Rapid training data generation from image sequences for pattern recognition

This study focuses on the development of a novel technique for the rapid generation of artificial neural network training data from video streams. Videos captured on an off-road terrain are used to train artificial neural networks that learn to differentiate road and non-road sections in the captured videos. Contrary to the times-taking frame-by-frame processing, in the proposed method, classification data of road pixels is created concurrently as the video plays. The proposed method is explained in detail and its performance is evaluated against the classical hand-classified image sequences on test videos. The proposed method can also be applied to several other applications using training for recognition.


Deep learning approach for laboratory mice grimace scaling
Eral, Mustafa; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2016)
Deep learning is extremely attractive research topic in pattern recognition and machine learning areas. Applications in speech recognition, natural language processing, and machine vision fields gained huge acceleration in performance by employing deep learning. In this thesis, deep learning is used for medical purposes in order to scale pain degree of drug stimulated mice by examining facial grimace. For this purpose each frame in the videos in the training set were scaled manually by experts according to ...
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This paper introduces two deep convolutional neural network training techniques that lead to more robust feature subspace separation in comparison to traditional training. Assume that dataset has M labels. The first method creates M deep convolutional neural networks called {DCNNi}i=1M" role="presentation">{DCNNi}Mi=1. Each of the networks DCNNi" role="presentation">DCNNi is composed of a convolutional neural network (CNNi" role="presentation">CNNi) and a fully connected neural network (FCNNi" role="pre...
Citation Formats
R. A. DİLAN, A. B. Koku, and E. İ. Konukseven, “Rapid training data generation from image sequences for pattern recognition,” 2011, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48768.