Research in object detection and recognition in cluttered scenes requires large image collections with ground truth labels. The labels should provide information about the object classes present in each image, as well as their shape and locations, and possibly other attributes such as pose. Such data is useful for testing, as well as for supervised learning. This project provides a web-based annotation tool that makes it easy to annotate images, and to instantly sharesuch annotations with the community. This tool, plus an initial set of 10,000 images (3000 of which have been labeled), can be found at http://www.csail.mit.edu/$\sim$brussell/research/LabelMe/intro.html

Artificial intelligence and machine learning is used in a lot of different areas, one of those areas is image recognition. In the production of a TV-show or film, image recognition can be used to help the editors to find specific objects, scenes, or people in the video content, which speeds up the production. But image recognition is not working perfect all the time and can not be used in the production of a TV-show or film as it is intended to. Therefore the image recognition algorithms needs to be trained on large datasets to become better. But to create these datasets takes time and tools that can let users create specific datasets and retrain algorithms to become better is needed. The aim of this master thesis was to investigate if it was possible to create a tool that can annotate objects and people in video content and using the data as training sets, and a tool that can retrain the output of an image recognition to make the image recognition become better. It was also important that the tools have a good workflow for the users. The study consisted of a theoretical study to gain more knowledge about annotation, and how to make a good UX-design with a good workflow. Interviews were also held to get more knowledge of what the requirements of the product was. It resulted in a user scenario and a workflow that was used together with the knowledge from the theoretical study to create a hi-fi prototype by using an iterative process with usability testing. This resulted in a final hi-fi prototype with a good design and a good workflow for the users, where it is possible to annotate objects and people with a bounding box, and where it is possible to retrain an image recognition program that has been used on video content. / Artificiell intelligens och maskininlärning används inom många olika områden, ett av dessa områden är bildigenkänning. Vid produktionen av ett TV-program eller av en film kan bildigenkänning användas för att hjälpa redigerarna att hitta specifika objekt, scener eller personer i videoinnehållet, vilket påskyndar produktionen. Men bildigenkänningsprogram fungerar inte alltid helt perfekt och kan inte användas i produktionen av ett TV-program eller film som det är tänkt att användas i det sammanhanget. För att förbättra bildigenkänningsprogram så behöver dess algoritm tränas på stora datasets av bilder och labels. Men att skapa dessa datasets tar tid och det behövs program som kan skapa datasets och återträna algoritmer för bildigenkänning så att de fungerar bättre. Syftet med detta examensarbete var att undersöka om det var möjligt att skapa ett verktyg som kan markera(annotera) objekt och personer i video och använda datat som träningsdata för algoritmer. Men även att skapa ett verktyg som kan återträna algoritmer för bildigenkänning så att de blir bättre utifrån datat man får från ett bildigenkänningprogram. Det var också viktigt att dessa verktyg hade ett bra arbetsflöde för användarna. Studien bestod av en teoretisk studie för att få mer kunskap om annoteringar i video och hur man skapar bra UX-design med ett bra arbetsflöde. Intervjuer hölls också för att få mer kunskap om kraven på produkten och vilka som skulle använda den. Det resulterade i ett användarscenario och ett arbetsflöde som användes tillsammans med kunskapen från den teoretiska studien för att skapa en hi-fi prototyp, där en iterativ process med användbarhetstestning användes. Detta resulterade i en slutlig hi-fi prototyp med bra design och ett bra arbetsflöde för användarna där det är möjligt att markera(annotera) objekt och personer med en bounding box och där det är möjligt att återträna algoritmer för bildigenkänning som har körts på video.

The goal of this work is to investigate options how to apply trainable edge detection algorithm Structured forest for fast edge detection to information extraction from historici maps and medical images. For the work, annotated dataset was created and the detektor was tested on it. Structured forest achieved better results on map data, compared with classical detectors. Success rate of finding edges of bones was similar at both approaches. Aim of the work is focused on comparing different image annotation styles, experiments with dataset, including determining parameters and evaluation of the methods.

