INTRODUCCIÓN AL PROCESADO DE IMAGEN
TEMA 2
INTRODUCCIÓN A LA IMAGEN DIGITAL
ADQUISICIÓN Y FORMACIÓN
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Image FormationImage Formation
objec
t
image plane
lens
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Image FormationImage Formationlig
ht so
urce
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Image FormationImage Formation
projection through lens
projection through lens
image of objectimage of object
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Image FormationImage Formation
projection onto discrete sensor
array.
projection onto discrete sensor
array. digital cameradigital camera
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Image FormationImage Formation
sensors register average color.
sensors register average color.
sampled imagesampled image
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Image FormationImage Formation
continuous colors, discrete locations.
continuous colors, discrete locations.
discrete real-valued image
discrete real-valued image
MUESTREO Y CUANTIFICACIÓN
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Sampling and Quantization
pixel grid
sampledreal image quantized sampled & quantized
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Sampling and Quantization
sampledreal image quantized sampled & quantized
pixel gridcolumn indexcolumn index
row
inde
xro
w in
dex
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Sampling
pixel grid
),( crI S ,CI
sampled image
Take the average within each square.
Take the average within each square.
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Sampling
),( crI S ,CI
continuous image sampled image
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Sampling
),( crI S ,CI
sampled image
Take the average within each square.
Take the average within each square.
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Sampling
Efectos del muestreo espacial:
•Teóricamente, bajo ciertas condiciones (las del teorema de muestreo), no hay pérdida de información en el muestreo.
•En la práctica, el muestreo limita la resolución de la imagen.
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continuous color input
disc
rete
col
or o
utpu
t
continuous colors mapped to a finite,
discrete set of colors.
continuous colors mapped to a finite,
discrete set of colors.
Quantification
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Cuantificación
Efecto de la cuantificación
•Si el número de niveles de intensidad usados para representar una imagen monocromo es pequeño, el ojo puede detectar efectos de contorno en el objeto•En el caso de imágenes B&W, con pocos tonos se observa ya alta calidad (con 100 niveles es suficiente, con 64 es muchas veces admisible)
32 niveles 16 niveles
8 niveles 4 niveles
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8 bits 256 levels 7 bits 128 levels 6 bits 64 levels 5 bits 32 levels
4 bits 16 levels 3 bits 8 levels 2 bits 4 levels 1 bit 2 levels
Cuantificación
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Intensity Quantization
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Cuantificación
•El tamaño de la “paleta de colores” (es decir, el número de colores utilizados para representar cada píxel es otro factor determinante de la calidad.
•Si este número es muy bajo, se apreciarán contornos artificiales que resultan visualmente molestos.
•Suelen ser necesarios 24 bits (3x8) ó 16 millones de colores (color real) para una visualización perfecta, con una paleta de 16 bits ó 65556 colores se aprecia casi sin distorsión, pero con una paleta de 8 bits ó 256 colores la calidad se ve muy deteriorada y con 16 la imagen es inservible).
16 mill
256
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Resampling
8× 16×nearest neighbornearest neighbor nearest neighbornearest neighbor
bicubic interpolationbicubic interpolation bicubic interpolationbicubic interpolation
(resizing)
IMAGEN DIGITAL
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Digital ImageDigital Image
a grid of squares, each of which
contains a single color
a grid of squares, each of which
contains a single color
each square is called a pixel (for picture element)
each square is called a pixel (for picture element)
Color images have 3 values per pixel; monochrome images have 1
value per pixel.
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A digital image, I, is a mapping from a 2D grid of uniformly spaced discrete points, {p = (r,c)}, into a set of positive integer values, {I( p)}, or a set of vector values, e.g., {[R G B]T(p)}.
At each column location in each row of I there is a value.
The pair ( p, I( p) ) is called a “pixel” (for picture element).
Pixels
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p = (r,c) is the pixel location indexed by row, r, and column, c.
I( p) = I(r,c) is the value of the pixel at location p.If I( p) is a single number then I is monochrome.If I( p) is a vector (ordered list of numbers) then I
has multiple bands (e.g., a color image).
Pixels
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Pixel Location: p = (r , c)
Pixel Value: I(p) = I(r , c) Pixel : [ p, I(p)]
Pixels
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Pixel : [ p, I(p)]Pixels
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Color Images
Are constructed from three intensity maps.
Each intensity map is pro-jected through a color filter (e.g., red, green, or blue, or cyan, magenta, or yellow) to create a monochrome image.
The intensity maps are overlaid to create a color image.
Each pixel in a color image is a three element vector.
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Color Images On a CRT*
Color Images On a CRT*
* Tubo de rayos catódicos
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Point ProcessingPoint Processing
original + gamma- gamma + brightness- brightness
original + contrast- contrast histogram EQhistogram mod
PERCEPCIÓN DEL COLOR (2)
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luminan
cehue
saturation
photo receptorsbrain
The eye has 3 types of photoreceptors: sensitive to red, green, or blue light.
The brain transforms RGB into separate brightness and color channels (e.g., LHS).
Color Sensing / Color Perception
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Color Perception
all bandsall bands luminanceluminance chrominancechrominance
redred greengreen blueblue
16× pixelization of:
luminance and chrominance (hue+saturation) are perceived with different resolutions, as are red, green and blue.
luminance and chrominance (hue+saturation) are perceived with different resolutions, as are red, green and blue.
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Color Balance and Saturation
Uniform changes in color components result in change of tint.
E.g., if all G pixel values are multiplied by > 1 then the image takes a green cast.
ALGUNAS OPERACIONES SOBRE IMAGEN
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Rotation
and motion blur
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Image Warping
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originalblurred sharpened
Spatial Filtering
FORMATOS DE IMAGEN
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Formatos de Imagen
Clasificación según el número de bandas o canales:•En color (RGB): se almacena RGB de alguna forma.•En escala de grises: se almacena Y de alguna forma.•Binarias: sólo existen dos colores, el blanco y el negro. Su origen suele ser el procesado de otras imágenes (ej: OCR’s).•Multi-canal: se almacenan más de 3 canales, típico en sensoresmulti-e hiper-espectrales para aplicaciones de observación terrestre.
JPEG
GIF
TIFF
PNG
BMP
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Image Compression: JPEGJP
EG
qua
lity
leve
lF
ile size in bytes
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JPE
G q
uali
ty le
vel
File size in
bytes
INTRODUCCIÓN AL PROCESADO DE IMAGEN
TEMA 2
INTRODUCCIÓN A LA IMAGEN DIGITAL
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