Teching aid in form of schoolbooks plays an importent role. The teacher uses schoolbooks as a tool in its tutoring. I have examined how and whether the pictures in the schoolbooks are presenting people and ethnic minorities biased and stereotypical. The material I have examined consisted of a series of schoolbooks (So-direkt 1-3, samhällskunskap av Bonnier 2003). To find out if that is the case I have used a theory by Stuart Hall, his theory is about people and how they are represented in different types of media, like pictures and text. I studied it by using the method of image analysis where I investigated and interpreted the denotation and konnontation of the pictures. Through image analysis I have looked at, examined and interpreted pictures that shows people in rich or poor contexts. From that I discoverd that people does not depict stereotypicaly in the pictures, but i did find myself seeing that people are positivly and/or negatively depicted depending on who are shown in the picture. Ethnic minorities are underrepresented in this series of books. Pictures which are depicting ethnic minorities are only shown in a total of nine pictures. In the way that these books are presenting Swedes they are only showing one typ of Swede. That Swede is nearly always light-skinned and has either blond or brown hair. The diversity that exist in reality is not representet in these schoolbooks. These schoolbooks tend to present certain people more positively then others through the pictures they depict. When it comes to show the environment and people it is done by showing the swedish population living in wealth. Swedish people are seen consuming and producing commodities, you can also see a variety of technological machines as tv:s, cellphones, phones, cars, computers, airplanes and trains surrounding them. Other people who are dark-skinned tend to be presented in a poor enviroment and in a negativ way in pictures.

Robotic automation has been a part of state-of-the-art manufacturing for many decades. Robotic manipulators are used for such tasks as welding, painting, pick and place tasks etc. Robotic manipulators are quite flexible and adaptable to new tasks, but a typical robot-based production cell requires extensive specification of the robot motion and construction of tools and fixtures for material handling. This incurs a large effort both in time and monetary expenses. The task of a vision system in this setting is to simplify the control and guidance of the robot and to reduce the need for supporting material handling machinery. This dissertation examines performance and properties of the current state-of-the-art local features within the setting of object pose estimation. This is done through an extensive set of experiments replicating various potential problems to which a vision system in a robotic cell could be subjected. The dissertation presents new local features which are shown to increase the performance of object pose estimation. A new local descriptor details how to use log-polar sampled image patches for truly rotational invariant matching. This representation is also extended to use a scale-space interest point detector which in turn makes it very competitive in our experiments. A number of variations of already available descriptors are constructed resulting in new and competitive features, among them a scale-space based Patch-duplet. In this dissertation a successful vision-based object pose estimation system is extended for multi-cue integration, yielding increased robustness and accuracy. Robustness is increased through algorithmic multi-cue integration, combining the individual strengths of multiple local features. Increased accuracy is achieved by utilizing manipulator movement and applying temporal multi-cue integration. This is implemented using a real flexible robotic manipulator arm. Besides work done on local features for ordinary image data a number of local features for range data has also been developed. This dissertation describes the theory behind and the application of the scene tensor to the problem of object pose estimation. The scene tensor is a fourth order tensor representation using projective geometry. It is shown how to use the scene tensor as a detector as well as how to apply it to the task of object pose estimation. The object pose estimation system is extended to work with 3D data. A novel way of handling sampling of range data when constructing a detector is discussed. A volume rasterization method is presented and the classic Harris detector is adapted to it. Finally, a novel region detector, called Maximally Robust Range Regions, is presented. All developed detectors are compared in a detector repeatability test.

