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Reiz, Achim; Albadawi, Mohamad; Sandkuhl, Kurt; Vahl, Matthias; Sidin, Dennis

Towards More Robust Fashion Recognition by Combining Deep-Learning-Based Detection with Semantic Reasoning

2021

Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021)

AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021) <2021, Online>

CEUR Workshop Proceedings, 2846

The company FutureTV produces and distributes self-produced videos in the fashion domain. It creates revenue through the placement of relevant advertising. The placement of apposite ads, though, requires an understanding of the contents of the videos. Until now, this tagging is created manually in a labor-intensive process. We believe that image recognition technologies can significantly decrease the need for manual involvement in the tagging process. However, the tagging of videos comes with additional challenges: Preliminary, new deep-learning models need to be trained on vast amounts of data obtained in a labor-intensive data-collection process. We suggest a new approach for the combining of deep-learning-based recognition with a semantic reasoning engine. Through the explicit declaration of knowledge fitting to the fashion categories present in the training data of the recognition system, we argue that it is possible to refine the recognition results and win extra knowledge beyond what is found in the neural net.

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Waack, Marco; Bunse, Christian [1. Betreuer]; Albadawi, Mohamad [2. Betreuer]

Evaluierung verschiedener Detektoren als Basis für Single Objekt Tracking in Multi Objekt Fisch Szenen mittels Convolutional Neural Networks

2020

Stralsund, Hochschule, Bachelor Thesis, 2020

Ziel dieser Bachelorarbeit ist es einen Überblick über Tracking verfahren zu geben, die neuronale Netze verwenden und aus diesen eine Architektur auszuwählen und diese exemplarisch zu implementieren. Dazu wird ein Ansatz ausgewählt, der sich für das Austauschen von den Detektoren anbietet, da ein weiteres Ziel dieser Bachelorarbeit ist herauszufinden ob es einen Unterschied in der Performanz der implementierten Architektur gibt, wenn Faster-RCNN [Gir15] oder Yolov3 [RF18] als Detektor verwendet werden. Weiterhin ist es das Ziel dieser Bachelorarbeit das Tracking von Fischen anhand des hier implementierten Systems zu testen. Dies ergibt sich aus dem hier gegebenem Dataset.

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Hashisho, Yousif; Albadawi, Mohamad; Krause, Tom; Lukas, Uwe von

Underwater Color Restoration Using U-Net Denoising Autoencoder

2019

Proceedings of the 11th International Symposium Image and Signal Processing and Analysis

International Symposium on Image and Signal Processing and Analysis (ISPA) <11, 2019, Dubrovnik, Croatia>

Visual inspection of underwater structures by vehicles, e.g. remotely operated vehicles (ROVs), plays an important role in scientific, military, and commercial sectors. However, the automatic extraction of information using software tools is hindered by the characteristics of water which degrade the quality of captured videos. As a contribution for restoring the color of underwater images, Underwater Denoising Autoencoder (UDAE) model is developed using a denoising autoencoder with U-Net architecture. The proposed network takes into consideration the accuracy and the computation cost to enable realtime implementation on underwater visual tasks using end-toend autoencoder network. Underwater vehicles perception is improved by reconstructing captured frames; hence obtaining better performance in underwater tasks. Related learning methods use generative adversarial networks (GANs) to generate color corrected underwater images, and to our knowledge this paper is the first to deal with a single autoencoder capable of producing same or better results. Moreover, image pairs are constructed for training the proposed network, where it is hard to obtain such dataset from underwater scenery. At the end, the proposed model is compared to a state-of-the-art method.

