Journal articles: 'Recognition of difference'

1

Lash, Scott, and Mike Featherstone. "Recognition and Difference." Theory, Culture & Society 18, no. 2-3 (June 2001): 1–19. http://dx.doi.org/10.1177/02632760122051751.

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2

Jones, Peter. "Equality, Recognition and Difference." Critical Review of International Social and Political Philosophy 9, no. 1 (March 2006): 23–46. http://dx.doi.org/10.1080/13698230500475457.

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MIYACHI, Hideo, and Nobuyuki TANIGUCHI. "Recognition Difference between Visualization Methods." Journal of the Visualization Society of Japan 24, Supplement2 (2004): 97–100. http://dx.doi.org/10.3154/jvs.24.supplement2_97.

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Lewin, Catharina, and Agneta Herlitz. "Sex differences in face recognition—Women’s faces make the difference." Brain and Cognition 50, no. 1 (October 2002): 121–28. http://dx.doi.org/10.1016/s0278-2626(02)00016-7.

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Davies, Bronwyn, Elisabeth De Schauwer, Lien Claes, Katrien De Munck, Inge Van De Putte, and Meggie Verstichele. "Recognition and difference: a collective biography." International Journal of Qualitative Studies in Education 26, no. 6 (July 2013): 680–91. http://dx.doi.org/10.1080/09518398.2013.788757.

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Wang, Liangliang, Ruifeng Li, and Yajun Fang. "Power difference template for action recognition." Machine Vision and Applications 28, no. 5-6 (June 14, 2017): 463–73. http://dx.doi.org/10.1007/s00138-017-0848-0.

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Vincent, Kerry. "Responding to schoolgirl pregnancy: the recognition and non-recognition of difference." Improving Schools 12, no. 3 (November 2009): 225–36. http://dx.doi.org/10.1177/1365480209342648.

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Teenage mothers in the UK have been found to be at risk of early school leaving, low levels of educational achievement and low levels of post-compulsory educational participation. Current policy in the UK emphasizes the importance of education as a way of improving the life chances of those who become pregnant while young and, as part of that, schools are encouraged to support the educational inclusion of those who become pregnant while still of statutory school age. Drawing on repeat qualitative interviews conducted over a 15-month period, this article examines the educational experiences of a group of students in one local authority in England who became pregnant while still at school. Particular attention is paid to how different schools addressed the ‘dilemma of difference’ posed by teenage pregnancy and how school attitudes and practices enhanced or inhibited educational participation.

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Wilson, Richard H., Bette A. Civitelld, and Robert H. Margolish. "Influence of Interaural Level Differences on the Speech Recognition Masking Level Difference." International Journal of Audiology 24, no. 1 (January 1985): 15–24. http://dx.doi.org/10.3109/00206098509070093.

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Salthouse, Timothy A., and Karen L. Siedlecki. "An Individual Difference Analysis of False Recognition." American Journal of Psychology 120, no. 3 (October 1, 2007): 429. http://dx.doi.org/10.2307/20445413.

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Piao, Nanzhou, and Rae-Hong Park. "Face Recognition Using Dual Difference Regression Classification." IEEE Signal Processing Letters 22, no. 12 (December 2015): 2455–58. http://dx.doi.org/10.1109/lsp.2015.2492980.

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Agyeman, Julian, and Jennifer Sien Erickson. "Culture, Recognition, and the Negotiation of Difference." Journal of Planning Education and Research 32, no. 3 (April 10, 2012): 358–66. http://dx.doi.org/10.1177/0739456x12441213.

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McCluskey, Gillean, Sheila Riddell, Elisabet Weedon, and Mariela Fordyce. "Exclusion from school and recognition of difference." Discourse: Studies in the Cultural Politics of Education 37, no. 4 (August 24, 2015): 529–39. http://dx.doi.org/10.1080/01596306.2015.1073015.

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13

Harris, C. "Beyond multiculturalism? Difference, recognition and social justice." Patterns of Prejudice 35, no. 1 (January 2001): 13–34. http://dx.doi.org/10.1080/003132201128811043.

