• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.2044-2059
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• 2022

Recent works have shown that the geometric constraint can be harnessed to boost the performance of CNN-based camera localization. However, the existing strategies are limited to imposing image-level constraint between pose pairs, which is weak and coarse-gained. In this paper, we introduce a pixel-level epipolar geometry constraint to vanilla localization framework without the ground-truth 3D information. Dubbed EpiLoc, our method establishes the geometric relationship between pixels in different images by utilizing the epipolar geometry thus forcing the network to regress more accurate poses. We also propose a variant called EpiSingle to cope with non-sequential training images, which can construct the epipolar geometry constraint based on a single image in a self-supervised manner. Extensive experiments on the public indoor 7Scenes and outdoor RobotCar datasets show that the proposed pixel-level constraint is valuable, and helps our EpiLoc achieve state-of-the-art results in the end-to-end camera localization task.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3312-3327
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• 2020

Plant diseases are a significant yield and quality constraint for farmers around the world due to their severe impact on agricultural productivity. Such losses can have a substantial impact on the economy which causes a reduction in farmer's income and higher prices for consumers. Further, it may also result in a severe shortage of food ensuing violent hunger and starvation, especially, in less-developed countries where access to disease prevention methods is limited. This research presents an investigation of Directional Local Quinary Patterns (DLQP) as a feature descriptor for plants leaf disease detection and Support Vector Machine (SVM) as a classifier. The DLQP as a feature descriptor is specifically the first time being used for disease detection in horticulture. DLQP provides directional edge information attending the reference pixel with its neighboring pixel value by involving computation of their grey-level difference based on quinary value (-2, -1, 0, 1, 2) in 0°, 45°, 90°, and 135° directions of selected window of plant leaf image. To assess the robustness of DLQP as a texture descriptor we used a research-oriented Plant Village dataset of Tomato plant (3,900 leaf images) comprising of 6 diseased classes, Potato plant (1,526 leaf images) and Apple plant (2,600 leaf images) comprising of 3 diseased classes. The accuracies of 95.6%, 96.2% and 97.8% for the above-mentioned crops, respectively, were achieved which are higher in comparison with classification on the same dataset using other standard feature descriptors like Local Binary Pattern (LBP) and Local Ternary Patterns (LTP). Further, the effectiveness of the proposed method is proven by comparing it with existing algorithms for plant disease phenotyping.

• Progress in Medical Physics
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• v.13 no.1
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• pp.32-36
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• 2002

For Intensity Modulated Radiation Therapy(IMRT), the spatial resolution of intensity map(IM) is limited by the width of multi-leaf collimator, which would make an effect on the conformity of the target, as well as organs at risk. Several Methods are suggested to increase the spatial resolution, which can be categorized by the hardware-dependent technique and the software-based technique. However the best solution might be to make the width of MLC finer. it has several obstacles in the respects of technical difficulty and cost. This preliminary study is designed to investigate the clinical effectiveness of the virtual-micro IMRT(VMIMRT) technique, one of the software-based technique. A particular intensity map was created, which has 42$\times$54 pixel dimension ,0.5cm pixel size and 15 intensity levels. Using this intensity map, segment fields of IMRT were generated with 1$\times$lcm, 0.5$\times$1cm, 0.5$\times$0.5cm(VMIM) beamlet size, respectively As results, we found that there was no evidence of improvement for VMIMRT, compared with the 0.5$\times$lcm beamlet size which can be delivered by 1cm width MLC. The reason seems to be due to the constraint of VMIMRT. Further study is required to prove the benefit of the VIMRT in clinical case like head and neck cancer, where is expected that higher resolution than 1cm is necessary.