We address the problem of segmenting a sequence of images of natural scenes into disjoint regions that are characterized by constant spatio-temporal statistics. We model the spatio-temporal dynamics in each region by Gauss-Markov models, and infer the model parameters as well as the boundary of the regions in a variational optimization framework. Numerical results demonstrate that – in contrast...

In this paper we introduce a feature-based neural language model, which is trained to estimate the probability of an element given its previous context features. In this way our feature-based language model can learn representation for more sophisticated features. We introduced the deep neural architecture into the Chinese Word Segmentation task. We got a significant improvement on segmenting p...

The problem of segmenting agricultural fields in aerial images is still a manual work in most Geographic Information System requiring repetitive, tedious and timeconsuming human work. Here, we address the problem of semiautomatic segmenting agricultural fields by region competition technique that integrates region growing and deformable models. The deformable model dynamically adapts its contou...

Characters recognition in natural images is a challenging problem, as it involves segmenting characters of various colours on various background. In this article, we present a method for segmenting images that use a colour perception graph. Our algorithm is inspired by graph cut segmentation techniques and it use an edge detection technique for filtering the graph before the graph-cut as well a...

Purpose:The paper presents the process of segmenting an organization’s customers through a process of K-mean clustering and subsequent expert judgment. Methodology/Approach: The paper adopts a case study methodology Findings:The case study illustrates the use of data mining techniques in segmenting and profiling of customers by a travel intermediary in Thailand. Research limitations/implication...

Many image segmentation techniques have been developed over the past two decades for segmenting the images, which help for object recognition, occlusion boundary estimation within motion or stereo systems, image compression, image editing. In this, there is a combined approach for segmenting the image. By using histogram equalization to the input image, from which it gives contrast enhancement ...

We are investigating techniques for producing textual summaries of time series data. Deep reasoning techniques have proven impractical because we lack perfect knowledge about users and their tasks. Data analysis techniques such as segmentation are more attractive, but they have been developed for data mining, not for communication. We examine how segmentation should be modified to make it suita...

Digital preservation of old newspapers contributes greatly to the historical register of a country's social, political and economical events. At the same time, newspaper preservation is an imperative necessity because of the fast paper deterioration and difficulty in tracing the overwhelming amount of information. Lambrakis Press S.A. owns a large collection of newspapers and periodicals that c...

In this paper we address the problem of segmenting foreground regions corresponding to a group of people given models of their appearance that were initialized before occlusion. We present a general framework that uses maximum likelihood estimation to estimate the best arrangement for people in terms of 2D translation that yields a segmentation for the foreground region. Given the segmentation ...

A method is presented for segmenting documents into conceptually related areas. Determining the equivalence of text is often based on the number of word repetitions. This approach is unsuitable for detecting short segments because terms tend not to be repeated across just a few sentences. In this paper we investigate the contribution of two other lexical features to find related words: collocat...