A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition.

https://www.mdpi.com/1424-8220/20/2/342/pdf

Face Recognition Systems: A Survey

Industrial robots, automated manufacturing, and efficient logistics processes are at the heart of the upcoming fourth industrial revolution. While there are seminal studies on the vulnerabilities of cyber-physical systems in the industry, as of today there has been no systematic analysis of the security of industrial robot controllers. We examine the standard architecture of an industrial robot and analyze a concrete deployment from a systems security standpoint. Then, we propose an attacker model and confront it with the minimal set of requirements that industrial robots should honor: precision in sensing the environment, correctness in execution of control logic, and safety for human operators. Following an experimental and practical approach, we then show how our modeled attacker can subvert such requirements through the exploitation of software vulnerabilities, leading to severe consequences that are unique to the robotics domain. We conclude by discussing safety standards and security challenges in industrial robotics.

/pdf/an-experimental-security-analysis-of-an-industrial-robot-17avaqe9qh.pdf

An Experimental Security Analysis of an Industrial Robot Controller

An auto fabric defect detection system via computer vision is used to replace manual inspection. In this paper, we propose a hardware accelerated algorithm based on a small-scale over-completed dictionary (SSOCD) via sparse coding (SC) method, which is realized on a parallel hardware platform (TMS320C6678). In order to reduce computation, the image patches projections in the training SSOCD are taken as features and the proposed features are more robust, and exhibit obvious advantages in detection results and computational cost. Furthermore, we introduce detection ratio and false ratio in order to measure the performance and reliability of the hardware accelerated algorithm. The experiments show that the proposed algorithm can run with high parallel efficiency and that the detection speed meets the real-time requirements of industrial inspection.

https://journals.sagepub.com/doi/pdf/10.5772/62058

Real-Time Fabric Defect Detection Using Accelerated Small-Scale Over-Completed Dictionary of Sparse Coding:

In Human-Robot Interactions (HRI), robots should be socially intelligent They should be able to respond appropriately to human affective and social cues in order to effectively engage in bi-directional communications Social intelligence would allow a robot to relate to, understand, and interact and share information with people in real-world human-centered environments This survey paper presents an encompassing review of existing automated affect recognition and classification systems for social robots engaged in various HRI settings Human-affect detection from facial expressions, body language, voice, and physiological signals are investigated, as well as from a combination of the aforementioned modes The automated systems are described by their corresponding robotic and HRI applications, the sensors they employ, and the feature detection techniques and affect classification strategies utilized This paper also discusses pertinent future research directions for promoting the development of socially intelligent robots capable of recognizing, classifying and responding to human affective states during real-time HRI

A Survey of Autonomous Human Affect Detection Methods for Social Robots Engaged in Natural HRI

With the rapid development of computers and the increasing, mass use of high-tech mobile devices, vision-based face recognition has advanced significantly. However, it is hard to conclude that the performance of computers surpasses that of humans, as humans have generally exhibited better performance in challenging situations involving occlusion or variations. Motivated by the recognition method of humans who utilize both holistic and local features, we present a computationally efficient hybrid face recognition method that employs dual-stage holistic and local feature-based recognition algorithms. In the first coarse recognition stage, the proposed algorithm utilizes Principal Component Analysis (PCA) to identify a test image. The recognition ends at this stage if the confidence level of the result turns out to be reliable. Otherwise, the algorithm uses this result for filtering out top candidate images with a high degree of similarity, and passes them to the next fine recognition stage where Gabor filte...

https://journals.sagepub.com/doi/pdf/10.5772/58473

An Efficient Hybrid Face Recognition Algorithm Using PCA and GABOR Wavelets

In this paper, the current international ISO activities on robots are introduced; ISO TC 184/SC 2, which has been active in developing standards for industrial robots and has widened its scope recently to include service robots as well as industrial robots. The aim of this paper is to introduce the existing standards and the new standards to be developed in SC 2, so that anyone who has interest in these emerging robotic areas could have information on the progress and the future planned developments.

Survey on ISO standards for industrial and service robots

Sejong face database: A multi-modal disguise face database

Heterogeneous face recognition (HFR) aims to match face images across different imaging domains such as visible-to-infrared and visible-to-thermal. Recently, the increasing utility of nonvisible imaging has increased the application prospects of HFR in areas such as biometrics, security, and surveillance. HFR is a challenging variate of face recognition due to the differences between different imaging domains. While the current research has proposed image preprocessing, feature extraction, or common subspace projection for HFR, the optimization of these multi-stage methods is a challenging task as each step needs to be optimized separately and the performance error accumulates over each stage. In this paper, we propose a unified end-to-end Cross-Modality Discriminator Network (CMDN) for HFR. The proposed network uses a Deep Relational Discriminator module to learn deep feature relations for cross-domain face matching. Simultaneously, the CMDN is used to extract modality-independent embedding vectors for face images. The CMDN parameters are optimized using a novel Unit-Class Loss that shows higher stability and accuracy over other popular metric-learning loss functions. The experimental results on five popular HFR datasets demonstrate that the proposed method achieves significant improvement over the existing state-of-the-art methods.

/pdf/heterogeneous-visible-thermal-and-visible-infrared-face-2xjzuka4.pdf

Heterogeneous Visible-Thermal and Visible-Infrared Face Recognition Using Cross-Modality Discriminator Network and Unit-Class Loss

Steel beam welding at a construction site is challenging due to the increasing thickness of steel members in today's buildings. In order to achieve high quality welding and resolve the problem caused by the shortage of skilled welders, robotic systems are in high demand. We have proposed a practical robotic system for steel beam welding, specifically designed for working on H‐shaped column structures that are known to be the most difficult structures for automation.

Seungbin Moon

Papers

An Efficient Hybrid Face Recognition Algorithm Using PCA and GABOR Wavelets

Survey on ISO standards for industrial and service robots

Sejong face database: A multi-modal disguise face database

Heterogeneous Visible-Thermal and Visible-Infrared Face Recognition Using Cross-Modality Discriminator Network and Unit-Class Loss

Portable robotic system for steel H‐beam welding