Name Strategy: Its Existence and Implications

It is argued that colour name strategy, object name strategy, and chunking strategy in memory are all aspects of the same general phenomena, called stereotyping, and this in turn is an example of a know-how representation. Such representations are argued to have their origin in a principle called the minimum duplication of resources. For most the subsequent discussions existence of colour name strategy suffices. It is pointed out that the Berlin†- Kay† universal partial ordering of colours and the frequency of traffic accidents classified by colour are surprisingly similar; a detailed analysis is not carried out as the specific colours recorded are not identical. Some consequences of the existence of a name strategy for the philosophy of language and mathematics are discussed: specifically it is argued that in accounts of truth and meaning it is necessary throughout to use real numbers as opposed to bi-valent quantities; and also that the concomitant label associated with sentences should not be of unconditional truth, but rather several real-valued quantities associated with visual communication. The implication of real-valued truth quantities is that the Continuum Hypothesis of pure mathematics is side-stepped, because real valued quantities occur ab initio. The existence of name strategy shows that thought/sememes and talk/phonemes can be separate, and this vindicates the assumption of thought occurring before talk used in psycho-linguistic speech production models.

Text Segmentation in Web Images Using Colour Perception and Topological Features

The research presented in this thesis addresses the problem of Text Segmentation in Web images. Text is routinely created in image form (headers, banners etc.) on Web pages, as an attempt to overcome the stylistic limitations of HTML. This text however, has a potentially high semantic value in terms of indexing and searching for the corresponding Web pages. As current search engine technology does not allow for text extraction and recognition in images, the text in image form is ignored. Moreover, it is desirable to obtain a uniform representation of all visible text of a Web page (for applications such as voice browsing or automated content analysis). This thesis presents two methods for text segmentation in Web images using colour perception and topological features. The nature of Web images and the implicit problems to text segmentation are described, and a study is performed to assess the magnitude of the problem and establish the need for automated text segmentation methods. Two segmentation methods are subsequently presented: the Split-and-Merge segmentation method and the Fuzzy segmentation method. Although approached in a distinctly different way in each method, the safe assumption that a human being should be able to read the text in any given Web Image is the foundation of both methods’ reasoning. This anthropocentric character of the methods along with the use of topological features of connected components, comprise the underlying working principles of the methods. An approach for classifying the connected components resulting from the segmentation methods as either characters or parts of the background is also presented

Use of colour for hand-filled form analysis and recognition

Colour information in form analysis is currently under utilised. As technology has advanced and computing costs have reduced, the processing of forms in colour has now become practicable. This paper describes a novel colour-based approach to the extraction of filled data from colour form images. Images are first quantised to reduce the colour complexity and data is extracted by examining the colour characteristics of the images. The improved performance of the proposed method has been verified by comparing the processing time, recognition rate, extraction precision and recall rate to that of an equivalent black and white system

General highlight detection in sport videos

Attention is a psychological measurement of human reflection against stimulus. We propose a general framework of highlight detection by comparing attention intensity during the watching of sports videos. Three steps are involved: adaptive selection on salient features, unified attention estimation and highlight identification. Adaptive selection computes feature correlation to decide an optimal set of salient features. Unified estimation combines these features by the technique of multi-resolution autoregressive (MAR) and thus creates a temporal curve of attention intensity. We rank the intensity of attention to discriminate boundaries of highlights. Such a framework alleviates semantic uncertainty around sport highlights and leads to an efficient and effective highlight detection. The advantages are as follows: (1) the capability of using data at coarse temporal resolutions; (2) the robustness against noise caused by modality asynchronism, perception uncertainty and feature mismatch; (3) the employment of Markovian constrains on content presentation, and (4) multi-resolution estimation on attention intensity, which enables the precise allocation of event boundaries

Proceedings of the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

This book is a collection of 15 reviewed technical reports summarizing the presentations at the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory. The covered topics include image processing, optical signal processing, visual inspection, pattern recognition and classification, human-machine interaction, world and situation modeling, autonomous system localization and mapping, information fusion, and trust propagation in sensor networks

A CAPTCHA model based on visual psychophysics: Using the brain to distinguish between human users and automated computer bots

Demand for the use of online services such as free emails, social networks, and online polling is increasing at an exponential rate. Due to this, online service providers and retailers feel pressurised to satisfy the multitude of end-user expectations. Meanwhile, automated computer robots (known as “bots”) are targeting online retailers and service providers by acting as human users and providing false information in order to abuse their service provisioning. CAPTCHA is a set of challenge/response protocol, which was introduced to protect online retailers and service providers from misuse and automated computer attacks. Text-based CAPTCHAs are the most popular form, and are used by most online service providers to differentiate between the human users and bots. However, the vast majority of text-based CAPTCHAs have been broken using the Optical Character Recognition (OCR) techniques and thus, reinforces the need for developing a secure and robust CAPTCHA model. Security and usability are the two fundamental issues that pose a trade-off in the design of a CAPTCHA; a hard CAPTCHA model could also be difficult for human users to resolve, which affects its usability, and vice versa. The model developed in this study uses the unsurpassed abilities of the Human Visual System (HVS) to superimpose and integrate complex information presented in individual frames, using the mechanism of trans-saccadic memory. In this context, the model integrates in its design the concept of persistence of vision, which enables humans to see the world in a continuous fashion. Preliminary results from the proposed model based on this technique are encouraging. To ensure the usability of the proposed CAPTCHA model, we set the threshold for the ORO parameter at 40%. This ensured that our CAPTCHA strings would be recognised by human observers at a rate of over 99% (or as close to 100% as is realistic). In turn, when examining the robustness of our VICAP model to computer programme attacks, we can observe that for the traditional case of OCR recognition, based on a single-frame scenario, the Computer Recognition Success Rate (CRSR) was about 0%, while in the case of a multi-frame scenario, the CRSR could increase to up to 50%

Tools to integrate organoleptic quality criteria into breeding programs

This technical booklet provides methodologies and guidance to implement sensory evaluations for organoleptic quality assessment in multi-actor-projects for organic agriculture. It presents five detailed tests that can be used in sensory evaluation, methodologies on how to prepare the samples and a glossary. This booklet has been developed under Solibam project and updated during Diversifood project