Marco La Cascia

3.3k total citations
76 papers, 2.0k citations indexed

About

Marco La Cascia is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Marco La Cascia has authored 76 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 61 papers in Computer Vision and Pattern Recognition, 8 papers in Artificial Intelligence and 7 papers in Media Technology. Recurrent topics in Marco La Cascia's work include Advanced Image and Video Retrieval Techniques (31 papers), Image Retrieval and Classification Techniques (21 papers) and Video Surveillance and Tracking Methods (16 papers). Marco La Cascia is often cited by papers focused on Advanced Image and Video Retrieval Techniques (31 papers), Image Retrieval and Classification Techniques (21 papers) and Video Surveillance and Tracking Methods (16 papers). Marco La Cascia collaborates with scholars based in Italy, United States and Switzerland. Marco La Cascia's co-authors include Stan Sclaroff, Liliana Lo Presti, Vassilis Athitsos, Leonid Taycher, Edoardo Ardizzone, Sunjay Sethi, Sebastiano Battiato, Alessandro Capra, Ilenia Tinnirello and Marco Morana and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Access.

In The Last Decade

Marco La Cascia

72 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marco La Cascia Italy 20 1.7k 305 214 201 180 76 2.0k
Guangyou Xu China 21 1.1k 0.6× 161 0.5× 176 0.8× 115 0.6× 138 0.8× 116 1.4k
Hyeran Byun South Korea 20 861 0.5× 444 1.5× 131 0.6× 141 0.7× 136 0.8× 137 1.6k
Giuseppe Serra Italy 24 2.0k 1.2× 355 1.2× 184 0.9× 210 1.0× 615 3.4× 95 2.4k
Pietro Pala Italy 24 2.2k 1.3× 324 1.1× 204 1.0× 380 1.9× 68 0.4× 108 2.5k
Yue Ming China 17 731 0.4× 225 0.7× 74 0.3× 143 0.7× 133 0.7× 97 1.1k
Raymond Ptucha United States 18 624 0.4× 333 1.1× 101 0.5× 141 0.7× 143 0.8× 76 1.1k
Vineeth N Balasubramanian India 23 870 0.5× 818 2.7× 134 0.6× 115 0.6× 92 0.5× 103 1.7k
Marco Bertini Italy 28 2.3k 1.4× 757 2.5× 62 0.3× 541 2.7× 123 0.7× 170 2.8k
Bing‐Kun Bao China 19 926 0.6× 311 1.0× 85 0.4× 77 0.4× 137 0.8× 85 1.3k
Masakazu Iwamura Japan 16 2.2k 1.3× 454 1.5× 203 0.9× 60 0.3× 817 4.5× 133 2.5k

Countries citing papers authored by Marco La Cascia

Since Specialization
Citations

This map shows the geographic impact of Marco La Cascia'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 Marco La Cascia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marco La Cascia more than expected).

Fields of papers citing papers by Marco La Cascia

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marco La Cascia. 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 Marco La Cascia. The network helps show where Marco La Cascia may publish in the future.

Co-authorship network of co-authors of Marco La Cascia

This figure shows the co-authorship network connecting the top 25 collaborators of Marco La Cascia. A scholar is included among the top collaborators of Marco La Cascia 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 Marco La Cascia. Marco La Cascia 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.
Tinnirello, Ilenia, et al.. (2022). Fake News Spreaders Detection: Sometimes Attention Is Not All You Need. Information. 13(9). 426–426. 22 indexed citations
2.
Cascia, Marco La, et al.. (2022). McRock at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Multi-Channel CNN, Hybrid LSTM, DistilBERT and XLNet. Nova Science Publishers (Nova Science Publishers, Inc.). 409–417. 2 indexed citations
3.
Presti, Liliana Lo, et al.. (2022). Iterative Multiple Bounding-Box Refinements for Visual Tracking. Journal of Imaging. 8(3). 61–61. 2 indexed citations
4.
Becattini, Federico, Andrea Ferracani, Alberto Del Bimbo, et al.. (2022). I-MALL An Effective Framework for Personalized Visits. Improving the Customer Experience in Stores. Florence Research (University of Florence). 11–19. 1 indexed citations
5.
Presti, Liliana Lo, Marco La Cascia, Mohamed Zaoui, et al.. (2020). Semiautomatic Behavioral Change-Point Detection: A Case Study Analyzing Children Interactions With a Social Agent. IEEE Transactions on Cognitive and Developmental Systems. 13(4). 779–790. 1 indexed citations
6.
Ardizzone, Edoardo, et al.. (2019). Automatic Generation of Custom Tourist Routes. Nova Science Publishers (Nova Science Publishers, Inc.). 3. 282–288. 1 indexed citations
7.
Presti, Liliana Lo & Marco La Cascia. (2015). Using Hankel matrices for dynamics-based facial emotion recognition and pain detection. Nova Science Publishers (Nova Science Publishers, Inc.). 26–33. 13 indexed citations
8.
Bruno, Alessandro, Luca Greco, & Marco La Cascia. (2014). Video Object Recognition and Modeling by SIFT Matching Optimization. Nova Science Publishers (Nova Science Publishers, Inc.). 662–670. 4 indexed citations
9.
Cascia, Marco La, et al.. (2013). Fully Automatic Saliency-based Subjects Extraction in Digital Images. Nova Science Publishers (Nova Science Publishers, Inc.). 129–136.
10.
Paola, Alessandra De, Marco La Cascia, Giuseppe Lo Re, Marco Morana, & Marco Ortolani. (2012). User detection through multi-sensor fusion in an AmI scenario. Nova Science Publishers (Nova Science Publishers, Inc.). 2502–2509. 14 indexed citations
11.
Ardizzone, Edoardo, et al.. (2012). Extracting Touristic Information from Online Image Collections. 482–488. 9 indexed citations
12.
Presti, Liliana Lo, Marco Morana, & Marco La Cascia. (2011). A data association approach to detect and organize people in personal photo collections. Multimedia Tools and Applications. 61(2). 321–352. 5 indexed citations
13.
Presti, Liliana Lo, Marco Morana, & Marco La Cascia. (2010). A Data Association Algorithm for People Re-identification in Photo Sequences. Nova Science Publishers (Nova Science Publishers, Inc.). 318–323. 7 indexed citations
14.
Ardizzone, Edoardo, Marco La Cascia, & Filippo Vella. (2008). Mean shift clustering for personal photo album organization. Nova Science Publishers (Nova Science Publishers, Inc.). 6820. 85–88. 7 indexed citations
15.
Capra, Alessandro, et al.. (2005). Extension of the depth of field using multi-focus input images. 146–150. 1 indexed citations
16.
Battiato, Sebastiano, et al.. (2004). 3D stereoscopic image pairs by depth-map generation. 124–131. 43 indexed citations
17.
Battiato, Sebastiano, et al.. (2004). 3D stereoscopic image pairs by depth-map generation. 124–131. 40 indexed citations
18.
Ardizzone, Edoardo & Marco La Cascia. (2002). Video indexing using optical flow field. 3. 831–834. 28 indexed citations
19.
Cascia, Marco La, Stan Sclaroff, & Vassilis Athitsos. (2000). Fast, reliable head tracking under varying illumination: an approach based on registration of texture-mapped 3D models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 22(4). 322–336. 388 indexed citations
20.
Cascia, Marco La, et al.. (1996). Content-based indexing of image and video databases by global and shape features. Nova Science Publishers (Nova Science Publishers, Inc.). 140–144 vol.3. 17 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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