ICPRAM

Key takeaways

ICPRAM belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect ICPRAM to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of ICPRAM from memory before moving on to harder problems.

Reference excerpt

The International Conference on Pattern Recognition Applications and Methods (ICPRAM) is held annually since 2012. From the beginning it is held in conjunction with two other conferences: ICAART - International Conference on Agents and Artificial Intelligence and ICORES - International Conference on Operations Research and Enterprise Systems. ICPRAM is composed by two main topics areas: theory and methods and applications. Each one of these areas is constituted by several sub-topics like Evolutionary Computation, Density Estimation, Spectral method, Combinatorial Optimization, Reinforcement learning, Meta learning, Convex optimization in the case of Theory and methods and Natural language processing, robotics, Signal processing, Information retrieval, perception in the applications area. The conference papers are made available at the SCITEPRESS digital library and are published in the conference proceedings. It's also made a selection of the best papers presented in the conference for publication in a Springer volume. Besides the presentation of papers from the authors, the conference is composed by tutorials. For example, the conference had a tutorial on Secure our society - Computer Vision Techniques for Video Surveillance given by Huiyu Zhou from the Queen's University Belfast, UK. Since the first edition, ICPRAM has counted on several keynote speakers like Tomaso Poggio, Josef Kittler, Hanan Samet, Nello Cristianini, John Shawe-Taylor and Antonio Torralba.

Editions ICPRAM 2020 - Valletta, Malta ICPRAM 2019 - Prague, Czech Republic ICPRAM 2018 - Funchal, Madeira, Portugal ICPRAM 2017 - Porto, Portugal ICPRAM 2016 - Rome, Italy ICPRAM 2015 - Lisbon, Portugal ICPRAM 2014 - ESEO, Angers, Loire Valley, France ICPRAM 2013 - Barcelona, Spain ICPRAM 2012 - Vilamoura, Algarve, Portugal

Best Paper Awards

2019 Area: Applications Best Paper Award: Yehezkel S. Resheff, Itay Lieder and Tom Hope. "All Together Now! The Benefits of Adaptively Fusing Pre-trained Deep Representations" Area: Applications Best Student Paper Award: Manex Serras, María Inés Torres and Arantza del Pozo. "Goal-conditioned User Modeling for Dialogue Systems using Stochastic Bi-Automata"

2018 Area: Theory and Methods Best Paper Award: Huanqian Yan, Yonggang Lu and Heng Ma. "Density-based Clustering using Automatic Density Peak Detection" Area: Applications Best Student Paper Award: Marcin Kopaczka, Marco Saggiomo, Moritz Guttler, Thomas Gries and Doreit Merhof. "Fully Automatic Faulty Weft Thread Detection using a Camera System and Feature-based Pattern Recognition"

2017 Area: Theory and Methods Best Paper Award: Seiya Satoh and Ryohei Nakano. "How New Information Criteria WAIC and WBIC Worked for MLP Model Selection" Best Student Award: Xiaoyi Chen and Régis Lengellé. "Domain Adaptation Transfer Learning by SVM Subject to a Maximum-Mean-Discrepancy-like Constraint" Area: Applications Best Paper Award:Sarah Ahmed and Tayyaba Azim. "Compression Techniques for Deep Fisher Vectors" Best Student Award:Niels Ole Salscheider, Eike Rehder and Martin Lauer. "Analysis of Regionlets for Pedestrian Detection"

2016 Area: Theory and Methods Best Paper Award: Anne C. van Rossum, Hai Xiang Lin, Johan Dubbeldam and H. Jaap van den Herik. "Nonparametric Bayesian Line Detection - Towards Proper Priors for Robotic Computer Vision " Best Student Award: Roghayeh Soleymani, Eric Granger and Giorgio Fumera. "Classifier Ensembles with Trajectory Under-Sampling for Face Re-Identification " Area: Applications Best Paper Award: Jeonghwan Park, Kang Li and Huiyu Zhou Archived 2017-07-01 at the Wayback Machine. "k-fold Subsampling based Sequential Backward Feature Elimination " Best Student Award: Julia Richter, Christian Wiede, Enes Dayangac, Markus Heß and Gangolf Hirtz. "Activity Recognition based on High-Level Reasoning - An Experimental Study Evaluating Proximity to Objects and Pose Information "

