Showing papers on "Image segmentation published in 1970"

Acoustic segmentation of speech

Abstract: A brief argument is presented for the need for automatic speech segmentation both to facilitate automatic speech recognition and for its theoretical linguistic importance. The problem of speech segmentation in the acoustic domain using a digital computer is examined in detail, that is, of determining an acoustic partition in time which has linguistic relevance. This problem is viewed, in more general terms, as that of detecting transitions, in a globally non-stationary process, from one local stationary state to another. Non-stationary analyses are approximated by considering short fixed length time series sections as seen through a window which moves by a fixed increment. Various non-stationary signal representations are explored in order to establish a feature space suitable for applications to segmentation. Spectral representations are generated only as a reference space for comparison of an automatic segmentation procedure with the linguistically determined segmentation of any given speech sample. Temporal representations of the zero crossings of speech signals are explored in detail. In particular the central sample moments of the reciprocal zero crossings as a function of time are used as input to a simple segmentation algorithm. The results of a demonstration of this algorithm show that speech segmentation as defined is possible by non-human means.

Detección de distracción en conductores mediante técnicas de visión de máquina

Abstract: This article presents a system for detecting states of distraction in drivers during daylight hours using machine vision techniques, which is based on the image segmentation of the eyes and mouth of a person, with a front-faceview camera From said segmentation states of motion of the mouth and head are established, thus allowing to infer the corresponding state of distraction Images are extracted from short videos with a resolution of 640x480 pixels and image processing techniques such as color space transformation and histogram analysis are applied A decision concerning the state of the driver is the result from a multilayer perceptron-type neural network with all extracted features as inputs Achieved performance is 90% for a controlled environment screening test and 86% in real environment, with an average response time of 30 ms

Recent Advances In Intelligent Inspection Of WoodBoards

Abstract: Automated Visual Inspection (AVI) is being proposed for the task of inspecting wood sheets for quality control. The motivation for this is that currently, the human graders employed for the task can only perform inspection with an accuracy of around 60%. For wood inspection, AVI operates by employing a camera to acquire an image of the sheet and then utilising appropriate image processing hardware and software routines to find and classify surface defects. A typical AVI framework includes the stages of image acquisition, image enhancement, segmentation, feature extraction, classification and grading. The image acquisition stage obtains an image of the sheet. Image enhancement improves the quality of the acquired image to facilitate segmentation. Frequently, this stage is not used because it is considered more important to obtain high quality images. Image segmentation divides the image into clear wood and defect regions. The result of this stage is called the segmented image which contains objects that represent the defects. Then, feature extraction is employed to calculate numerical values to represent each object. The classification stage determines the type of each object based on its features. Finally, the board is given a grade based on the number of defects found and the size and severity of each one. Grading is a task which can be implemented with the aid of a grading table and a simple expert system. Therefore, it offers few opportunities for research. This paper gives details of advances in the areas of image acquisition, image enhancement, segmentation, feature extraction and classification for Automated Visual Inspection of wood boards. Artificial intelligence techniques, such as neural networks, genetic algorithms and fuzzy logic, are concentrated upon. Transactions on Information and Communications Technologies vol 20, © 1998 WIT Press, www.witpress.com, ISSN 1743-3517