Ludovic Macaire

1.5k total citations
42 papers, 476 citations indexed

About

Ludovic Macaire is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Ludovic Macaire has authored 42 papers receiving a total of 476 indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Computer Vision and Pattern Recognition, 20 papers in Media Technology and 6 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Ludovic Macaire's work include Image Retrieval and Classification Techniques (21 papers), Advanced Image and Video Retrieval Techniques (21 papers) and Remote-Sensing Image Classification (16 papers). Ludovic Macaire is often cited by papers focused on Image Retrieval and Classification Techniques (21 papers), Advanced Image and Video Retrieval Techniques (21 papers) and Remote-Sensing Image Classification (16 papers). Ludovic Macaire collaborates with scholars based in France, Algeria and Lebanon. Ludovic Macaire's co-authors include Nicolas Vandenbroucke, Olivier Losson, J.‐G. Postaire, Alice Porebski, Denis Hamad, Damien Muselet, Kamal Hammouche, A. Tounzi, Yvonnick Le Menach and Aurélien Duménil and has published in prestigious journals such as Sensors, Pattern Recognition and Journal of the Optical Society of America A.

In The Last Decade

Ludovic Macaire

40 papers receiving 444 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ludovic Macaire France 10 352 177 63 36 33 42 476
Arto Kaarna Finland 12 255 0.7× 150 0.8× 51 0.8× 25 0.7× 21 0.6× 58 480
Chee-Way Chong Malaysia 7 504 1.4× 97 0.5× 38 0.6× 18 0.5× 17 0.5× 11 585
Abdullah Bal Türkiye 11 175 0.5× 173 1.0× 64 1.0× 34 0.9× 12 0.4× 49 385
Rıfat Kurban Türkiye 12 284 0.8× 327 1.8× 68 1.1× 14 0.4× 14 0.4× 29 547
Wele Gedara Chaminda Bandara United States 11 233 0.7× 240 1.4× 38 0.6× 55 1.5× 5 0.2× 20 495
J.F. Haddon United Kingdom 8 239 0.7× 76 0.4× 55 0.9× 16 0.4× 9 0.3× 30 347
S.X. Liao Canada 9 612 1.7× 103 0.6× 73 1.2× 16 0.4× 5 0.2× 13 691
H.M.G. Stokman Netherlands 9 293 0.8× 113 0.6× 22 0.3× 19 0.5× 81 2.5× 20 373
Muhammad Suzuri Hitam Malaysia 7 344 1.0× 153 0.9× 31 0.5× 10 0.3× 24 0.7× 43 511
Shasha Mao China 11 194 0.6× 52 0.3× 142 2.3× 18 0.5× 17 0.5× 39 390

Countries citing papers authored by Ludovic Macaire

Since Specialization
Citations

This map shows the geographic impact of Ludovic Macaire's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ludovic Macaire with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ludovic Macaire more than expected).

Fields of papers citing papers by Ludovic Macaire

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ludovic Macaire. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ludovic Macaire. The network helps show where Ludovic Macaire may publish in the future.

Co-authorship network of co-authors of Ludovic Macaire

This figure shows the co-authorship network connecting the top 25 collaborators of Ludovic Macaire. A scholar is included among the top collaborators of Ludovic Macaire based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ludovic Macaire. Ludovic Macaire is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Losson, Olivier, et al.. (2023). MSFA-Net: A convolutional neural network based on multispectral filter arrays for texture feature extraction. Pattern Recognition Letters. 168. 93–99. 8 indexed citations
2.
Hammouche, Kamal, et al.. (2022). Fuzzy Color Aura Matrices for Texture Image Segmentation. Journal of Imaging. 8(9). 244–244. 3 indexed citations
3.
Hammouche, Kamal, et al.. (2021). Constrained feature selection for semisupervised color-texture image segmentation using spectral clustering. Journal of Electronic Imaging. 30(1). 5 indexed citations
4.
Hammouche, Kamal, et al.. (2020). Similarity-based constraint score for feature selection. Knowledge-Based Systems. 209. 106429–106429. 5 indexed citations
5.
Losson, Olivier, et al.. (2018). Spatio-spectral binary patterns based on multispectral filter arrays for texture classification. Journal of the Optical Society of America A. 35(9). 1532–1532. 3 indexed citations
6.
Losson, Olivier, et al.. (2016). Color local binary patterns: compact descriptors for texture classification. Journal of Electronic Imaging. 25(6). 61404–61404. 23 indexed citations
7.
Macaire, Ludovic, et al.. (2015). Intrinsic camera calibration equipped with Scheimpflug optical device. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9534. 953416–953416. 10 indexed citations
8.
Vandenbroucke, Nicolas, et al.. (2015). Unsupervised color-image segmentation by multicolor space iterative pixel classification. Journal of Electronic Imaging. 24(2). 23032–23032. 6 indexed citations
9.
Hamad, Denis, et al.. (2013). Constraint Score Evaluation for Spectral Feature Selection. Neural Processing Letters. 38(2). 155–175. 3 indexed citations
10.
Losson, Olivier, Alice Porebski, Nicolas Vandenbroucke, & Ludovic Macaire. (2013). Color texture analysis using CFA chromatic co-occurrence matrices. Computer Vision and Image Understanding. 117(7). 747–763. 17 indexed citations
11.
Losson, Olivier & Ludovic Macaire. (2012). Colour texture classification from colour filter array images using various colour spaces. IET Image Processing. 6(8). 1192–1204. 2 indexed citations
12.
Fernández-Maloigne, Christine, et al.. (2012). Digital Color Imaging. HAL (Le Centre pour la Communication Scientifique Directe). 4 indexed citations
13.
Losson, Olivier & Ludovic Macaire. (2012). CFA local binary patterns for fast illuminant-invariant color texture classification. Journal of Real-Time Image Processing. 10(2). 387–401. 9 indexed citations
14.
Porebski, Alice, et al.. (2010). Constraint score for semi-supervised selection of color texture features. 275–279. 1 indexed citations
15.
Macaire, Ludovic, et al.. (2010). Constraint scores for semi-supervised feature selection: A comparative study. Pattern Recognition Letters. 32(5). 656–665. 49 indexed citations
16.
Porebski, Alice, Nicolas Vandenbroucke, & Ludovic Macaire. (2008). Neighborhood and Haralick feature extraction for color texture analysis. Conference on Colour in Graphics Imaging and Vision. 4(1). 316–321. 5 indexed citations
17.
Muselet, Damien, Brian Funt, Lilong Shi, & Ludovic Macaire. (2007). OBJECT RECOGNITION AND POSE ESTIMATION ACROSS ILLUMINATION CHANGES. Summit (Simon Fraser University). 264–267. 1 indexed citations
18.
Vandenbroucke, Nicolas, et al.. (2006). Scale space filter based on homogeneity degree for color image segmentation. Conference on Colour in Graphics Imaging and Vision. 3(1). 12–17. 1 indexed citations
19.
Trémeau, Alain, Christine Fernández-Maloigne, Olivier Colot, et al.. (2004). Image Numérique Couleur : de l'acquisition au traitement. SPIRE - Sciences Po Institutional REpository. 1 indexed citations
20.
Vandenbroucke, Nicolas, Ludovic Macaire, & J.‐G. Postaire. (1997). <title>Soccer player recognition by pixel classification in a hybrid color space</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 3071. 23–33. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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