Compute channel features at a single scale given an input image.
Compute the channel features as described in:
P. Dollár, Z. Tu, P. Perona and S. Belongie
"Integral Channel Features", BMVC 2009.
Channel features have proven very effective in sliding window object
detection, both in terms of *accuracy* and *speed*. Numerous feature
types including histogram of gradients (hog) can be converted into
channel features, and overall, channels are general and powerful.
Given an input image I, a corresponding channel is a registered map of I,
where the output pixels are computed from corresponding patches of input
pixels (thus preserving overall image layout). A trivial channel is
simply the input grayscale image, likewise for a color image each color
channel can serve as a channel. Other channels can be computed using
linear or non-linear transformations of I, various choices implemented
here are described below. The only constraint is that channels must be
translationally invariant (i.e. translating the input image or the
resulting channels gives the same result). This allows for fast object
detection, as the channels can be computed once on the entire image
rather than separately for each overlapping detection window.
Currently, three channel types are available by default (to date, these
have proven the most effective for sliding window object detection):
(1) color channels (computed using rgbConvert.m)
(2) gradient magnitude (computed using gradientMag.m)
(3) quantized gradient channels (computed using gradientHist.m)
For more information about each channel type, including the exact input
parameters and their meanings, see the respective m-files which perform
the actual computatons (chnsCompute is essentially a wrapper function).
The converted color channels serve as input to gradientMag/gradientHist.
Additionally, custom channels can be specified via an optional struct
array "pCustom" which may have 0 or more custom channel definitions. Each
custom channel is generated via a call to "chns=feval(hFunc,I,pFunc{:})".
The color space of I is determined by pColor.colorSpace, use the setting
colorSpace='orig' if the input image is not an 'rgb' image and should be
left unchanged (e.g. if I has multiple channels). The input I will have
type single and the output of hFunc should also have type single.
"shrink" (which should be an integer) determines the amount to subsample
the computed channels (in applications such as detection subsamping does
not affect performance). The params for each channel type are described
in detail in the respective function. In addition, each channel type has
a param "enabled" that determines if the channel is computed. If
chnsCompute() is called with no inputs, the output is the complete
default params (pChns). Otherwise the outputs are the computed channels
and additional meta-data (see below). The channels are computed at a
single scale, for (fast) multi-scale channel computation see chnsPyramid.
An emphasis has been placed on speed, with the code undergoing heavy
optimization. Computing the full set of channels used in the BMVC09 paper
referenced above on a 480x640 image runs over *100 fps* on a single core
of a machine from 2011 (although runtime depends on input parameters).
USAGE
pChns = chnsCompute()
chns = chnsCompute( I, pChns )
INPUTS
I - [hxwx3] input image (uint8 or single/double in [0,1])
pChns - parameters (struct or name/value pairs)
.shrink - [4] integer downsampling amount for channels
.pColor - parameters for color space:
.enabled - [1] if true enable color channels
.smooth - [1] radius for image smoothing (using convTri)
.colorSpace - ['luv'] choices are: 'gray', 'rgb', 'hsv', 'orig'
.pGradMag - parameters for gradient magnitude:
.enabled - [1] if true enable gradient magnitude channel
.colorChn - [0] if>0 color channel to use for grad computation
.normRad - [5] normalization radius for gradient
.normConst - [.005] normalization constant for gradient
.full - [0] if true compute angles in [0,2*pi) else in [0,pi)
.pGradHist - parameters for gradient histograms:
.enabled - [1] if true enable gradient histogram channels
.binSize - [shrink] spatial bin size (defaults to shrink)
.nOrients - [6] number of orientation channels
.softBin - [0] if true use "soft" bilinear spatial binning
.useHog - [0] if true perform 4-way hog normalization/clipping
.clipHog - [.2] value at which to clip hog histogram bins
.pCustom - parameters for custom channels (optional struct array):
.enabled - [1] if true enable custom channel type
.name - ['REQ'] custom channel type name
.hFunc - ['REQ'] function handle for computing custom channels
.pFunc - [{}] additional params for chns=hFunc(I,pFunc{:})
.padWith - [0] how channel should be padded (e.g. 0,'replicate')
.complete - [] if true does not check/set default vals in pChns
OUTPUTS
chns - output struct
.pChns - exact input parameters used
.nTypes - number of channel types
.data - [nTypes x 1] cell [h/shrink x w/shrink x nChns] channels
.info - [nTypes x 1] struct array
.name - channel type name
.pChn - exact input parameters for given channel type
.nChns - number of channels for given channel type
.padWith - how channel should be padded (0,'replicate')
EXAMPLE - default channels
I=imResample(imread('peppers.png'),[480 640]); pChns=chnsCompute();
tic, for i=1:100, chns=chnsCompute(I,pChns); end; toc
figure(1); montage2(cat(3,chns.data{:}));
EXAMPLE - default + custom channels
I=imResample(imread('peppers.png'),[480 640]); pChns=chnsCompute();
hFunc=@(I) 5*sqrt(max(0,max(convBox(I.^2,2)-convBox(I,2).^2,[],3)));
pChns.pCustom=struct('name','Std02','hFunc',hFunc); pChns.complete=0;
tic, chns=chnsCompute(I,pChns); toc
figure(1); im(chns.data{4});
See also rgbConvert, gradientMag, gradientHist, chnsPyramid
Piotr's Computer Vision Matlab Toolbox Version 3.23
Copyright 2014 Piotr Dollar & Ron Appel. [pdollar-at-gmail.com]
Licensed under the Simplified BSD License [see external/bsd.txt]