Internet of Things växer fort och förutspås bli en del av vardagen. Detta öppnar möjligheter för att skapa produkter som förenklar vardagslivet. Automatisk objektsidentifiering kombinerad med en automatiserad lagerstatus kan underlätta inventering, något som kan användas till exempel i smarta kylskåp för att göra vardagen enklare genom Internet of Things.Detta examensarbete studerar metoder inom objektsidentifikation för att ta fram ett system som automatiskt kan identifiera objekt och hantera lagerstatus. En prototyp framställdes och testades för att se vilka möjligheter som finns. Systemet använder en Raspberry Pi som basenhet, vilken använder Dlib-bibliotek för att identifiera objekt som har blivit fördefinierade. Vid okända objekt identifierar användaren objekt i en mobilapplikation, systemet kan genom detta lära sig identifiera nya objekt. Samma applikation används för att se lagerstatusen på de olika objekt som har registrerats av systemet. Prototypen klarar av att identifiera kända objekt samt att lära sig nya, enligt projektets mål. / Internet of Things is growing fast and is predicted to become a part of everyday life. This can be used to create products which will make everyday life easier. Automated object detection combined with an automated inventory check can make it easier to manage what is in stock, this is something that can be used in smart refrigerators as an example, to make life more convenient through Internet of Things. This Bachelor thesis studies methods regarding object detection with the purpose to build a system which automatically identifies objects and manages the inventory status. A prototype was built and tested to see what the possibilities there is with such a system. The Prototype uses a Raspberry Pi as core unit, which uses Dlib libraries to identify predefined objects. The user will identify unknown objects via the mobile phone application, which makes it possible for the system to learn how to identify new objects. The same application is used to check the inventory status for the different objects that has been identified by the system. The prototype can identify objects and learn to identify new ones, according to the goals of the project.

Invariant object recognition is maybe the most basic and fundamental property of our visual system. It is the basis of many other cognitive tasks, like motor actions and social interactions. Hence, the theoretical understanding and modeling of invariant object recognition is one of the central problems in computational neuroscience. Indeed, object recognition consists of two different tasks: classification and identification. The focus of this thesis is on object identification under the basic geometrical transformations shift, scaling, and rotation. The visual system can perform shift, size, and rotation invariant object identification. This thesis consists of two parts. In the first part, we present and investigate the VisNet model proposed by Rolls. The generalization problems of VisNet triggered our development of a new neural network model for invariant object identification. Starting point for an improved generalization behavior is the search for an operation that extracts images features that are invariant under shifts, rotations, and scalings. Extracting invariant features guarantees that an object seen once in a specific pose can be identified in any pose. We present and investigate our model in the second part of this thesis.

Video thumbnails are essential to represent the content and summary of a video. This thesis proposed a thumbnail extraction approach for fashion videos based on the presence of clothing items, sharpness, and contrast. Furthermore, this thesis investigated how the proposed thumbnail selection method performed concerning user engagement. Other research has been done on user engagement; however, the impact of clothing item presence has yet to be investigated. Firstly, a YOLOv7 model was trained on a fashion dataset to identify clothing items. The proposed selection method used the model to extract labels to determine what frames contain the maximum number of clothing items. The selected frames were filtered based on a contrast threshold, and the sharpest frame was kept as the proposed thumbnail from the remaining frames. The contrast was measured by calculating the standard deviation of the pixels in each frame. The sharpness was measured with the Laplacian operator. The user engagement was investigated by surveying 119 participants on thumbnail preference. The participants were presented with three frames, the thumbnail extracted with the proposed method, and two control frames: the middle frame of the video and a frame where the YOLOv7 model had only identified one object. The results show that the proposed thumbnail selection method performs well, receiving 59.75% of the total votes, compared to a middle frame and a single-item frame that received 17.46% and 22.79% of the votes, respectively. The results indicate that the proposed parameters for the thumbnail extraction could lead to higher user engagement. / Video-miniatyrbilder är en essentiell del av att presentera och sammanfatta videoinnehåll. Den här uppsatsen föreslår en miniatyrbilds extraktionsmetod för modevideos baserat på klädesplagg, skärpa och kontrast. Denna uppsats utvärderade hur den föreslagna metoden presterar i relation till användarengagemang. Tidigare forskning har utvärderat användarengagemang på miniatyrbilder, dock inte kopplat till närvaro av klädesplagg. Först tränades en YOLOv7 modell på ett modedataset för att identifiera klädesplagg. Den föreslagna metoden använde modellen för att extrahera etiketter för att fastställa vilka bilder som inkluderade flest klädesplagg. De utvalda bilderna filtrerades med en kontrast-tröskel, och den skarpaste bilden av de resterande bilderna behölls som en föreslagen miniatyrbild. Kontrasten mättes med standardavvikelsen mellan pixlar i varje bild. Skärpan mättes med Laplaceoperatorn. Användarengagemanget undersöktes med en enkät genomförd av 119 deltagare för att identifiera vilken miniatyrbild som föredrogs. Deltagarna blev presenterade med tre bilder, en extraherad med den föreslagna metoden och två kontrollbilder: mittenbilden från videon och en bild där YOLOv7 modellen endast identifierat ett objekt. Resultaten visar att den föreslagna metoden presterar bra, den fick 59,75% av rösterna, jämfört med mittenbilden och bilden med ett objekt, som fick respektive 17.46% och 22.79%. Resultaten indikerar att den föreslagna parametrarna kan bidra till ökat användarengagamang i modefilmer.

