https://hal-mines-paristech.archives-ouvertes.fr/hal-01689709/document

Harris corner detection on a NUMA manycore

ROV 3D project aims at developing innovative tools which link underwater photogrammetry and acoustic measurements from an active underwater sensor. The results will be 3D high resolution surveys of underwater sites. The new means and methods developed aim at reducing the investigation time in situ, and proposing comprehensive and non-intrusive measurement tools for the studied environment. In this paper, we apply a pre-processing pipe line to increase the SIFT and SURF descriptors extraction quality in order to solve the problem of surveying an underwater archaeological wreck in a very high condition of turbidity. We work in the Rhodano river, in south of France on a roman wreck with 20 centimeters visibility. Under these conditions a standard process is not efficient and water turbidity is a real obstacle to feature extraction. Nevertheless the mission was not dedicated to an exhaustive survey of the wreck, but only a test to show and evaluate the feasibility. The results are positive even if the main problem seems now to be the time processing, indeed the poor visibility increase drastically the number of photographs

Underwater image preprocessing for automated photogrammetry in high turbidity water: An application on the Arles-Rhone XIII roman wreck in the Rhodano river, France

Image Stitching with Combined Moment Invariants and Sift Features

ROV 3D project aims at developing innovative tools which link underwater photogrammetry and acoustic measurements from an active underwater sensor. The results will be 3D high resolution surveys of underwater sites. The new means and methods developed aim at reducing the investigation time in situ, and proposing comprehensive and non-intrusive measurement tools for the studied environment. In this paper, we made an investigation to find at first a pre-processing method of underwater images that do not require a priori knowledge of the scene in order to increase the repeatability of SIFT and SURF descriptors and, in a second time, finding a method to compute distances which will be less costly in terms of execution time for finding corresponding points.

/pdf/performances-analysis-of-underwater-image-preprocessing-4fpn052cle.pdf

Performances Analysis of Underwater Image Preprocessing Techniques on the Repeatability of SIFT and SURF Descriptors

Artificial Intelligence (AI) technologies and their related applications are now developing at a rapid pace. Indoor positioning will be one of the core technologies that enable AI applications because people spend 80% of their time indoors. Humans can locate themselves related to a visually well-defined object, e.g., a door, based on their visual observations. Can a smartphone camera do a similar job when it points to an object? In this paper, a visual positioning solution was developed based on a single image captured from a smartphone camera pointing to a well-defined object. The smartphone camera simulates the process of human eyes for the purpose of relatively locating themselves against a well-defined object. Extensive experiments were conducted with five types of smartphones on three different indoor settings, including a meeting room, a library, and a reading room. Experimental results shown that the average positioning accuracy of the solution based on five smartphone cameras is 30.6 cm, while that for the human-observed solution with 300 samples from 10 different people is 73.1 cm.

https://www.mdpi.com/1424-8220/17/11/2645/pdf

Visual Positioning Indoors: Human Eyes vs. Smartphone Cameras.

Harris is one of the most widely used corner detection algorithms which is based on intensity. Because of the practice of Gaussian smoothing link, Harris algorithm has a good performance on its stability and robustness, but it is also the direct reason of the limitation of its computing speed. Furthermore, its positioning accuracy of T-type, L-type and Arrow-type corners is low. In this paper, an improved algorithm is presented to solve the efficiency and accuracy problem of Harris algorithm. Firstly, we integrated the corner response function of MIC algorithm into the Harris algorithm to reduce the calculation amount of Gaussian smoothing link, so that the computing speed is obviously increased. Then we investigate a 3×3 mask to calculate the number of the pixels which their gray values are similar to the central point of the mask. We found that for real corner, the number of its similar points is the minimum among its 8 neighborhoods. In view of this, we added a comparison function before the No Max Suppression step to exclude some disturbance points nearby the real corners. Hence the positioning accuracy is increased significantly. In order to compare the accuracy of the improved algorithm and Harris algorithm, an evaluation standard is also proposed. The experimental results show that the detection time of the improved algorithm is only 31.2% that of the original Harris algorithm, and also, the improvement on positioning accuracy of L-type, T-type and Arrow-type corners are realized.

A Fast and Accurate Corner Detector Based on Harris Algorithm

Background Noninvasive preoperative prediction of histological grading is essential for clinical management of cerebral glioma. Purpose This study aimed to investigate the association between the image quality assessment of 1H magnetic resonance spectroscopy and accurate grading of glioma. Materials and Methods 98 glioma patients confirmed by pathology were retrospectively recruited in this single-center study. All patients underwent 1H-MRS examination at 3.0T before surgery. According to WHO standards, all cases were divided into two groups: low-grade glioma (grade I and II, 48 cases) and high-grade glioma (grades III and IV, 50 cases). The metabolite ratios in both grades were calculated before and after image quality assessment. The area under the receiver operating characteristic (ROC) curve was used to evaluate the capacity of each ratio in glioma grading. Results The Cho/Cr, Cho/NAA and NAA/Cr metabolite ratios had certain differences in each glioma group before and after MRS image quality assessment. In the low-grade glioma group, there was a dramatic difference in the Cho/Cr ratio before and after image quality assessment (p = 0.011). After MRS image quality assessment, the accuracy of glioma grading was significantly improved. The Cho/Cr ratio with 83.3% sensitivity and 93.7% specificity is the best index of glioma grading, with the optimal cutoff value of the Cho/Cr ratio being 3.72. Conclusion The image quality of MRS does affect the metabolite ratios and the results of glioma grading. MRS image quality assessment can observably improve the accuracy rate of glioma grading. The Cho/Cr ratio has the best diagnostic performance in differentiating high-grade from low-grade glioma.

Guo Chenguang

Papers

A Fast and Accurate Corner Detector Based on Harris Algorithm

1H-MR spectroscopy in grading of cerebral glioma: A new view point, MRS image quality assessment