scholarly journals Object segmentation in cluttered environment based on gaze tracing and gaze blinking

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Photchara Ratsamee ◽  
Yasushi Mae ◽  
Kazuto Kamiyama ◽  
Mitsuhiro Horade ◽  
Masaru Kojima ◽  
...  
Keyword(s):  
Object Segmentation ◽  
Target Object ◽  
Complex Scene ◽  
Robot Interaction ◽  
Cluttered Environment ◽  
Motor Paralysis ◽  
Object Search ◽  
Core Module ◽  
Different Types ◽  
Visual Segmentation

AbstractPeople with disabilities, such as patients with motor paralysis conditions, lack independence and cannot move most parts of their bodies except for their eyes. Supportive robot technology is highly beneficial in supporting these types of patients. We propose a gaze-informed location-based (or gaze-based) object segmentation, which is a core module of successful patient-robot interaction in an object-search task (i.e., a situation when a robot has to search for and deliver a target object to the patient). We have introduced the concepts of gaze tracing (GT) and gaze blinking (GB), which are integrated into our proposed object segmentation technique, to yield the benefit of an accurate visual segmentation of unknown objects in a complex scene. Gaze tracing information can be used as a clue as to where the target object is located in a scene. Then, gaze blinking can be used to confirm the position of the target object. The effectiveness of our proposed method has been demonstrated using a humanoid robot in experiments with different types of highly cluttered scenes. Based on the limited gaze guidance from the user, we achieved an 85% F-score of unknown object segmentation in an unknown environment.

Object Extraction Using Topological Models from Complex Scene Image

2017 ◽  
pp. 335-357
Author(s):  
Uday Pratap Singh ◽  
Sanjeev Jain
Keyword(s):  
Object Recognition ◽  
Object Segmentation ◽  
Multimedia Data ◽  
Region Merging ◽  
Complex Scene ◽  
Object Contour ◽  
Merging Process ◽  
Feasible Solutions ◽  
Neighbourhood Graph ◽  
Topological Models

Efficient and effective object recognition from a multimedia data are very complex. Automatic object segmentation is usually very hard for natural images; interactive schemes with a few simple markers provide feasible solutions. In this chapter, we propose topological model based region merging. In this work, we will focus on topological models like, Relative Neighbourhood Graph (RNG) and Gabriel graph (GG), etc. From the Initial segmented image, we constructed a neighbourhood graph represented different regions as the node of graph and weight of the edges are the value of dissimilarity measures function for their colour histogram vectors. A method of similarity based region merging mechanism (supervised and unsupervised) is proposed to guide the merging process with the help of markers. The region merging process is adaptive to the image content and it does not need to set the similarity threshold in advance. To the validation of proposed method extensive experiments are performed and the result shows that the proposed method extracts the object contour from the complex background.

scholarly journals Bin-Picking for Planar Objects Based on a Deep Learning Network: A Case Study of USB Packs

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3602 ◽  
Author(s):  
Tuan-Tang Le ◽  
Chyi-Yeu Lin
Keyword(s):  
Deep Learning ◽  
Image Data ◽  
Target Object ◽  
Successful Implementation ◽  
Pose Prediction ◽  
Robust Solution ◽  
Cloud Data ◽  
Cluttered Environment ◽  
Bin Picking ◽  
3D Information

Random bin-picking is a prominent, useful, and challenging industrial robotics application. However, many industrial and real-world objects are planar and have oriented surface points that are not sufficiently compact and discriminative for those methods using geometry information, especially depth discontinuities. This study solves the above-mentioned problems by proposing a novel and robust solution for random bin-picking for planar objects in a cluttered environment. Different from other research that has mainly focused on 3D information, this study first applies an instance segmentation-based deep learning approach using 2D image data for classifying and localizing the target object while generating a mask for each instance. The presented approach, moreover, serves as a pioneering method to extract 3D point cloud data based on 2D pixel values for building the appropriate coordinate system on the planar object plane. The experimental results showed that the proposed method reached an accuracy rate of 100% for classifying two-sided objects in the unseen dataset, and 3D appropriate pose prediction was highly effective, with average translation and rotation errors less than 0.23 cm and 2.26°, respectively. Finally, the system success rate for picking up objects was over 99% at an average processing time of 0.9 s per step, fast enough for continuous robotic operation without interruption. This showed a promising higher successful pickup rate compared to previous approaches to random bin-picking problems. Successful implementation of the proposed approach for USB packs provides a solid basis for other planar objects in a cluttered environment. With remarkable precision and efficiency, this study shows significant commercialization potential.

