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We present a biologically inspired long-term object tracking system based on Virtual Generalizing Random Access Memory (VG-RAM) Weightless Neural Networks (WNN). VG-RAM WNN is an effective machine learning technique that offers simple implementation and fast training. Our system models the biological saccadic eye movement, the transformation suffered by the images captured by the eyes from the retina...
The Virtual Generalizing Random Access Memory Weightless Neural Network (VG-RAM WNN) is a type of WNN that only requires storage capacity proportional to the training set. As such, it is an effective machine learning technique that offers simple implementation and fast training – it can be made in one shot. However, the VG-RAM WNN test time for applications that require many training samples can be...
We present a biologically inspired approach to traffic sign detection based on Virtual Generalizing Random Access Memory Weightless Neural Networks (VG-RAM WNN). VG-RAM WNN are effective machine learning tools that offer simple implementation and fast training and test. Our VG-RAM WNN architecture models the saccadic eye movement system and the transformations suffered by the images captured by the...
Virtual Generalizing Random Access Memory Weightless Neural Networks (VG-RAM WNN) is an effective machine learning technique that offers simple implementation and fast training and test. In this paper, we present a new approach for traffic sign recognition based on VG-RAM WNN. We evaluate its performance using the German Traffic Sign Recognition Benchmark (GTSRB), a large multi-class classification...
Virtual Generalizing Random Access Memory Weightless Neural Networks (VG-RAM WNN) is an effective machine learning technique that offers simple implementation and fast training and test. We examined the performance of VG-RAM WNN on binocular dense stereo matching using the Middlebury Stereo Datasets. Our experimental results showed that, even without tackling occlusions and discontinuities in the...
We developed two parallel versions of the Constant Space Belief Propagation algorithm (CSBP - one of the best stereo algorithms currently known): one in OpenMP and one in C+CUDA. For images with 640x480 pixels, the sequential version has a performance of 1.16 frames per second (FPS), the OpenMP parallel version has a performance of 3.7 FPS, while the C+CUDA version has a performance of 17.3 FPS in...
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