Feature detection using a connectionist network

Avoiding obstacles using a connectionist network

In this article, visual data obtained by a binocular active vision system is integrated, together with ultrasonic range measurements; in the development of a obstacle detection and avoidance system based on a connectionist grid. The traditional notion of probabilistic occupation grid is extended through the use of a three-layer structure of connectionist networks which allows the integration of...

A Transfer Learning Based Feature Extractor for Polyphonic Sound Event Detection Using Connectionist Temporal Classification

Sound event detection is the task of detecting the type, onset time, and offset time of sound events in audio streams. The mainstream solution is recurrent neural networks (RNNs), which usually predict the probability of each sound event at every time step. Connectionist temporal classification (CTC) has been applied in order to relax the need for exact annotations of onset and offset times; th...

Smart Feature Detection Using an Invariance Network Architecture . A .

Phishing website detection using weighted feature line embedding

The aim of phishing is tracing the users' s private information without their permission by designing a new website which mimics the trusted website. The specialists of information technology do not agree on a unique definition for the discriminative features that characterizes the phishing websites. Therefore, the number of reliable training samples in phishing detection problems is limited. M...

Quad-pixel edge detection using neural network

One of the most fundamental features of digital image and the basic steps in image processing, analysis, pattern recognition and computer vision is the edge of an image where the preciseness and reliability of its results will affect directly on the comprehension machine system made objective world. Several edge detectors have been developed in the past decades, although no single edge detector...