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2019 IEEE/RSJ International conference on Intelligent Robots and systems (IROS)

作者： Hanbo Zhang Xuguang Lan Site Bai Xinwen Zhou Zhiqiang Tian Nanning Zheng National Engineering Laboratory for Visual Information Processing and Applications School of Electronic and Information Engineering Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University No.28 Xianning Road Xi’an Shaanxi China

Grasp detection considering the affiliations between grasps and their owner in object overlapping scenes is a necessary and challenging task for the practical use of the robotic grasping approach. In this paper, a robotic grasp detection algorithm named ROI-GD is proposed to provide a feasible solution to this problem based on Region of Interest (ROI), which is the region proposal for objects. ROI-GD uses features from ROIs to detect grasps instead of the whole scene. It has two stages: the first stage is to provide ROIs in the input image and the second-stage is the grasp detector based on ROI features. We also contribute a multi-object grasp dataset, (a) which is much larger than Cornell Grasp Dataset, by labeling Visual Manipulation Relationship Dataset. Experimental results demonstrate that ROI-GD performs much better in object overlapping scenes and at the meantime, remains comparable with state-of-the-art grasp detection algorithms on Cornell Grasp Dataset and Jacquard Dataset. Robotic experiments demonstrate that ROI-GD can help robots grasp the target in single-object and multi-object scenes with the overall success rates of 92.5% and 83.8% respectively.

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A Survey on Performance of Various image Compression Techniques 3

A Survey on Performance of Various Image Compression Techniq...

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3rd International conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (IGEECCOT)

作者： Gireesh, H. R. Rangaiah, L. VTU Belagavi India RRCE Dept ECE Bangalore Karnataka India

ISBN: (纸本)9781538651308

image compression enables quite exciting solutions in many fields, such as image analysis, bio-medical image processing, wireless systems and seems to be a key application in today's digital and smart world. image compression seems to be a powerful tool in case of transmission and storage of large data images in various applications such as big data, medical etc. However due to exclusive and quality soft tissue contrast, Dynamic Magnetic Resonance Imaging (MRI) has been a field of attraction with increasing attention in recent decades. Moreover, MRI is considered as one of the most effective and strongest diagnosis system making the extensive usage of magnetic and radio waves in order to diagnose the human organs. This diagnosis is capable of generating 3D images with detailed anatomical features without any X-ray radiations. The prime purpose of this survey is to provide a comprehensive report of different image compression schemes in order to design an efficient compression scheme for dynamic MRI images. In this paper, author has surveyed different image compression schemes which are either sole implementations or hybrid of two or more algorithms. The author has also presented a comparative analysis for the surveyed compression schemes. This survey paper finally makes inroads for further researches in the domain of image compression schemes for dynamic MRI images.

关键词： Dynamic MRI image Compression Compression Ratio Redundancy PSNR SSIM

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Real-time Geoprojection and Stabilization on Airborne GPU-enabled Embedded systems 8

Real-time Geoprojection and Stabilization on Airborne GPU-en...

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conference on Geospatial Informatics, Motion imagery, and Network Analytics Viii

作者： Teters, Evan AliAkbarpour, Hadi Palaniappan, Kannappan Seetharaman, Guna Univ Missouri Dept Comp Sci CIVA Lab Columbia MO 65201 USA

ISBN: (数字)9781510618022

ISBN: (纸本)9781510618022

We describe the challenges and capabilities of implementing a fast, efficient georegistration system on low-power GPU-enabled embedded systems. The input to this are high-resolution aerial images and refined camera metadata, the output are registered aerial images that can be used e.g. in moving object detection and tracking algorithms. The transformations required in the geoprojections in this implementation are obtained from our recent fast Structure-from-Motion (SfM) and georegistration method-BA4S. A real-time warping of the highresolution 3-channel imaged is implemented on GPU in this work which allows a fast geoprojection on a low-power embedded system. Our benchmarks show the effectiveness of the implementation and compared its performance on different hardware platforms. We propose future application in real-time on-device processing, given initial speeds on embedded systems.

