The analysis and identification of texture is a key area in image processing and computer vision. One of the most prominent texture analysis algorithms is the Gabor Filter. These filters are used by convolving an image with a family of self similar filters or wavelets through the selection of a suitable number of scales and orientations, which are responsible for aiding in the identification of textures of differing coarseness and directions respectively. While extensively used in a variety of applications, including, biometrics such as iris and facial recognition, their effectiveness depend largely on the manual selection of different parameters values, i.e. the centre frequency, the number of scales and orientations, and the standard deviations. Previous studies have been conducted on how to determine optimal values. However the results are sometimes inconsistent and even contradictory. Furthermore, the selection of the mask size and tile size used in the convolution process has received little attention, presumably since they are image set dependent. This research attempts to verify specific claims made in previous studies about the influence of the number of scales and orientations, but also to investigate the variation of the filter mask size and tile size for water body extraction from satellite imagery. Optical satellite imagery may contain texture samples that are conceptually the same (belong to the same class), but are structurally different or differ due to changes in illumination, i.e. a texture may appear completely different when the intensity or position of a light source changes. A systematic testing of the effects of varying the parameter values on optical satellite imagery is conducted. Experiments are designed to verify claims made about the influence of varying the scales and orientations within predetermined ranges, but also to show the considerable changes in classification accuracy when varying the filter mask and tile size. Heuristic techniques such as Genetic Algorithms (GA) can be used to find optimum solutions in application domains where an enumeration approach is not feasible. Hence, the effectiveness of a GA to automate the process of determining optimum Gabor filter parameter values for a given image dataset is also investigated. The results of the research can be used to facilitate the selection of Gabor filter parameters for applications that involve multi-textured image segmentation or classification, and specifically to guide the selection of appropriate filter mask and tile sizes for automated analysis of satellite imagery. / Thesis (M.Sc.)-University of KwaZulu-Natal, Durban, 2012.

智慧型手機的用途已從語音溝通延伸轉變為多功能導向的的生活工具。目 前多數的智慧型手機均具備攝影鏡頭,而此模組更已被公認為基本的標準 配備。使用者透過手機,可以輕易且自然地拍攝感興趣的物體、景色或文 字等,並且建立屬於自己的影像資料庫。在眾多的手機軟體中,旅遊類的 程式是其中一種常見整合內容與多項感測模組的應用實例。在行動平台上, 設計一個影像辨識系統服務可以大幅地協助遊客們在旅途中去瞭解、認識 知名的地標、建築物、或別具意義的物體與文字等。 然而在行動平台上的可用資源是有限的,因此想要在行動平台上開發有效 率的影像辨識系統,是頗具挑戰性的任務。如何在準確率與計算成本之間 取得最佳的平衡點往往是行動平台上開發影像辨識技術的最重要課題。 根據上述的目標,本研究擬於行動平台上設計、開發行動影像搜尋與智慧 型文字辨識系統。具體而言,我們將在影像搜尋上整合兩個全域的特徵描 述子,並針對印刷與手寫字體去開發智慧型文字辨識系統。實驗結果顯示, 在行動影像搜尋與文字辨識的效能測試部分,前三名的辨識率皆可達到的 80%。 / The roles of smart phones have extended from simple voice communications to multi-purpose applications. Smart phone equipped with miniaturized image capturing modules are now considered standard. Users can easily take pictures of interested objects, scenes or texts, and build their own image database. Travel-type mobile app is one example that takes advantage of the array of sensors on the device. A mobile image search engine can bring much convenience to tourists when they want to retrieve information regarding specific landmarks, buildings, or other objects. However, devising an effective image recognition system for smart phone is a quite challenging task due to the complexity of image search and pattern recognition algorithms. Image recognition techniques that strike a balance between accuracy and efficiency need to be developed to cope with limited resources on mobile platforms. Toward the above goal, this thesis seeks to design effective mobile visual search and intelligent character recognition systems on mobile platforms. Specifically, we propose two global feature descriptors for efficient image search. We also develop an intelligent character recognition engine that can handle both printed and handwritten texts. Experimental results show that the accuracy reaches 80% for top-3 candidates in visual search and intelligent character recognition tasks.

Thesis (M. Eng. Sc.)--University of Adelaide, 1987. / Typescript. Includes a copy of a paper presented at TADSEM '85 --Australian Seminar on Devices for Expressive Communication and Environmental Control, co-authored by the author. Includes bibliographical references (leaves [115-121]).

