scholarly journals Accuracy of High- and Low-Resolution Cone-Beam Computed Tomographic Scans in the Detection of Impacted Tooth-Induced External Root Resorption: An Ex-Vivo Study

2020 ◽  
Author(s):  
Arash Dabbaghi ◽  
Sanaz Sharifi ◽  
Masoud Esmaeili
Keyword(s):  
High Resolution ◽  
Diagnostic Accuracy ◽  
Cone Beam ◽  
Low Resolution ◽  
Impacted Tooth ◽  
External Root Resorption ◽  
Tooth Impaction ◽  
Significant Difference ◽  
High Resolution Images ◽  
Low Resolution Images

Objectives: Cone-beam computed tomography (CBCT) is used in diagnostic situations, as well as tooth impaction and its complications. A possible sequela of tooth impaction is resorption of adjacent teeth, complicating the treatment plans. This study aimed to determine the diagnostic accuracy of high- and low-resolution CBCT scans in the detection of external root resorptions (ERRs), caused by an adjacent impacted tooth in the cementoenamel junction (CEJ), mid-root, and apical areas. Materials and Methods: Forty-five intact single-rooted teeth were divided into three groups of 15. Each group was dedicated to each zone of the root. Slight, moderate, and severe ERRs were formed, and CBCT scans were taken before and after the formation of ERRs. The diagnostic accuracy was assessed, and the Proportion test was used to compare the results. Results: The statistical analyses of high- and low-resolution images showed a significant difference (P<0.05), which implies the higher accuracy of high-resolution images. The highest diagnostic accuracy among different zones was related to the mid-root, and the lowest was related to the apical zone. In terms of the size of ERRs, the diagnostic accuracy was the lowest for slight ERRs. Conclusion: The most reliable and accurate diagnostic mode was found in high-resolution images, in the mid-root zone, and with severe ERRs. The lowest diagnostic accuracy was found in low-resolution images, in the apical zone, and with slight ERRs.

Stereo Matching Algorithm Based on Pyramid Double Dynamic Programming

2014 ◽  
Vol 981 ◽  
pp. 352-355 ◽  
Author(s):  
Ji Zhou Wei ◽  
Shu Chun Yu ◽  
Wen Fei Dong ◽  
Chao Feng ◽  
Bing Xie
Keyword(s):  
Dynamic Programming ◽  
High Resolution ◽  
Stereo Matching ◽  
Control Points ◽  
Low Resolution ◽  
Matching Algorithm ◽  
Resolution Image ◽  
High Resolution Images ◽  
Candidate Control ◽  
Low Resolution Images

A stereo matching algorithm was proposed based on pyramid algorithm and dynamic programming. High and low resolution images was computed by pyramid algorithm, and then candidate control points were stroke on low-resolution image, and final control points were stroke on the high-resolution images. Finally, final control points were used in directing stereo matching based on dynamic programming. Since the striking of candidate control points on low-resolution image, the time is greatly reduced. Experiments show that the proposed method has a high matching precision.

PALM PRINTS 'LINES DO TELL'- AN INNOVATING PARAPHERNALIA AS BIOMETRIC AUTHENTICATION

2021 ◽  
pp. 37-38
Author(s):  
Sameera Shamim Khan ◽  
Smitha Naik ◽  
Arshad Khan
Keyword(s):  
High Resolution ◽  
Software Systems ◽  
Biometric Authentication ◽  
Low Resolution ◽  
Classi Cation ◽  
Cation System ◽  
High Resolution Images ◽  
Palm Print ◽  
Commercial Applications ◽  
Low Resolution Images

Authentication in personal identication using palm print method provides valuable evidence in one's identication. It has been investigated over years by different methods employed by both high resolution images which are further processed by different computerized techniques and software systems and low resolution images which have attracted many researchers attention. This paper proposes a brief introduction about palm prints its different methods employed and the current classication system which is less time consuming followed for research to be carried out for biometric authentication and scientic evidences which is useful for civil and commercial applications.

scholarly journals Spatial Super Resolution of Real-World Aerial Images for Image-Based Plant Phenotyping

2021 ◽  
Vol 13 (12) ◽  
pp. 2308
Author(s):  
Masoomeh Aslahishahri ◽  
Kevin G. Stanley ◽  
Hema Duddu ◽  
Steve Shirtliffe ◽  
Sally Vail ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Real World ◽  
Super Resolution ◽  
Aerial Images ◽  
Plant Phenotyping ◽  
Low Resolution ◽  
High Resolution Images ◽  
Synthetic Images ◽  
Low Resolution Images

Unmanned aerial vehicle (UAV) imaging is a promising data acquisition technique for image-based plant phenotyping. However, UAV images have a lower spatial resolution than similarly equipped in field ground-based vehicle systems, such as carts, because of their distance from the crop canopy, which can be particularly problematic for measuring small-sized plant features. In this study, the performance of three deep learning-based super resolution models, employed as a pre-processing tool to enhance the spatial resolution of low resolution images of three different kinds of crops were evaluated. To train a super resolution model, aerial images employing two separate sensors co-mounted on a UAV flown over lentil, wheat and canola breeding trials were collected. A software workflow to pre-process and align real-world low resolution and high-resolution images and use them as inputs and targets for training super resolution models was created. To demonstrate the effectiveness of real-world images, three different experiments employing synthetic images, manually downsampled high resolution images, or real-world low resolution images as input to the models were conducted. The performance of the super resolution models demonstrates that the models trained with synthetic images cannot generalize to real-world images and fail to reproduce comparable images with the targets. However, the same models trained with real-world datasets can reconstruct higher-fidelity outputs, which are better suited for measuring plant phenotypes.

