Visual Tracking with Object Center Displacement and CenterNet

Modern artificial intelligence systems have revolutionized approaches to scientific and technological challenges in a variety of fields, thus remarkable improvements in the quality of state-of-the-art computer vision and other techniques are observed; object tracking in video frames is a vital field of research that provides information about objects and their trajectories. This paper presents an object tracking method basing on optical flow generated between frames and a ConvNet method. Initially, optical center displacement is employed to detect possible the bounding box center of the tracked object. Then, CenterNet is used for object position correction. Given the initial set of points (i.e., bounding box) in first frame, the tracker tries to follow the motion of center of these points by looking at its direction of change in calculated optical flow with next frame, a correction mechanism takes place and waits for motions that surpass a correction threshold to launch position corrections.

Transfer and generalization of learned manipulation between unimanual and bimanual tasks

Successful object manipulation, such as preventing object roll, relies on the modulation of forces and centers of pressure (point of application of digits on each grasp surface) prior to lift onset to generate a compensatory torque. Whether or not generalization of learned manipulation can occur after adding or removing effectors is not known. We examined this by recruiting participants to perform lifts in unimanual and bimanual grasps and analyzed results before and after transfer. Our results show partial generalization of learned manipulation occurred when switching from a (1) unimanual to bimanual grasp regardless of object center of mass, and (2) bimanual to unimanual grasp when the center of mass was on the thumb side. Partial generalization was driven by the modulation of effectors’ center of pressure, in the appropriate direction but of insufficient magnitude, while load forces did not contribute to torque generation after transfer. In addition, we show that the combination of effector forces and centers of pressure in the generation of compensatory torque differ between unimanual and bimanual grasping. These findings highlight that (1) high-level representations of learned manipulation enable only partial learning transfer when adding or removing effectors, and (2) such partial generalization is mainly driven by modulation of effectors’ center of pressure.

Penerapan Teori Golden Section Pada Foto Landscape Pantai Di Gunungkidul

Technical problems in the digital photography era have now beenovercome by the sophistication of cameras. The problem thattechnology cannot solve is the composition of the image.Composition is a skill that requires an understanding ofphotographic objects, visual elements, and artistic experience.Composition is related to the placement of objects and how to build aflow of view in viewing photos that have an impact on the impressionof the photo. The decision to press the camera's shutter button is aconsideration of composition, and is not easily understood bystudents or beginners who are just learning photography. So thatwith knowledge of the composition of this golden section,photographers can produce more optimal work. Careful at everyvisual element that exists, skilled in arranging objects so that bettervisuals are created. Super-sophisticated camera technology cannotsearch for objects on its own, therefore this research is offered. TheGolden Section Theory has been successfully applied to the field ofarchitecture in the 5th century BC and in the field of painting duringthe renessance era. In this study, the author tries to apply thisformula to determine the main object (center of attention) in a beachscene photo in Gunungkidul in 2: 3 image format or the defaultDSLR camera format.

Stellar occultations enable milliarcsecond astrometry for Trans-Neptunian objects and Centaurs

Context. Trans-Neptunian objects (TNOs) and Centaurs are remnants of our planetary system formation, and their physical properties have invaluable information for evolutionary theories. Stellar occultation is a ground-based method for studying these distant small bodies and has presented exciting results. These observations can provide precise profiles of the involved body, allowing an accurate determination of its size and shape. Aims. The goal is to show that even single-chord detections of TNOs allow us to measure their milliarcsecond astrometric positions in the reference frame of the Gaia second data release (DR2). Accurate ephemerides can then be generated, allowing predictions of stellar occultations with much higher reliability. Methods. We analyzed data from various stellar occultation detections to obtain astrometric positions of the involved bodies. The events published before the Gaia era were updated so that the Gaia DR2 stellar catalog is the reference, thus providing accurate positions. Events with detection from one or two different sites (single or double chord) were analyzed to determine the event duration. Previously determined sizes were used to calculate the position of the object center and its corresponding error with respectto the detected chord and the International Celestial Reference System propagated Gaia DR2 star position. Results. We derive 37 precise astrometric positions for 19 TNOs and four Centaurs. Twenty-one of these events are presented here for the first time. Although about 68% of our results are based on single-chord detection, most have intrinsic precision at the submilliarcsecond level. Lower limits on the diameter of bodies such as Sedna, 2002 KX14, and Echeclus, and also shape constraints on 2002 VE95, 2003 FF128, and 2005 TV189 are presented as valuable byproducts. Conclusions. Using the Gaia DR2 catalog, we show that even a single detection of a stellar occultation allows improving the object ephemeris significantly, which in turn enables predicting a future stellar occultation with high accuracy. Observational campaigns can be efficiently organized with this help, and may provide a full physical characterization of the involved object, or even the study of topographic features such as satellites or rings.

