https://www.youtube.com/watch?v=UhjQOCGSb1A
This presentation was delivered at the 25th annual Stereoscopic Displays and Applications conference (3-5 February 2014) held in San Francisco, USA.More pres

https://spie.org/Documents/ConferencesExhibitions/EI14-final-L.pdf
Using fMRI To Reverse Engineer the Human Visual System Jack L. Gallant, Univ. of California, Berkeley (United States) Abstract: The human brain is the most sophisticated image processing system known, capable of impressive feats of recognition and discrimination under challenging natural conditions. Reverse-engineering the brain might enable us

https://openaccess.thecvf.com/content/WACV2024/papers/Xia_DREAM_Visual_Decoding_From_Reversing_Human_Visual_System_WACV_2024_paper.pdf
ture and color from the original visual stimuli. This phe-nomenon arises due to the absence of proper color guidance. Following above analysis, we propose DREAM, a visual Decoding method from REversing humAn visual systeM. It aims to mirror the forward process from visual stimuli to fMRI recordings (Sec. 3).

https://arxiv.org/abs/2310.02265
In this work we present DREAM, an fMRI-to-image method for reconstructing viewed images from brain activities, grounded on fundamental knowledge of the human visual system. We craft reverse pathways that emulate the hierarchical and parallel nature of how humans perceive the visual world. These tailored pathways are specialized to decipher semantics, color, and depth cues from fMRI data

https://ieeexplore.ieee.org/book/9100608
Book Abstract: This milestone interdisciplinary work brings you to the cutting edge of emerging technologies inspired by human sight, ranging from semiconductor photoreceptors based on novel organic polymers and retinomorphic processing circuitry to low-powered devices that replicate spatial and temporal processing in the brain. Moreover, it is the first work of its kind that integrates the

http://www.stereoscopic.org/2014/program.html
"Using fMRI To Reverse Engineer the Human Visual System" [9011-301] Jack L. Gallant, Univ. of California, Berkeley (United States) Abstract: The human brain is the most sophisticated image processing system known, capable of impressive feats of recognition and discrimination under challenging natural conditions. Reverse-engineering the brain

https://www.imaging.org/IST/IST/Conferences/EI/EI2024/EI2024.aspx?EntryCCO=4
Plenary Recordings. Using fMRI to Reverse Engineer the Human Visual System; Jack L. Gallant, University of California, Berkeley (United States) Integrated Imaging: Creating Images from the Tight Integration of Algorithms, Computation, and Sensors; Charles A. Bouman, Purdue University (United States) Program; Final Program ; Abstracts ; EI 2013

https://www.nature.com/articles/nature06713
Visual stimuli were delivered using the VisuaStim goggles system (Resonance Technology). ... Gallant, J. L. Parametric reverse correlation reveals spatial linearity of retinotopic human V1 BOLD

https://www.nature.com/articles/s41597-023-02471-x
To our knowledge, there are only three large-scale fMRI datasets that have been specifically collected for the study of the neural basis of human visual processing under naturalistic scenes: the

https://www.semanticscholar.org/paper/Next-generation-artificial-vision-systems%3A-reverse-Faber/df154e2acac3a6a08a002084b4148e91cc4786d1
Semantic Scholar extracted view of "Next generation artificial vision systems: reverse engineering the human visual system" by T. Faber. Skip to search form Skip to main content Skip to account menu. Semantic Scholar's Logo. Search 218,940,644 papers from all fields of science. Search

https://www.pnas.org/doi/10.1073/pnas.0804110105
Functional (f)MRI has revolutionized the field of human brain research. fMRI can noninvasively map the spatial architecture of brain function via localized increases in blood flow after sensory or cognitive stimulation. Recent advances in fMRI have led to enhanced sensitivity and spatial accuracy of the measured signals, indicating the

https://www.sciencedirect.com/science/article/pii/S1053811922006516
Vision science enables specific predictions about expected fMRI responses, including their location, strength, and the timing of their activation. Thus, visual stimulation is well-suited to detect anomalous responses and then link these responses to vascular artefacts (e.g., Lee et al. 1995 and Winawer et al. 2010 ).

