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2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), 2019
In keypoint regression, models are trained to consume an image and produce the x, y coordinates of some entity, e.g., a person's nose or corner of a document. Typical methods for doing so include linear regression over deep features to directly produce x, y coordinates and training networks to regress a dense heatmap around each keypoint.
Christopher Tensmeyer, Tony R. Martinez
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In keypoint regression, models are trained to consume an image and produce the x, y coordinates of some entity, e.g., a person's nose or corner of a document. Typical methods for doing so include linear regression over deep features to directly produce x, y coordinates and training networks to regress a dense heatmap around each keypoint.
Christopher Tensmeyer, Tony R. Martinez
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2021
Effective assisted living environments must be able to perform inferences on how their occupants interact with their environment. Gaze direction provides strong indications of how people interact with their surroundings. In this paper, we propose a gaze tracking method that uses a neural network regressor to estimate gazes from keypoints and integrates
Paris Her +4 more
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Effective assisted living environments must be able to perform inferences on how their occupants interact with their environment. Gaze direction provides strong indications of how people interact with their surroundings. In this paper, we propose a gaze tracking method that uses a neural network regressor to estimate gazes from keypoints and integrates
Paris Her +4 more
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Accelerated Keypoint Extraction
2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services, 2008Keypoints have become a fundamental feature in image and video processing. This article presents a wavelet-inspired structure to speed up the keypoint extraction process. The method restricts the extraction process to high variation areas solely, deemed likely to provide keypoints.
Rémi Trichet, Bernard Mérialdo
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Complementary Keypoint Descriptors
2016We examine the use of complementary descriptors for keypoint recognition in digital images. The descriptors combine multiple types of information, including shape, color, and texture. We first review several keypoint descriptors and propose new descriptors that use normalized brightness/color spatial histograms.
Clark F. Olson +3 more
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Keypoint detection by cascaded fast
2014 IEEE International Conference on Image Processing (ICIP), 2014When the FAST method for detecting corner features at high speed is applied to images that include complex textures (regions that include foliage, shrubbery, etc.), many corners that are not needed for object recognition are detected because FAST defines corner features on the basis of a 16-pixel bounding circle.
Takahiro Hasegawa +4 more
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Phase Invariant Keypoint Detection
2007 15th International Conference on Digital Signal Processing, 2007This paper introduces extensions to the complex wavelet keypoint detection paper [1], Keypoints are generated by finding local peaks in accumulated, interpolated maps of the product of magnitudes of directional complex filter responses, as in earlier work.
Anil Anthony Bharath, Nick G. Kingsbury
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Scale-invariant corner keypoints
2014 IEEE International Conference on Image Processing (ICIP), 2014Effective and efficient generation of keypoints from images is the first step of many computer vision applications, such as object matching. The last decade presented us with an arms race toward faster and more robust keypoint detection, feature description and matching.
Bo Li, Haibo Li 0001, Ulrik Söderström
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Multi-scale Local Implicit Keypoint Descriptor for Keypoint Matching
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023JongMin Lee, Eunhyeok Park, Sungjoo Yoo
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Detection of general edges and keypoints
1992A computational framework for extracting (1) edges with an arbitrary profile function and (2) keypoints such as corners, vertices and terminations is presented. Using oriented filters with even and odd symmetry we combine their convolution outputs to oriented energy resulting in a unified representation of edges, lines and combinations thereof.
Lukas Rosenthaler +3 more
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Spatial histogram of keypoints (SHIK)
2013 IEEE International Conference on Image Processing, 2013Among a variety of feature extraction approaches, special attention has been given to the SIFT algorithm which delivers good results for many applications. However, the non fixed and huge dimensionality of the extracted SIFT feature vector cause certain limitations when it is used in machine learning frameworks.
Zenonas Theodosiou, Nicolas Tsapatsoulis
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