关键词： Aerial image stabilization embedded systems georegistration geo-registration wide area motion imagery (WAMI) real-time CUDA

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FSG: A statistical approach to line detection via fast segments grouping

FSG: A statistical approach to line detection via fast segme...

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25th IEEE/RSJ International conference on Intelligent Robots and systems (IROS)

作者： Suarez, Iago Munoz, Enrique Buenaposada, Jose M. Baumela, Luis The Graffter Ctr Empresas Campus Montegancedo S-N Pozuelo De Alarcon 28223 Spain Univ Rey Juan Carlos ETSII C Tulipan S-N Mostoles 28933 Spain Univ Politecn Madrid Dept Inteligencia Artificial Campus Montegancedo S-N Boadilla Del Monte 28660 Spain

ISBN: (纸本)9781538680940

Line extraction is a preliminary step in various visual robotic tasks performed in low textured scenes such as city and indoor settings. Several efficient line segment detection algorithms such as LSD and EDLines have recently emerged. However, the state of the art segment grouping methods are not robust enough or not amenable for detecting lines in real-time. In this paper we present FSG, a fast and robust line detection algorithm. It is based on two independent components. A proposer that greedily cluster segments suggesting plausible line candidates and a probabilistic model that decides if a group of segments is an actual line. In the experiments we show that our procedure is more robust and faster than the best methods in the literature and achieves state-of-the art performance in a high level robot localization task such as vanishing points detection.

关键词： image segmentation Probabilistic logic Estimation Simultaneous localization and mapping Task analysis Detection algorithms

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Categorizing License Plates Using Convolutional Neural Network with Residual Learning

Categorizing License Plates Using Convolutional Neural Netwo...

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Asia-Pacific conference on Intelligent Robot systems (ACIRS)

作者： John Anthony C. Jose Jose Martin Z. Maningo Jayson P. Rogelio Argel A. Bandala Ryan Rhay Vicerra Edwin Sybingco Phoebe Mae L. Ching Elmer P. Dadios Electronics and Communications Engineering Department De La Salle University Manila Philippines Manufacturing Engineering and Management Department De La Salle University Manila Philippines Industrial Engineering Department De La Salle University Manila Philippines

Like other countries, the Philippines uses various license plate standards wherein some purely text while some are hybrid graphic-text plates. And to harness its generalizability, this study developed a classification algorithm utilized as a pre-processing scheme for the multi-standard license plate. With an input image captured at a different perspective, it was feed into the neural network and classify as Rizal monument series (2001 base and 2003 base), 2014 series and conduction sticker for new vehicles. In total, there are 303 different images captured for this study. Around 100 conduction sticker images, 103 Rizal Monument images, 100 black and white images. Furthermore, this study focused on using transfer learning technique, wherein a trained network utilized, then only the last layer was reset and retrained on the new dataset. To measure the performance of the classification model and optimized it cross-entropy and stochastic gradient descent was employed respectively at a learning rate of 0.001 and reduced by 10 for every seven (7) epochs. The progression of accuracy results in increasing the epochs, and for the 25 epochs, the training completed in 4 minutes and 7 seconds with the best validation accuracy of 82.61%.

关键词： Licenses Convolutional neural networks Training Classification algorithms image classification Convolution

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Experimental approach to detect water stress in ornamental plants using sUAS-imagery 3

Experimental approach to detect water stress in ornamental p...