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Hashisho, Yousif; Lukas, Uwe von [Gutachter]; Staadt, Oliver [Gutachter]; Albadawi, Mohamad [Supervising Advisor]; Krause, Tom [Supervising Advisor]

Underwater Image Enhancement Using Autoencoders

2019

Rostock, Univ., Master Thesis, 2019

Visual inspection of underwater structures by vehicles, e.g. remotely operated vehicles (ROVs), plays an important role in scientific, military, and commercial aspects. However, the automatic extraction of information using software tools is hindered by the optical characteristics of water which degrade the quality of the videos. As a contribution for enhancing underwater images, we develop an algorithm using a single denoising autoencoder to restore the color and remove the disturbances such as marine snow from underwater images. Marine snow in some images is only partially removed using the proposed network; however, we show the reason behind this failure. Related learning methods use generative adversarial networks (GANs) for generating color corrected underwater images, and to our knowledge this thesis is the first to deal with a single autoencoder capable of producing same or better results. Moreover, underwater aligned image pairs are established for the training of the proposed network, where it is hard to obtain such dataset from underwater scenery. The objective is to increase the accuracy and reliability on automatic underwater operations that rely on robotic perception without human interference by improving the quality of the captured frames. At the end, the proposed network is evaluated using Mean Squared Error (MSE), Peak Signal-to- Noise Ratio (PSNR), and Structural Similarity Index Metric (SSIM) quality metrics. Additionally, we compare our experiment with a related method. The proposed network takes into consideration the computation cost and the accuracy to have real-time implementation on visual-driven tasks using a single autoencoder.

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Sidin, Dennis; Ferrein, Alexander [Betreuender Professor]; Albadawi, Mohamad [Betreuender Mitarbeiter]

Unterwasserdetektion von Munitionsaltlasten auf Sonarbildern durch Convolutional Neural Networks

2019

Rostock, Univ., Bachelor Thesis, 2019

In dieser Bachelorarbeit werden Munitionsaltlasten auf Sonarbildern mit Hilfe von verschiedenen Convolutional Neural Networks detektiert. Dabei wird der Einfluss von Transfer Learning, Data Augmentation und der Variation der Datenmenge untersucht. Zu diesem Zweck werden mehrere neuronale Netze auf Basis von Faster R-CNN und SSD trainiert und evaluiert. Auf Testbildern wird so ein mAP-Wert von 0,763 und ein Recall von 0,897 erreicht.

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Judzinsky, Nils; Lukas, Uwe von [Gutachter]; Urban, Bodo [Zweitgutachter]; Albadawi, Mohamad [Betreuer]; Krause, Tom [Betreuer]

Automated Object Pose Recognition by a Combination of Stereo Cameras and 2D Object Detection

2018

Rostock, Univ., Master Thesis, 2018

The ability to visually recognize the 3D pose of objects would be helpful in many industrial applications. Existing approaches on this topic restrict the pose estimation to simplified scenarios, e.g. where a pose consists of just a 2D position and an angle around the vertical axis, or they require a priori knowledge. In this thesis methods are investigated to estimate a 6 DoF pose and 3D extends of arbitrary objects captured by a stereo camera. No knowledge about the shape of objects is required beforehand. A prototype implementation is provided. It first detects the objects in 2D employing a CNN trained for object detection. Then, the depth information is used to reconstruct a 3D point cloud and isolate the objects of interest. Finally, pose and size are estimated based on dense and sparse registration methods. At the end, the whole method is tested on artificially generated stereo images of fish. The results show remaining challenges especially regarding the robustness.

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Albadawi, Mohamad; Lukas, Uwe von [Supervising professor]; Krause, Tom [Tutor]

Resolving Classification Ambiguities in Convolutional Neural Networks Using Hierarchical Structures

2018

Rostock, Univ., Master Thesis, 2018

We have recently witnessed the revolution of deep learning and convolutional neural networks enabled by the powerful machines available today. Convolutional neural networks have demonstrated excellent performance on various vision tasks, most importantly classification and detection. Nevertheless, there are some difficulties in the way of perfect performance. One problem is discriminating among objects that look extremely similar visually but semantically they are different. Another problem is the high cost of training large detection models. The same cost applies when the model is required to detect a new type of objects. In this work those problems are handled by introducing visual concepts and the use of hierarchical structures. We will see how the accuracy of classifying similar objects can be highly improved and how the time of accommodating for new objects in a detection model can be reduced from days to hours.