scholarly journals Shape binary patterns: an efficient local descriptor and keypoint detector for point clouds

2022 ◽  
Author(s):  
Cristina Romero-González ◽  
Ismael García-Varea ◽  
Jesus Martínez-Gómez
Keyword(s):  
Autonomous Robots ◽  
Robot Vision ◽  
Point Clouds ◽  
Relevant Information ◽  
Local Descriptor ◽  
Local Descriptors ◽  
Visual Tasks ◽  
Research Problems ◽  
Real Value ◽  
Keypoint Detector

AbstractMany of the research problems in robot vision involve the detection of keypoints, areas with salient information in the input images and the generation of local descriptors, that encode relevant information for such keypoints. Computer vision solutions have recently relied on Deep Learning techniques, which make extensive use of the computational capabilities available. In autonomous robots, these capabilities are usually limited and, consequently, images cannot be processed adequately. For this reason, some robot vision tasks still benefit from a more classic approach based on keypoint detectors and local descriptors. In 2D images, the use of binary representations for visual tasks has shown that, with lower computational requirements, they can obtain a performance comparable to classic real-value techniques. However, these achievements have not been fully translated to 3D images, where research is mainly focused on real-value approaches. Thus, in this paper, we propose a keypoint detector and local descriptor based on 3D binary patterns. The experimentation demonstrates that our proposal is competitive against state-of-the-art techniques, while its processing can be performed more efficiently.

scholarly journals Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation

2020 ◽  
Vol 12 (11) ◽  
pp. 1870 ◽  
Author(s):  
Qingqing Li ◽  
Paavo Nevalainen ◽  
Jorge Peña Queralta ◽  
Jukka Heikkonen ◽  
Tomi Westerlund
Keyword(s):  
Autonomous Robots ◽  
Laser Scanner ◽  
Point Clouds ◽  
Time Data ◽  
Unstructured Environments ◽  
Accurate Localization ◽  
Novel Approach ◽  
Local Point ◽  
Feature Based ◽  
Real Time Data Processing

Autonomous harvesting and transportation is a long-term goal of the forest industry. One of the main challenges is the accurate localization of both vehicles and trees in a forest. Forests are unstructured environments where it is difficult to find a group of significant landmarks for current fast feature-based place recognition algorithms. This paper proposes a novel approach where local point clouds are matched to a global tree map using the Delaunay triangularization as the representation format. Instead of point cloud based matching methods, we utilize a topology-based method. First, tree trunk positions are registered at a prior run done by a forest harvester. Second, the resulting map is Delaunay triangularized. Third, a local submap of the autonomous robot is registered, triangularized and matched using triangular similarity maximization to estimate the position of the robot. We test our method on a dataset accumulated from a forestry site at Lieksa, Finland. A total length of 200 m of harvester path was recorded by an industrial harvester with a 3D laser scanner and a geolocation unit fixed to the frame. Our experiments show a 12 cm s.t.d. in the location accuracy and with real-time data processing for speeds not exceeding 0.5 m/s. The accuracy and speed limit are realistic during forest operations.

LOCAL DESCRIPTOR MATCHING WITH SUPPORT VECTOR MACHINES

2010 ◽  
Vol 07 (01) ◽  
pp. 59-80
Author(s):  
D. CHENG ◽  
S. Q. XIE ◽  
E. HÄMMERLE
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Local Descriptor ◽  
Local Descriptors ◽  
Vector Machines ◽  
Three Stages ◽  
Image Transformations

Local descriptor matching is the most overlooked stage of the three stages of the local descriptor process, and this paper proposes a new method for matching local descriptors based on support vector machines. Results from experiments show that the developed method is more robust for matching local descriptors for all image transformations considered. The method is able to be integrated with different local descriptor methods, and with different machine learning algorithms and this shows that the approach is sufficiently robust and versatile.

scholarly journals Geoarchaeological site documentation and analysis of 3D data derived by terrestrial laser scanning

2014 ◽  
Vol II-5 ◽  
pp. 173-179 ◽  
Author(s):  
D. Hoffmeister ◽  
S. Zellmann ◽  
K. Kindermann ◽  
A. Pastoors ◽  
U. Lang ◽  
...  
Keyword(s):  
Cross Sections ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
3D Data ◽  
Research Problems ◽  
Survey Results ◽  
Rtk Gps ◽  
Derived Data