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conference on Autonomous Air and Ground Sensing systems for Agricultural Optimization and Phenotyping iii

作者： de Castro, Ana I. Maja, Joe Mari Owen, Jim Robbins, James Pena, Jose M. CSIC Inst Sustainable Agr Cordoba Spain Clemson Univ Edisto Res & Educ Ctr Blackville SC USA Virginia Tech Hampton Rd Agr Res & Extens Ctr Virginia Beach VA USA Univ Arkansas Div Agr Little Rock AR 72204 USA CSIC Inst Agr Sci Madrid Spain

ISBN: (数字)9781510618404

ISBN: (纸本)9781510618404

Efficiency in irrigation management is crucial to optimize water use in agriculture. A good irrigation strategy requires accurate and reliable measurements of crop water status that provide dynamic data and timely spatial information. However, this is not feasible with time-consuming manual measurements, which are also prone to cumulative errors due to subjective estimations. Ornamental horticulture crops offer challenges for applying small unmanned aircraft systems (sUAS) technology due to the relatively small area of production and its diversity of plant species. sUAS can operate on demand at low flight height and to carry a wide range of sensors allows capturing the variation of plant traits over time, making it a timely alternative to ground-based data collection in nursery systems. This research evaluated the potential of sUAS-based images to estimate crop water status under three different irrigation regimes. sUAS-imagery of experimental plots was acquired in August 2017 using several multispectral sensors. Container-grown ornamental plants used in the study were Cornus, Hydrangea, Spiraea, Buddleia and Physocarpus. An algorithm based on the object-based image analysis (OBIA) paradigm was applied to retrieve spectral information from each individual plant. Preliminary one-way analysis of variance (ANOVA) identified water stressed and non-stressed plants from data of each study sensor, although spectral separation was higher when information from the sensors was combined. Our results revealed the potential of the sUAS to monitor water status in container-grown ornamental plants, although further analysis is needed to explore vegetation indices and data analysis algorithms.

关键词： object-based image analysis (OBIA) water stress ornamental horticulture image analysis sensors drones

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Segmentation of the hippocampus by transferring algorithmic knowledge for large cohort processing

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MEDICAL image ANALYSIS 2018年 43卷 214-228页

作者： Thyreau, Benjamin Sato, Kazunori Fukuda, Hiroshi Taki, Yasuyuki Tohoku Univ Tohoku Med Megabank Org Sendai Miyagi Japan Tohoku Univ Inst Dev Aging & Canc Sendai Miyagi Japan Tohoku Pharmaceut Univ Sendai Miyagi Japan

The hippocampus is a particularly interesting target for neuroscience research studies due to its essential role within the human brain. In large human cohort studies, bilateral hippocampal structures are frequently identified and measured to gain insight into human behaviour or genomic variability in neuropsychiatric disorders of interest. Automatic segmentation is performed using various algorithms, with FreeSurfer being a popular option. In this manuscript, we present a method to segment the bilateral hippocampus using a deep-learned appearance model. Deep convolutional neural networks (ConvNets) have shown great success in recent years, due to their ability to learn meaningful features from a mass of training data. Our method relies on the following key novelties: (i) we use a wide and variable training set coming from multiple cohorts (ii) our training labels come in part from the output of the FreeSurfer algorithm, and (iii) we include synthetic data and use a powerful data augmentation scheme. Our method proves to be robust, and it has fast inference (<30s total per subject), with trained model available online (https://***/bthyreau/hippodeep). We depict illustrative results and show extensive qualitative and quantitative cohort-wide comparisons with FreeSurfer. Our work demonstrates that deep neural network methods can easily encode, and even improve, existing anatomical knowledge, even when this knowledge exists in algorithmic form. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Deep neural networks

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Low-cost and Real-time Hardware Implementation of Stereo Vision System on FPGA

Low-cost and Real-time Hardware Implementation of Stereo Vis...

