Fiber Defect Detection of Inhomogeneous Voluminous Textiles
Carrasco-Ochoa, Jesús Ariel (Ed.) et al.: Pattern Recognition : MCPR 2017. Springer, 2017. (Lecture Notes in Computer Science (LNCS) 10267), pp. 278-287
Mexican Conference on Pattern Recognition (MCPR) <9, 2017, Huatulco, Mexico>
Quality assurance of dry cleaned industrial textiles is still a mostly manually operated task. In this paper, we present how computer vision and machine learning can be used for the purpose of automating defect detection in this application. Most existing systems require textiles to be spread flat, in order to detect defects. In contrast, we present a novel classification method that can be used when textiles are in inhomogeneous, voluminous shape. Normalization and classification methods are combined in a decision-tree model, in order to detect different kinds of textile defects. We evaluate the performance of our system in realworld settings with images of piles of textiles, taken using stereo vision. Our results show, that our novel classification method using key point pre-selection and convolutional neural networks outperform competitive methods in classification accuracy.
Separation of Subjects in High-Security Locks by Using Capacitive Sensing
Bremen, Hochschule, Master Thesis, 2017
A reliable distinction between one and more than one person in the automated access control is of great importance. When access to high-security area e.g. bank or in border control, here personal interlock is used. These systems ensure without human influence, that only a single individual can pass through a particular transit area (Mantrap Portal). Existing technical approaches use thermal imaging (Body Heat), RGB-D Images, Camera image based and computer vision algorithm to verify if there are one or more persons in the transit area. Other known systems use weight or photo sensor based methods for verification. In this Master's Thesis, we will investigate using capacitive sensors for this application. The most suitable capacitive sensing technique, as well as the number of sensors and their position, will be examined in this work. The performance of the developed system will be measured empirical testing and includes test scenarios in which an attacker tries to spoof the system. The system performance using capacitive sensors will be measured. Receiver operating characteristics (ROC) or Detection Error Tradeoff (DET) curves will show how the developed system performs compared with other solution. The work will conclude with a feasibility analysis of the capacitive sensor technique in a possible practical usage.
Talis - A Design Study for a Wearable Device to Assist People with Depression
Reisman, Sorel (Ed.) et al.: 2017 IEEE 41st Annual Computer Software and Applications Conference Workshops : COMPSACW. Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2017, pp. 543-548
IEEE International COMPSAC Workshop on User Centered Design and Adaptive Systems (UCDAS) <4, 2017, Torino, Italy>
One of the major diseases affecting the global population, depression has a strong emotional impact on its sufferers. In this design study, "Talis" is presented as a wearable device which uses emotion recognition as an interface between patient and machine to support psychotherapeutic treatment. We combine two therapy methods, "Cognitive Behavioral Therapy" and "Well- Being Therapy", with interactive methods thought to increase their practical application potential. In this study, we draw on the results obtained in the area of "affective computing" for the use of emotions in empathic devices. The positive and negative phases experienced by the patient are identified through speech recognition and used for direct communication and later evaluation. After considering the design possibilities and suitable hardware, the future realization of such technology appears feasible. In order to design the wearable, user studies and technical experiments were carried out. The results of these suggest that the device could be beneficial for the treatment of patients with depression.
Vision Based Food Recognition: Automated Checkout System for Canteen Operators
Darmstadt, TU, Master Thesis, 2017
Food recognition has always been a complex task to achieve due to deformable nature of food items. A lot of different food items can look pretty much identical because of their same shape and colour and the wide rage of diversity they are present in. Some time it is even difficult for humans to distinguish between different food items based on their visual characteristics. Main goal of this thesis is to see if it is possible to provide a viable solution to canteen owner so that they can do price calculation of their food products and to analyze different techniques that could be used in food recognition for cashier systems. All this cost estimation should be done in a reasonable amount of time (One customer should not spend more than 5 to 10 secs waiting for cost evaluation of his/her food). Flow of system should also be simple so that every customer can adopt to it very easily. Objective is to provide a simple solution in which purchased items could be identified from an image. Customer would only be required to place tray under a camera and no further interaction from customer point of view would be required in cost evaluation. Enrolment of new components in system should also be easy and less time consuming. To achieve the end target, system would be relying on cutting edge Deep Learning(DL) techniques. As over the past few years (Especially after Alexnet Which came in 2012) DL has shown very promising results in the areas of image classification and image segmentation. DL has also been very helpful when it comes to finding similarities between different images or for Maximum Likely-hood Estimation. Readers of this thesis document will see how different DL techniques can be combined to achieve food classification task. This report will give a detailed analysis about how different techniques, which are used to solve other complex problems like face recognition or signature verification, can be modified to perform food recognition. To achieve optimum classification results I examined different Neural Network(NN) architectures and evaluated their results. Best performance was achieved by training a Convolutional Neural Network( CNN) based on Inception architecture against the Triplet Loss. This method achieved a HTER of 10.3%. It was further investigated that these results could be further enhanced with the application of appropriate supervised image segmentation techniques.
