The construction of images of people and place: Labelling Liverpool and stereotyping Scousers.

On 28th August 1207, King John created the Borough of Liverpool by granting its first charter. During the ensuing 800 years Liverpool has experienced a complex and changing social, economic and political history resulting in powerful images of the city and its people. This paper examines the labelling of Liverpool and stereotypes of Scousers. It explains how historical and contemporary events, and their coverage in various arms of the media, construct social and spatial imaginations of the city. This involves a more systematic contribution to the how and why dimensions of negative place imagery and social stereotypes, and enhances our understanding of the processes and issues affecting our interpretations of people and place. The analysis is both historical and contemporaneous in teasing out how previous and current events shape the perceptions of insiders and outsiders. This paper reveals that despite concerted efforts to re-brand Liverpool the city continues to face difficult challenges with ongoing bad publicity and negative place imagery.

Robust Extraction of Text from Camera Images using Colour and Spatial Information Simultaneously

The importance and use of text extraction from camera based coloured scene images is rapidly increasing with time. Text within a camera grabbed image can contain a huge amount of meta data about that scene. Such meta data can be useful for identification, indexing and retrieval purposes. While the segmentation and recognition of text from document images is quite successful, detection of coloured scene text is a new challenge for all camera based images. Common problems for text extraction from camera based images are the lack of prior knowledge of any kind of text features such as colour, font, size and orientation as well as the location of the probable text regions. In this paper, we document the development of a fully automatic and extremely robust text segmentation technique that can be used for any type of camera grabbed frame be it single image or video. A new algorithm is proposed which can overcome the current problems of text segmentation. The algorithm exploits text appearance in terms of colour and spatial distribution. When the new text extraction technique was tested on a variety of camera based images it was found to out perform existing techniques (or something similar). The proposed technique also overcomes any problems that can arise due to an unconstraint complex background. The novelty in the works arises from the fact that this is the first time that colour and spatial information are used simultaneously for the purpose of text extraction.

Segmentation of optic disc in retinal images using an improved gradient vector flow algorithm

Image segmentation plays an important role in the analysis of retinal images as the extraction of the optic disk provides important cues for accurate diagnosis of various retinopathic diseases. In recent years, gradient vector flow (GVF) based algorithms have been used successfully to successfully segment a variety of medical imagery. However, due to the compromise of internal and external energy forces within the resulting partial differential equations, these methods can lead to less accurate segmentation results in certain cases. In this paper, we propose the use of a new mean shift-based GVF segmentation algorithm that drives the internal/external energies towards the correct direction. The proposed method incorporates a mean shift operation within the standard GVF cost function to arrive at a more accurate segmentation. Experimental results on a large dataset of retinal images demonstrate that the presented method optimally detects the border of the optic disc.