https://cbmm.mit.edu/video/reverse-engineering-human-visual-intelligence
Reverse Engineering Human Visual Intelligence. Date Posted: August 12, 2019. Date Recorded: August 12, 2019. CBMM Speaker (s): James DiCarlo. All Captioned Videos. Brains, Minds and Machines Summer Course 2019. Associated CBMM Pages: James DiCarlo. BMM Summer Course 2019.

https://link.springer.com/chapter/10.1007/978-1-4899-7591-1_15
Vision is the dominant sense in humans, and the visual system covers about 25 % of the human cerebral cortex. The visual cortex contains many maps of the visual world and many functional regions implicated in processing distinct perceptual qualities of the visual scene. This chapter provides an overview of the organization and function of

https://link.springer.com/chapter/10.1007/978-3-540-33679-2_8
■ This chapter reviews work on the method of functional magnetic resonance imaging (fMRI), which has been used to describe the structural and functional anatomy of the human visual system. ■ Exploitation of the endogenous paramagnetic contrast agent

https://www.academicradiology.org/article/S1076-6332(09)00041-5/fulltext
Next Generation Artificial Vision Systems, edited by Anil Bharath and Maria Petrou of the Imperial College in London, is a broad-scoped review that presents current technology in the physiology of vision and the software and hardware developments inspired by it. Overall, the book is almost as remarkable as the vision system itself in its attempts to integrate such a wide range of material into

https://books.google.com/books/about/Next_Generation_Artificial_Vision_System.html?id=TfAeAQAAIAAJ
Bharath initiated the Basic Technology Project "Reverse Engineering Human Visual Processes," which aims to create an engineering blueprint for a subset of processes in the human visual system. Maria Petrou, Ph.D. is a professor of signal processing and the head of the Communications and Signal Processing Group at Imperial College in London.

https://www.gbv.de/dms/ilmenau/toc/574386823.PDF
The Human Visual System: An Engineering Challenge 1 1.1 Introduction 1 1.2 Overview of the Human Visual System 2 1.2.1 The Human Eye 3 1.2.1.1 Issues to Be Investigated 8 1.2.2 Lateral Geniculate Nucleus (LGN) 10 1.2.3 The VI Region of the Visual Cortex 12 1.2.3.1 Issues to Be Investigated 14 1.2.4 Motion Analysis and V5 15

https://www.jneurosci.org/content/jneuro/44/2/e0803232023.full.pdf
To interpret this rich visual input, the visual system processes information spa-tially and temporally through computations by receptive fields. Prior research has separately characterized spatial recep-tive elds in primate (Hubel and Wiesel, 1968) and human fi visual cortex (Dumoulin and Wandell, 2008; Wandell et al., 2009; Kay et al., 2013

https://www.nature.com/articles/s41467-022-35117-4
Using fMRI, we discovered three major brain networks driving conscious visual perception independent of report: first, increases in signal detection regions in visual, fusiform cortex, and frontal

https://link.springer.com/chapter/10.1007/978-3-031-10909-6_29
FMRI mapping with a rotating checkered wedge produces a brain map with information that is complementary to that provided by the annular stimulus (Figs. 29.1b and 29.2a). (Note that the color codes for Figs. 29.1 and 29.2 are different. The horizontal meridian is purple in Fig. 29.1b but green in Fig. 29.2a.)Again consistent with early human studies, the left and right halves of the visual

https://onlinelibrary.wiley.com/doi/pdf/10.1002/hbm.23945
A human visual-stimulus EEG, MEG, and fMRI test was performed, and the experimental data revealed that FITC and wFITC displayed more focal areas than fMNE and MNE. In conclusion, the proposed FITC method is able to better resolve the spatial mismatch problems encountered in fMRI-constrained EEG/MEG source imaging.