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Iranian conference on Electrical Engineering (ICEE)

作者： Mostafa Fathi Samad Sheikhaei Javad Tavakoli Advancom Lab College of Engineering University of Tehran Tehran Iran

In this work, one of local stereo vision algorithms named SAD approach, which is used in image depth estimation, has been surveyed, and an efficient and real-time new hardware implementation has been proposed. The proposed method has been verified and tested using C implementation. The acceptable simulation results along with the detailed explanation of numerous pre-processing steps are also presented. Our innovations could be divided into two sections: architecture and algorithm. In architecture section, by using a specific architecture, memory access has been lowered and therefore, speed has been increased. In algorithm section, a part of local algorithm, known as refinement, has been substituted with a simpler and more efficient algorithm. Cyclone IV has been utilized as our hardware platform. In this article, this point would demonstrate how to use an exact but less complicated controller, which results in less area, and how to use pipeline architecture and remove repetitive and redundant memory accesses, which conduct our stereo vision system to meet the real-time constraints. Suggested hardware implementation could reach to 53fps processing speed with 100MHz clock. No processing IP core has been used. In comparison with related work, our proposed method is more efficient in logic elements usage, and accordingly in power consumption.

关键词： Stereo vision Hardware Field programmable gate arrays Computer architecture Real-time systems Graphics processing units Cameras

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Non-invasive Assessment of Psoriasis from Snapshot Hyperspectral Imaging

Non-invasive Assessment of Psoriasis from Snapshot Hyperspec...

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IEEE International conference on systems, Man, and Cybernetics (SMC)

作者： Chen, Hsian-Min Chen, Hsin-Hua Chen, Yi-Ming Fu, Yu-Wen Taichung Vet Gen Hosp Dept Med Res Ctr Quantitat Imaging Med CQUIM Taichung Taiwan Taichung Vet Gen Hosp Dept Med Res Taichung Taiwan Taichung Vet Gen Hosp Div Allergy Immunol & Rheumatol Taichung Taiwan

ISBN: (纸本)9781538666500

Psoriasis is a chronic, immune-mediated inflammatory skin disease and affecting about 125 million people worldwide. It ranges in severity from a few scattered red, scaly plaques to involvement of almost the entire body surface. Currently, clinicians use visual and haptic methods for diagnosis the disease severity. This does not help them in stratification and risk assessment of the lesion stage and grade. In this paper, we present a new snapshot hyperspectral imaging approach to real time processing algorithms for evaluation of skin involvement. The Spectral Angle Mapper (SAM) was used to calculate the difference between two reflectance spectra of healthy skin and psoriasis. The results showed that the proposed method can identify differences of uninvolved and involved psoriatic skin of a patient. In addition, we also compared spectral differences of skin from a psoriasis patient and healthy individuals, which could be valuable in characterization and mapping psoriasis. In conclusion, the non-invasive and snapshot hyperspectral imaging method described in this study sheds light on its clinical application in patients with psoriasis further confirmatory studies are still needed to validate this assessment tool.

关键词： Hyperspectral image psoriasis snapshot spectral angle mapper

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Fractal Dimension of GrayScale images

Fractal Dimension of GrayScale Images

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International conference on Computing, Analytics and Networking (ICCAN)

作者： Nayak, Soumya Ranjan Mishra, Jibitesh Jena, Pyari Mohan Coll Engn & Technol Dept Informat Technol Bhubaneswar India Coll Engn & Technol Dept Comp Sci & Applicat Bhubaneswar India

ISBN: (纸本)9789811078712;9789811078705

Fractal dimension (FD) is a necessary aspect for characterizing the surface roughness and self-similarity of complex objects. However, fractal dimension gradually established its importance in the area of image processing. A number of algorithms for estimating fractal dimension of digital images have been reported in many literatures. However, different techniques lead to different results. Among them, the differential box-counting (DBC) was most popular and well-liked technique in digital domain. In this paper, we have presented an efficient differential box-counting mechanism for accurate estimation of FD with less fitting error as compared to existing methods like original DBC, relative DBC (RDBC), and improved box-counting (IBC) and improved DBC (IDBC). The experimental work is carried out by one set of fourteen Brodatz images. From this experimental result, we found that the proposed method performs best among the existing methods in terms of less fitting error.

关键词： Fractal dimension DBC RDBC IBC IDBC Grayscale image

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