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Florica Mindru

dblp:39/142 · DBLP profile ↗
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4ranked-venue papers
3as first author
0since 2021 · last 2004
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 3 first-authorGraphics, computer vision, multimedia, augmented reality and games · 3 · 2 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Image recognition and object detection · 50% 3D vision · 50%
Computer graphics and multimedia
2 papers
Image and video processing · 100%

Topics — the 2 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
viewpoint invariance
0.011999
Recognizing Color Patterns Irrespective of Viewpoint and Illumination · CVPR 1999
Image and video processing › color image processing
illumination invariance
0.011999
Recognizing Color Patterns Irrespective of Viewpoint and Illumination · CVPR 1999

Methods — techniques the papers use, named apart from their topics

moment invariants · 0.0affine invariance · 0.0invariant descriptor · 0.0
YearPublicationVenuePosition
2004 Moment invariants for recognition under changing viewpoint and illumination
Florica Mindru, Tinne Tuytelaars, Luc Van Gool, Theo Moons
Comput. Vis. Image Underst.1
2002 Comparing Intensity Transformations and Their Invariants in the Context of Color Pattern Recognition
Florica Mindru, Theo Moons, Luc Van Gool
ECCV (4)1
1999 Recognizing Color Patterns Irrespective of Viewpoint and Illumination
abstract
New invariant features are presented that can be used for the recognition of planar color patterns such as labels, logos, pictograms, etc., irrespective of the viewpoint or the illumination conditions, and without the need for error prone contour extraction. The new features are based on moments of powers of the intensities in the individual color bands and combinations thereof. These moments implicitly characterize the shape, the intensity and the color distribution of the pattern in a uniform manner. The paper gives a classification of all functions of such moments which are invariant under both affine deformations of the pattern (thus achieving viewpoint invariance) as well as linear changes of the intensity values of the color bands (hence, coping with changes in the irradiance pattern due to different lighting conditions and/or viewpoints). The discriminant power and classification performance of the new invariants for color pattern recognition is tested on a data set of images of outdoors advertising panels. A comparison to moment invariants presented in literature is included as well.
Florica Mindru, Theo Moons, Luc Van Gool
CVPR1
1997 Fully Unsupervised Clustering Using Center-Surround Receptive Fields with Applications to Color-Segmentation
Eric J. Pauwels, Peter Fiddelaers, Florica Mindru
CAIP3