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Kang, Oh-Han. "Analysis of Recognition Difference Regarding Informatics Textbook." Journal of the Korea Industrial Information Systems Research 16, no. 2 (June 30, 2011): 191–98. http://dx.doi.org/10.9723/jksiis.2011.16.2.191.

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Wen, Ying, Lianghua He, and Pengfei Shi. "Face recognition using difference vector plus KPCA." Digital Signal Processing 22, no. 1 (January 2012): 140–46. http://dx.doi.org/10.1016/j.dsp.2011.08.004.

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S.G, Siddharth, Arunkumar M, and Valarmathi S. "Authentication by Palmprint Recognition using Phase-Difference Trained by Probability Neural Network." SIJ Transactions on Computer Networks & Communication Engineering 02, no. 01 (February 13, 2014): 01–04. http://dx.doi.org/10.9756/sijcnce/v2i1/0201510101.

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17

Stone, Alison. "Hegel's Dialectic and the Recognition of Feminine Difference." Philosophy Today 47, no. 9999 (2003): 132–39. http://dx.doi.org/10.5840/philtoday200347supplement17.

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18

Taniguchi, Kosuke. "Difference in shape information correlated to object recognition." Proceedings of the Annual Convention of the Japanese Psychological Association 81 (September 20, 2017): 3B—039–3B—039. http://dx.doi.org/10.4992/pacjpa.81.0_3b-039.

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19

Lee, Yejin, and Kwang Tae Jung. "Gender Difference on Recognition Characteristics of Tactile Stimuli." Journal of the Ergonomics Society of Korea 38, no. 6 (December 31, 2019): 517–26. http://dx.doi.org/10.5143/jesk.2019.38.6.517.

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20

Moore, Henrietta L. "Difference and Recognition: Postmillennial Identities and Social Justice." Signs: Journal of Women in Culture and Society 25, no. 4 (July 2000): 1129–32. http://dx.doi.org/10.1086/495532.

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21

Sui, Jie, Chang Hong Liu, and Shihui Han. "Cultural difference in neural mechanisms of self-recognition." Social Neuroscience 4, no. 5 (October 2009): 402–11. http://dx.doi.org/10.1080/17470910802674825.

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22

Chen, Si, and Yan Yan. "Discriminative local difference patterns for robust face recognition." Electronics Letters 51, no. 25 (December 2015): 2108–9. http://dx.doi.org/10.1049/el.2015.2802.

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Badrinath, G. S., and Phalguni Gupta. "Palmprint based recognition system using phase-difference information." Future Generation Computer Systems 28, no. 1 (January 2012): 287–305. http://dx.doi.org/10.1016/j.future.2010.11.029.

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Pan, Hang, Lun Xie, Juan Li, Zeping Lv, and Zhiliang Wang. "Micro‐expression recognition by two‐stream difference network." IET Computer Vision 15, no. 6 (March 30, 2021): 440–48. http://dx.doi.org/10.1049/cvi2.12030.

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25

Ning, Xiaoyan. "A Novel Recognition Algorithm for XQPSK Based on Phase Difference Annular Statistics and SVM." International Journal of Signal Processing Systems 7, no. 1 (March 2019): 1–6. http://dx.doi.org/10.18178/ijsps.7.1.1-6.

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26

Bohušová, Hana. "Development of the common standard for revenue recording as a part of the US GAAP and IAS/IFRS systems convergence." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 57, no. 6 (2009): 29–44. http://dx.doi.org/10.11118/actaun200957060029.

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The most significant difference between US GAAP and IFRSs is in the area of general approach. IFRSs are based on basic accounting principles1 with limited application guidance, US GAAPs are based especially on rules with specific application guidance. FASB and IASB initiated their joint project on revenue recording to converge IFRS and US GAAP in this area. The main objective of this paper is comparative analysis of revenue recognition under both systems, evaluation of the most significant differences in revenue recognition and measurements as a starting point for the preparation of the new general standard for revenue recognition and the new approach to the revenue recognition development.In this paper, the current approaches to revenue recognition under both systems are compared. The most significant difference is the general approach to revenue recognition. There is the Conceptual Framework where revenue is defined, two standards on revenue recognition and interpretations concerning revenue recognition and measurement in the IAS/IFRS. On the other hand, there are many standards and guidance concerning revenue in the US GAAP. Revenue is defined in the Statements of Financial Accounting concepts (CON 5, CON 6). There is not any general standard for revenue recognition under the US GAAP. The most significant differences in revenue recognition concern the long-term contracts and deferred payments. Despite this difference, there are many similarities between both systems.Based on the results of the comparative analysis which was done in the paper, a new approach for revenue recognition based on principles for the new general standard for revenue recognition common for both systems is being developed.

