of inaccurate segmentation, 2) helps to clarify the semantics of a particular region, and 3) enables a simple querying interface for region-based image retrieval systems. The application of SIMPLIcity to several databases, including a database of about 200,000 general-purpose images, has demonstrated that our

We present a new, interactive tool called Intelligent Scissors which we use for image segmentation and composition. Fully automated segmentation is an unsolved problem, while manual tracing is inaccurate and laboriously unacceptable. However, Intelligent Scissors allow objects within digital images

We present a new, interactive tool called Intelligent Scissors which we use for image seg-mentation. Fully automated segmentation is an unsolved problem, while manual tracing is inaccu-rate and laboriously unacceptable. However, Intelligent Scissors allow objects within digital images

on individual regions, the overall similarity approach reduces the influence of inaccurate segmentation, helps to clarify the semantics of a particular region, and enables a simple querying interface for region-based image retrieval systems. The IRM has been implemented as a part of our experimental SIMPLIcity

with crisp-valued feature representations, the UFM measure greatly reduces the inuence of inaccurate segmentation, and provides a very intuitive quantification. The UFM has been implemented as a part of our experimental SIMPLIcity image retrieval system. The performance of the system is illustrated using

Abstract: Text line segmentation is an important step because inaccurately segmented text lines will cause errors in the recognition stage. Text line segmentation of the handwritten documents is still one of the most complicated problems in developing a reliable OCR. The nature of handwriting

This paper studies the problem of extracting data from a Web page that contains several structured data records. The objective is to segment these data records, extract data items/fields from them and put the data in a database table. This problem has been studied by several researchers. However

, computer-aided detection systems may be highly likely to fail to depict those abnormal regions because of inaccurate segmentation methods. In particular, abnormali-ties such as pleural effusions, consolidations, and masses often cause inaccurate lung segmentation, which greatly limits the use of image

between each pair of adjacent segmentation points, and, finally, obtaining graphical representations of substrokes by fitting graphical primitives to them. Since a stroke inevitably contains noises, the first stage may produce wrong or inaccurate segmentation points, resulting in the wrong substroke

tomography images. The level set algorithm has some advantages over the classical snake deformable models but it has difficulties with large gaps in the boundary of segmented region. Such boundary gaps may cause inaccurate segmentation that requires manual correction by the user while our goal is to keep