5027_latecki2016.jpg

Longin Jan Latecki

Computer Vision Pattern Recognition Image Analysis Artificial Intelligence

About

J. Latecki, Ph.D. is a Professor in the Department of Computer and Information Sciences at Temple University. His research focuses on computer vision, pattern recognition, and related areas in artificial intelligence and image analysis.

Research Performance Summary

372
Total Papers
16235
Total Citations
67
H-Index
1990-2026
Active Research Span

First Recorded Paper

Topologie in Depiktionen

Year: 1990

Citations: 0

Venue: GWAI-90 14th German Workshop on Artificial Intelligence: Eringerfeld, 10.–14

Latest Recorded Paper

Self-Supervised Learning of Deep Embeddings for Classification and Identification of Dental Implants

Year: 2026

Citations: 0

Venue: Journal of Imaging 12 (1)

Last 10 Years Publication Activity

This timeline shows the professor's yearly publication activity.

2026
2
2025
10
2024
15
2023
11
2022
6
2021
9
2020
10
2019
13
2018
8
2017
10
2016
10

Publication Venues and Collaboration

Journal, Conference, and Book Publication Breakdown

Conference 163
Journal 46
Book 6

Top Coauthors

Latecki Bai Liu

Research Impact by Period

2025-2026

Period Stats
Papers 12
Citations 15
Avg. Citations / Paper 1.2
H-Index 2

2020-2024

Period Stats
Papers 51
Citations 1253
Avg. Citations / Paper 24.6
H-Index 20

2015-2019

Period Stats
Papers 48
Citations 2870
Avg. Citations / Paper 59.8
H-Index 22

2010-2014

Period Stats
Papers 66
Citations 2750
Avg. Citations / Paper 41.7
H-Index 27

2005-2009

Period Stats
Papers 81
Citations 5116
Avg. Citations / Paper 63.2
H-Index 29

2000-2004

Period Stats
Papers 47
Citations 2692
Avg. Citations / Paper 57.3
H-Index 17

1995-1999

Period Stats
Papers 52
Citations 1395
Avg. Citations / Paper 26.8
H-Index 14

1990-1994

Period Stats
Papers 15
Citations 144
Avg. Citations / Paper 9.6
H-Index 4

Contact and Professional Links

Contact Information

latecki@temple.edu
2152045781

Detected Research Keywords

Well Composed Sets Discrete Curve Evolution Shape Similarity Measure Maximum Weight Cliques Convolutional Neural Networks Well Composed Pictures Network Semantic Segmentation Partial Shape Similarity Mesh Visual Quality Local Search Maximum