object classes
Recently Published Documents


TOTAL DOCUMENTS

121
(FIVE YEARS 16)

H-INDEX

21
(FIVE YEARS 2)

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6740
Author(s):  
Guillem Vallicrosa ◽  
Khadidja Himri ◽  
Pere Ridao ◽  
Nuno Gracias

This paper presents a method to build a semantic map to assist an underwater vehicle-manipulator system in performing intervention tasks autonomously in a submerged man-made pipe structure. The method is based on the integration of feature-based slam and 3D object recognition using a database of a priori known objects. The robot uses dvl, pressure, and ahrs sensors for navigation and is equipped with a laser scanner providing non-coloured 3D point clouds of the inspected structure in real time. The object recognition module recognises the pipes and objects within the scan and passes them to the slam, which adds them to the map if not yet observed. Otherwise, it uses them to correct the map and the robot navigation if they were already mapped. The slam provides a consistent map and a drift-less navigation. Moreover, it provides a global identifier for every observed object instance and its pipe connectivity. This information is fed back to the object recognition module, where it is used to estimate the object classes using Bayesian techniques over the set of those object classes which are compatible in terms of pipe connectivity. This allows fusing of all the already available object observations to improve recognition. The outcome of the process is a semantic map made of pipes connected through valves, elbows and tees conforming to the real structure. Knowing the class and the position of objects will enable high-level manipulation commands in the near future.


Kalbotyra ◽  
2021 ◽  
Vol 74 ◽  
pp. 35-48
Author(s):  
Joanna Cholewa

This article aims to disambiguate the French verb baisser, which describes the downward movement of an entity, and to present its conceptual structure. Our approach is strongly based on the belief that the meaning of the word is conceptual, and that it reflects the world being looked at, not the real world (Honeste 1999, 2005). Our interest will focus on the locative and abstract meanings of the chosen verb, the uses of which we will study. Each use is a set formed by a predicate, defined by its arguments whose field is delimited by the predicate itself (Gross 2015). Arguments are defined using object classes. Each use is illustrated by a single sentence and a translation into Polish, the translation being a synonym of a word in another language. The type of event described by the verb will be studied, taking into account: the situation described by the verb (kinematic, dynamic, according to Desclés 2003, 2005); belonging to one of the four groups of verbs of movement, distinguished by Aurnague (2012) according to two parameters: change of location and change of elementary locative relwation; polarity (initial, median and final, according to Borillo 1998). Baisser has twelve uses (locative and abstract). Their invariant meaning is downwards movement, which is conceptualized in different ways: displacement of an entity downwards in physical space, but also as a decrease along a scale: of quantifiable value, of sound, of luminosity, intensity or quality, and finally of physical strength and of quality.


Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6108
Author(s):  
Sukhan Lee ◽  
Yongjun Yang

Deep learning approaches to estimating full 3D orientations of objects, in addition to object classes, are limited in their accuracies, due to the difficulty in learning the continuous nature of three-axis orientation variations by regression or classification with sufficient generalization. This paper presents a novel progressive deep learning framework, herein referred to as 3D POCO Net, that offers high accuracy in estimating orientations about three rotational axes yet with efficiency in network complexity. The proposed 3D POCO Net is configured, using four PointNet-based networks for independently representing the object class and three individual axes of rotations. The four independent networks are linked by in-between association subnetworks that are trained to progressively map the global features learned by individual networks one after another for fine-tuning the independent networks. In 3D POCO Net, high accuracy is achieved by combining a high precision classification based on a large number of orientation classes with a regression based on a weighted sum of classification outputs, while high efficiency is maintained by a progressive framework by which a large number of orientation classes are grouped into independent networks linked by association subnetworks. We implemented 3D POCO Net for full three-axis orientation variations and trained it with about 146 million orientation variations augmented from the ModelNet10 dataset. The testing results show that we can achieve an orientation regression error of about 2.5° with about 90% accuracy in object classification for general three-axis orientation estimation and object classification. Furthermore, we demonstrate that a pre-trained 3D POCO Net can serve as an orientation representation platform based on which orientations as well as object classes of partial point clouds from occluded objects are learned in the form of transfer learning.


2021 ◽  
Vol 8 ◽  
Author(s):  
Gal Gorjup ◽  
Lucas Gerez ◽  
Minas Liarokapis

Robot grasping in unstructured and dynamic environments is heavily dependent on the object attributes. Although Deep Learning approaches have delivered exceptional performance in robot perception, human perception and reasoning are still superior in processing novel object classes. Furthermore, training such models requires large, difficult to obtain datasets. This work combines crowdsourcing and gamification to leverage human intelligence, enhancing the object recognition and attribute estimation processes of robot grasping. The framework employs an attribute matching system that encodes visual information into an online puzzle game, utilizing the collective intelligence of players to expand the attribute database and react to real-time perception conflicts. The framework is deployed and evaluated in two proof-of-concept applications: enhancing the control of a robotic exoskeleton glove and improving object identification for autonomous robot grasping. In addition, a model for estimating the framework response time is proposed. The obtained results demonstrate that the framework is capable of rapid adaptation to novel object classes, based purely on visual information and human experience.


