We continue to work on IPCV module and we are pleased to announce the release of Scilab IPCV 2.0 with bugs fixed and some great new features!
Support on Deep Learning Network!
One of the exiting features in IPCV 2.0 is the support for deep learning network for inference system. This feature allows the user to import DNN models build and trained with caffe and tensorflow into Scilab to build a fast prototype or even end desktop base end solution in short time!
On top of that, visualization features of Scilab also assist the students to understand the DNN faster, which is important to trigger their interest in Deep Learning and move further to explore in this exiting field!
Using OpenCV 3.4.1 As The Engine
By using the latest stable release of OpenCV, IPCV “fuses” the power of both OSS – Scilab and OpenCV and makes a powerful software to replace some other expensive proprietary software in Image Processing, Computer Vision, and Deep Learning.
Some other changes and bugs fixed, please refer to the change log in the module.
In order to install the module, simple run atomsInstall(“IPCV”) under Scilab 6.0.1.
Currently we just compile for windows 64 bit version due to lack of resource due to funding, support for Linux will be coming soon. However, we might drop the support for the 32 bit system simple because there are more features which would perform (if not only work on) much faster and better in 64 bit system.