Computational Model for Image Processing in the Minds of People with Visual Agnosia using Fuzzy Cognitive Map
محورهای موضوعی : Data Mining
Elham Askari
1
,
Sara Motamed
2
1 - Department of Computer Engineering, Fouman and Shaft Branch, Islamic Azad University, Fouman, Iran
2 - Department of Computer Engineering, Fouman and Shaft Branch, Islamic Azad University, Fouman, Iran
کلید واژه: Visual Agnosia, Fuzzy Cognitive Mapping, Visual Model, Mind,
چکیده مقاله :
The Agnosia is a neurological condition that leads to an inability to name, recognize, and extract meaning from the visual, auditory, and sensory environment, despite the fact that the receptor organ is perfect. Visual agnosia is the most common type of this disorder. People with agnosia have trouble communicating between the mind and the brain. As a result, they cannot understand the images seen. In this paper, a model is proposed that is based on the visual pathway so that it first receives the visual stimulus and then, after understanding, the object is identified. In this paper, a model based on the visual pathway is proposed and using intelligent Fuzzy Cognitive Map will help improve image processing in the minds of these patients. First, the proposed model that is inspired by the visual perception pathway, is designed. Then, appropriate attributes that include the texture and color of the images are extracted and the concept of the seen image is perceived using Fuzzy Cognitive Mapping, the meaning recognition and the relationships between objects. This model reduces the difficulty of perceiving and recognizing objects in patients with visual agnosia. The results show that the proposed model, with 98.1% accuracy, shows better performance than other methods.
The Agnosia is a neurological condition that leads to an inability to name, recognize, and extract meaning from the visual, auditory, and sensory environment, despite the fact that the receptor organ is perfect. Visual agnosia is the most common type of this disorder. People with agnosia have trouble communicating between the mind and the brain. As a result, they cannot understand the images seen. In this paper, a model is proposed that is based on the visual pathway so that it first receives the visual stimulus and then, after understanding, the object is identified. In this paper, a model based on the visual pathway is proposed and using intelligent Fuzzy Cognitive Map will help improve image processing in the minds of these patients. First, the proposed model that is inspired by the visual perception pathway, is designed. Then, appropriate attributes that include the texture and color of the images are extracted and the concept of the seen image is perceived using Fuzzy Cognitive Mapping, the meaning recognition and the relationships between objects. This model reduces the difficulty of perceiving and recognizing objects in patients with visual agnosia. The results show that the proposed model, with 98.1% accuracy, shows better performance than other methods.
[1] M.J. Yang, T.J. Nail, J. Winer, “Left Parietal Tumors Presenting with Smartphone Icon Visual Agnosia: Two Cases of a Modern Presentation of Gerstmann Syndrome”, World Neurosurgery, Vol.142, 2020, pp. 233-238.
[2] N. Seijdel, H.S. Scholte, E.H.F.de. Haan, “Visual features drive the category-specific impairments on categorization tasks in a patient with object agnosia”, Neuropsychologia, Vol.161, 2021.
[3] MJ. Farah," Visual agnosia: Disorders of object recognition and what they tell us about normal vision", MIT Press, American Psychological Association, 1990, pp: 322-329.
[4] P.S. Vecera, S.K. Gilds, S, "What processing is impaired in appreceptive agnosia? Evidence from normal subjects", Journal of Cognitive Neuroscience, Vol. 10. NO. 5, 1998, pp.568–580.
[5] M. Goodale, AD. Milner AD, "Separate visual pathways for perception and action",Trends Neurosci, Vol. 15. No. 1, 1992, pp.5-20.
[6] E. Golderg,"Associative Agnosias and the functions of the left hemisphere",Vol. 12, 1990, pp: 467-484.
[7] R. Cecere, N. Dundon, C. Bertini,"When apperceptive agnosia is explained by a deficit of primary visual processing, Elsevier Ltd., 2014, pp:12-27.
[8] L. Iterson, L. T.L.Sie, P.B. Augustijn, A.C.S. Rooze, F.E. Jansen, “Acquired visual agnosia as an uncommon presentation of epileptic encephalopathy in a 6-year-old boy with CSWS”, Epilepsy & Behavior Reports, 2021, Vol.16.
[9] K. Erikssona, A. Inenb, K. Hirvonenc, P. Nieminena, M. Koivikko,"Visual agnosia in a child with non-lesional occipito-temporal CSWS", Brain and Development, Vol.25, No.4, 2003, pp.262–267.