scholarly journals BFRVSR: A Bidirectional Frame Recurrent Method for Video Super-Resolution

2020 ◽  
Author(s):  
Xiongxiong Xue ◽  
Zhenqi Han ◽  
Weiqin Tong ◽  
Mingqi Li ◽  
Lizhuang Liu
Keyword(s):  
High Resolution ◽  
Computational Cost ◽  
Super Resolution ◽  
Temporal Consistency ◽  
Low Resolution ◽  
Resolution Method ◽  
Resolution Image ◽  
High Resolution Image ◽  
High Resolution Images ◽  
Low Resolution Images

Video super-resolution, which utilizes the relevant information of several low-resolution frames to generate high-resolution images, is a challenging task. One possible solution called sliding window method tries to divide the generation of high-resolution video sequences into independent sub-tasks, and only adjacent low-resolution images are used to estimate the high-resolution version of the central low-resolution image. Another popular method named recurrent algorithm proposes to utilize not only the low-resolution images but also the generated high-resolution images of previous frames to generate the high-resolution image. However, both methods have some unavoidable disadvantages. The former one usually leads to bad temporal consistency and requires higher computational cost while the latter method always can not make full use of information contained by optical flow or any other calculated features. Thus more investigations need to be done to explore the balance between these two methods. In this work, a bidirectional frame recurrent video super-resolution method is proposed. To be specific, a reverse training is proposed that the generated high-resolution frame is also utilized to help estimate the high-resolution version of the former frame. With the contribution of reverse training and the forward training, the idea of bidirectional recurrent method not only guarantees the temporal consistency but also make full use of the adjacent information due to the bidirectional training operation while the computational cost is acceptable. Experimental results demonstrate that the bidirectional super-resolution framework gives remarkable performance that it solves the time-related problems when the generated high-resolution image is impressive compared with recurrent-based video super-resolution method.

scholarly journals Effects of Spatial Resolution on the Satellite Observation of Floating Macroalgae Blooms

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1761
Author(s):  
Xinhua Wang ◽  
Qianguo Xing ◽  
Deyu An ◽  
Ling Meng ◽  
Xiangyang Zheng ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Yellow Sea ◽  
Satellite Images ◽  
Satellite Observation ◽  
The Yellow Sea ◽  
Low Resolution ◽  
High Resolution Satellite Images ◽  
High Resolution Images ◽  
Low Resolution Images

Satellite images with different spatial resolutions are widely used in the observations of floating macroalgae booms in sea surface. In this study, semi-synchronous satellite images with different resolutions (10 m, 16 m, 30 m, 50 m, 100 m, 250 m and 500 m) acquired over the Yellow Sea, are used to quantitatively assess the effects of spatial resolution on the observation of floating macroalgae blooms of Ulva prolifera. Results indicate that the covering area of macroalgae-mixing pixels (MM-CA) detected from high resolution images is smaller than that from low resolution images; however, the area affected by macroalgae blooms (AA) is larger in high resolution images than in low resolution ones. The omission rates in the MM-CA and the AA increase with the decrease of spatial resolution. These results indicate that satellite remote sensing on the basis of low resolution images (especially, 100 m, 250 m, 500 m), would overestimate the covering area of macroalgae while omit the small patches in the affected zones. To reduce the impacts of overestimation and omission, high resolution satellite images are used to show the seasonal changes of macroalgae blooms in 2018 and 2019 in the Yellow Sea.

scholarly journals Estimates of Maize Plant Density from UAV RGB Images Using Faster-RCNN Detection Model: Impact of the Spatial Resolution

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
K. Velumani ◽  
R. Lopez-Lozano ◽  
S. Madec ◽  
W. Guo ◽  
J. Gillet ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Management Practices ◽  
Plant Density ◽  
Maize Plant ◽  
Low Resolution ◽  
High Resolution Images ◽  
Rgb Images ◽  
The Impact ◽  
Low Resolution Images

Early-stage plant density is an essential trait that determines the fate of a genotype under given environmental conditions and management practices. The use of RGB images taken from UAVs may replace the traditional visual counting in fields with improved throughput, accuracy, and access to plant localization. However, high-resolution images are required to detect the small plants present at the early stages. This study explores the impact of image ground sampling distance (GSD) on the performances of maize plant detection at three-to-five leaves stage using Faster-RCNN object detection algorithm. Data collected at high resolution (GSD≈0.3 cm) over six contrasted sites were used for model training. Two additional sites with images acquired both at high and low (GSD≈0.6 cm) resolutions were used to evaluate the model performances. Results show that Faster-RCNN achieved very good plant detection and counting (rRMSE=0.08) performances when native high-resolution images are used both for training and validation. Similarly, good performances were observed (rRMSE=0.11) when the model is trained over synthetic low-resolution images obtained by downsampling the native training high-resolution images and applied to the synthetic low-resolution validation images. Conversely, poor performances are obtained when the model is trained on a given spatial resolution and applied to another spatial resolution. Training on a mix of high- and low-resolution images allows to get very good performances on the native high-resolution (rRMSE=0.06) and synthetic low-resolution (rRMSE=0.10) images. However, very low performances are still observed over the native low-resolution images (rRMSE=0.48), mainly due to the poor quality of the native low-resolution images. Finally, an advanced super resolution method based on GAN (generative adversarial network) that introduces additional textural information derived from the native high-resolution images was applied to the native low-resolution validation images. Results show some significant improvement (rRMSE=0.22) compared to bicubic upsampling approach, while still far below the performances achieved over the native high-resolution images.

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