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27

Bauman, Zygmunt. "The Great War of Recognition." Theory, Culture & Society 18, no. 2-3 (June 2001): 137–50. http://dx.doi.org/10.1177/02632760122051823.

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With the removal of the ‘final state’ vision from the perception of historical process recasts the coexistence of (proliferating) differences as a perpetual condition of modernity. Given that ‘difference’ masks all too often inequality, perpetuity of the ‘wars of recognition’ is therefore a likely prospect, since the instability of all extant and emerging power settings triggers reconnaissance-through-battle. The politics of recognition, though, tends to be viewed and practiced, wrongly, as an alternative rather than complement of distributive justice, thereby inflaming rather than mollifying the intensity of ‘recognition wars’.

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28

Lu, Yu-An. "The effect of dialectal variation on word recognition." Language and Linguistics / 語言暨語言學 20, no. 4 (September 24, 2019): 535–68. http://dx.doi.org/10.1075/lali.00048.lu.

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Abstract Previous studies on Chinese dialect variation have mostly focused on the description of dialects, the regions where these dialects are spoken, attitudes towards dialects, and acoustic differences across dialects. The present study draws on experimental evidence concerning a vowel difference in two Taiwan Southern Min (TSM) dialects to provide more understanding on how non-contrastive, dialectal variations may affect speakers’ processing of speech. The variation of interest is a phonemic difference, [ə] and [ɔ], in the vowel inventory in two TSM dialects, in which the difference signals a lexical contrast in one dialect (e.g. [ə-a] ‘oyster’ vs. [ɔ-a] ‘taro’) but not in the other ([ɔ-a] ‘oyster, taro’). A long-term repetition-priming experiment investigating the word recognition involving the two vowels revealed a dialect effect on TSM speakers’ word recognition in accordance with prior exposure, native-ness and variant frequency. Implications of the findings are provided.

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29

Foerch, Christian, Bjoern Misselwitz, Matthias Sitzer, Klaus Berger, Helmuth Steinmetz, and Tobias Neumann-Haefelin. "Difference in recognition of right and left hemispheric stroke." Lancet 366, no. 9483 (July 2005): 392–93. http://dx.doi.org/10.1016/s0140-6736(05)67024-9.

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30

Liu, Yong-jun, Cai-kou Chen, and Zheng-qun Wang. "Modified Maximum Scatter-difference Discriminant Analysis and Face Recognition." Journal of Electronics & Information Technology 30, no. 1 (February 17, 2011): 190–93. http://dx.doi.org/10.3724/sp.j.1146.2006.00811.

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31

Qi, Yong Feng, and Yuan Lian Huo. "Locality Preserving Maximum Scatter Difference Projection for Face Recognition." Applied Mechanics and Materials 411-414 (September 2013): 1179–84. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1179.

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Maximum Scatter Difference (MSD) aims to preserve discriminant information of sample space, but it fails to find the essential structure of the samples with nonlinear distribution. To overcome this problem, an efficient feature extraction method named as Locality Preserving Maximum Scatter Difference (LPMSD) projection is proposed in this paper. The new algorithm is developed based on locality preserved embedding and MSD criterion. Thus, the proposed LPMSD not only preserves discriminant information of sample space but also captures the intrinsic submanifold of sample space. Experimental results on ORL, Yale and CAS-PEAL face database indicate that the LPMSD method outperforms the MSD, MMSD and LDA methods under various experimental conditions.

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32

Chen, Jingying, Ruyi Xu, and Leyuan Liu. "Deep peak-neutral difference feature for facial expression recognition." Multimedia Tools and Applications 77, no. 22 (March 30, 2018): 29871–87. http://dx.doi.org/10.1007/s11042-018-5909-5.

