On the Comparison of Classic and Deep Keypoint Detector and Descriptor Methods

This work proposes a novel keypoint detector called QSIF (Quality and Spatial based Invariant Feature Detector). The primary contributions include: (1) a multilevel box filter is used to build the image scales efficiently and precisely, (2) by examining pixels in quality and spatial space simultaneously, QSIF can directly locate the keypoints without scale space extrema detection in the entire image spatial space, (3) QSIF can precisely control the number of output keypoints while maintaining almost the same repeatability of keypoint detection. This characteristic is essential in many real-time application fields. Extensive experimental results with images under scale, rotation, viewpoint and illumination changes demonstrate that the proposed QSIF has a stable and satisfied repeatability, and it can greatly speed up the keypoint detect and matching.

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A Vessel Keypoint Detector for junction classification

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) ◽

10.1109/isbi.2017.7950657 ◽

2017 ◽

Cited By ~ 4

Author(s):

Chetan L Srinidhi ◽

Priyadarshi Rath ◽

Jayanthi Sivaswamy

Keyword(s):

Keypoint Detector

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

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-ii-3-w5-121-2015 ◽

2015 ◽

Vol II-3/W5 ◽

pp. 121-128 ◽

Cited By ~ 16

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.

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AI Based Monitoring of Different Risk Levels in COVID-19 Context

Sensors ◽

10.3390/s22010298 ◽

2021 ◽

Vol 22 (1) ◽

pp. 298

Author(s):

César Melo ◽

Sandra Dixe ◽

Jaime C. Fonseca ◽

António H. J. Moreira ◽

João Borges

Keyword(s):

Synthetic Data ◽

Virus Transmission ◽

Data Generation ◽

Average Precision ◽

Risk Levels ◽

Health Authorities ◽

Synthetic Data Generation ◽

Political Effects ◽

Thermal Cameras ◽

Keypoint Detector

COVID-19 was responsible for devastating social, economic, and political effects all over the world. Although the health authorities imposed restrictions provided relief and assisted with trying to return society to normal life, it is imperative to monitor people’s behavior and risk factors to keep virus transmission levels as low as possible. This article focuses on the application of deep learning algorithms to detect the presence of masks on people in public spaces (using RGB cameras), as well as the detection of the caruncle in the human eye area to make an accurate measurement of body temperature (using thermal cameras). For this task, synthetic data generation techniques were used to create hybrid datasets from public ones to train state-of-the-art algorithms, such as YOLOv5 object detector and a keypoint detector based on Resnet-50. For RGB mask detection, YOLOv5 achieved an average precision of 82.4%. For thermal masks, glasses, and caruncle detection, YOLOv5 and keypoint detector achieved an average precision of 96.65% and 78.7%, respectively. Moreover, RGB and thermal datasets were made publicly available.

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Detection of Pediatric Femur Configuration on X-ray Images

Applied Sciences ◽

10.3390/app11209538 ◽

2021 ◽

Vol 11 (20) ◽

pp. 9538

Author(s):

Marta Drążkowska

Keyword(s):

Medical Image ◽

Image Data ◽

Knee Joints ◽

Experimental Protocol ◽

X Ray ◽

Gradient Based ◽

Overall Performance ◽

Fully Automatic ◽

Medical Image Data ◽

Keypoint Detector

In this paper, we present a fully automatic solution for denoting bone configuration on two-dimensional images. A dataset of 300 X-ray images of children’s knee joints was collected. The strict experimental protocol established in this study increased the difficulty of post-processing. Therefore, we tackled the problem of obtaining reliable information from medical image data of insufficient quality. We proposed a set of features that unambiguously denoted configuration of the bone on the image, namely the femur. It was crucial to define the features that were independent of age, since age variability of subjects was high. Subsequently, we defined image keypoints directly corresponding to those features. Their positions were used to determine the coordinate system denoting femur configuration. A complex keypoint detector was proposed, composed of two different estimator architectures: gradient-based and based on the convolutional neural network. The positions of the keypoints were used to determine the configuration of the femur on each image frame. The overall performance of both estimators working in parallel was evaluated using X-ray images from the publicly available LERA dataset.

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