Detection and classification of road signs in natural environments

An automatic road sign recognition system first locates road signs within images captured by an imaging sensor on-board of a vehicle, and then identifies the detected road signs. This paper presents an automatic neural-network-based road sign recognition system. First, a study of the existing road sign recognition research is presented. In this study, the issues associated with automatic road sign recognition are described, the existing methods developed to tackle the road sign recognition problem are reviewed, and a comparison of the features of these methods is given. Second, the developed road sign recognition system is described. The system is capable of analysing live colour road scene images, detecting multiple road signs within each image, and classifying the type of road signs detected. The system consists of two modules: detection and classification. The detection module segments the input image in the hue-saturation-intensity colour space, and then detects road signs using a Multi-layer Perceptron neural-network. The classification module determines the type of detected road signs using a series of one to one architectural Multi-layer Perceptron neural networks. Two sets of classifiers are trained using the Resillient-Backpropagation and Scaled-Conjugate-Gradient algorithms. The two modules of the system are evaluated individually first. Then the system is tested as a whole. The experimental results demonstrate that the system is capable of achieving an average recognition hit-rate of 95.96% using the scaled-conjugate-gradient trained classifiers.

Analysis of environmental sounds as indexical signs in film

In this paper, we investigate the problem of classifying a subset of environmental sounds in movie audio tracks that indicate specific indexical semiotic use. These environmental sounds are used to signify and enhance events occurring in film scenes. We propose a classification system for detecting the presence of violence and car chase scenes in film by classifying ten various environmental sounds that form the constituent audio events of these scenes using a number of old and new audio features. Experiments with our classification system on pure test sounds resulted in a correct event classification rate of 88.9%. We also present the results of the classifier on the mixed audio tracks of several scenes taken from The Mummy and Lethal Weapon 2. The classification of sound events is the first step towards determining the presence of the complex sound scenes within film audio and describing the thematic content of the scenes.

Detecting indexical signs in film audio for scene interpretation

In this paper, we study the sound tracks in films and their indexical semiotic usage by developing a classification system that detects complex sound scenes and their constituent sound events in cinema. We investigate two main issues in this paper: Determination of what constitutes the presence of a high level sound scene and inferences about the thematic content of the scene that can be drawn from this presence, and classification of environmental sounds in the audio track of the scene, to assist in the automatic detection of the high level scene. Experiments with our classification system on pure sounds resulted in a correct event classification rate of 88.9%. When the audio content of a number of film scenes was examined, though a lower accuracy resulted with sound event detection due to the presence of mixed sounds, the film audio samples were generally classified with the correct high-level sound scene label, enabling correct inferences about the story content of the scenes.

Vital signs

Vital sign assessment and interpretation is integral in determining a patient's health status. An important aim of assessment of vital signs is to identify early patient deterioration. This chapter will provide guidelines to facilitate the incorporation of vital sign measurement into nursing practice.