A review on image segmentation techniques

An Introduction to MultiAgent Systems.

There are now a wide Abstract There are now a wide variety of image segmentation techniques, some considered general purpose and some designed for specific classes of images. These techniques can be classified as: measurement space guided spatial clustering, single linkage region growing schemes, hybrid linkage region growing schemes, centroid linkage region growing schemes, spatial clustering schemes, and split-and-merge schemes. In this paper, we define each of the major classes of image segmentation techniques and describe several specific examples of each class of algorithm. We illustrate some of the techniques with examples of segmentations performed on real images.

Image Segmentation Techniques

https://www.cin.ufpe.br/~rps/Artigos/Face%20Detection%20-%20%20A%20Survey.pdf

Face Detection

This review considers the application of artificial neural networks (ANNs) to rainfall-runoff modelling and flood forecasting. This is an emerging field of research, character- ized by a wide variety of techniques, a diversity of geographical contexts, a general absence of intermodel comparisons, and inconsistent reporting of model skill. This article begins by outlining the basic principles of ANN modelling, common network architectures and training algorithms. The discussion then addresses related themes of the division and preprocessing of data for model calibration/validation; data standardization techniques; and methods of evaluating ANN model performance. A literature survey underlines the need for clear guidance in current modelling practice, as well as the comparison of ANN methods with more conven- tional statistical models. Accordingly, a template is proposed in order to assist the construction of future ANN rainfall-runoff models. Finally, it is suggested that research might focus on the extraction of hydrological 'rules' from ANN weights, and on the development of standard performance measures that penalize unnecessary model complexity.

Hydrological modelling using artificial neural networks

Estimating the flows of rivers can have significant economic impact, as this can help in agricultural water management and in protection from water shortages and possible flood damage. The first goal of the paper is to apply neural networks to the problem of forecasting the flow of the River Nile in Egypt. The second goal of the paper is to utilize time series as a benchmark to compare between several neural-network forecasting methods. We compare four different methods to preprocess the inputs and outputs, including a novel method proposed here based on discrete Fourier series. We also compare three different methods for the multistep ahead forecast problem: the direct method, the recursive method, and the recursive method trained using a backpropagation through time scheme. We also include a theoretical comparison between these three methods. The final comparison is between different methods to perform a longer horizon forecast, and that includes ways to partition the problem into several subproblems of forecasting K steps ahead.

A comparison between neural-network forecasting techniques-case study: river flow forecasting

Sign language recognition using a combination of new vision based features

The objective of a computer vision system is to outline the objects in a picture and label them with an appropriate interpretation. This paper proposes a new paradigm for a modular computer vision system which is both data directed and knowledge based. The system consists of three different types of units, two of which are associative data memories implemented as relational databases. The short-term memory (STM) contains the raw color picture data and the most current interpretations and deductions about the original scene. The long-term memory (LTM) contains a detailed model of the scene under consideration. A collection of analysis processors, each of which is specialized for a particular task, can communicate with both of these memories. The information in the LTM remains unchanged during the analysis, while the STM is being continually updated and revised by the appropriate processors. The latter may be conceived of as being activated by certain data conditions in the STM, and using the information in both the LTM and STM to alter the status of the STM.

A Modular Computer Vision System for Picture Segmentation and Interpretation

Automated medical image processing and analysis offers a powerful tool for medical diagnosis. In this work we tackle the problem of white blood cell shape analysis based on the morphological characteristics of their outer contour and nuclei. The paper presents a set of preprocessing and segmentation algorithms along with a set of features that are able to recognize and classify different categories of normal white blood cells. The system was tested on gray level images obtained from a CCD camera through a microscope and produced a correct classification rate close to 91%.

Samir I. Shaheen

Papers

A comparison between neural-network forecasting techniques-case study: river flow forecasting

Sign language recognition using a combination of new vision based features

A Modular Computer Vision System for Picture Segmentation and Interpretation

Segmentation and classification of white blood cells

Landmark identification in computerized posteroanterior cephalometrics.