Familiarity with images affects how dogs (Canis familiaris) process life-size video projections of humans

2018 ◽  
Vol 71 (6) ◽  
pp. 1457-1468
Author(s):  
Péter Pongrácz ◽  
András Péter ◽  
Ádám Miklósi
Keyword(s):  
Search Task ◽  
Visual Stimuli ◽  
Target Object ◽  
Important Conclusion ◽  
Motion Cues ◽  
Visual Elements ◽  
Video Images ◽  
Object Search ◽  
Actual Content ◽  
Video Presentations

A central problem of behavioural studies providing artificial visual stimuli for non-human animals is to determine how subjects perceive and process these stimuli. Especially in the case of videos, it is important to ascertain that animals perceive the actual content of the images and are not just reacting to the motion cues in the presentation. In this study, we set out to investigate how dogs process life-sized videos. We aimed to find out whether dogs perceive the actual content of video images or whether they only react to the videos as a set of dynamic visual elements. For this purpose, dogs were presented with an object search task where a life-sized projected human was hiding a target object. The videos were either normally oriented or displayed upside down, and we analysed dogs’ reactions towards the projector screen after the video presentations, and their performance in the search task. Results indicated that in the case of the normally oriented videos, dogs spontaneously perceived the actual content of the images. However, the ‘Inverted’ videos were first processed as a set of unrelated visual elements, and only after some exposure to these videos did the dogs show signs of perceiving the unusual configuration of the depicted scene. Our most important conclusion was that dogs process the same type of artificial visual stimuli in different ways, depending on the familiarity of the depicted scene, and that the processing mode can change with exposure to unfamiliar stimuli.

Young children's sensitivity to listener knowledge and perceptual context in choosing referring expressions

2005 ◽  
Vol 26 (4) ◽  
pp. 541-558 ◽  
Author(s):  
ANGELIKA WITTEK ◽  
MICHAEL TOMASELLO
Keyword(s):  
Target Object ◽  
Referring Expressions ◽  
Different Types

Speakers use different types of referring expressions depending on what the listener knows or is attending to; for example, they use pronouns for objects that are already present in the immediate discourse or perceptual context. In a first study we found that 2.5- and 3.5-year-old children are strongly influenced by their interlocutor's knowledge of a referent as expressed in her immediately preceding utterance. Specifically, when they are asked a question about a target object (“Where is the broom?”), they tend to use null references or pronouns to refer to that object (“On the shelf” or “It's on the shelf”); in contrast, when they are asked more general questions (“What do we need?”) or contrast questions (“Do we need a mop?”) that reveal no knowledge of the target object they tend to use lexical nouns (“A broom” or “No, a broom”). In a second study we found that children at around their second birthday are not influenced by immediately preceding utterances in this same way. Finally, in a third study we found that 2.5- and 3.5-year-old children's choice of referring expressions is very little influenced by the physical arrangements of objects in the perceptual context, whether it is absent or needs to be distinguished from a close-by alternative, when they request a target object from a silent adult. These results are discussed in terms of children's emerging understanding of the knowledge and attentional states and other persons.

scholarly journals The Application of Image Processing to Solve Occlusion Issue in Object Tracking

2018 ◽  
Vol 152 ◽  
pp. 03001
Author(s):  
Yun Zhe Cheong ◽  
Wei Jen Chew
Keyword(s):  
Object Tracking ◽  
Moving Objects ◽  
Object Segmentation ◽  
Target Object ◽  
Occlusion Handling ◽  
Multiple Objects ◽  
The Road ◽  
Occluded Objects ◽  
On The Road ◽  
Tracking Object

Object tracking is a computer vision field that involves identifying and tracking either a single or multiple objects in an environment. This is extremely useful to help observe the movements of the target object like people in the street or cars on the road. However, a common issue with tracking an object in an environment with many moving objects is occlusion. Occlusion can cause the system to lose track of the object being tracked or after overlapping, the wrong object will be tracked instead. In this paper, a system that is able to correctly track occluded objects is proposed. This system includes algorithms such as foreground object segmentation, colour tracking, object specification and occlusion handling. An input video is input to the system and every single frame of the video is analysed. The foreground objects are segmented with object segmentation algorithm and tracked with the colour tracking algorithm. An ID is assigned to each tracked object. Results obtained shows that the proposed system is able to continuously track an object and maintain the correct identity even after is has been occluded by another object.

