IoT based image processing filters

Multichannel filters for image processing

New filters for multichannel image processing are introduced and analysed. The proposed methodology constitutes a unifying and powerful framework for multichannel signal processing. The new filters use fuzzy membership functions based on different distance measures among the image vectors to adapt to local data in the image. Fuzzy aggregators are utilized to determine the weights in the propose...

Biomedical Image Processing with Morphology-Based Nonlinear Filters

Image Processing Filters for Line Detection-Based Spoken Term Detection

Spoken term detection (STD) from oral presentations is addressed. Specifically, we regard STD as a line detection problem in an image file, in which each pixel holds a syllabledistance between query term and automatic speech recognition (ASR) results. Since such kind of image file essentially includes ASR errors, line detection in noisy image should be investigated. In this paper, we propose li...

Adaptive Filters for Image Processing Based on Artificial Neural Networks

This paper presents our research in image processing (filters used for edges detection, color transformation, and distortion correction) using Artificial Neural Networks. We have developed a tool, Neuron Color, in order to do this kind of experiments and improving our adaptive methods.

Multiresolution Particle Filters in Image Processing

Recursively estimating the likelihood of a set of parameters, given a series of observations, is a common problem in signal processing. The Kalman filter is a The particle filter is now a well-known alternative to the Kalman filter. It represents the likelihood as a set of samples with associated weights and so can approximate any distribution. It can be applied to problems where the process mo...