![]() Scilab Enterprises was created in 2010 out of the Scilab Consortium, which was itself created in 2003 as part of an initiative backed by INRIA, the French National Institute for computer sciences and applied mathematics. Our shared vision will provide the engineering community with the latest generation of analytical solutions to meet current and future numerical simulation challenges." A global community with more than a million engineering users Raphaël Auphan, CEO of Scilab Enterprises, said: “We are very enthusiastic about joining ESI, a numerical simulation and Virtual Prototyping global leader, to bring Scilab to a wider range of industrial, academic and research players. It paves the way towards the more elaborate 3D-4D numerical simulations of the full Virtual Prototyping and eventually of the all-encompassing “Immersive Virtual Engineering” transformative solutions of Industry 4.0.” It is aligned with our objective to expand our user base to include all stakeholders involved in the industrial product creation process, starting from the earliest stages of analytical modeling. Scilab provides a world class powerful environment for engineering computation and scientific applications.Ĭommenting on this acquisition, Vincent Chaillou, ESI Group’s COO, said: “This acquisition fits perfectly with ESI Group’s technology investment strategy. The slight differences are due to the number format which we used causing some rounding errors, however, this is just to illustrate how CNN works and how the training of the model produce the filters to extract the features from the image automatically.ESI Group (FR0004110310 – ESI), pioneer and leader in Virtual Prototyping solutions, today announces the acquisition of Scilab Enterprises SAS, publisher of Scilab, widely regarded as the most compelling open source alternative to MATLAB® 1, the commercial software for analytical numerical solutions. The results are similar to the one in figure 2. Here’s the result:įigure 6: Manually Reproducing the Feature Map with Filtering Technique (Convolution) ![]() After that, we applied each of the filter on the same image to obtain the feature maps which is similar to the one we are getting from the forward pass of the model. Sout(:,:,i) = imfilter(S3',para1(:,:,i)) Īs we could see, doing it manually required some extra steps, including converting image to grayscale, convert to double, and resize the image to the same size with the model input. The first convolution layer consist of 6 filters, the following lines will shows the 6 feature maps (output of the convolution filters) and also filters parameters which produce such maps. ![]() ![]() The above lines of codes will load the pre-trained tensorflow model with the following architecture: We use ~ as the background is black and the object is whiteįigure 1 : Output Prediction with Original Image Net = dnn_readmodel(dnn_path + 'lenet5.pb','','tensorflow') Out = dnn_forward (net ,~S, ) disp (out ' ) = max (out ) scf ( ) imshow (S ) xnumb ( 10, 10 ,maxI - 1 ) ĭnn_path = fullpath(getIPCVpath() + '/images/dnn/') S = imread (dnn_path + '3.jpg' ) // We use ~ as the background is black and the object is white Net = dnn_readmodel (dnn_path + 'lenet5.pb', '', 'tensorflow' ) // Read Image Dnn_path = fullpath (getIPCVpath ( ) + '/images/dnn/' )
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