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33

Yueming Wang, Jianzhuang Liu, and Xiaoou Tang. "Robust 3D Face Recognition by Local Shape Difference Boosting." IEEE Transactions on Pattern Analysis and Machine Intelligence 32, no. 10 (October 2010): 1858–70. http://dx.doi.org/10.1109/tpami.2009.200.

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34

Liu, Q., X. Tang, H. Lu, and S. Ma. "Face Recognition Using Kernel Scatter-Difference-Based Discriminant Analysis." IEEE Transactions on Neural Networks 17, no. 4 (July 2006): 1081–85. http://dx.doi.org/10.1109/tnn.2006.875970.

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35

Sheffer, Dan, and Dov Ingman. "The informational difference concept in analyzing target recognition issues." Journal of the Optical Society of America A 14, no. 7 (July 1, 1997): 1431. http://dx.doi.org/10.1364/josaa.14.001431.

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36

HATANO, Aya, Shinji KITAGAMI, and Jun KAWAGUCHI. "Effect of difference of verbalized information on facial recognition." Proceedings of the Annual Convention of the Japanese Psychological Association 77 (September 19, 2013): 3AM—097–3AM—097. http://dx.doi.org/10.4992/pacjpa.77.0_3am-097.

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37

Wu, Taixia, Bo Yuan, Shudong Wang, Guanghua Li, and Yong Lei. "A Normalized Difference Spectral Recognition Index for Azurite Pigment." Applied Spectroscopy 74, no. 5 (May 2020): 571–82. http://dx.doi.org/10.1177/0003702820909435.

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Hyperspectral technology is a nondestructive, fast, and reliable method for the detection and restoration of relics. Most of the band characteristics of mineral pigment are concentrated between 2200 and 2400 nm, and these data are expensive to obtain (the required imaging sensor is expensive). We are pursuing a hyperspectral index mean that can effectively distinguish pigments in shorter band ranges to achieve high application value that is much less expensive. In this study, based on the spectral features of azurite at 400–1500 nm, we created an azurite normalized difference spectral index (ANDSI) through feature band selection, derivation of characteristic formulae, and discrimination analysis. Reflectivity bands at 458, 806, and 1373 nm were selected to build the ANDSI. Azurite was compared with 25 other common pigments and it was found that the discrimination values between azurite and the other pigments exceeded 0.88 (where values >0.5 indicate discriminable pigments), demonstrating that the ANDSI is suitable for detecting azurite.

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38

Mike, Dennis G. "COMPUTER MEDIATED WORD RECOGNITION: POISED TO MAKE A DIFFERENCE?" Reading & Writing Quarterly 17, no. 1 (January 2001): 99–102. http://dx.doi.org/10.1080/10573560117720.

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39

Chen, Gang, and Feihu Qi. "Difference feature neural network in recognition of human faces." Journal of Electronics (China) 18, no. 2 (April 2001): 167–73. http://dx.doi.org/10.1007/s11767-001-0023-6.

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40

Li, Zhaokui, Yan Wang, Xing Zhou, Guohui Ding, Xiangbin Shi, and Runze Wan. "Mean Laplacian mappings-based difference LDA for face recognition." Multimedia Tools and Applications 76, no. 2 (January 11, 2016): 2243–65. http://dx.doi.org/10.1007/s11042-015-3207-z.

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41

Liu, Leyuan, Rubin Jiang, Jiao Huo, and Jingying Chen. "Self-Difference Convolutional Neural Network for Facial Expression Recognition." Sensors 21, no. 6 (March 23, 2021): 2250. http://dx.doi.org/10.3390/s21062250.

