Analysis of feature detector and descriptor combinations with a localization experiment for various performance metrics

The purpose of this study is to provide a detailed performance comparison of feature detector/descriptor methods, particularly when their various combinations are used for image-matching. The localization experiments of a mobile robot in an indoor environment are presented as a case study. In these experiments, 3090 query images and 127 dataset images were used. This study includes five methods for feature detectors (features from accelerated segment test (FAST), oriented FAST and rotated binary robust independent elementary features (BRIEF) (ORB), speeded-up robust features (SURF), scale invariant feature transform (SIFT), and binary robust invariant scalable keypoints (BRISK)) and five other methods for feature descriptors (BRIEF, BRISK, SIFT, SURF, and ORB). These methods were used in 23 different combinations and it was possible to obtain meaningful and consistent comparison results using the performance criteria defined in this study. All of these methods were used independently and separately from each other as either feature detector or descriptor. The performance analysis shows the discriminative power of various combinations of detector and descriptor methods. The analysis is completed using five parameters: (i) accuracy, (ii) time, (iii) angle difference between keypoints, (iv) number of correct matches, and (v) distance between correctly matched keypoints. In a range of 60{\deg}, covering five rotational pose points for our system, the FAST-SURF combination had the lowest distance and angle difference values and the highest number of matched keypoints. SIFT-SURF was the most accurate combination with a 98.41% correct classification rate. The fastest algorithm was ORB-BRIEF, with a total running time of 21,303.30 s to match 560 images captured during motion with 127 dataset images.Comment: 11 pages, 3 figures, 1 tabl

Safe Human-Robot Interaction Using Variable Stiffness, Hyper-Redundancy, and Smart Robotic Skins

In service robotics, safe human-robot interaction (HRI) is still an open research topic, requiring developments both in hardware and in software as well as their integration. In UMAY1 and MEDICARE-C2projects, we addressed both mechanism design and perception aspects of a framework for safe HRI. Our first focus was to design variable stiffness joints for the robotic neck and arm to enable inherent compliance to protect a human collaborator. We demonstrate the advantages of variable stiffness actuators (VSA) in compliancy, safety, and energy efficiency with applications in exoskeleton and rehabilitation robotics. The variable-stiffness robotic neck mechanism was later scaled down and adopted in the robotic endoscope featuring hyper-redundancy. The hyper-redundant structures are more controllable, having efficient actuation and better feedback. Lastly, a smart robotic skin is introduced to explain the safety support via enhancement of tactile perception. Although it is developed for a hyper-redundant endoscopic robotic platform, the artificial skin can also be integrated in service robotics to provide multimodal tactile feedback. This chapter gives an overview of systems and their integration to attain a safer HRI. We follow a holistic approach for inherent compliancy via mechanism design (i.e., variable stiffness), precise control (i.e., hyper-redundancy), and multimodal tactile perception (i.e., smart robotic-skins)

Bank efficiency and non-performing loans: Evidence from Turkey

This study analyses technical and allocative efficiencies in Turkish banks from December 2002 to December 2017, under the assumption of constant returns to scale. We apply a modified version of the Data Envelopment Analysis (DEA) approach introduced by Aparico et al. (2015), which employs a directional distance model to provide estimates of efficiency, with a focus on Non-Performing Loans (NPLs) as an undesirable output. In addition, we examine the determinants of efficiency by applying quantile regressions to panel data. The results obtained support the thesis that NPLs exert a negative impact in terms of technical efficiency, which confirms the “bad management” hypothesis in the banking sector. We also find that the level of efficiency of Turkish banks differs, depending on the ownership structure in place

Fast re-OBJ: real-time object re-identification in rigid scenes

Re-identifying objects in a rigid scene across varying viewpoints (object Re-ID) is a challenging task, in particular when there are similar, even identical objects coexist in the same environment. Discriminative features play no doubt an essential role in addressing this challenge, while for practical deployment, real-time performance is another desired attribute. We therefore propose a novel framework, named Fast re-OBJ, that is able to improve both Re-ID accuracy and processing speed via tight coupling between the instance segmentation module and embedding generation module. The rich object encoding in the instance segmentation backbone is directly shared to the embedding generation module for training a more discriminative representation via a triplet network. Moreover, we create datasets with the segmentation outputs using real-time object detectors to train and evaluate our object embedding module. With extensive experiments, we prove that our proposed Fast re-OBJ improves the object Re-ID accuracy by 5% and the speed is 5× faster compared to the state-of-the-art methods. The dataset and code repository are publicly available at: https://tinyurl.com/bdsb53c4

