* 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

Fixed a buffer overflow which occurs when setImage is called with different images and default image_id.

Incremental commit.

Theory updated.

Theory finished.

Inpainting in progress.

Inpainting done plus minor fix in kernel computation.

Tutorials done.

Documentation done.

Whitespace fix.

Update.

Conversion to jpg.

Documentation links fixed.