Print Mottle is perhaps one of the most disturbing factors influencing overall Print Quality. Mottle has traditionally been evaluated by estimating the reflectance variation in the print. Although the amplitude of the reflectance variation is probably the most important aspect of print mottle, other aspects may also influence the perceptibility of mottle. Since the human visual system is optimised to fit the conditions prevailing in its surroundings, it is also important to consider aspects such as mean reflectance factor level, spatial frequency content, structure of the mottle, and colour variations. In this thesis, a new evaluation model for the estimation of print mottle is proposed. The model is best explained as a six-step chain. First, a digital RGB image of the print is acquired with a scanner. The digital RGB image is then calibrated and transformed into the L*a*b* colour space. Next, the three colour components are transformed into the frequency domain by a Fourier transform and the power spectra are calculated. The power spectra are thereafter filtered with respect to the contrast sensitivity functions representing the human eye’s sensitivity to spatial variations in the three colour channels. To account for systematic variations in the sample, the spectra are filtered a second time with texture enhancement filters, which are based on local calculations of chi-square measures in the power spectra. The energy within the visually detectable area of the filtered power spectra is then integrated to obtain a single measure of the variation for each colour component. A single mottle estimate is obtained as the square root of the sum of the squared variation measures for the three components. To acknowledge the influence of mean lightness level on perceived print mottle in a way that agrees with the results presented in Paper I, the mottle estimate obtained is finally multiplied by the sixth root of the mean reflectance factor level. The theoretical foundations of the model are consecutively developed through the first five papers of the thesis. The first paper considers the influence of the mean reflectance level on perceived print mottle. The second and third papers describe the contrast sensitivity filter and the texture enhancement filter applied. The fourth paper compares the new model with other models for print mottle evaluation. The fifth paper extends the grey-scale version of the model into colour. The sixth paper presents the unified model that takes all the mentioned factors into account. To test the model, samples from both simulated sets of prints with various degrees of colour and/or systematic mottle and sets of real prints from various conventional presses were analysed a) visually, b) with traditional print mottle evaluation models, and c) with the new model. Results obtained using the different evaluation models were compared with visual assessments of the sets of prints. In each one of the evaluations the new model was found to be as good as or superior to the traditional print mottle evaluation models in its agreement with visual assessment. The new model is particularly promising in cases where the evaluated prints show colour and/or systematic disturbances. / QC 20101012

Thresholding algorithms and filters are of great importance when utilizing OCR to extract information from text documents such as invoices. Invoice documents vary greatly and since the performance of image processing methods when applied to those documents will vary accordingly, selecting appropriate methods is critical if a high recognition rate is to be obtained. This paper aims to determine if a document recognition system that automatically selects optimal processing methods, based on the characteristics of input images, will yield a higher recognition rate than what can be achieved by a manual choice. Such a recognition system, including a learning framework for selecting optimal thresholding algorithms and filters, was developed and evaluated. It was established that an automatic selection will ensure a high recognition rate when applied to a set of arbitrary invoice images by successfully adapting and avoiding the methods that yield poor recognition rates.

This report contains a description on how it is possible to guide a robot along an edge, by using a camera mounted on the robot. If stereo matching is used to calculate 3Dcoordinates of an object or an edge, it requires two images from different known positions and orientations to calculate where it is. In the image analysis in this project, the Canny edge filter has been used. The result from the filter is not useful directly, because it finds too many edges and it misses some pixels. The Canny edge result must be sorted and finally filled up before the final calculations can be started. This additional work with the image decreases unfortunately the accuracy in the calculations. The accuracy is estimated through comparison between measured coordinates of the edge using a coordinate measuring machine and the calculated coordinates. There is a deviation of up to three mm in the calculated edge. The camera calibration has been described in earlier thesis so it is not mentioned in this report, although it is a prerequisite of this project.

Image based screening is an important tool used in research for development of drugs to fight cancer. Phase contrast video microscopy - a cheap and fast image screening technology - enables a rapid generation of large amounts of data, which requires a fast method for analysis of this data. As videos contain a lot of redundant information, the difficulty is to extract usable information in form of features from the videos, by compressing available information, or filter out redundant data. In this thesis, the problem is approached in an experimental fashion where three different methods have been devised and tested, to evaluate different ways to automatically extract features from phase contrast microscopy videos containing cultured cancer cells. The three methods considered are, in order: an adaptive linear filter, an on-line clustering algorithm, and an artificial neural network. The ambition is that outputs from these methods can create time-varying histograms of features that can be used in further mathematical modeling of cell dynamics. It is concluded that, while the results of the first method is not impressive and can be dismissed, the remaining two are more promising and are able to successfully extract features automatically and aggregate them into time-varying histograms.