Annotation is essential in machine learning. Building an accurate object detec-tion model requires a large, diverse dataset, which poses challenges due to thetime-consuming nature of manual annotation. This thesis was made in collabora-tion with Project Ngulia, which aims at developing technical solutions to protectand monitor wild animals. A contribution of this work was to integrate an effi-cient semi-automatic image annotation tool within the Ngulia system, with theaim of streamlining the annotation process and improving the employed objectdetection models. Through research into available annotation tools, a custom toolwas deemed the most cost-effective and flexible option. It utilizes object detec-tion model predictions as annotation suggestions, improving the efficiency of theannotation process. The efficiency was evaluated through a user test, with partic-ipants achieving an average reduction of approximately 2 seconds in annotationspeed when utilizing suggestions. This reduction was supported as statisticallysignificant through a one-way ANOVA test. Additionally, it was investigated which images should be prioritized for an-notation in order to obtain the the most accurate predictions. Different samplingmethods were investigated and compared. The performance of the obtained mod-els remained relatively consistent, although with the even distribution methodat top. This indicate that the choice of sampling method may not substantiallyimpact the accuracy of the model, as the performance of the methods was rela-tively comparable. Moreover, different methods of selecting training data in there-training process was compared. The difference in performance was consider-ately small, likely due to the limited and balanced data pool. The experimentsdid however indicate that incorporating previously seen data with unseen datacould be beneficial, and that a reduced dataset can be sufficient. However, furtherinvestigation is required to fully understand the extent of these benefits.

Translation alignment is an essential task in Digital Humanities and Natural Language Processing, and it aims to link words/phrases in the source text with their translation equivalents in the translation. In addition to its importance in teaching and learning historical languages, translation alignment builds bridges between ancient and modern languages through which various linguistics annotations can be transferred. This thesis focuses on word-level translation alignment applied to historical languages in general and Ancient Greek and Latin in particular. As the title indicates, the thesis addresses four interdisciplinary aspects of translation alignment. The starting point was developing Ugarit, an interactive annotation tool to perform manual alignment aiming to gather training data to train an automatic alignment model. This effort resulted in more than 190k accurate translation pairs that I used for supervised training later. Ugarit has been used by many researchers and scholars also in the classroom at several institutions for teaching and learning ancient languages, which resulted in a large, diverse crowd-sourced aligned parallel corpus allowing us to conduct experiments and qualitative analysis to detect recurring patterns in annotators’ alignment practice and the generated translation pairs. Further, I employed the recent advances in NLP and language modeling to develop an automatic alignment model for historical low-resourced languages, experimenting with various training objectives and proposing a training strategy for historical languages that combines supervised and unsupervised training with mono- and multilingual texts. Then, I integrated this alignment model into other development workflows to project cross-lingual annotations and induce bilingual dictionaries from parallel corpora. Evaluation is essential to assess the quality of any model. To ensure employing the best practice, I reviewed the current evaluation procedure, defined its limitations, and proposed two new evaluation metrics. Moreover, I introduced a visual analytics framework to explore and inspect alignment gold standard datasets and support quantitative and qualitative evaluation of translation alignment models. Besides, I designed and implemented visual analytics tools and reading environments for parallel texts and proposed various visualization approaches to support different alignment-related tasks employing the latest advances in information visualization and best practice. Overall, this thesis presents a comprehensive study that includes manual and automatic alignment techniques, evaluation methods and visual analytics tools that aim to advance the field of translation alignment for historical languages.