Attack Detection in an Autonomous Entrance System using Optical Flow
7th International Conference on Imaging for Crime Detection and Prevention : ICDP 2016 [online]. [cited 11 July 2017] Available from: [http://digital-library.theiet.org/content/conferences/2016/0006], 2016, 6 p.
International Conference on Imaging for Crime Detection and Prevention (ICDP) <7, 2016, Madrid, Spain>
Unstaffed access control portals are becoming more common in high security areas. Existing systems require expensive hardware, or are sensitive to changing environmental conditions. We present a single camera system for a mantrap which is able to verify that only one individual is in the designated transit area. Our novel approach combines optical flow and machine-learning classification. A database was created that consists of images of attempted attacks and regular verification. The results show that our approach provides competitive results and outperforms detection rates in several attack scenarios.
Combining Low-level Features of Offline Questionnaires for Handwriting Identification
Aurélio, Campilho (Ed.) et al.: Image Analysis and Recognition : ICIAR 2016. Switzerland: Springer International Publishing, 2016. (Lecture Notes in Computer Science (LNCS) 9730), pp. 46-54
International Conference on Image Analysis and Recognition (ICIAR) <13, 2016, Póvoa de Varzim, Portugal>
When using anonymous offline questionnaires for reviewing services or products it is often not guaranteed that a reviewer does this only once as intended. In this paper an applied combination of different features of handwritten characteristics and its fusion is presented to expose such manipulations. The presented approach covers the aspects of alignment normalization, segmentation, feature extraction, classification and fusion. Nine features from handwritten text, numbers and checkboxes are extracted and used to recognize handwriter duplicates. The proposed method has been tested on a novel database containing pages of handwritten text produced by 1,734 writers. Furthermore we show that the unified biometric decision using a weighted sum combination rule can significantly improve writer identification performance even on low level features.
Emotional User Interface: Emotionen als Ein- und Ausgabemedium
Darmstadt, Hochschule, Bachelor Thesis, 2016
The objective of this bachelor thesis is to augment the interaction between human and machine with emotion as an interface. It is intended to demonstrate the importance of emotion in Human-Computer-Interaction and how the latter can be improved through the use of emotion recognition and emotional design principles. The conception and prototypical realization of "Talis" within this bachelor thesis illustrates a draft of a future generation of devices, that are not only characterized by their function but also communicate on an emotional level, and contribute to their owners' development and well being. "Talis" is a therapy-supporting wearable for patients with depressive moods, mild and moderate depression. It was developed together with therapists based on methods of cognitive behavioral therapy and well-being therapy and in cooperation with the Fraunhofer Institute for Computer Graphics Research. It is based on a preceding research thesis on emotions in design and technology. "Talis" accompanies patients during their everyday life and therapy sessions. The wearable helps them to recognize and reflect the positive in their lives. Positive moments are automatically recognized, saved and played back when a decrease in the patients' mood is detected. Additionally patients have the possibility to actively record positive moments or listen to one of them. Negative phases are to be decreased or avoided this way. Through emotion recognition and automation patients are relieved from inner hurdles, caused by their condition, like avolition, that inhibit the success of therapy methods. Patients experience additional emotional support through the special haptics of "Talis". They function as a tactile representation, a memory, of all the positive in their lives. This way the effect of the therapeutic methods is increased and the psychological strain is decreased.
Rapid Classification of Textile Fabrics Arranged in Piles
Callegari. Christian (Ed.) et al.: Proceedings of the 13th International Joint Conference on e-Business and Telecommunications Volume 5 : ICETE 2016. SciTePress, 2016, pp. 99-105
International Joint Conference on e-Business and Telecommunications (ICETE) <13, 2016, Lisbon, Portugal>
Research on the quality assurance of textiles has been a subject of much interest, particularly in relation to defect detection and the classification of woven fibers. Known systems require the fabric to be flat and spread-out on 2D surfaces in order for it to be classified. Unlike other systems, this system is able to classify textiles when they are presented in piles and in assembly-line like environments. Technical approaches have been selected under the aspects of speed and accuracy using 2D camera image data. A patch-based solution was chosen using an entropy-based pre-selection of small image patches. Interest points as well as texture descriptors combined with principle component analysis were part of this evaluation. The results showed that a classification of image patches resulted in less computational cost but reduced accuracy by 3.67%.
