Aware Image Rescaling." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (June 26, 2023): 3155–63. http://dx.doi.org/10.1609/aaai.v37i3.25420.

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High-resolution (HR) images are usually downscaled to low-resolution (LR) ones for better display and afterward upscaled back to the original size to recover details. Recent work in image rescaling formulates downscaling and upscaling as a unified task and learns a bijective mapping between HR and LR via invertible networks. However, in real-world applications (e.g., social media), most images are compressed for transmission. Lossy compression will lead to irreversible information loss on LR images, hence damaging the inverse upscaling procedure and degrading the reconstruction accuracy. In this paper, we propose the Self-Asymmetric Invertible Network (SAIN) for compression-aware image rescaling. To tackle the distribution shift, we first develop an end-to-end asymmetric framework with two separate bijective mappings for high-quality and compressed LR images, respectively. Then, based on empirical analysis of this framework, we model the distribution of the lost information (including downscaling and compression) using isotropic Gaussian mixtures and propose the Enhanced Invertible Block to derive high-quality/compressed LR images in one forward pass. Besides, we design a set of losses to regularize the learned LR images and enhance the invertibility. Extensive experiments demonstrate the consistent improvements of SAIN across various image rescaling datasets in terms of both quantitative and qualitative evaluation under standard image compression formats (i.e., JPEG and WebP). Code is available at https://github.com/yang-jin-hai/SAIN.

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Van, Thanh The, and Thanh Manh Le. "Content-based image retrieval using a signature graph and a self-organizing map." International Journal of Applied Mathematics and Computer Science 26, no. 2 (June 1, 2016): 423–38. http://dx.doi.org/10.1515/amcs-2016-0030.

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Abstract In order to effectively retrieve a large database of images, a method of creating an image retrieval system CBIR (contentbased image retrieval) is applied based on a binary index which aims to describe features of an image object of interest. This index is called the binary signature and builds input data for the problem of matching similar images. To extract the object of interest, we propose an image segmentation method on the basis of low-level visual features including the color and texture of the image. These features are extracted at each block of the image by the discrete wavelet frame transform and the appropriate color space. On the basis of a segmented image, we create a binary signature to describe the location, color and shape of the objects of interest. In order to match similar images, we provide a similarity measure between the images based on binary signatures. Then, we present a CBIR model which combines a signature graph and a self-organizing map to cluster and store similar images. To illustrate the proposed method, experiments on image databases are reported, including COREL,Wang and MSRDI.

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Nguyen, Toan Duc, Duc-Tai Le, Junghyun Bum, Seongho Kim, Su Jeong Song, and Hyunseung Choo. "Self-FI: Self-Supervised Learning for Disease Diagnosis in Fundus Images." Bioengineering 10, no. 9 (September 16, 2023): 1089. http://dx.doi.org/10.3390/bioengineering10091089.

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Self-supervised learning has been successful in computer vision, and its application to medical imaging has shown great promise. This study proposes a novel self-supervised learning method for medical image classification, specifically targeting ultra-wide-field fundus images (UFI). The proposed method utilizes contrastive learning to pre-train a deep learning model and then fine-tune it with a small set of labeled images. This approach reduces the reliance on labeled data, which is often limited and costly to obtain, and has the potential to improve disease detection in UFI. This method employs two contrastive learning techniques, namely bi-lateral contrastive learning and multi-modality pre-training, to form positive pairs using the data correlation. Bi-lateral learning fuses multiple views of the same patient’s images, and multi-modality pre-training leverages the complementary information between UFI and conventional fundus images (CFI) to form positive pairs. The results show that the proposed contrastive learning method achieves state-of-the-art performance with an area under the receiver operating characteristic curve (AUC) score of 86.96, outperforming other approaches. The findings suggest that self-supervised learning is a promising direction for medical image analysis, with potential applications in various clinical settings.

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49

Heikkilä, Martta. "From the Self-Image to the Image Itself." Glimpse 22, no. 1 (2021): 18–22. http://dx.doi.org/10.5840/glimpse20212214.

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In this article, I examine the idea of the portrait from two viewpoints: the ‘classical’ portrait as it appears in Jean-Luc Nancy’s post-phenomenological philosophy, and the recent self-portrait photographs or ‘selfies’ on social media. First, I consider the portrait’s value in Nancy’s theories of art: for him, portraits hold an important position among the genres of visual art, since they present themselves as distinctive images by extracting the innermost force of the portrayed person. Secondly, I take up the philosophical and political implications of Nancy’s notion of the portrait vis-a-vis the contemporary selfie culture. I suggest that, instead of emphasizing the model’s singularity as traditional artistic portraits do, the flow of selfies tends to create similarity. I begin by clarifying Nancy’s paradoxical claim that the human portrait may resemble a person only on the condition of not representing him or her. After this, I inquire about the philosophical position of selfies as constructed portraits that make visible the absence of the self. However, as I argue, they do this in a sense that differs from Nancy’s account of the portrait. As a result, I propose that the repetition and circulation of selfies has remarkably changed our view on the significance and, finally, the ontology of the portrait.

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50

Kracsay, Susanne. "Self Image ‐ Alien Image: A Bilateral Video Project." Educational Media International 32, no. 2 (June 1995): 93–96. http://dx.doi.org/10.1080/0952398950320209.

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Journal articles on the topic 'Self image'

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