scholarly journals Power Tillers for Demining: Blast Test

10.5772/5690 ◽  
2007 ◽  
Vol 4 (2) ◽  
pp. 28 ◽  
Author(s):  
Emanuela Elisa Cepolina ◽  
Manjula Udayanga Hemapala
Keyword(s):  
Shock Wave ◽  
Developing Countries ◽  
Large Scale ◽  
Cutting Tool ◽  
Participatory Approach ◽  
Humanitarian Demining ◽  
Core Module ◽  
Different Types ◽  
Preliminary Version ◽  
Power Tiller

Power tillers are very simple and versatile machines with large scale diffusion in developing countries, where they are commonly used both for agriculture and for transportation purposes. A new integrated participatory approach that makes use of and improves local end-users knowledge has been used to design a new robotic system for humanitarian demining applications in Sri Lanka, using power tiller as core module. A demining machine composed by a tractor unit, a ground processing tool and a vegetation cutting tool is here presented together with results obtained from the first blast test on the preliminary version of tractor unit armouring. Different breakable connections between wheels and axle have been designed to cause physical detachment and interrupt the transmission of the shock wave released by the explosion of a mine under one wheel. Effects of explosions on different types of wheels and on the chassis have been recorded and commented.

scholarly journals Gentle Versus Strong Touch Classification: Preliminary Results, Challenges, and Potentials

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3033
Author(s):  
Soheil Keshmiri ◽  
Masahiro Shiomi ◽  
Hidenobu Sumioka ◽  
Takashi Minato ◽  
Hiroshi Ishiguro
Keyword(s):  
Preliminary Investigation ◽  
Real Life ◽  
Human Robot Interaction ◽  
Machine Learning Algorithms ◽  
Robot Interaction ◽  
Affective Communication ◽  
Assistive Robots ◽  
Socially Assistive Robots ◽  
Average Accuracy ◽  
Different Types

Touch plays a crucial role in humans’ nonverbal social and affective communication. It then comes as no surprise to observe a considerable effort that has been placed on devising methodologies for automated touch classification. For instance, such an ability allows for the use of smart touch sensors in such real-life application domains as socially-assistive robots and embodied telecommunication. In fact, touch classification literature represents an undeniably progressive result. However, these results are limited in two important ways. First, they are mostly based on overall (i.e., average) accuracy of different classifiers. As a result, they fall short in providing an insight on performance of these approaches as per different types of touch. Second, they do not consider the same type of touch with different level of strength (e.g., gentle versus strong touch). This is certainly an important factor that deserves investigating since the intensity of a touch can utterly transform its meaning (e.g., from an affectionate gesture to a sign of punishment). The current study provides a preliminary investigation of these shortcomings by considering the accuracy of a number of classifiers for both, within- (i.e., same type of touch with differing strengths) and between-touch (i.e., different types of touch) classifications. Our results help verify the strength and shortcoming of different machine learning algorithms for touch classification. They also highlight some of the challenges whose solution concepts can pave the path for integration of touch sensors in such application domains as human–robot interaction (HRI).

scholarly journals Editorial for Special Issue “Remote Sensing for Target Object Detection and Identification”

2020 ◽  
Vol 12 (1) ◽  
pp. 196 ◽  
Author(s):  
Gemine Vivone ◽  
Paolo Addesso ◽  
Amanda Ziemann
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Unmanned Aerial Vehicles ◽  
Target Object ◽  
Special Issue ◽  
Remotely Sensed Data ◽  
Aerial Vehicles ◽  
Detection And Identification ◽  
Different Types ◽  
Identification Techniques

This special issue gathers fourteen papers focused on the application of a variety of target object detection and identification techniques for remotely-sensed data. These data are acquired by different types of sensors (both passive and active) and are located on various platforms, ranging from satellites to unmanned aerial vehicles. This editorial provides an overview of the contributed papers, briefly presenting the technologies and algorithms employed as well as the related applications.

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