Stereo-Image Normalization of Voluminous Objects Improves Textile Defect Recognition
Bebis, George (Ed.) et al.: Advances in Visual Computing. 12th International Symposium, ISVC 2016 : Proceedings, Part I. Springer International Publishing, 2016. (Lecture Notes in Computer Science (LNCS) 10072), pp. 181-192
International Symposium on Visual Computing (ISVC) <12, 2016, Las Vegas, NV, USA>
The visual detection of defects in textiles is an important application in the textile industry. Existing systems require textiles to be spread flat so they appear as 2D surfaces, in order to detect defects. In contrast, we show classification of textiles and textile feature extraction methods, which can be used when textiles are in inhomogeneous, voluminous shape. We present a novel approach on image normalization to be used in stain-defect recognition. The acquired database consist of images of piles of textiles, taken using stereo vision. The results show that a simple classifier using normalized images outperforms other approaches using machine learning in classification accuracy.
Verification of Single-Person Access in a Mantrap Portal Using RGB-D Images
Pistori, Hemerson (Ed.) et al.: XII Workshop de Visão Computacional. Proceedings : WVC 2016. [cited 13 June 2017] Available from: [http://wvc2016.weebly.com/uploads/1/3/5/3/13538287/final_program_wvc2016_proceedings.pdf], 2016, pp. 177-182
Workshop de Visão Computacional <2016, Campo Grande, Brasil>
Automatic entrance systems are increasingly gaining importance to guarantee security in e.g. critical infrastructure. A pipeline is presented which verifies that only a single, authorized subject can enter a secured area. Verification scenarios are carried out by using a set of RGB-D images. Features, invariant to rotation and pose are used and classified by different metrics to be applied in real-time. The performance was evaluated by using scenarios in which the system was attacked by a second subject. The results show that the presented approach outerperforms competitive methods. It concludes with a summary of strengths and weaknesses and gives an outlook for future work.
Verifying Isolation in a Mantrap Portal via Thermal Imaging
Minarik, Ivan (Ed.) et al.: IWSSIP 2016. Proceedings : 23rd International Conference on Systems, Signals and Image Processing. Bratislava, 2016, pp. 149-152
International Conference on Systems, Signals and Image Processing (IWSSIP) <23, 2016, Bratislava, Slovakia>
This work presents a system that can be used to ensure that only one individual can pass through a designated transit area (mantrap portal). The developed technical approach uses thermal images to detect humans based on their body heat. A special focus was on the behaviour of the system placed under attack when an intruder tries to overcome the system. The performance was evaluated in empirical testing with a test group, selected according to their physical characteristics. The test scenarios cover changing appearances of individuals and possibly carried objects into the mantrap. Receiver Operating Characteristics (ROC) curves show how the developed system performs. This work concludes with a discussion about a number of challenges and gives an outlook for possible solutions.
Prototypical Development of an In-Shop Advertisment System using Body Dimension Recognition
Darmstadt, Hochschule, Master Thesis, 2014
This thesis outlines a system created to give consumers in the fashion industry an idea of how an item of clothing will look on them before trying it on. In the form of a short video, items of clothing are projected virtually onto an image of the user. Through the use of this system, retailers and manufacturers have the chance to immediately display their clothes on potential customers.
Virtual Fitting Pipeline: Body Dimension Recognition, Cloth Modeling, and On-Body Simulation
Bender, Jan (Ed.) et al.: VRIPHYS 14: 11th Workshop in Virtual Reality Interactions and Physical Simulations. Goslar: Eurographics Association, 2014, pp. 99-107
International Workshop in Virtual Reality Interaction and Physical Simulations (VRIPHYS) <11, 2014, Bremen, Germany>
This paper describes a solution for 3D clothes simulation on human avatars. The proposed approach consists of three parts, the collection of anthropometric human body dimensions, cloths scanning, and the simulation on 3D avatars. The simulation and human machine interaction has been designed for application in a passive In- Shop advertisement system. All parts have been evaluated and adapted under the aim of developing a low-cost automated scanning and post-production system. Human body dimension recognition was achieved by using a landmark detection based approach using both two 2D and 3D cameras for front and profile images. The human silhouettes extraction solution based on 2D images is expected to be more robust to multi-textured background surfaces than existing solutions. Eight measurements corresponding to the norm of body dimensions defined in the standard EN-13402 were used to reconstruct a 3D model of the human body. The performance is evaluated against the ground-truth of our newly acquired database. For 3D scanning of clothes, different scanning methods have been evaluated under apparel, quality and cost aspects. The chosen approach uses state of the art consumer products and describes how they can be combined to develop an automated system. The scanned cloths can be later simulated on the human avatars, which are created based on estimation of human body dimensions. This work concludes with software design suggestions for a consumer oriented solution such as a virtual fitting room using body metrics. A number of future challenges and an outlook for possible solutions are also discussed.