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Facial expression recognition (FER) is a challenging problem due to the intra-class variation caused by subject identities. In this paper, a self-difference convolutional network (SD-CNN) is proposed to address the intra-class variation issue in FER. First, the SD-CNN uses a conditional generative adversarial network to generate the six typical facial expressions for the same subject in the testing image. Second, six compact and light-weighted difference-based CNNs, called DiffNets, are designed for classifying facial expressions. Each DiffNet extracts a pair of deep features from the testing image and one of the six synthesized expression images, and compares the difference between the deep feature pair. In this way, any potential facial expression in the testing image has an opportunity to be compared with the synthesized “Self”—an image of the same subject with the same facial expression as the testing image. As most of the self-difference features of the images with the same facial expression gather tightly in the feature space, the intra-class variation issue is significantly alleviated. The proposed SD-CNN is extensively evaluated on two widely-used facial expression datasets: CK+ and Oulu-CASIA. Experimental results demonstrate that the SD-CNN achieves state-of-the-art performance with accuracies of 99.7% on CK+ and 91.3% on Oulu-CASIA, respectively. Moreover, the model size of the online processing part of the SD-CNN is only 9.54 MB (1.59 MB ×6), which enables the SD-CNN to run on low-cost hardware.

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42

Kang, Kimoon, and Hyun-Chool Shin. "EMG Based Gesture Recognition Using the Unbiased Difference Power." Applied Sciences 11, no. 4 (February 8, 2021): 1526. http://dx.doi.org/10.3390/app11041526.

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In this paper, we propose an unbiased difference power that is robust against noise as a feature for electromyography (EMG)-based gesture recognition. The proposed unbiased difference power is obtained by subtracting the noise-biased part from the difference power. We derive the difference power equation and discover that the difference power is biased by twice the noise power. For noise power estimation, we utilized the characteristics of the EMG signal and estimated the noise power from the resting period. For performance evaluation, we used EMG signals provided by the open source Ninapro project database. We used the recognition accuracy as an evaluation index. We compare the recognition accuracy of the case using the proposed unbiased feature with those of two conventional cases. Experimental results show that the proposed unbiased difference power improves the accuracy compared with conventional ones. As the noise level increases, cases where the proposed unbiased difference power is used show a clear improvement in accuracy compared with the two conventional cases. For the signal-to-noise ratio (SNR) of 0 dB, the proposed unbiased difference power improves the average accuracy by more than 12%.

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43

SÁNCHEZ, LYDIA, and MANUEL CAMPOS. "Object recognition and content." Empedocles: European Journal for the Philosophy of Communication 2, no. 2 (December 8, 2011): 207–26. http://dx.doi.org/10.1386/ejpc.2.2.207_1.

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Puzzles concerning attitude reports are at the origin of traditional theories of content. According to most of these theories, content has to involve some sort of conceptual entities, like senses, which determine reference. Conceptual views, however, have been challenged by direct reference theories and informational perspectives on content. In this paper we lay down the central elements of the more relevant strategies for solving cognitive puzzles. We then argue that the best solution available to those who maintain a view of content as truth conditions is to abandon the idea that content is the only element of mental attitudes that can make a difference as to the truth value of attitude reports. We finally resort to means of recognition of objects as one obvious element that helps explain differences in attitudes.

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44

Zhong, Ruan. "Image Color Recognition System Based on Labview." Applied Mechanics and Materials 713-715 (January 2015): 2073–76. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.2073.

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In order to solve the problem such as measuring errors and faults in the color detection because of fatigue and individual differences, and to solve the problems of poor relevant of equipment and weak data-processing ability on the color detection. The solution of images color difference recognition system based on Labview is proposed. The solution is establishing the system simulation platform on Labview software.

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45

Samus, Quincy M., Amrita Vavilikolanu, Lawrence Mayer, Matthew McNabney, Jason Brandt, Constantine G. Lyketsos, and Adam Rosenblatt. "Cohort differences in dementia recognition and treatment indicators among assisted living residents in Maryland: did a change in the resident assessment tool make a difference?" International Psychogeriatrics 25, no. 12 (September 23, 2013): 2047–56. http://dx.doi.org/10.1017/s1041610213001610.

