* enabled convolution & activation fusion

* a few more optimizations:
+ optimized the common case when the indices of max pooling layer are not used. in this case we use the more efficient branch that computes just maximums over the aperture.
+ optimized the convolution + activation fusion when the activation is relu, which is another common case
+ convolution can now be fused with batch norm. It's the zero-cost fusion. If the batch norm is followed by relu, all three (conv + batchnorm + relu) are fused together. this modification seriously improved ENet performance

* hopefully fixed warnings on Windows

* first commit

* first commit

* adjust code layout

* round mean value

* add missed header

* remove useless header

* remove useless header

* first commit

* first commit

* first commit

* Encapsule function averageHash by class

* remove export macro

* encapsulate phash algo by class

* first commit

* fix bugs of createHash and fillBlcoks

* 1 : add create function
2 : add overload functions

* implement get set functions

* fix bug--destination depth should be CV_32F

* first commit

* first commit

* 1 : fix bug--forgot '"'
2 : forgot to include iostream

* fix warnings

* remove tab

* remove trailing white space

* remove trailing white space

* remove trailing white space

* remove trailing white space

* remove trailing white space

* remove trailing white space

* first commit

* remove trailing white space

* remove trailing white space

* remove trailing white space

* reduce redundance operation

* add explanation of img_hash

* remove useless comments

* remove trailing space

* first commit

* fix missed symbol

* add new defgroup and change all defgroup to ihash

* fix namespace confliction

* change namespace from ihash to img_hash

* change ihash to img_hash

* change include guard

* forbid implicit conversion

* first commit

* 1 : declare function findFeatureVector
2 : forward declare test class--RavialVarHashTester as friend

* first commit

* replace auto with explicit type

* export some symbols, for initialization and testing

* remove trailing white space

* add namespace cv

* fix type cast warning and define default constrcutor/destructor

* declare and define RadialVarHashTester in namespace

* remove default constructor/destructor

* exports functions findFeatures and destructor

* remove trailing white space

* fix bug--wrong definition of destructor

* remove trailing white space

* implement findFeatureVector

* add test case for findFeatureVector

* 1 : fix bug--forgot to allocate space for input
2 : fix bug--compare the results of pixPerLine with wrong matrix

* remove trailing space

* implement hashCalculate

* add test case for hashCalculate

* remove trailing white space

* avoid hiding parameter

* refine codes and keep the original range

* adjust expected hash value since the range of hash change

* add comment

* reduce scope

* remove trailing white space

* adjust format

* add new function compare

* implement compute functions

* use array as buffer of cv::Mat, avoid memory allocation

* remove trailing whitespace

* 1 : implement cross-correlation rather than using matchTemplate since the
results of matchTemplate are weird
2 : remove gamma param, although the paper said PHash apply gamma
correction on the image, but the codes do not do that(I think it is a bug
of PHash)
3 : create function can specify sigma and numOfAngleLine
4 : Use blurImg to replace normalizeImg
5 : remove useless parameters which related to gamma correction

* add example of radial variance hash

* use buffer to avoid memory allocation and use enum to specify hash size

* remove useless header

* fix bug--constructor only accept two params

* add comments

* transpose projection matrix, friendlier for cache hit

* use pointer to access projection value

* function able to specify sigma and numOfAngleLine

* add get/set functions

* implement image hash algo--block mean hash

* include block mean hash and add comments

* remove trailing whitespace

* fix warning--compare with sign and unsigned value

* implement destructor and change mode type to size_t

* add example of block mean hash

* compress the bits of hash

* function--blockMeanHash able to set mode

* fix type cast warning and style

* change expected result to bool

* compress the hash value from 16 bytes to 8 bytes

* update comments

* compress hash from 16 bytes to 8 bytes

* use peak value of cross correlation as comparison result

* add limit header

* first commit

* add group and header file of color moment hash

* should not use auto, it is c++11 feature

* support python binding

* implement destructor of AverageHash

* support python binding

* support python binding

* support python binding

* change type to inputArray and outputArray, easier for python binding

* all algorithms support input type CV_8UC4,CV_8UC3 and CV_8UC1

* Provide better instructions

* Make it more pleasant to read

* Add information of speed comparsion

* remove trailing white space

* remove useless blank line

* refine comments of example

* fix link error

* refine title

* 1 : implement function "getDefaultName"
2 : adjust style

* Update README.md

1 : Fix wrong branch
2 : Add another solution to download img_hash

* img_hash: refactored interfaces to use pImpl

* remove trailing white space

* img_hash: use type-safe pImpl

* change name, easier to find the source file

* 1 : narrow scope of ImgHashImpl
2 : use static cast to replace dynamic cast because class hierarchy of
img_hash is very straightforward

* should not declare ImgHashImpl api in the header file of ImgHashBase, this increase chance of breaking ABI

* should not declare ImgHashImpl api in the header file of ImgHashBase, this increase chance of breaking ABI

* fix warning, because unelaborated friend declaration is a C++11 extension

* first commit

* fix bug, except of windows, other platforms cannot access private member.
pimpl only accessable by the class itself, therefore I think declare
everything as public is quite safe

* first commit comparison and computation charts

* update chart link

* some further optimizations and cleanups in dnn:
+ got rid of dnn::gemm; it's not perf critical anymore (perhaps)
+ embedded col2im functionality into convolution_layer.cpp, since it's not used anywhere else
+ parallel max pooling. even better performance can be achieved if we knew that max indices are not needed (and they are not needed in most networks)
+ somewhat optimized deconvolution layer: optimized bias addition (merged it with col2im), optimized col2im slightly.
+ hopefully fixed incorrect memory access in fully-connected layer; restored aligned memory reads (they should work fine now)

* hopefully fixed regressions in ENet performance

* fixed some typos in deconvolution; added SIMD optimization for the max pooling layer

* fixed warnings in SIMD-less build configuration

* rewritten the following layers to be [much] more efficient: convolution, fully connected, activations (relu, tanh, ...), LRN. Use optional AVX optimization for the first two.

* eliminated trailing whitespaces

The while loop in member function buildPyramid in deepflow.cpp terminated as a result of the break statement and never as a result of the termination criteria because the "i" variable in the while loop was never incremented.

Changed the while loop into a for loop

Changed the while loop into a for loop in the member function buildPyramid in deepFlow.cpp

removed trailing whitespace

* Reuse deep learning output blobs

* Changed order for iterating through blobs while seeking memory. Refactored a little.

fix java wrappers for ERFilter, OCRHMMDecoder, OCRBeamSearchDecoder (#1210)

* fix java wrappers for ERFilter, OCRHMMDecoder, OCRBeamSearchDecoder

* fix comments