Part II: Early Vision in One Image

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فهرست عناوین اصلی در این پاورپوینت

● Why study Computer Vision?
● Properties of Vision
● Part I: The Physics of Imaging
● Part II: Early Vision in One Image
● Representing an image patch
● Texture
● Shape from texture
● Part III: Early Vision in Multiple Images
● Part IV: Mid-Level Vision
● Part V: High Level Vision (Geometry)
● Part VI: High Level Vision (Probabilistic)
● 3D Reconstruction from multiple views
● Part VII: Some Applications in Detail
● Some applications of recognition
● What are the problems in recognition?
● History
● History-II
● Segmentation
● Matching templates
● Relations between templates
● Representing the 3D world
● People
● Horse grouper
● Returned data set
● Tracking
● The nasty likelihood

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گروه موضوعی: زمان استخراج مطلب: 2019/05/16 06:20:51

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., image, object, texture, vision, part, point, view, different, bar, representation, property,

توجه: این مطلب در تاریخ 2019/05/16 06:20:51 به صورت خودکار از فضای وب آشکار توسط موتور جستجوی پاورپوینت جمع آوری شده است و در صورت اعلام عدم رضایت تهیه کننده ی آن، طبق قوانین سایت از روی وب گاه حذف خواهد شد. این مطلب از وب سایت زیر استخراج شده است و مسئولیت انتشار آن با منبع اصلی است.

http://luthuli.cs.uiuc.edu/~daf/book/bookpages/Slides/Introduction.ppt

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عبارات پرتکرار و مهم در این اسلاید عبارتند از: ., image, object, texture, vision, part, point, view, different, bar, representation, property,

مشاهده محتوای متنیِ این اسلاید ppt

مشاهده محتوای متنیِ این اسلاید ppt

why study computer vision images and movies are everywhere fast growing collection of useful applications building representations of the ۳d world from pictures automated surveillance who’s doing what movie post processing face finding various deep and attractive scientific mysteries how does object recognition work greater understanding of human vision properties of vision one can see the future cricketers avoid being hit in the head there’s a reflex when the right eye sees something going left and the left eye sees something going right move your head fast. gannets pull their wings back at the last moment gannets are diving birds they must steer with their wings but wings break unless pulled back at the moment of contact. area of target over rate of change of area gives time to contact. properties of vision ۳d representations are easily constructed there are many different cues. useful to humans avoid bumping into things planning a grasp etc. in computer vision build models for movies . cues include multiple views motion stereopsis texture shading properties of vision people draw distinctions between what is seen object recognition this could mean is this a fish or a bicycle it could mean is this george washington it could mean is this poisonous or not it could mean is this slippery or not it could mean will this support my weight great mystery how to build programs that can draw useful distinctions based on image properties. part i the physics of imaging how images are formed cameras what a camera does how to tell where the camera was light how to measure light what light does at surfaces how the brightness values we see in cameras are determined color the underlying mechanisms of color how to describe it and measure it part ii early vision in one image representing small patches of image for three reasons we wish to establish correspondence between say points in different images so we need to describe the neighborhood of the points sharp changes are important in practice known as edges representing texture by giving some statistics of the different kinds of small patch present in the texture. tigers have lots of bars few spots leopards are the other way representing an image patch filter outputs essentially form a dot product between a pattern and an image while shifting the pattern across the image strong response image locally looks like the pattern e.g. derivatives measured by filtering with a kernel that looks like a big derivative bright bar next to dark bar convolve this image with this kernel to get this at this point one should point out that the image is represented with largest value bright and smallest value dark. the kernel is large and positive at light points large and negative at dark points and mid grey at zeros. the output has the same properties. notice that a strong response comes when the image looks like the kernel. the output is a representation that tells us when there are large x derivatives in the image. worth getting the class to guess what derivative is being estimated. texture many objects are distinguished by their texture tigers cheetahs grass trees we represent texture with statistics of filter outputs for tigers bar filters at a coarse scale respond strongly for cheetahs spots at the same scale for grass long narrow bars for the leaves of trees extended spots objects with different textures can be segmented the variation in textures is a cue to shape here one points out that a filter is basically a template. if we have lots of different templates and count the relative proportions of the different types we have a texture representation. texture determines surface properties wetness roughness sliminess etc. and gives us shape cues as well the basics of texture representation vertical and horizontal bars count the mean response and classify on that statistic. of course for more detail one would have more filters and more statistics. shape from texture markings on a surface distort when seen in a camera the pattern of distortions is a cue to the normal of the surface. the details remain vague but the effect is very widespread and useful e.g. the surface graph . part iii early vision in multiple images the geometry of multiple views where could it appear in camera ۲ ۳ etc. given it was here in ۱ ۱ and ۲ etc. stereopsis what we know about the world from having ۲ eyes structure from motion what we know about the world from having many eyes or more commonly our eyes moving. part iv mid level vision finding coherent structure so as to break the image or movie into big units segmentation breaking images and videos into useful pieces e.g. finding video sequences that correspond to one shot e.g. finding image components that are coherent in internal appearance tracking keeping track of a moving object through a long sequence of views part v high level vision geometry the relations between object geometry and image geometry model based vision find the position and orientation of known objects smooth surfaces and outlines how the outline of a curved object is formed and what it looks like aspect graphs how the outline of a curved object moves around as you view it from different directions range data part vi high level vision probabilistic using classifiers and probability to recognize objects templates and classifiers how to find objects that look the same from view to view with a classifier relations break up objects into big simple parts find the parts with a classifier and then reason about the relationships between the parts to find the object. geometric templates from spatial relations extend this trick so that templates are formed from relations between much smaller parts ۳d reconstruction from multiple views multiple views arise from stereo motion strategy triangulate from distinct measurements of the same thing issues correspondence which points in the images are projections of the same ۳d point the representation what do we report noise how do …

کلمات کلیدی پرکاربرد در این اسلاید پاورپوینت: ., image, object, texture, vision, part, point, view, different, bar, representation, property,

این فایل پاورپوینت شامل 45 اسلاید و به زبان انگلیسی و حجم آن 5.86 مگا بایت است. نوع قالب فایل ppt بوده که با این لینک قابل دانلود است. این مطلب برگرفته از سایت زیر است و مسئولیت انتشار آن با منبع اصلی می باشد که در تاریخ 2019/05/16 06:20:51 استخراج شده است.

http://luthuli.cs.uiuc.edu/~daf/book/bookpages/Slides/Introduction.ppt

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