Modulation of Grasp Parameters using Arbitrary Cues about Object Property in Older Adults

ABSTRACTDexterous manipulation may be guided by explicit information about object property. Such a manipulation requires fine modulation of digit position and forces using explicit cues. Young adults can form arbitrary cue-object property associations for accurate modulation of digit position and forces. Aging, in contrast, might alter this conditional learning. Older adults are impaired in accurately modulating their digit forces using explicit cues about object property. However, it is not known whether older adults can use explicit cues about object property to modulate digit position. In this study, we instructed ten healthy older and ten young adults to learn a manipulation task using arbitrary color cues about object center of mass location. Subjects were required to exert clockwise, counterclockwise, or no torque on the object according to the color cue and lift the object while minimizing its tilt across sixty trials. Older adults produced larger torque error during the conditional learning trials than young adults. This resulted in a significantly slower rate of learning in older adults. Older, but not young adults, failed to modulate their digit position and forces using the color cues. Similar aging-related differences were not observed while learning the task using implicit knowledge about object property. Our findings suggest that aging impairs the ability to use explicit cues about object property to modulate both digit position and forces for dexterous manipulation. We discuss our findings in relation to age-related changes in the processes and the neural network for conditional learning.

Field Network—A New Method to Detect Directional Object

As the development of object detection technology in computer vision, identifying objects is always an active yet challenging task, and even more efficient and accurate requirements are being imposed on state-of-the-art algorithms. However, many algorithms perform object box regression based on RPN(Region Proposal Network) and anchors, which cannot accurately describe the shape information of the object. In this paper, we propose a new object detection method called Field Network (FN) and Region Fitting Algorithm (RFA). It can solve these problems by Center Field. Center field reflects the probability of the pixel approaching the object center. Different from the previous methods, we abandoned anchors and ROI technologies, and propose the concept of Field. Field is the intensity of the object area, reflecting the probability of the object in the area. Based on the distribution of the probability density of the object center in the visual field perception area, we add the Object Field in the output part. And we abstract it into an Elliptic Field with normal distribution and use RFA to fit objects. Additionally, we add two fields to predict the x,y components of the object direction which contain the neural units in the field array. We extract the objects through these Fields. Moreover, our model is relatively simple and have smaller size, which is only 73 M. Our method improves performance considerably over baseline systems on DOTA, MS COCO and PASCAL VOC datasets, with overall performance competitive with recent state-of-the-art systems.

USING NEURAL NETWORKS TO DETECT OBJECTS IN MLS POINT CLOUDS BASED ON LOCAL POINT NEIGHBORHOODS

<p><strong>Abstract.</strong> This paper presents an approach which uses a <i>PointNet</i>-like neural network to detect objects of certain types in MLS point clouds. In our case, it is used for the detection of pedestrians, but the approach can easily be adapted to other object classes. In the first step, we process local point neighborhoods with the neural network to determine a descriptive feature. This is then further processed to generate two outputs of the network. The first output classifies the neighborhood and determines if it is part of an object of interest. If this is the case, the second output determines where it is located in relation to the object center. This regression output allows us to use a voting process for the actual object detection. This processing step is inspired by approaches based on implicit shape models (ISM). It is able to deal with a certain amount of incorrectly classified neighborhoods, since it combines the results of multiple neighborhoods for the detection of an object. A benefit of our approach as compared to other machine learning methods is its low demand for training data. In our experiments, we achieved a promising detection performance even with less than 1000 training examples.</p>