Computer vision is an area in computer science that attempts to give computers the ability to see and recognise objects using varying sources of input, such as video or pictures. This problem is usually solved by using artificial intelligence (AI) techniques. The most common being deep learning. The project investigates the possibility of using these techniques to recognisetraffic signs in real time. This would make it possible in the future to build a user application that does this. The case study gathers information about available AI techniques, and three object detection deep learning models are selected. These are YOLOv3, SSD, and Faster R-CNN. The chosen models are used in a case study to find out which one is best suited to the task of identifying parking signs in real-time. Faster R-CNN performed the best in terms of recall and precision combined. YOLOv3 slacked behind in recall, but this could be because of how we chose to label the training data. Finally, SSD performed the worst in terms of recall, but was also relatively fast. Evaluation of the case study shows that it is possible to detect parking signs in real time. However, the hardware necessary is more powerful than that offered by currently available mobile platforms. Therefore it is concluded that a cloud solution would be optimal, if the techniques tested were to be implemented in a parking sign reading mobile app. / Datorseende är ett område inom datorvetenskap som fokuserar på att ge maskiner förmågan att se och känna igen objekt med olika typer av input, såsom bilder eller video. Detta är ett problem som ofta löses med hjälp av artificiell intelligens (AI). Mer specifikt, djupinlärning. I detta projekt undersöks möjligheten att använda djupinlärning för att känna igen trafikskyltar i realtid. Detta så att i framtiden kunna bygga en applikation, som kan byggas att känna igen parkeringsskyltar i realtid. Fallstudien samlar information om tillgängliga AI-tekniker, och tre djupinlärningsmodeller väljs ut. Dessa är YOLOv, SSD, och Faster R-CNN. Dessa modeller används i en fallstudie för att ta reda på vilken av dem som är bäst lämpad för uppgiften att känna igen parkeringsskyltar i realtid. Faster R-CNN presterade bäst vad gäller upptäckande av objekt och precision tillsammans. YOLOv3 upptäckte färre object, men det är sannolikt att detta berodde på hur vi valde att markera träningsdatan. Slutligen upptäckte SSD minst antal objekt, men presterade också relativt snabbt. Bedömning av fallstudien visar att det är möjligt att känna igen parkeringsskyltar i realtid. Den nödvändiga hårdvaran är dock kraftfullare än den som erbjuds av mobiler för närvarande. Därför dras slutsatsen att en molnlösning skulle vara optimal, om de testade teknikerna skulle användas för att implementera en app för att känna igen parkeringskyltar.

Les études sont mitigées sur les séquelles cognitives des commotions cérébrales, certaines suggèrent qu’elles se résorbent rapidement tandis que d’autres indiquent qu’elles persistent dans le temps. Par contre, aucunes données n’existent pour indiquer si une tâche cognitive comme l’imagerie mentale visuelle fait ressortir des séquelles à la suite d’une commotion cérébrale. Ainsi, la présente étude a pour objet d’évaluer l’effet des commotions cérébrales d’origine sportive sur la capacité d’imagerie mentale visuelle d’objets et d’imagerie spatiale des athlètes. Afin de répondre à cet objectif, nous comparons les capacités d’imagerie mentale chez des joueurs de football masculins de calibre universitaire sans historique répertorié de commotions cérébrales (n=15) et chez un second groupe d’athlète ayant été victime d’au moins une commotion cérébrale (n=15). Notre hypothèse est que les athlètes non-commotionnés ont une meilleure imagerie mentale que les athlètes commotionnés. Les résultats infirment notre hypothèse. Les athlètes commotionnés performent aussi bien que les athlètes non-commotionnés aux trois tests suivants : Paper Folding Test (PFT), Visual Object Identification Task (VOIT) et Vividness of Visual Imagery Questionnaire (VVIQ). De plus, ni le nombre de commotions cérébrales ni le temps écoulé depuis la dernière commotion cérébrale n’influent sur la performance des athlètes commotionnés. / The research is mitigated on the cognitive after-effects of a concussion. Some studies suggest the effects disappear rapidly whereas others observe a continuation in their manifestation. However, no research has been done to indicate whether a cognitive task like mental imagery brings out these effects following a concussion. This study will evaluate the effects of sport-related concussions on object and spatial visual mental imagery of athletes. To achieve this goal, we compare the mental imagery capacity between two groups of male football athletes of University level. The first group (n=15) with no history of concussions and the second group (n=15) with one or more concussions. We hypothesize that the non-concussed athletes visualize better than the concussed athletes. Our results invalidate our hypothesis. Both groups have similar results on the three following measures: Paper Folding Test (PFT), Visual Object Identification Task (VOIT) and Vividness of Visual Imagery Questionnaire (VVIQ). Furthermore, the quantity of concussions and the time past since the last concussion seems to have no impact on the visual mental imagery performance of concussed athletes.