過去研究指出因為欠缺基礎數位素養，敏銳度不足造成國內學生面對大量網路訊息時，降低了過濾資訊的能力，因此建立起資訊篩選與評估的機制，培養數位閱讀能力與資訊素養，成為近幾年來熱門的議題。本研究以「閱讀知識合作標註學習系統」結合網路探究式學習，發展「合作式標註工具輔助網路探究式學習模式」，期望能創新資訊素養教學，為學生找到有效提升資訊尋求能力的新方法。 研究採用準實驗研究法，以新北市某國小五年級兩班共50名學生為研究對象，進行「網路資訊評估與判斷」的主題合作探究學習，其中一班25名學生被隨機分派到採用「合作式標註工具輔助網路探究式學習模式」為實驗組，另一班25名學生被隨機分派到採用「討論版工具輔助網路探究式學習模式」為控制組，以先備知識及認知風格作為背景變項，探討兩種不同學習模式的學生在學習成效、認知負荷、科技接受度與學習滿意度的影響與差異。 研究結果發現，相較於「討論版工具輔助網路探究式學習模式」，採用「合作式標註工具輔助網路探究式學習模式」對於中、低先備知識者以及場地獨立型風格學生的學習成效有很大助益；不論是採用哪一種學習模式的學習者在學習中，並不會產生過大的認知負荷；而在評估科技接受度以及學習滿意度上，低先備知識的學生認為採用「合作式標註工具」比採用「討論版工具」輔助網路探究式學習的幫助更大，同時在學習滿意度也更為顯著。 最後基於研究結果，提出發揮工具的優勢發展系列推廣課程，以及延伸應用批判性思考學習對教師進行教學的建議，以及未來可深入長時間發展、探究式學習的互動歷程行為、學習遷移等相關探討與研究，希望能作為資訊素養教育推廣下，研究領域探討議題的新方向。 / The past studies have suggested that the lack of basic digital literacy and acuteness has reduced Taiwanese students’ ability to filter information when facing a vast amount of Internet information. As a result, establishing a mechanism for selecting and assessing information, as well as cultivating digital reading ability and information literacy have been the hot topics in recent years. By combing the Reading Knowledge Collaborative Annotation Tool (CAT) with the Web-based inquiry-based learning, this study has developed the “Web-based Inquiry-based Learning Model with the Collaborative Annotation Tool,” hoping to innovate the information literacy instruction and find new ways to effectively improve students’ information search capabilities. In this study, a quasi-experimental study method was adopted, and 50 fifth-graders from two classes in a certain elementary school in New Taipei City were selected as the research subjects to conduct the collaborative inquiry-based learning on the theme of “Internet Information Assessment and Judgment.” Among them, 25 students from one class were randomly assigned to the experimental group of adopting the “Web-based Inquiry-based Learning Model with the Collaborative Annotation Tool,” while 25 students from another class were randomly assigned to the control group of adopting the “Web-based Inquiry-based Learning Model with the Discussion Board Tool.” With prior knowledge and cognitive style as background variables, the influences and differences in students’ learning effectiveness, cognitive load, technology acceptance, and learning satisfaction in two different learning models were thoroughly explored. The research results found that compared to the “Web-based Inquiry-based Learning Model with the Discussion Board Tool,” the “Web-based Inquiry-based Learning Model with the Collaborative Annotation Tool” showed much higher benefits in the learning effectiveness for students with middle and low prior knowledge and with field independence. Both of these two models produce would not produce excessive cognitive load on students during the learning process. As for the assessments on technology acceptance and learning satisfaction, students with low prior knowledge considered that the Web-based Inquiry-based Learning Model with the Collaborative Annotation Tool was more helpful for them than the one with the Discussion Board Tool, and they also showed a higher significant level of learning satisfaction. Lastly, based on the research results, this study suggests that the advantages of the tool can be used to further develop a series of promotion courses, and the use of critical thinking learning can be extended to the teaching for teachers. Also, this study suggests that the long-term in-depth explorations of the interactive course behavior of inquiry-based learning, transfer of learning, and other relevant studies can be conducted in the future, hoping to provide as new directions of topics for the research field when promoting information literacy instruction.