2015 Area: Theory and Methods Best Paper Award: Mohamed-Rafik Bouguelia, Yolande Belaïd and Abdel Belaïd. "Stream-based Active Learning in the Presence of Label Noise" Best Student Paper: João Costa and Jaime S. Cardoso. "oAdaBoost" Area: Applications Best Paper Award: Wei Quan, Bogdan Matuszewski and Lik-Kwan Shark. "3-D Shape Matching for Face Analysis and Recognition" Best Student Paper: Julia Richter, Christian Wiede and Gangolf Hirtz. "Mobility Assessment of Demented People Using Pose Estimation and Movement Detection"

2014 Area: Theory and Methods Best Paper Award: Jameson Reed, Mohammad Naeem and Pascal Matsakis. "A First Algorithm to Calculate Force Histograms in the Case of 3D Vector Objects" Best Student Paper: Johannes Herwig, Timm Linder and Josef Pauli. "Removing Motion Blur using Natural Image Statistics" Area: Applications Best Paper Award: Sebastian Kurtek, Chafik Samir and Lemlih Ouchchane. "Statistical Shape Model for Simulation of Realistic Endometrial Tissue" Best Student Paper: Florian Baumann, Jie Lao, Arne Ehlers and Bodo Rosenhahn. "Motion Binary Patterns for Action Recognition"

2013 Area: Theory and Methods Best Paper Award: Barbara Hammer, Andrej Gisbrecht and Alexander Schulz. "Applications of Discriminative Dimensionality Reduction" Best Student Paper: Cristina Garcia-Cardona, Arjuna Flenner and Allon G. Percus. "Multiclass Diffuse Interface Models for Semi-supervised Learning on Graphs" Area: Applications Best Paper Award: Yoshito Otake, Carneal Catherine, Blake Lucas, Gaurav Thawait, John Carrino, Brian Corner, Marina Carboni, Barry DeCristofano, Michale Maffeo, Andrew Merkle and Mehran Armand. "Prediction of Organ Geometry from Demographic and Anthropometric Data based on Supervised Learning Approach using Statistical Shape Atlas" Best Student Paper: James Lotspeich and Mathias Kolsch. "Tracking Subpixel Targets with Critically Sampled Optics"

2012 Area: Theory and Methods Best Paper Award: Martin Emms and Hector-Hugo Franco-Penya. "ON ORDER EQUIVALENCES BETWEEN DISTANCE AND SIMILARITY MEASURES ON SEQUENCES AND TREES" Best Student Paper: Anna C. Carli, Mario A. T. Figueiredo, Manuele Bicego and Vittorio Murino. "GENERATIVE EMBEDDINGS BASED ON RICIAN MIXTURES" Area: Applications Best Paper Award: Laura Antanas, Martijn van Otterlo, José Oramas, Tinne Tuytelaars and Luc De Raedt. "A RELATIONAL DISTANCE-BASED FRAMEWORK FOR HIERARCHICAL IMAGE UNDERSTANDING" Best Student Paper: Laura Brandolini and Marco Piastra. "COMPUTING THE REEB GRAPH FOR TRIANGLE MESHES WITH ACTIVE CONTOURS"

References

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Worked examples

Example 1 — a first encounter with ICPRAM

Start with the simplest possible case. Write down what ICPRAM claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to ICPRAM before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about ICPRAM ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of ICPRAM

In research: ICPRAM appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses ICPRAM in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: ICPRAM is common in secondary-school and first-year university syllabi. It links to neighbouring topics Academic conferences, Computer science conferences, Information systems conferences, so understanding it makes those chapters shorter.
In everyday life: Look for ICPRAM outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

How to study ICPRAM in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what ICPRAM means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain ICPRAM out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is ICPRAM in simple terms?

The International Conference on Pattern Recognition Applications and Methods (ICPRAM) is held annually since 2012. From the beginning it is held in conjunction with two other conferences: ICAART - International Conference on Agents and Artificial Intelligence and ICORES - International Conference o…

Why does ICPRAM matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study ICPRAM?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on ICPRAM.