Although the origins of Radio Frequency Identification (RFID) technology can be traced back for many years, it is only recently that RFID has experienced rapid growth. That growth is mainly due to the increasing application of this technology in various supply chains. The widening of the implementation of RFID technology in supply chains has posed many challenges and one of the biggest is the degradation of the RFID system performance when tagging metallic objects, or when the RFID system operates in a metallic environment. This thesis focuses on tackling the issue of having metallic objects in an Ultra High Frequency (UHF) RFID system. The work presented in this thesis contributes to the research on UHF RFID systems involving metallic objects in several ways: (a) the development of novel RFID tags that range from a simple tag for general applications to tags suitable for metallic object identification; (b) the tag designs target the criteria of minimal tag size and cost to embrace the vision of item level tagging; and (c) the analysis of the performance (through theoretical predictions and practical measurements) of an RFID tag near metallic structures of various shapes and sizes. The early part of this thesis provides a brief introduction to RFID and reviews the background information related to metallic object identification for UHF RFID systems. The process of designing a basic tag, and additional information and work done related to the process, are outlined in the early part of this thesis. As part of this fundamental research process, and before proceeding to the designing of tags specifically for metallic objects, a small and low cost RFID tag for general applications was developed. Details of the design of this tag, with the application of this tag for animal identification, are presented. In the later parts of the work, different tag design approaches were explored and this has generated three rather different RFID tags suitable for attaching to metallic objects. The aim of this research is not just to design tags for metallic objects but also to tackle the constraints of having tags that are small in size, cost effective and suited in size to some familiar objects. Hence, in the later part of this research, the work took a step further where one of the three tags designed for metallic objects addressed the challenge of identifying individual small metallic beverage cans. RFID involves tagging of different types of objects and a tag may be required to be located in a depression of a metallic object. In the final part of this research, the read range performance of one of the RFID tags designed for metallic objects was analysed when the tag was located in metallic depressions of various shapes and sizes. The analysis was performed from a combination of theoretical calculation and simulation perspectives, and also through practical real-life measurements. Metallic objects are very common around us. Their presence is unavoidable and so to identify them, having the appropriate RFID tags suitable for operation on metallic surfaces is essential. Frequently the tags must be small in size and low in cost to allow identification at item level of individual small metallic objects. Understanding and being aware of the potential effects of metallic structures of various shapes and sizes on the tag performance is thus important. The research in this thesis into all the above can bring the industry further towards full deployment of RFID down to item level tagging. / Thesis (Ph.D.) - University of Adelaide, School of Electrical and Electronic Engineering, 2008

This paper deals with the topic of numerical arrangement of fibers in a heat exchanger. The heat exchanger is scanned in an industrial tomograph and the acquired data are represented by the field of voxels. The method used in this paper is based on tracing the fiber fragments through the use of image analysis and the subsequent numerical connection of the fragments. The result is a set of fibers that are represented by points in the field through which they are passing.

We formulate Local Shape Descriptor selection for model-based object recognition in range data as an optimization problem and offer a platform that facilitates a solution. The goal of object recognition is to identify and localize objects of interest in an image. Recognition is often performed in three phases: point matching, where correspondences are established between points on the 3-D surfaces of the models and the range image; hypothesis generation, where rough alignments are found between the image and the visible models; and pose refinement, where the accuracy of the initial alignments is improved. The overall efficiency and reliability of a recognition system is highly influenced by the effectiveness of the point matching phase. Local Shape Descriptors are used for establishing point correspondences by way of encapsulating local shape, such that similarity between two descriptors indicates geometric similarity between their respective neighbourhoods. We present a generalized platform for constructing local shape descriptors that subsumes a large class of existing methods and allows for tuning descriptors to the geometry of specific models and to sensor characteristics. Our descriptors, termed as Variable-Dimensional Local Shape Descriptors, are constructed as multivariate observations of several local properties and are represented as histograms. The optimal set of properties, which maximizes the performance of a recognition system, depend on the geometry of the objects of interest and the noise characteristics of range image acquisition devices and is selected through pre-processing the models and sample training images. Experimental analysis confirms the superiority of optimized descriptors over generic ones in recognition tasks in LIDAR and dense stereo range images. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2009-09-01 11:07:32.084