The Visual Brain CSE 332 Visualization Introduction Over 50 off the O th human h b brain i iis d dedicated di t d tto vision i i and d visual representations decoding visual information high level processing of visual information thinking with visual metaphors Kl Klaus M Mueller ll Computer Science Department Stony Brook University Input Device The Eye Sensor The Cones and Rods Two types T t off receptors t on retina ti rods and cones Rods spread all over the retinal surface 75 150 million low resolution resolution no color vision vision but very sensitive to low light scotopic or dimlight vision Cones a dense array around the central portion of the retina the fovea centralis 6 7 million high resolution color vision but require brighter light photopic or bright light bright light vision Wiring The Visual Pathways Processing Unit The Visual Cortex V1 V2 Visuall cortex Vi t b breaks k iinputt up iinto t diff differentt aspects t color shape motion depth LGN Lateral Geniculate Nucleus Pre Attentive Processing If you wantt it or not t some features f t are always l detected d t t d Pre Attentive Processing Wh iis it so ffast Why t And fast within 200 ms or less Well because 50 of the brain is dedicated to vision Vision is a MASSIVELY parallel processor dedicated to detect analyze l recognize reason with visual input Pre Attentive Processing Sensitivity S iti it tto diff differences iin color orientation size shape motion shading 3D depth Pre Attentive Processing B t there But th are limits li it conjunctions j ti d don t t work k wellll quick find the blue circle Pre Attentive Processing S Some ffeatures cues t are stronger t than th others th Pre Attentive Processing Words W d are patterns tt which hi h fform strong t pre attentive tt ti features f t this would have been different if this had been done in Arabic There are limits however let s see the next experiment p Pre Attentive Processing Pre Attentive Processing R di 1 Reading Aoccdrnig to a rscheearch at an Elingsh uinervtisy it deosn t mttaer in waht oredr the Now is tihs ture Raed on ltteers in a wrod are the olny iprmoetnt tihng is taht frist and lsat ltteer is at the rghit pclae The rset can be a toatl mses and yyou can sitll raed it wouthit porbelm Tihs is bcuseae we do not raed ervey lteter by it slef but the wrod as a wlohe Pre Attentive Processing Pre Attentive Processing R di 2 Reading R di 2 Reading Anidroccg to crad cniyrrag lcitsiugnis According to card carrying linguistics planoissefors at an uemannd utisreviny professionals at an unnamed university in Bsitirh Cibmuloa and crartnoy to the in British Columbia and contrary to the duoibus cmials of the ueticnd rcraeseh dubious claims of the uncited research a slpmie p macinahcel ioisrevnn of a slpmie p macinahcel ioisrevnn of ianretnl cretcarahs araepps sneiciffut to ianretnl cretcarahs araepps sneiciffut to csufnoe the eadyrevy oekoolnr csufnoe the eadyrevy oekoolnr Pre Attentive Processing R di 2 Reading According to card carrying linguistics professionals at an unnamed university in British Columbia and contrary to the dubious claims of the uncited research a simple p mechanical inversion of internal characters appears sufficient to confuse the everyday onlooker What To Learn From This The human Th h visual i l system t HSV ttolerates l t visual i l noise i very well it can read the randomly garbled text very well machines equipped with computer vision are poor at this Humans have only limited computational capacity hard h d tto execute t a fi fixed d rule l tto d decipher i h ttextt especially once the text gets longer 7 2 rule of working memory this is where computers excel The fact that computers deal poorly with noisy patterns is exploited in CAPTCHA CAPTCHA Completely Automated Public Turing Test to tell Computers and Humans Apart used to ensure that an actual human is interacting with a system some examples creating a new gmail or yahoo account prevent spammer accounts submitting files data email CAPTCHA CAPTCHA noisy CAPTCHA i and d vastly tl di distorted t t d patterns tt th thatt are diffi difficult lt to recognize by machines New Field Visual Analytics and d thi this is i also l the th motivation ti ti for f a new emerging i field fi ld Visual Analytics the science of reasoning with visual information Pairs machine intelligence computing bit representations with human intelligence g creativity y visual representations p Completely Automated Public Turing test to tell Computers and Humans Apart CAPTCHA One More Butt computer B t vision i i algorithms l ith h have b become more sophisticated at CAPTCHA character recognition the latest approach is object recognition Pre Attentive Processing Pre Attentive Processing C Count t the th black bl k d dots t More Optical Illusions Optical Illusions Optical Illusions Optical Illusions Optical Illusions Optical Illusions Optical Illusions Optical Illusions Optical Illusions Are the purple lines straight or bent Which circle in the middle is bigger Optical Illusions Optical Illusions You should see a man s face and also a word Hint Try tilting your head to the right g the world begins g with L Do you see gray areas in between the squares Now where did they come from Optical Illusions Sidewalk Art Julian Beever Optical Illusions Sidewalk Art Julian Beever Optical Illusions Sidewalk Art Julian Beever Optical Illusions Sidewalk Art Julian Beever Optical Illusions Sidewalk Art Julian Beever Optical Illusions Sidewalk Art Julian Beever Explanation The Power of the Visional System ti screen retina real world view focal point So th S the human h visual i l system t HSV iis nott perfect f t b butt it it s extremely powerful Vision is an integral part of life Vision is the gateway to higher level regions of the brain drawn illusion equally perceived Exploit this fast and powerful processor for complex data analyses creative tasks communicating ideas The science of visualization Visualization Domains Visualization Examples Visualization Examples Visualization Examples Visualization Examples Visualization Examples Visualization Examples Visualization Examples Visualization Examples Visualization Examples