![3d object converter 4.80 3d object converter 4.80](https://img.informer.com/p7/3d-object-converter-for-windows-v6.3-main-window-picture.png)
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3D OBJECT CONVERTER 4.80 CODE
To facilitate replication of our experiments, the MATLAB source code will be available at ( ).Conversions supported for documents, spreadsheets, video, 3D models, CAD drawings, presentations, images, audio, archives, websites and more! The Wilcoxon signed rank test is used to compare the different methods moreover, the independence of the different methods is studied using the Q-statistic. The computer vision system based on the proposed approach was tested on many different datasets, demonstrating the generalizability of the proposed approach thanks to the strong performance recorded. For the handcrafted features, a wide variety of state-of-the-art algorithms are considered: Local Ternary Patterns, Local Phase Quantization, Rotation Invariant Co-occurrence Local Binary Patterns, Completed Local Binary Patterns, Rotated local binary pattern image, Globally Rotation Invariant Multi-scale Co-occurrence Local Binary Pattern, and several others. Three approaches are used for the non-handcrafted features: deep transfer learning features based on convolutional neural networks (CNN), principal component analysis network (PCAN), and the compact binary descriptor (CBD). The application of feature transform techniques to reduce the dimensionality of feature sets coming from the deep layers represents one of the main contributions of this paper. Three substructures are proposed for creating the generic computer vision system starting from handcrafted and non-handcrafter features: i) one that remaps the output layer of a trained CNN to classify a different problem using an SVM ii) a second for exploiting the output of the penultimate layer of a trained CNN as a feature vector to feed an SVM and iii) a third for merging the output of some deep layers, applying a dimensionality reduction method, and using these features as the input to an SVM.
![3d object converter 4.80 3d object converter 4.80](https://img.informer.com/p7/3d-object-converter-for-windows-v4.2-batch-converter.png)
Such a system is a single structure that can be used for synthesizing a large number of different image classification tasks. This work presents a generic computer vision system designed for exploiting trained deep Convolutional Neural Networks (CNN) as a generic feature extractor and mixing these features with more traditional hand-crafted features.