Author(s):  
Mikhail Sergeevich Kopylov

Nowadays, scripting is becoming a basic functionality in a very large number of different applications. This paper considers the experience of expanding the program capabilities of the optical modeling system using the Python scripting language. A brief overview of existing solutions is discussed. The approach based on the method of using the unified entity interface is proposed, which makes the process of expansion of the system simple and convenient for both its developers and end users. The new program modules like script interpreter,script editor and built-in parametric object libraries have been designed and integrated into the optical modeling system to work with scenarios are considered in detail. Software extension mechanism by means of adding new script-based object classes is provided. Examples of using Python API for a number of simple operations and examples of work with some simulation and automation modules based on scenarios are considered.


2020 ◽  
Vol 499 (1) ◽  
pp. 524-542
Author(s):  
Daniel K Giles ◽  
Lucianne Walkowicz

ABSTRACT In the present era of large-scale surveys, big data present new challenges to the discovery process for anomalous data. Such data can be indicative of systematic errors, extreme (or rare) forms of known phenomena, or most interestingly, truly novel phenomena that exhibit as-of-yet unobserved behaviours. In this work, we present an outlier scoring methodology to identify and characterize the most promising unusual sources to facilitate discoveries of such anomalous data. We have developed a data mining method based on k-nearest neighbour distance in feature space to efficiently identify the most anomalous light curves. We test variations of this method including using principal components of the feature space, removing select features, the effect of the choice of k, and scoring to subset samples. We evaluate the performance of our scoring on known object classes and find that our scoring consistently scores rare (<1000) object classes higher than common classes. We have applied scoring to all long cadence light curves of Quarters 1–17 of Kepler’s prime mission and present outlier scores for all 2.8 million light curves for the roughly 200k objects.


2020 ◽  
Vol 2 (1) ◽  
pp. 40
Author(s):  
Syahid Al Irfan ◽  
Nuryono Satya Widodo

In a soccer game the ability of humanoid robots that one needs to have is to see the ball object in real time. Development of the ability of humanoid robots to see the ball has been developed but the level of accuracy of object recognition and adaptation during matches still needs to be improved. The architecture designed in this study is Convolutional Neural Network or CNN which is designed to have 6 hidden layers with implementation of the robot program using the Tensorflow library. The pictures taken are used in the training process to have 9 types of images based on where the pictures were taken. Each type of image is divided into 2 classes, namely 2000 images for ball object classes and 2000 images for non-ball object classes. The test is done in real time using a white ball on green grass. From the architectural design and white ball detection test results obtained a success rate of 67%, five of the nine models managed to recognize the ball. The model can recognize objects with an image processing speed of a maximum of 13 FPS.Dalam pertandingan sepak bola kemampuan robot humanoid yang perlu dimiliki salah satunya adalah melihat objek bola secara real time. Pengembangan kemampuan robot humanoid untuk melihat bola telah dikembangkan tetapi tingkat akurasi pengenalan objek dan adaptasi saat pertandingan masih perlu ditingkatkan. Arsitektur yang dirancang pada penelitian ini yaitu Convolutional Neural Network atau CNN yang dirancang memiliki 6 hidden layer dengan implementasi pada program robot menggunakan library Tensorflow. Gambar yang diambil digunakan dalam proses training memiliki 9 jenis gambar berdasarkan tempat pengambilan gambar. Tiap jenis gambar terbagi menjadi 2 class yaitu 2000 gambar untuk class objek bola dan 2000 gambar untuk class objek bukan bola. Pengujian dilakukan secara real time dengan menggunakan bola berwarna putih di atas rumput hijau. Dari perancangan arsitektur dan hasil pengujian pendeteksian bola putih didapatkan persentase keberhasilan 67% yaitu lima dari sembilan model berhasil mengenali bola. Model dapat mengenali objek dengan kecepatan pengolahan gambar adalah maksimal 13 FPS.


Data in Brief ◽  
2020 ◽  
Vol 29 ◽  
pp. 105302
Author(s):  
Yaniv Morgenstern ◽  
Filipp Schmidt ◽  
Roland W. Fleming
Keyword(s):  

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 380 ◽  
Author(s):  
Agnese Chiatti ◽  
Gianluca Bardaro ◽  
Emanuele Bastianelli ◽  
Ilaria Tiddi ◽  
Prasenjit Mitra ◽  
...  

To assist humans with their daily tasks, mobile robots are expected to navigate complex and dynamic environments, presenting unpredictable combinations of known and unknown objects. Most state-of-the-art object recognition methods are unsuitable for this scenario because they require that: (i) all target object classes are known beforehand, and (ii) a vast number of training examples is provided for each class. This evidence calls for novel methods to handle unknown object classes, for which fewer images are initially available (few-shot recognition). One way of tackling the problem is learning how to match novel objects to their most similar supporting example. Here, we compare different (shallow and deep) approaches to few-shot image matching on a novel data set, consisting of 2D views of common object types drawn from a combination of ShapeNet and Google. First, we assess if the similarity of objects learned from a combination of ShapeNet and Google can scale up to new object classes, i.e., categories unseen at training time. Furthermore, we show how normalising the learned embeddings can impact the generalisation abilities of the tested methods, in the context of two novel configurations: (i) where the weights of a Convolutional two-branch Network are imprinted and (ii) where the embeddings of a Convolutional Siamese Network are L2-normalised.


Sign in / Sign up

Export Citation Format

Share Document