When firing a handheld weapon, the shooter tries to aim at the point where he wants the bullet to hit. However, due to imperfections in the human body, this can be quite hard. The weapon moves relative to the target and the shooter has to use precise timing to fire the shot exactly when the weapon points to the intended target position. This can be very hard, especially when shooting at long range using a magnifying rifle scope. In this thesis, a solution to this problem using image analysis is described and tested. Using a digital video camera and software, the system helps the shooter to fire at the appropriate time. The system is designed to operate in real-time conditions on a PC. The tests carried out have shown that the solution is promising and helps to achieve better accuracy. However it needs to be optimized to run smoothly on a smaller scale embedded system. / Då en skytt avfyrar ett handhållet vapen försöker skytten sikta mot den punkt där han vill att kulan ska träffa. Eftersom den mänskliga kroppen inte är helt stabil kommer vapnet att röra sig runt denna punkt och skytten måste försöka avfyra skottet precis vid den tidpunkt då vapnet pekar mot rätt punkt. Detta är särskilt svårt vid stora avstånd, då små vinkelskillnader i vapnets pipa ger större utslag med ökande avstånd till målet.  I denna uppsats beskrivs och utvärderas ett system konstruerat för att minimera inverkan av de ofrivilliga rörelserna. Systemet använder sig av en videokamera monterad i siktet och en dator med mjukvara som utför analys och behandling av videoströmmen för att avgöra när vapnet bör avfyras. Tanken är att i ett färdigt system implementera algoritmen i ett portabelt inbyggt system som kan monteras i kikarsiktet tillsammans med kameran. Mjukvaran kan sedan styra avfyrningen elektroniskt efter att skytten gett sitt godkännande genom att lägga tryck på avtryckaren. Testen som genomförts visar att angreppssättet är lovande. Systemet fick i samtliga fall bättre resultat än då skyttarna avfyrade skott manuellt.

This thesis is focused on developing a robust algorithm for recognition of road signs including all stages of a machine vision system i.e. image acquisition, pre-processing, colour segmentation, labelling and classifi-cation. Images are acquired by two different imaging systems and noise removal is done by applying Mean filter. Furthermore, different colour segmentation methods are investigated to find out the most high-performance approach and after applying dynamic segmentation based on blue channel in YCbCr colour space, the obtained binary image is transferred to a personal computer through the developed PC software using standard serial port and further processing and classification is run on the PC. Histogram of Oriented Gradients (HOG) is used as the main feature for recognition of road signs and finally the classification task is fulfilled by employing hardware efficient Minimum Distance Classifier (MDC).

Work done by several researchers reveals that unbound aggregate materials show nonlinear cross-anisotropic behavior. The incorporation of cross-anisotropic properties significantly improves the predictions of stress distribution by reducing tensile stresses computed within granular layers. Existing pavement analysis and design approaches, however, generally assume the pavement structure to be linear isotropic layered system. This assumption is motivated by the difficulties in determining cross-anisotropic resilient material properties from laboratory experiments and lack of pavement anisotropic analysis programs. Recently, the International Center for Aggregates Research (ICAR) developed a methodology to characterize unbound aggregate layers by considering stress-sensitivity and nonlinear cross-anisotropy. The ICAR model requires nine coefficients to account for stress-sensitivity and anisotropy of vertical, horizontal, and shear moduli. Unfortunately, ICAR testing protocol is time-consuming and expensive to perform and certainly do not lend themselves to routine testing. Since it is important to be able to consider the stress-sensitive and anisotropic nature of unbound granular materials, a simple procedure was proposed by accounting for the effects of aggregate gradation and shape properties in predicting the cross-anisotropic modular ratio of unbound granular materials. Variable confining pressure type repeated load triaxial tests were performed on six aggregate sources with three different gradations and three different moisture contents. The experimental results were analyzed within the framework of nonlinear cross-anisotropic elastic model in order to determine the model coefficients. Image analysis techniques were utilized to measure aggregate shape properties. The gradation and shape properties were fitted using a cumulative distribution function and nonlinear regression analysis, which is capable of capturing the complete distribution of these properties. The experimental and analytical results indicate that the vertical resilient modulus is greater than the horizontal resilient modulus and that aggregate physical properties significantly affect the anisotropic resilient behavior. Based on finite element analysis, the anisotropic resilient behavior has substantial effect on the critical pavement responses. Thus, it is extremely valuable to approximate the degree of cross-anisotropy in unbound aggregates and to use it as input in the pavement analysis programs to adequately model unbound aggregate bases for pavement design and analysis.

In the conventional diffraction tomography a linear array is used to receive forward scattered field. Then a standard algorithm like back propagation or Fourier domain interpolation is used for reconstruction of the object. A circular array which captures both forward and backward scattered field has been proposed. A new theorem is proposed, which states that the scattered field measured with a large circular array surrounding the object is proportional to the Fourier transform of the object profile taken on the circumference of a circle of radius equa1 to the wave number and centered at (-k0 cosZO, -k0 sin Z0). The circular array outperforms in two counts. Firstly, a larger bandwidth of Fourier transform is used for reconstruction. Secondly, in circular array since the scattered field itself is related to the object Fourier transform, the reconstruction is free from the errors induced by finite array size. The effect of broad band illumination has been studied. A fewer number of illuminations appear to produce a reconstruction which is possible only with a large number of illuminations but narrow band illumination. Thus a trade off between the number of illumination angles and the bandwidth of the source exists.