[10] E. Askari, S.K. Setarehdan, A. Sheikhani, M.R. Mohamadi, M. Teshnelab, "Modeling the connections of brain regions in children with autism using cellular neural networks and electroencephalography analysis", Artificial Intelligence in Medicine, Vol. 89, pp.40-50.
[11] R. Ptak, F. Lazeyras, “Functional connectivity and the failure to retrieve meaning from shape in visual object agnosia”, Brain and Cognition, Vol.131, 2019, pp.94-101.
[12] X. Duanmu, "Image Retrieval Using Color Moment Invariant", in Seventh International Conference on Information Technology,2010, pp: 200-203.
[13] F. yaghouti, S. motamed. “Recognition of Facial Expression of Emotions Based on Brain Emotional Learning (BEL) Model”, Advances in Cognitive Science, Vol. 20, No. 4, 2019, pp.46-61.
[14] J. Morén, “Emotion and learning- a computational model of the Amygdala”, Ph.D. thesis, Lund, Sweden: Lund University, 2002.
[15] E. Lotfi, “Mathematical modeling of emotional brain for classification problems, Proceeding of IAM”, Vol. 2, No. 1, 2013, pp.60-71.
[16] S.M. Khaligh Razavi, N. Kriegeskorte, “Deep supervised, but not unsupervised, models may explain IT cortical representation”, PLoS computational biology, Vol. 10. No. 11, 2014.
[17] R.M. Cichy, A. Khosla, D. Pantazis, A. Torralba, A. Oliva, “Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence”, Scientific Reports, Vol. 6, No. 27755, 2016.
[18] U. Güçlü,, M.A.J. van Gerven, “Deep Neural Networks Reveal a Gradient in the Complexityof Neural Representations across the Ventral Stream”, The Journal of Neuroscience, Vol. 35, No. 27, 2015, pp. 10005-10014.
[19] N. Seijdel, H.S. Scholte, E.H.F. de Haan. “Visual features drive the category-specific impairments on categorization tasks in a patient with object agnosia”, Neuropsychologia, Vol. 161, 2021.
[20] H. Karnath, J. Rüter, A. Mandler, M. Himmelbach, "The anatomy of object recognition—Visual form agnosia caused by medial occipitotemporal stroke", The Journal of Neuroscience", Vol. 29, No. 18, 2009, pp: 5854–5862.
[21] B. Kolb, I. O. Whishaw, "Fundamentals of Human Neuropsychology 6th ed", New York, NY., Worth Publishers, ISBN 978-0-7167-9586-5, 2009,pp:325-331.
[22] H. Lissauer,"A case of visual agnosia with a contribution to theory", Cogn Neuropsychol, Vol.5, 1988, pp.153–192.
[23] B. LFinlay, “Focusing the eyes and recognizing objects: evo-devo and the sensitive period“, Current Opinion in Behavioral Sciences,Vol. 36, 2020, pp.36-41.
[24] H. Sufikarimi, K. Mohammadi, “Role of the secondary visual cortex in HMAX model for object recognition”, Cognitive System Research, Vol. 64, 2020, pp.15-28.
[25] M. Mishkin, L. G. Ungerleider, K. A. Macko, “Object vision and spatial vision: Two cortical pathways”, Trends in Neurosciences, Vol. 6, 1983, pp.414-417.
[26] P. Lazzaro, D. Murra, B. Schwortz, “Pattern recognition after image processing of low-contrast images, the case of the Shroud of Turin”, Pattern Recognition, Vol. 46, No. 7, 2013, pp.1964-1970.
[27] S. Etemad, M. Amirmazlaghani, “Color texture image retrieval based on Copula multivariate modeling in the Shearlet domain”, Engineering Applications of Artificial Intelligence, Vol. 102, 2021.
[28] C. Singh, E. Walia, K. PreetKaur, “Color texture description with novel local binary patterns for effective image retrieval”, Pattern Recognition, Vol.76, 2018, pp.50-68.
[29] J.K. Kamarainen, V. Kyrki, H. Kalviainen, “Invariance properties of gabor filter-based features-overview and applications”, IEEE Transactions on Image Processing, Vol. 15, 2006, pp.1088-1099.
[30] EP. Georgiou, A. Kannappan, "Fuzzy cognitive map ensemble learning paradigm to solve classification problems: Application to autism identification", Applied Soft Computing, Vol. 12, 2012, pp:3798-3809.