Terrestrial laser scanning was conducted to document and analyse sites of geoarchaeological interest in Jordan, Egypt and Spain. In those cases, the terrestrial laser scanner LMS-Z420i from Riegl was used in combination with an accurate RTK-GPS for georeferencing of the point clouds. Additionally, local surveying networks were integrated by established transformations and used for indirect registration purposes. All data were integrated in a workflow that involves different software and according results. The derived data were used for the documentation of the sites by accurate plans and cross-sections. Furthermore, the 3D data were analysed for geoarchaeological research problems, such as volumetric determinations, the ceiling thickness of a cave and lighting simulations based on path tracing. The method was reliable in harsh environmental conditions, but the weight of the instrument, the measuring time and the minimum measurement distance were a drawback. However, generally an accurate documentation of the sites was possible. Overall, the integration in a 3D GIS is easily possible by the accurate georeference of the derived data. In addition, local survey results are also implemented by the established transformations. Enhanced analyses based on the derived 3D data shows promising results.

scholarly journals Behavioral Measures of Attention and Cognitive Control During a New Auditory Working Memory Paradigm

2019 ◽  
Author(s):  
Jürgen Kayser ◽  
Lidia Y.X. Wong ◽  
Elizabeth Sacchi ◽  
Lindsey Casal-Roscum ◽  
Jorge E. Alvarenga ◽  
...  
Keyword(s):  
Working Memory ◽  
Cognitive Control ◽  
Cognitive Impairments ◽  
Relevant Information ◽  
Auditory Hallucinations ◽  
Proactive Control ◽  
Behavioral Measures ◽  
Response Sensitivity ◽  
Functional Deficits ◽  
Visual Tasks

Proactive control is the ability to manipulate and maintain goal-relevant information within working memory (WM), allowing individuals to selectively attend to important information while inhibiting irrelevant distractions. Deficits in proactive control may cause multiple cognitive impairments seen in schizophrenia. However, studies of cognitive control have largely relied on visual tasks, even though functional deficits in schizophrenia are more frequent and severe in the auditory domain (i.e., hallucinations). Hence, we developed an auditory analog of a visual Ignore/Suppress paradigm. Healthy adults (N=40) listened to a series of 4 letters (600-ms SOA) presented alternately to each ear, followed by a 3.2-s maintenance interval and a probe. Participants were directed to either selectively ignore (I) to-be-presented letters to one ear, suppress (S) letters already presented to one ear, or remember (R) all presented letters. The critical cue was provided either before (I) or after (S) the encoding series, or simultaneously with the probe (R). Probes were encoding items presented to the attended/not suppressed ear (“Valid”), the ignored/suppressed ear (“Lure”), or not presented (“Control”). Replicating prior findings during visual Ignore/Suppress tasks, response sensitivity and latency revealed poorer performance for Lure than Control trials, particularly during the Suppress condition. Shorter Suppress than Remember latencies suggested a behavioral advantage when discarding encoded items from WM. Paradigm-related internal consistencies and 1-week test-retest reliabilities (n=38) were good to excellent. Findings validate these auditory WM tasks as a reliable manipulation of proactive control and set the stage for studies with schizophrenia patients who experience auditory hallucinations.

scholarly journals Selecting Discriminative Binary Patterns for a Local Feature

2015 ◽  
Vol 15 (3) ◽  
pp. 104-113
Author(s):  
Yingying Li ◽  
Jieqing Tan ◽  
Jinqin Zhong
Keyword(s):  
Object Recognition ◽  
Image Matching ◽  
State Of The Art ◽  
Local Feature ◽  
High Dimensionality ◽  
Learning Method ◽  
Local Descriptor ◽  
Local Descriptors

Abstract The local descriptors based on a binary pattern feature have state-of-the-art distinctiveness. However, their high dimensionality resists them from matching faster and being used in a low-end device. In this paper we propose an efficient and feasible learning method to select discriminative binary patterns for constructing a compact local descriptor. In the selection, a searching tree with Branch&Bound is used instead of the exhaustive enumeration, in order to avoid tremendous computation in training. New local descriptors are constructed based on the selected patterns. The efficiency of selecting binary patterns has been confirmed by the evaluation of these new local descriptors’ performance in experiments of image matching and object recognition.

scholarly journals Effective Smoke Detection Using Spatial-Temporal Energy and Weber Local Descriptors in Three Orthogonal Planes (WLD-TOP)

2018 ◽  
Vol 18 (01) ◽  
pp. e05 ◽  
Author(s):  
John Adedapo Ojo ◽  
Jamiu Alabi Oladosu
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Robot Vision ◽  
Fire Detection ◽  
Support Vector ◽  
False Alarms ◽  
High Detection Rate ◽  
Local Descriptor ◽  
Video Frames

