201,141 research outputs found
Semantic segmentation priors for object discovery
© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Reliable object discovery in realistic indoor scenes is a necessity for many computer vision and service robot applications. In these scenes, semantic segmentation methods have made huge advances in recent years. Such methods can provide useful prior information for object discovery by removing false positives and by delineating object boundaries. We propose a novel method that combines bottom-up object discovery and semantic priors for producing generic object candidates in RGB-D images. We use a deep learning method for semantic segmentation to classify colour and depth superpixels into meaningful categories. Separately for each category, we use saliency to estimate the location and scale of objects, and superpixels to find their precise boundaries. Finally, object candidates of all categories are combined and ranked. We evaluate our approach on the NYU Depth V2 dataset and show that we outperform other state-of-the-art object discovery methods in terms of recall.Peer ReviewedPostprint (author's final draft
Saliency Methods for Object Discovery Based on Image and Depth Segmentation
Object discovery is a recent paradigm in computer and robotic vision where the process of interpreting an image starts by proposing a set of candidate regions that potentially correspond to objects; these candidates can be validated later on by object recognition modules or by robot interaction. In this thesis, we propose a novel method for object discovery that works on single RGB-D images and aims at achieving higher recall than current state-of-the-art methods with fewer candidates. Our approach uses saliency as a cue to roughly estimate the location and extent of the objects, and segmentation processes in order to identify the candidates' precise boundaries. We investigate the performance of four different segmentation methods based on colour, depth, an early and a late fusion of colour and depth, and conclude that the late fusion is the most successful. The object candidates are sorted according to a novel ranking strategy based on a combination of features such as 3D convexity and saliency. We evaluate our method and compare it to other state-of-the-art approaches in object discovery on challenging real world sequences from three different public datasets containing a high degree of clutter. The results show that our approach consistently outperforms the other methods. In the second part of this thesis, we turn to streams of images. Here, our goal is to generate as few object candidates per frame as necessary in order to find as many objects as possible throughout the sequence. Therefore, we propose to extend our object discovery system with a so called spatial inhibition of return mechanism to inhibit object candidates that correspond to objects that have already been generated in the past. The challenge here is to inhibit the candidates consistently with viewpoint change, and therefore, we root our inhibition of return mechanism in 3D spatial coordinates. In the final part of this thesis we show an application of our object discovery method to the task of salient object segmentation. The results show that our method achieves state-of-the-art performance
The First Brown Dwarf Discovered by the Backyard Worlds: Planet 9 Citizen Science Project
The Wide-field Infrared Survey Explorer (WISE) is a powerful tool for finding
nearby brown dwarfs and searching for new planets in the outer solar system,
especially with the incorporation of NEOWISE and NEOWISE-Reactivation data. So
far, searches for brown dwarfs in WISE data have yet to take advantage of the
full depth of the WISE images. To efficiently search this unexplored space via
visual inspection, we have launched a new citizen science project, called
"Backyard Worlds: Planet 9," which asks volunteers to examine short animations
composed of difference images constructed from time-resolved WISE coadds. We
report the discovery of the first new substellar object found by this project,
WISEA J110125.95+540052.8, a T5.5 brown dwarf located approximately 34 pc from
the Sun with a total proper motion of 0.7 as yr. WISEA
J110125.95+540052.8 has a WISE magnitude of , this
discovery demonstrates the ability of citizen scientists to identify moving
objects via visual inspection that are 0.9 magnitudes fainter than the
single-exposure sensitivity, a threshold that has limited prior motion-based
brown dwarf searches with WISE.Comment: 9 pages, 4 figures, 1 table. Accepted for publication in the
Astrophysical Journal Letter
StampNet: unsupervised multi-class object discovery
Unsupervised object discovery in images involves uncovering recurring
patterns that define objects and discriminates them against the background.
This is more challenging than image clustering as the size and the location of
the objects are not known: this adds additional degrees of freedom and
increases the problem complexity. In this work, we propose StampNet, a novel
autoencoding neural network that localizes shapes (objects) over a simple
background in images and categorizes them simultaneously. StampNet consists of
a discrete latent space that is used to categorize objects and to determine the
location of the objects. The object categories are formed during the training,
resulting in the discovery of a fixed set of objects. We present a set of
experiments that demonstrate that StampNet is able to localize and cluster
multiple overlapping shapes with varying complexity including the digits from
the MNIST dataset. We also present an application of StampNet in the
localization of pedestrians in overhead depth-maps
ULAS J234311.93-005034.0: A gravitational lens system selected from UKIDSS and SDSS
We report the discovery of a new gravitational lens system. This object, ULAS
J234311.93-005034.0, is the first to be selected by using the new UKIRT
Infrared Deep Sky Survey (UKIDSS), together with the Sloan Digital Sky Survey
(SDSS). The ULAS J234311.93-005034.0 system contains a quasar at redshift 0.788
which is doubly imaged, with separation 1.4". The two quasar images have the
same redshift and similar, though not identical, spectra. The lensing galaxy is
detected by subtracting point-spread functions from R-band images taken with
the Keck telescope. The lensing galaxy can also be detected by subtracting the
spectra of the A and B images, since more of the galaxy light is likely to be
present in the latter. No redshift is determined from the galaxy, although the
shape of its spectrum suggests a redshift of about 0.3. The object's lens
status is secure, due to the identification of two objects with the same
redshift together with a lensing galaxy. Our imaging suggests that the lens is
found in a cluster environment, in which candidate arc-like structures, that
require confirmation, are visible in the vicinity. Further discoveries of
lenses from the UKIDSS survey are likely as part of this programme, due to the
depth of UKIDSS and its generally good seeing conditions.Comment: Accepted by MNRA
RGB-D datasets using microsoft kinect or similar sensors: a survey
RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms
3D Object Discovery and Modeling Using Single RGB-D Images Containing Multiple Object Instances
Unsupervised object modeling is important in robotics, especially for
handling a large set of objects. We present a method for unsupervised 3D object
discovery, reconstruction, and localization that exploits multiple instances of
an identical object contained in a single RGB-D image. The proposed method does
not rely on segmentation, scene knowledge, or user input, and thus is easily
scalable. Our method aims to find recurrent patterns in a single RGB-D image by
utilizing appearance and geometry of the salient regions. We extract keypoints
and match them in pairs based on their descriptors. We then generate triplets
of the keypoints matching with each other using several geometric criteria to
minimize false matches. The relative poses of the matched triplets are computed
and clustered to discover sets of triplet pairs with similar relative poses.
Triplets belonging to the same set are likely to belong to the same object and
are used to construct an initial object model. Detection of remaining instances
with the initial object model using RANSAC allows to further expand and refine
the model. The automatically generated object models are both compact and
descriptive. We show quantitative and qualitative results on RGB-D images with
various objects including some from the Amazon Picking Challenge. We also
demonstrate the use of our method in an object picking scenario with a robotic
arm
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