589,942 research outputs found
Transfer learning or design a custom CNN for tactile object recognition
International Workshop on Robotac: New Progress in Tactile Perception and Learning in RoboticsNovel tactile sensors allow treating pressure lectures as standard images due to its highresolution. Therefore, computer vision algorithms such as Convolutional Neural Networks
(CNNs) can be used to identify objects in contact. In this work, a high-resolution tactile
sensor has been attached to a robotic end-effector to identify objects in contact. Moreover,
two CNNs-based approaches have been tested in an experiment of classification of pressure
images. These methods include a transfer learning approach using a pre-trained CNN on an
RGB images dataset and a custom-made CNN trained from scratch with tactile information.
A comparative study of performance between them has been carried out.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Spanish project DPI2015-65186-R, the European Commission under grant agreement BES-2016-078237, the educational project PIE-118 of the
University of Malag
Perceptual Generative Adversarial Networks for Small Object Detection
Detecting small objects is notoriously challenging due to their low
resolution and noisy representation. Existing object detection pipelines
usually detect small objects through learning representations of all the
objects at multiple scales. However, the performance gain of such ad hoc
architectures is usually limited to pay off the computational cost. In this
work, we address the small object detection problem by developing a single
architecture that internally lifts representations of small objects to
"super-resolved" ones, achieving similar characteristics as large objects and
thus more discriminative for detection. For this purpose, we propose a new
Perceptual Generative Adversarial Network (Perceptual GAN) model that improves
small object detection through narrowing representation difference of small
objects from the large ones. Specifically, its generator learns to transfer
perceived poor representations of the small objects to super-resolved ones that
are similar enough to real large objects to fool a competing discriminator.
Meanwhile its discriminator competes with the generator to identify the
generated representation and imposes an additional perceptual requirement -
generated representations of small objects must be beneficial for detection
purpose - on the generator. Extensive evaluations on the challenging
Tsinghua-Tencent 100K and the Caltech benchmark well demonstrate the
superiority of Perceptual GAN in detecting small objects, including traffic
signs and pedestrians, over well-established state-of-the-arts
Formalization of higher-level intelligence through integration of intelligent tutoring tools : a thesis presented in partial fulfilment of the requirements for the degree of Master of Information Systems, Department of Information Systems, Massey University, Palmerston North, New Zealand
In contrast with a traditional Intelligent Tutoring System (ITS), which attempts to be fairly comprehensive and covers enormous chunks of a discipline's subject matter, a basic Intelligent Tutoring Tool (ITT) (Patel & Kinshuk, 1997) has a narrow focus. It focuses on a single topic or a very small cluster of related topics. An ITT is regarded as a building block of a larger and more comprehensive tutoring system, which is fundamentally similar with the emerging technology "Learning Objects" (LOs) (LTSC, 2000a). While an individual ITT or LO focuses on a single topic or a very small cluster of knowledge, the importance of the automatic integration of interrelated ITTs or LOs is very clear. This integration can extend the scope of an individual ITT or LO, it can guide the user from a simple working model to a complex working model and provide the learner with a rich learning experience, which results in a higher level of learning. This study reviews and analyses the Learning Objects technology, as well as its advantages and difficulties. Especially, the LOs integration mechanisms applied in the existing learning systems are discussed in detail. As a result, a new ITT integration framework is proposed which extends and formalizes the former ITT integration structures (Kinshuk & Patel, 1997, Kinshuk, et al. 2003) in two ways: identifying and organizing ITTs, and describing and networking ITTs. The proposed ITTs integration framework has the following four notions: (1) Ontology, to set up an explicit conceptualisation in a particular domain, (2) Object Design and Sequence Theory, to identify and arrange learning objects in a pedagogical way through the processes of decomposing principled skills, synthesising working models and placing these models on scales of increasing complexity, (3) Metadata, to describe the identified ITTs and their interrelationships in a cross-platform XML format, and (4) Integration Mechanism, to detect and activate the contextual relationship
A census of Oph candidate members from Gaia DR2
The Ophiuchus cloud complex is one of the best laboratories to study the
earlier stages of the stellar and protoplanetary disc evolution. The wealth of
accurate astrometric measurements contained in the Gaia Data Release 2 can be
used to update the census of Ophiuchus member candidates. We seek to find
potential new members of Ophiuchus and identify those surrounded by a
circumstellar disc. We constructed a control sample composed of 188 bona fide
Ophiuchus members. Using this sample as a reference we applied three different
density-based machine learning clustering algorithms (DBSCAN, OPTICS, and
HDBSCAN) to a sample drawn from the Gaia catalogue centred on the Ophiuchus
cloud. The clustering analysis was applied in the five astrometric dimensions
defined by the three-dimensional Cartesian space and the proper motions in
right ascension and declination. The three clustering algorithms systematically
identify a similar set of candidate members in a main cluster with astrometric
properties consistent with those of the control sample. The increased
flexibility of the OPTICS and HDBSCAN algorithms enable these methods to
identify a secondary cluster. We constructed a common sample containing 391
member candidates including 166 new objects, which have not yet been discussed
in the literature. By combining the Gaia data with 2MASS and WISE photometry,
we built the spectral energy distributions from 0.5 to 22\microm for a subset
of 48 objects and found a total of 41 discs, including 11 Class II and 1 Class
III new discs. Density-based clustering algorithms are a promising tool to
identify candidate members of star forming regions in large astrometric
databases. If confirmed, the candidate members discussed in this work would
represent an increment of roughly 40% of the current census of Ophiuchus.Comment: A&A, Accepted. Abridged abstrac
Mining Object Parts from CNNs via Active Question-Answering
Given a convolutional neural network (CNN) that is pre-trained for object
classification, this paper proposes to use active question-answering to
semanticize neural patterns in conv-layers of the CNN and mine part concepts.
For each part concept, we mine neural patterns in the pre-trained CNN, which
are related to the target part, and use these patterns to construct an And-Or
graph (AOG) to represent a four-layer semantic hierarchy of the part. As an
interpretable model, the AOG associates different CNN units with different
explicit object parts. We use an active human-computer communication to
incrementally grow such an AOG on the pre-trained CNN as follows. We allow the
computer to actively identify objects, whose neural patterns cannot be
explained by the current AOG. Then, the computer asks human about the
unexplained objects, and uses the answers to automatically discover certain CNN
patterns corresponding to the missing knowledge. We incrementally grow the AOG
to encode new knowledge discovered during the active-learning process. In
experiments, our method exhibits high learning efficiency. Our method uses
about 1/6-1/3 of the part annotations for training, but achieves similar or
better part-localization performance than fast-RCNN methods.Comment: Published in CVPR 201
3D CNN Based Phantom Object Removing from Mobile Laser Scanning Data
In this paper we introduce a new deep learning based approach to detect and remove phantom objects from point clouds produced by mobile laser scanning (MLS) systems. The phantoms are caused by the presence of scene objects moving concurrently with the MLS platform, and appear as long, sparse
but irregular point cloud segments in the measurements. We propose a new 3D CNN framework working on a voxelized column-grid to identify the phantom regions. We quantitatively evaluate the proposed model on real MLS test data, and compare it to two different reference approaches
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