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ABSTRACTBackground:There is a lack of empirical evidence about the impact of regulations on dementia care quality in assisted living (AL). We examined cohort differences in dementia recognition and treatment indicators between two cohorts of AL residents with dementia, evaluated prior to and following a dementia-related policy modification to more adequately assess memory and behavioral problems.Methods:Cross-sectional comparison of two AL resident cohorts was done (Cohort 1 [evaluated 2001–2003] and Cohort 2 [evaluated 2004–2006]) from the Maryland Assisted Living studies. Initial in-person evaluations of residents with dementia (n = 248) were performed from a random sample of 28 AL facilities in Maryland (physician examination, clinical characteristics, and staff and family recognition of dementia included). Adequacy of dementia workup and treatment was rated by an expert consensus panel.Results:Staff recognition of dementia was better in Cohort 1 than in Cohort 2 (77% vs. 63%, p = 0.011), with no significant differences in family recognition (86% vs. 85%, p = 0.680), or complete treatment ratings (52% vs. 64%, p = 0.060). In adjusted logistic regression, cognitive impairment and neuropsychiatric symptoms correlated with staff recognition; and cognitive impairment correlated with family recognition. Increased age and cognitive impairment reduced odds of having a complete dementia workup. Odds of having complete dementia treatment was reduced by age and having more depressive symptoms. Cohort was not predictive of dementia recognition or treatment indicators in adjusted models.Conclusions:We noted few cohort differences in dementia care indicators after accounting for covariates, and concluded that rates of dementia recognition and treatment did not appear to change much organically following the policy modifications.

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Zhao, Shuhuan, and Zheng-ping Hu. "Occluded Face Recognition Based on Double Layers Module Sparsity Difference." Advances in Electronics 2014 (August 18, 2014): 1–6. http://dx.doi.org/10.1155/2014/687827.

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Image recognition with occlusion is one of the popular problems in pattern recognition. This paper partitions the images into some modules in two layers and the sparsity difference is used to evaluate the occluded modules. The final identification is processed on the unoccluded modules by sparse representation. Firstly, we partition the images into four blocks and sparse representation is performed on each block, so the sparsity of each block can be obtained; secondly, each block is partitioned again into two modules. Sparsity of each small module is calculated as the first step. Finally, the sparsity difference of small module with the corresponding block is used to detect the occluded modules; in this paper, the small modules with negative sparsity differences are considered as occluded modules. The identification is performed on the selected unoccluded modules by sparse representation. Experiments on the AR and Yale B database verify the robustness and effectiveness of the proposed method.

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47

Souza, Pamela, Eric Hoover, and Frederick Gallun. "Application of the Envelope Difference Index to Spectrally Sparse Speech." Journal of Speech, Language, and Hearing Research 55, no. 3 (June 2012): 824–37. http://dx.doi.org/10.1044/1092-4388(2011/10-0301).

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Purpose Amplitude compression is a common hearing aid processing strategy that can improve speech audibility and loudness comfort but also has the potential to alter important cues carried by the speech envelope. In previous work, a measure of envelope change, the Envelope Difference Index (EDI; Fortune, Woodruff, & Preves, 1994), was moderately related to recognition of spectrally robust consonants. This follow-up study investigated the relationship between the EDI and recognition of spectrally sparse consonants. Method Stimuli were vowel-consonant-vowel tokens processed to reduce spectral cues. Compression parameters were chosen to achieve a range of EDI values. Recognition was measured for 20 listeners with normal hearing. Results Both overall recognition and perception of consonant features were reduced at higher EDI values. Similar effects were noted with noise-vocoded and sine-vocoded processing and regardless of whether periodicity cues were available. Conclusion The data provide information about the acceptable limits of envelope distortion under constrained conditions. These limits can be used to consider the impact of envelope distortions in situations where other cues are available to varying extents.

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Choi, Seong-Rak. "The Study on the Difference of Regulation Recognition among Generations." Journal of the Korea Contents Association 16, no. 3 (March 28, 2016): 586–95. http://dx.doi.org/10.5392/jkca.2016.16.03.586.

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49

Ebine, Hideyuki, and Osamu Nakamura. "The recognition of facial expressions considering the difference between individualities." IEEJ Transactions on Electronics, Information and Systems 119, no. 4 (1999): 474–81. http://dx.doi.org/10.1541/ieejeiss1987.119.4_474.

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50

Park, So-Young, Dong-Keun Kim, and Min-Cheol Whang. "Maximum Entropy-based Emotion Recognition Model using Individual Average Difference." Journal of the Korean Institute of Information and Communication Engineering 14, no. 7 (July 30, 2010): 1557–64. http://dx.doi.org/10.6109/jkiice.2010.14.7.1557.

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Journal articles on the topic 'Recognition of difference'

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