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A License Plate-Recognition Algorithm for Intelligent Transportation System Applications
TLDR
A review in the related literature presented in this paper reveals that better performance has been reported, when limitations in distance, angle of view, illumination conditions are set, and background complexity is low.
License Plate Recognition From Still Images and Video Sequences: A Survey
TLDR
This paper offers to researchers a link to a public image database to define a common reference point for LPR algorithmic assessment and issues such as processing time, computational power, and recognition rate are addressed.
Vehicle Logo Recognition Using a SIFT-Based Enhanced Matching Scheme
TLDR
It is shown that the enhanced matching approach proposed in this paper boosts the recognition accuracy compared with the standard SIFT-based feature-matching method.
Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011
TLDR
Important topics from different classification techniques, such as databases available for experimentation, appropriate feature extraction and selection methods, classifiers and performance issues are discussed, with emphasis on research published in the last decade.
Comparison of Different Classifiers for Emotion Recognition
TLDR
A comparison of two classifiers for speech signal emotion recognition is presented and a speaker-dependent and speaker independent emotion recognition classification is concentrated on.
Intelligent Modification of Colors in Digitized Paintings for Enhancing the Visual Perception of Color-blind Viewers
TLDR
A novel daltonization method that targets a specific type of color vision deficiency, namely protanopia and is adapted in order to improve perception, while ensuring that the adapted colors do not conflict with colors in the first category.
Intelligent modification for the daltonization process of digitized paintings
Daltonization is a procedure for adapting colors in an image or a sequence of images for improving the color perception by a color-deficient viewer. In this paper an intelligent/enhanced
Classification on Speech Emotion Recognition-A Comparative Study
TLDR
A comparative analysis of four classifiers for speech signal emotion recognition, focusing on speaker and utterance (phrase) dependent and independent framework, finds that the speaker dependent framework reaches very high accuracy(94%) and the speaker independent framework reaches 80%.
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