A low-cost UAV framework towards ornamental plant detection and counting in the wild

Object detection still keeps its role as one of the fundamental challenges within the computer vision territory. In particular, achieving satisfying results concerning object detection from outdoor images occupies a considerable space. In this study, in addition to comparing handcrafted feature detector/descriptor performance with deep learning methods over ornamental plant images at the outdoor, we propose a framework to improve the detection of these plants. Firstly, we take query images in the RGB format from the onboard UAV camera. Secondly, our model classifies the scene as a planting or an urban area. Thirdly, if the images are from planting area, thirdly, we filter the field according to the color and acquire only the green parts. Lastly, we feed the object detector model with the filtered area and obtain the category and localization of the plants as a result. In parallel, we also estimate the number of interested plants using the geometrical relations and predefined average plant size, then we verify the outputs of the object detector with this results. The conducted experiments show that deep learning based object detection methods overtake conventional feature detector/descriptor techniques in terms of accuracy, recall, precision, and sensitivity rates. The field classifier model, VGGNet, achieves a 98.17% accuracy for this task, whilst YoloV3 achieves 91.6% accuracy with 0.12 IOU for object detection as the best method. The proposed framework also improves the overall performance of these algorithms by 1.27% for accuracy and 0.023 for IOU. By specifying the limits thoroughly and developing task-dependent approaches, we reveal the great potential of our framework plant detection and counting in the wild consisting of basic image preprocessing techniques, geometrical operations, and deep neural network.Scientific and Technological Research Council of Turkey (TBTAK) [1139B411900149]The authors would like to thank Dr. Levent Calli and Arifiye Cicekcilik Fidancilik Ltd. Co. for their help in data collection using the UAV from the field for the outdoor experiments. This work is supported by The Scientific and Technological Research Council of Turkey (TBTAK) under Grant No. 1139B411900149.WOS:0005613462000012-s2.0-8508752375

A hybrid image dataset toward bridging the gap between real and simulation environments for robotics

The primary motivation of computer vision in the robotics field is to obtain a perception level that is as close as possible to human visual system. To achieve this, the inclusion of large datasets is necessary, sometimes involving less-frequent and seemingly irrelevant data to increase the system robustness. To minimize the effort and time in forming such extensive datasets from real world, the preferred method is to utilize simulation environments, replicating real-world conditions as much as possible. Following this solution path, the machine vision problems in robotics (i.e., object detection, recognition, and manipulation) often employ synthetic images in datasets and, however, do not mix them with real-world images. When the systems are trained only using the synthetic images and tested within the simulated world, the tasks requiring object recognition in robotics can be accomplished. However, the systems trained using this procedure cannot be directly used in the real-world experiments or end-user products due to the inconsistencies between real and simulation environments. Therefore, we propose a hybrid image dataset including annotated desktop objects from real and synthetic worlds (ADORESet). This hybrid dataset provides purposeful object categories with a sufficient number of real and synthetic images. ADORESet is composed of colored images with the dimension of 300 7300 pixels within 30 categories. Each class has 2500 real-world images acquired from the wild web and 750 synthetic images that are generated within Gazebo simulation environment. This hybrid dataset enables researchers to implement their own algorithms for both real-world and simulation environment conditions. ADORESet is composed of fully annotated object images. The limits of objects are manually specified, and the bounding box coordinates are provided. The successor objects are also labeled to give statistical information and the likelihood about the relations of the objects within the dataset. To further demonstrate the benefits of this dataset, it is tested in object recognition tasks by fine-tuning the state-of-the-art deep convolutional neural networks such as VGGNet, InceptionV3, ResNet, and Xception. The possible combinations regarding the data types for these models are compared in terms of time, accuracy, and loss values. As a result of the conducted object recognition experiments, training with all-real images yields approximately 49% validation accuracy for simulation images. When the training is performed with all-synthetic images and validated using all-real images, the accuracy becomes lower than 10%. If the complete ADORESet is employed for training and validation, the hybrid dataset validation accuracy reaches approximately to 95%. This result proves further that including the real and synthetic images together in the training and validation sessions increases the overall system accuracy and reliability

Low-cost variable stiffness joint design using translational variable radius pulleys

