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Jean Monnet University, France

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5 Papers

Knowledge Amalgamation for Single-Shot Context-Aware Emotion Recognition

Topics: Face and Expression Recognition; Object Detection and Localization; Deep Learning for Visual Understanding ; Multi-task learning

Authors: Tristan Cladière , Olivier Alata , Christophe Ducottet , Hubert Konik and Anne-Claire Legrand

Affiliation: Université Jean Monnet Saint-Etienne, CNRS, Institut d’Optique Graduate School, Laboratoire Hubert Curien UMR 5516, F-42023, Saint-Etienne, France

Digital Object Identifier: 10.5220/0013169800003912

Proceedings of VISAPP 2025, ISBN: 978-989-758-728-3

Quick Abstract

Fine-grained emotion recognition using the whole context inside images is a challenging task. Usually, the approaches to solve this problem analyze the scene from different aspects, for example people, place, object or interactions, and make a final prediction that takes all this information into account. Despite giving promising results, this requires specialized pre-trained models, and multiple pre-processing steps, which inevitably results in long and complex frameworks. To obtain a more practicable solution that would work in real time scenario with limited resources, we propose a method inspired by the amalgamation process to incorporate specialized knowledge from multiple teachers inside a student composed of a single architecture. Moreover, the student is not only capable of treating all subjects simultaneously by creating emotion maps, but also to detect the subjects in a bottom-up manner. We also compare our approach with the traditional method of fine-tuning pre-trained models, and show its superiority on two databases used in the context-aware emotion recognition field.

Subtle Motion Analysis and Spotting using the Riesz Pyramid

Topics: Optical Flow and Motion Analyses; Video Surveillance and Event Detection

Authors: Carlos Andres Arango , Olivier Alata , Rémi Emonet , Anne-Claire Legrand and Hubert Konik

Affiliation: Univ. Lyon, UJM-Saint-Etienne, CNRS and Institut d Optique Graduate School, France

Digital Object Identifier: 10.5220/0006620004460454

Proceedings of VISAPP 2018/VISIGRAPP, ISBN: 978-989-758-290-5

Quick Abstract

Analyzing and temporally spotting motions which are almost invisible to the human eye might reveal interesting information about the world. However, detecting these events is difficult due to their short duration and low intensities. Taking inspiration from video magnification techniques, we design a workflow for analyzing and temporally spotting subtle motions based on the Riesz pyramid. In addition, we propose a filtering and masking scheme that segments motions of interest without producing undesired artifacts or delays. In order to be able to evaluate the spotting accuracy of our method, we introduce our own database containing videos of subtle motions. Experiments are carried out under different types and levels of noise. Finally, we show that our method is able to outperform other state of the art methods in this challenging task.

Related Ontology Subjects/Areas/Topics (4)

Computer Vision, Visualization and Computer Graphics		Optical Flow and Motion Analyses		Video Surveillance and Event Detection
Motion, Tracking and Stereo Vision

Towards an Electronic Orientation Table: Using Features Extracted From the Image to Register Digital Elevation Model

Topics: Entertainment Imaging Applications; Mobile Imaging

Authors: Leo Nicolle ¹ , Julien Bonneton ¹ , Hubert Konik ² , Damien Muselet ² and Laure Tougne ¹

Affiliations: ¹ LIRIS, France , ² Univ. Lyon and UJM-Saint-Etienne, France

Digital Object Identifier: 10.5220/0006115700280038

Proceedings of VISAPP 2017/VISIGRAPP, ISBN: 978-989-758-227-1

Quick Abstract

The generation of a virtual representation of the bones and fragments is an artificial step required in order to obtain helpful models to work with in a simulation. Nowadays, the Marching Cubes algorithm is a de facto standard for the generation of geometric models from medical images. However, bone fragments models generated by Marching Cubes are huge and contain many unconnected geometric elements inside the bone due to the trabecular tissue. The development of new methods to generate geometrically simple 3D models from CT image stacks that preserve the original information extracted from them would be of great interest. In order to achieve that, a preliminary study for the development of a new method to generate triangle meshes from segmented medical images is presented. The method does not modify the points extracted from CT images, and avoid generating triangles inside the bone. The aim of this initial study is to analyse if a spatial decomposition may help in the process of generating a triangle mesh by using a divide-and-conquer approach. The method is under development and therefore this paper only presents some initial results and exposes the detected issues to be improved.

Related Ontology Subjects/Areas/Topics (4)

Applications and Services		Entertainment Imaging Applications		Mobile Imaging
Computer Vision, Visualization and Computer Graphics

Deterministic Method for Automatic Visual Grading of Seed Food Products

Topics: Feature Selection and Extraction; Shape Representation; Object Recognition

Authors: Pierre Dubosclard ¹ , Stanislas Larnier ¹ , Hubert Konik ² , Ariane Herbulot ¹ and Michel Devy ¹

Affiliations: ¹ CNRS and Univ. de Toulouse, France , ² Laboratoire Hubert Curien, France

Digital Object Identifier: 10.5220/0005188102120217

Proceedings of ICPRAM 2015, ISBN: 978-989-758-076-5

Quick Abstract

This paper presents a deterministic method for automatic visual grading, designed to solve the industrial problem of evaluation of seed lots. The sample is thrown in bulk onto a tray placed in a chamber for acquiring color image. An image processing method had been developed to separate and characterize each seed. Shape learning is performed on isolated seeds. The collected information is used for the segmentation. A first step is made based on simple criteria such as regions, edges and normals to the boundary. Then, an active contour with shape prior is performed to improve the results.

Related Ontology Subjects/Areas/Topics (7)

Applications	Pattern Recognition	Software Engineering
Feature Selection and Extraction	Shape Representation	Theory and Methods
Object Recognition

Human Visual System Based Framework For Gender Recognition

Topics: Machine Learning; Cognitive Systems; Vision and Perception; Pattern Recognition

Authors: Cherinet G. Zewdie and Hubert Konik

Affiliation: Université de Lyon and Université Jean Monnet, France

Digital Object Identifier: 10.5220/0005176102540260

Proceedings of ICAART 2015, ISBN: 978-989-758-074-1

Quick Abstract

A face reveals a great deal of information to a perceiver including gender. Humans use specific information (cue) from a face to recognize gender. The focus of this paper is to find out this cue when the Human Visual System (HVS) decodes gender of a face. The result can be used by a Computer Vision community to develop HVS inspired framework for gender recognition. We carried out a Pyscho-visual experiment to find which face region is most correlated with gender. Eye movements of 15 observers were recorded using an eye tracker when they performed gender recognition task under controlled and free viewing condition. Analysis of the eye movement shows that the eye region is the most correlated with gender recognition. We also proposed a HVS inspired automatic gender recognition framework based on the Psycho-visual experiment. The proposed framework is tested on FERET database and is shown to achieve a high recognition accuracy.

Related Ontology Subjects/Areas/Topics (21)

Artificial Intelligence	Health Engineering and Technology Applications	Neurotechnology, Electronics and Informatics
Biomedical Engineering	Human-Computer Interaction	Pattern Recognition
Biomedical Signal Processing	Knowledge Discovery and Information Retrieval	Physiological Computing Systems
Cognitive Systems	Knowledge-Based Systems	Sensor Networks
Computational Intelligence	Machine Learning	Soft Computing
Data Manipulation	Methodologies and Methods	Symbolic Systems
Evolutionary Computing	Neurocomputing	Vision and Perception

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