Thesis (Ph.D.)--University of Wollongong, 2006. / Typescript. Includes bibliographical references: leaf 208-226.

Denna rapport redogör för skapandet av ett API som kan extrahera information från bilder på kvitton. Informationen som APIet skulle kunna ta fram var organisationsnummer, datum, tid, summa och moms. Här ingår även en fördjupning om tekniken OCR (optical character recognition) som omvandlar bilder och dokument till text. Examensarbetet utfördes åt Flex Applications AB. Examensarbetet utfördes åt Flex Applications AB. / This report describes the creation of an API that can extract information from pictures of receipts. Registration number, date, time, sum and tax are the information that the API was going to be able to deliver. In this thesis there is also a deepening of the technology OCR (optical character recognition) that transforms pictures and documents to text. The thesis was performed for Flex Applications AB.

This thesis introduces readers to the basic types of the text and information inputs and processing to the computer. Thesis also includes historical contexts, current trends and future perspective of computer data input technologies and their use in practice. The first part of the thesis is a summary of a particular forms of entering and processing of the text data and information. The following part presents technological trends on the market concentrated on the automatic speech recognition systems along with the possibilities of their application in the business sphere. The rest of the thesis consists of a survey between Czech IT companies and based on it's results comes a suggestion of which technologies should be used as a part of the information systems.

The thesis consists of three parts. Theoretical description of digital image processing, optical character recognition and design of system for car licence plate recognition (LPR) in image or video sequence. Theoretical part describes image representation, smoothing, methods used for blob segmentation and proposed are two methods for optical character recognition (OCR). Concern of practical part is to find solution and design procedure for LPR system included OCR. The design contain image pre-processing, blob segmentation, object detection based on its properties and OCR. Proposed solution use grayscale trasformation, histogram processing, thresholding, connected component,region recognition based on its patern and properties. Implemented is also optical recognition method of licence plate where acquired values are compared with database used to manage entry of vehicles into object.

This work focuses on the theory of artificial neural networks: the history, individual ways of learning and architecture of networks. It is also necessary to desribe the image processing blocks from scanning and image processing through segmentation to object recognition. The next part is focused on connecting the previous two parts, and therefore on the use of neural networks in image processing, specifically the identification of objects. In the practical part of the work is designed the user application for recognizing characters such as numbers, uppercase and lowercase letters.

Encoding hieroglyphic texts is time-consuming. If a text already exists as hand-written transcription, there is an alternative, namely OCR. Off-the-shelf OCR systems seem difficult to adapt to the peculiarities of Ancient Egyptian. Presented is a proof-of-concept tool that was designed to digitize texts of Urkunden IV in the hand-writing of Kurt Sethe. It automatically recognizes signs and produces a normalized encoding, suitable for storage in a database, or for printing on a screen or on paper, requiring little manual correction. The encoding of hieroglyphic text is RES (Revised Encoding Scheme) rather than (common dialects of) MdC (Manuel de Codage). Earlier papers argued against MdC and in favour of RES for corpus development. Arguments in favour of RES include longevity of the encoding, as its semantics are font-independent. The present study provides evidence that RES is also much preferable to MdC in the context of OCR. With a well-understood parsing technique, relative positioning of scanned signs can be straightforwardly mapped to suitable primitives of the encoding.

This paper proposes an end-to-end, scale invariant, visual object recognition system, composed of computational components that mimic the cortex in the brain. The system uses a two stage process. The first stage is a filter that extracts scale invariant features from the visual field. The second stage uses inference based spacio-temporal analysis of these features to identify objects in the visual field. The proposed model combines Numenta's Hierarchical Temporal Memory (HTM), with HMAX developed by MIT's Brain and Cognitive Science Department. While these two biologically inspired paradigms are based on what is known about the visual cortex, HTM and HMAX tackle the overall object recognition problem from different directions. Image pyramid based methods like HMAX make explicit use of scale, but have no sense of time. HTM, on the other hand, only indirectly tackles scale, but makes explicit use of time. By combining HTM and HMAX, both scale and time are addressed. In this paper, I show that HTM and HMAX can be combined to make a com- plete cortex inspired object recognition model that explicitly uses both scale and time to recognize objects in temporal sequences of images. Additionally, through experimentation, I examine several variations of HMAX and its