Video-based fire detection (VFD) technologies have received significant attention from both academic and industrial communities recently. However, existing VFD approaches are still susceptible to false alarms due to changes in illumination, camera noise, variability of shape, motion, colour, irregular patterns of smoke and flames, modelling and training inaccuracies. Hence, this work aimed at developing a VSD system that will have a high detection rate, low false-alarm rate and short response time. Moving blocks in video frames were segmented and analysed in HSI colour space, and wavelet energy analysis of the smoke candidate blocks was performed. In addition, Dynamic texture descriptors were obtained using Weber Local Descriptor in Three Orthogonal Planes (WLD-TOP). These features were combined and used as inputs to Support Vector Classifier with radial based kernel function, while post-processing stage employs temporal image filtering to reduce false alarm. The algorithm was implemented in MATLAB 8.1.0.604 (R2013a). Accuracy of 99.30%, detection rate of 99.28% and false alarm rate of 0.65% were obtained when tested with some online videos. The output of this work would find applications in early fire detection systems and other applications such as robot vision and automated inspection.

scholarly journals FINDING A GOOD FEATURE DETECTOR-DESCRIPTOR COMBINATION FOR THE 2D KEYPOINT-BASED REGISTRATION OF TLS POINT CLOUDS

2015 ◽  
Vol II-3/W5 ◽  
pp. 121-128 ◽  
Author(s):  
S. Urban ◽  
M. Weinmann
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Scale Invariant ◽  
Point Cloud Registration ◽  
Speeded Up Robust Features ◽  
Extensive Evaluation ◽  
Feature Correspondences ◽  
Intensity Images ◽  
Keypoint Detector

The automatic and accurate registration of terrestrial laser scanning (TLS) data is a topic of great interest in the domains of city modeling, construction surveying or cultural heritage. While numerous of the most recent approaches focus on keypoint-based point cloud registration relying on forward-projected 2D keypoints detected in panoramic intensity images, little attention has been paid to the selection of appropriate keypoint detector-descriptor combinations. Instead, keypoints are commonly detected and described by applying well-known methods such as the Scale Invariant Feature Transform (SIFT) or Speeded-Up Robust Features (SURF). In this paper, we present a framework for evaluating the influence of different keypoint detector-descriptor combinations on the results of point cloud registration. For this purpose, we involve five different approaches for extracting local features from the panoramic intensity images and exploit the range information of putative feature correspondences in order to define bearing vectors which, in turn, may be exploited to transfer the task of point cloud registration from the object space to the observation space. With an extensive evaluation of our framework on a standard benchmark TLS dataset, we clearly demonstrate that replacing SIFT and SURF detectors and descriptors by more recent approaches significantly alleviates point cloud registration in terms of accuracy, efficiency and robustness.

scholarly journals Target Recognition in SAR Image via Keypoint based Local Descriptor—Foundation

2018 ◽  
Author(s):  
Ganggang Dong ◽  
Jocelyn Chanussot
Keyword(s):  
Target Recognition ◽  
Incidence Angle ◽  
Recognition Performance ◽  
Computational Cost ◽  
Geometric Distortion ◽  
Linear Regression Models ◽  
Global Features ◽  
Local Descriptor ◽  
Local Descriptors ◽  
Radar Images

This paper considers target characterization and recognition in radar images with keypoint-based local descriptor. Most of the preceding works rely on the global features or raw intensity values, and hence produce the limited recognition performance. Moreover, the global features are sensitive to the real-world sources of variability, such as aspect view, configu-ration, and incidence angle changes, clutter, articulation, and occlusion. Keypoint-based local descriptor was developed as a powerful strategy to address invariance to contrast change and geometric distortion. This property inspires us to investigate whether the family of local features are relevant for radar target recognition. Most of the preceding works typically devote to finding the correspondences between a collected image and a reference one. The representative applications include image register and change detection. Little work was pursued to target recognition in SAR images. This is because the huge number of local descriptors resulting from radar images make the computational cost and memory consumption unacceptable. To handle the problems, this paper develops two families of methods. The proposed methods are used to achieve target recognition by means of local descriptors. Our first solver refers to building multiple linear regression models, and addresses the problem by the theory of sparse representation. The second scheme rebuilds a new feature by the feature quantization skill, from which the inference can be drawn. Multiple comparative studies are pursued to verify the performance of detectors and descriptors popularly used. The source code was publicly released on https://ganggangdong.github.io/homepage/.

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