Robot joints are expected to be safe, compliant, compact, simple and low-cost. Gravity compensation, zero backlash, energy efficiency and stiffness adjustability are some desired features in the robotic joints. The variable radius pulleys (VRPs) provide a simple, compact and low-cost solution to the stiffness adjustment problem. VRP mechanisms maintain a preconfigured nonlinear force-elongation curve utilizing off-the-shelf torsional spring and pulley profile. In this paper, three synthesis algorithms are presented for VRP mechanisms to obtain desired force-elongation curve. In addition, a feasibility condition is proposed to determine the torsional spring coefficient. Using the synthesis methods and the feasibility condition, a variable stiffness mechanism is designed and manufactured which uses two VRPs in an antagonistic cable driven structure. Afterwards, the outputs of three synthesis methods are compared to force-elongation characteristics in the tensile testing experiment. A custom testbed is manufactured to measure the pulley rotation, cable elongation and tensile force at the same time. Using the experiment as the baseline, the best algorithm achieved to reproduce the desired curve with a root-mean-square (RMS) error of 13.3%. Furthermore, VRP-VSJ is implemented with a linear controller to reveal the performance of the mechanism in terms of position accuracy and stiffness adjustability

One-year clinical evaluation of different types of bulk-fill composites

Colak, Hakan/0000-0001-8262-0913; Bayraktar, Yusuf/0000-0001-6250-5651; Hamidi, Mehmet Mustafa/0000-0003-3461-7925; ERCAN, Ertugrul/0000-0002-4753-6553WOS: 000407264700008PubMed: 26800647Aim: In the present study, we evaluated the 1-year clinical performance of a conventional posterior composite resin and three bulk-fill composite resins. Methods: Fifty patients with four class II restorations under occlusion were enrolled in the present study. A total of 200 restorations were placed in the cavity, 50 for each material (Clearfil Photo Posterior, Filtek Bulk-Fill Flowable and Filtek P60, Tetric EvoCeram Bulk-Fill, and SonicFill). One operator placed the restorations in the cavity, and 1 week later the patients were called for baseline examination. Two calibrated examiners evaluated the restorations once every 3 months for 1 year, according to United States Public Health Service criteria. The data were analyzed using SPSS. Non-parametric tests (Kruskal-Wallis, Mann-Whitney U-test, and Friedman) were used for the analysis at a confidence level of 95%. Results: The 1-year recall rate was 86%. All restorations showed minor modifications after 1 year. However, no statistically-significant differences were detected between the materials' performance at baseline and after 1 year for all criteria (P > 0.05). Conclusions: The bulk-fill composite resin materials showed similar clinical performance when compared with a conventional posterior composite resin. Further evaluations are necessary for the long-term clinical performance of these materials

External Force/Torque Estimation With Only Position Sensors for Antagonistic VSAs

Recent use scenarios involving human-robot collaboration have revealed that the robots require elastic joints to safely interact with humans. It is also critical to know applied force/torque (f/t) during the interaction for control and motion planning purposes. In this article, we estimate the external f/t values without using any sensors other than low-cost encoders by exploiting the inherent elastic properties of the joint. For estimation, the following two different approaches are used: model based and model free. In the model-based approach, an extended Kalman filter (EKF) and an external force observer (EFOB) are used considering the dynamical behavior of the system to estimate the interaction force. In the model-free approach, the artificial neural network (ANN) utilizes the data gathered from mechanical systems. In comparative analysis, we have, therefore, considered three different estimation methods, two of which are model based and the remaining one is model free (i.e., data driven). Implementing these estimation algorithms experimentally on a variable stiffness joint, we performed an extensive evaluation of their performances. All methods show similar level of performance in terms of the root-mean-square (RMS) error with 0.0847, 0.0841, and 0.1082 N for the EKF, EFOB, and ANN, respectively. Model-based methods do not require continuous data stream through the experimental set up. On the other hand, the ANN does not need an explicit model of the system; therefore, it may become preferable when the detailed model derivation is not possible

A typical method for decellularization of plants as biomaterials

Decellularization is a process by which cells are removed from tissues or organs, leaving behind the extracellular matrix (ECM) structure. This process has gained interest in the fields of tissue engineering and regenerative medicine as a way to prepare suitable scaffolds for tissue reconstruction. Although the initial efforts come with the animal tissues, this technique can also be applied to various plant tissues with simple modifications, as plant-derived biomaterials have the benefit of being biocompatible and serving as a safe, all-natural substitute for synthetic or animal originated materials. Additionally, plant-derived biomaterials may help cells grow and differentiate, creating a three-dimensional environment for tissue regeneration and repair. Here we demonstrate a general method for plant tissue decellularization, including already experienced approaches and techniques. • Exhibit the basic steps for plant decellularization, which may be applied to several other plant tissues. • The proposed approach may be optimized considering various intended uses. • Gives basic information for the determination of decellularization efficiency