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Auflistung nach Schlagwort "image processing"

1 - 4 von 4
  • Konferenzbeitrag
    A digital weed counting system for the weed control performance evaluation
    (42. GIL-Jahrestagung, Künstliche Intelligenz in der Agrar- und Ernährungswirtschaft, 2022) Pamornnak, Burawich; Scholz, Christian; Becker, Silke; Ruckelshausen, Arno
    The weed counting method is one of the keys to indicate the performance of the weed control process. This article presents a digital weed counting system to use instead of a conventional manual counting system called “Göttinger Zähl- und Schätzrahmen” or “Göttinger Rahmen” due to the limitation of human counting on big-scale field experiment areas. The proposed method demonstrated on the maize field consists of two main parts, a virtual weed counting frame and a weed counting core, respectively. The system was implemented as a mobile application for the smartphone (Android) with server-based processing. The pre-processed image on the mobile phone will be sent to the weed counting core based on the pre-trained convolution neural network model (CNN or deep learning) on the server. Finally, the number of detected weeds will be sent back to the mobile phone to show the results. In the first implementation, 100 frames on a 1-hectare field area were evaluated. The absolute weed counting errors were categorized into three groups, A-Group (0-10 weeds error) achieves 73 %, B-Group (11-20 weeds error) achieves 17 %, and C-Group (21-30 weeds error) achieves 10 %, respectively. For overall performance, the system achieves the  = 0.97 from the correlation and 12.8 % counting error. These results show the digital version of “Göttinger Rahmen” has the potential to become a practical tool for weed control evaluations.
  • Textdokument
    Evaluation of CNN-based algorithms for human pose analysis of persons in red carpet scenarios
    (INFORMATIK 2017, 2017) Kowerko, Danny; Richter, Daniel; Heinzig, Manuel; Kahl, Stefan; Helmert, Stefan; Brunnett, Guido
    We evaluate two CNN-based algorithms for keypoint-based human pose analysis on two image test sets containing red carpet scenarios, one taken under controlled conditions in a TV studio environment and another more heterogeneous data set taken from FlickR without any restriction but to contain a red carpet. We focus on the pose of persons standing directly on the red carpet. A web application is presented allowing collaborative work to confirm or modify already pre-localised body keypoints given from the method presented in [Ca17]. These annotations helped to quickly define ground truth for the subsequent evaluation of several hundreds of persons standing on a red carpet. An own evaluation formalism is presented that adopts to the size of the respective keypoints. The TV studio data set includes coarsely defined body and head poses. Using the angular information, we are able to quantitatively define the optimum head pose angle range and limitations of facial keypoint determination.
  • Textdokument
    Fingerprint Damage Localizer and Detector of Skin Diseases from Fingerprint Images
    (BIOSIG 2017, 2017) Barotova,Stepanka; Drahansky,Martin
    This article describes a novel approach for detection and classification of skin diseases in fingerprints using three methods - Block Orientation Field, Histogram Analysis and Flood Fill. The combination of these methods brings a surprising results and using a rule descriptor for selected skin diseases, we are able to classify the disease into a group or concrete name.
  • Konferenzbeitrag
    An overview of visual servoing for robotic manipulators in digital agriculture
    (43. GIL-Jahrestagung, Resiliente Agri-Food-Systeme, 2023) Shamshiri, Redmond R; Dworak, Volker; ShokrianZeini, Mostafa; Navas, Eduardo; Käthner, Jana; Höfner, Nora; Weltzien, Cornelia
    Innovations in terms of robotic manipulator control in digital agriculture have advanced considerably in the last decade with the aim of reducing costs and increasing efficiencies. The availability of compact imaging sensors such as digital cameras that can perceive depth information besides the flexibility of open-source image processing software that can be trained for different applications has played significant roles in accelerating this sector. Preliminary studies have shown that the majority of available robot manipulators in agriculture are using Image-Based Visual Servo (IBVS) control to reach a target position. The presented study provides an overview of different redundant manipulators that are controlled by means of visual servoing for automating various field tasks in digital agriculture including (i) pruning, thinning, and trimming, (ii) harvesting, and (iii) inspection and target spraying. The reviewed works suggest that developing optimal tree shapes and planting techniques is necessary to improve the performance of visual servo control and automate farming operations with robots. In addition, selection of the right imaging sensors, graphics processing units, and training of the computer vision algorithms with more fruits and plants datasets have been highlighted as the three main elements for improving the functionality of IBVS in manipulator control for agricultural applications.