Les risques psychosociaux sont un enjeu de santé publique majeur, en particulier à cause des troubles qu'ils peuvent engendrer : stress, changements d'humeurs, burn-out, etc. Bien que le diagnostic de ces troubles doive être réalisé par un professionel, l'Affective Computing peut apporter une contribution en améliorant la compréhension des phénomènes. L'Affective Computing (ou Informatique Affective) est un domaine pluridisciplinaire, faisant intervenir des concepts d'Intelligence Artificielle, de psychologie et de psychiatrie, notamment. Dans ce travail de recherche, on s'intéresse à deux éléments pouvant faire l'objet de troubles : l'état émotionnel et l'état dépressif des individus.Le concept d'émotion couvre un très large champ de définitions et de modélisations, pour la plupart issues de travaux en psychiatrie ou en psychologie. C'est le cas, par exemple, du circumplex de Russell, qui définit une émotion comme étant la combinaison de deux dimensions affectives, nommées valence et arousal. La valence dénote le caractère triste ou joyeux d'un individu, alors que l'arousal qualifie son caractère passif ou actif. L'évaluation automatique des états émotionnels a suscité, dans la dernière décénie, un regain d'intérêt notable. Des méthodes issues de l'Intelligence Artificielle permettent d'atteindre des performances intéressantes, à partir de données capturées de manière non-invasive, comme des vidéos. Cependant, il demeure un aspect peu étudié : celui des intensités émotionnelles, et de la possibilité de les reconnaître. Dans cette thèse, nous avons exploré cet aspect au moyen de méthodes de visualisation et de classification pour montrer que l'usage de classes d'intensités émotionnelles, plutôt que de valeurs continues, bénéficie à la fois à la reconnaissance automatique et à l'interprétation des états.Le concept de dépression connaît un cadre plus strict, dans la mesure où c'est une maladie reconnue en tant que telle. Elle atteint les individus sans distinction d'âge, de genre ou de métier, mais varie en intensité ou en nature des symptômes. Pour cette raison, son étude tant au niveau de la détection que du suivi, présente un intérêt majeur pour la prévention des risques psychosociaux.Toutefois, son diagnostic est rendu difficile par le caractère parfois anodin des symptômes et par la démarche souvent délicate de consulter un spécialiste. L'échelle de Beck et le score associé permettent, au moyen d'un questionnaire, d'évaluer la sévérité de l'état dépressif d'un individu. Le système que nous avons développé est capable de reconnaître automatiquement le score dépressif d'un individu à partir de vidéos. Il comprend, d'une part, un descripteur visuel spatio-temporel bas niveau qui quantifie les micro et les macro-mouvements faciaux et, d'autre part, des méthodes neuronales issues des sciences cognitives. Sa rapidité autorise des applications de reconnaissance des états dépressifs en temps réel, et ses performances sont intéressantes au regard de l'état de l'art. La fusion des modalités visuelles et auditives a également fait l'objet d'une étude, qui montre que l'utilisation de ces deux canaux sensoriels bénéficie à la reconnaissance des états dépressifs.Au-delà des performances et de son originalité, l'un des points forts de ce travail de thèse est l'interprétabilité des méthodes. En effet, dans un contexte pluridisciplinaire tel que celui posé par l'Affective Computing, l'amélioration des connaissances et la compréhension des phénomènes étudiés sont des aspects majeurs que les méthodes informatiques sous forme de "boîte noire" ont souvent du mal à appréhender. / Psychosocial risks are a major public health issue, because of the disorders they can trigger : stress, mood swings, burn-outs, etc. Although propoer diagnosis can only be made by a healthcare professionnel, Affective Computing can make a contribution by improving the understanding of the phenomena. Affective Computing is a multidisciplinary field involving concepts of Artificial Intelligence, psychology and psychiatry, among others. In this research, we are interested in two elements that can be subject to disorders: the emotional state and the depressive state of individuals.The concept of emotion covers a wide range of definitions and models, most of which are based on work in psychiatry or psychology. A famous example is Russell's circumplex, which defines an emotion as the combination of two emotional dimensions, called valence and arousal. Valence denotes an individual's sad or joyful character, while arousal denotes his passive or active character. The automatic evaluation of emotional states has generated a significant revival of interest in the last decade. Methods from Artificial Intelligence allow to achieve interesting performances, from data captured in a non-invasive manner, such as videos. However, there is one aspect that has not been studied much: that of emotional intensities and the possibility of recognizing them. In this thesis, we have explored this aspect using visualization and classification methods to show that the use of emotional intensity classes, rather than continuous values, benefits both automatic recognition and state interpretation.The concept of depression is more strict, as it is a recognized disease as such. It affects individuals regardless of age, gender or occupation, but varies in intensity or nature of symptoms. For this reason, its study, both at the level of detection and monitoring, is of major interest for the prevention of psychosocial risks.However, his diagnosis is made difficult by the sometimes innocuous nature of the symptoms and by the often delicate process of consulting a specialist. The Beck's scale and the associated score allow, by means of a questionnaire, to evaluate the severity of an individual's state of depression. The system we have developed is able to automatically recognize an individual's depressive score from videos. It includes, on the one hand, a low-level visual spatio-temporal descriptor that quantifies micro and macro facial movements and, on the other hand, neural methods from the cognitive sciences. Its speed allows applications for real-time recognition of depressive states, and its performance is interesting with regard to the state of the art. The fusion of visual and auditory modalities has also been studied, showing that the use of these two sensory channels benefits the recognition of depressive states.Beyond performance and originality, one of the strong points of this thesis is the interpretability of the methods. Indeed, in a multidisciplinary context such as that of Affective Computing, improving knowledge and understanding of the studied phenomena is a key point that usual computer methods implemeted as "black boxes" can't deal with.

<p>Hyperspectral reflectance data are utilised in many applications, where measured data are processed and converted into physical, chemical and/or biological properties of the target objects and/or processes being studied. It has been proven that crop reflectance data can be used to detect, characterise and quantify disease severity and plant density.</p><p>In this thesis, various methods were proposed and used for detection, characterisation and quantification of disease severity and plant density utilising data acquired by hand-held spectrometers. Following this direction, hyperspectral images provide both spatial and spectral information opening for more efficient analysis.</p><p>Hence, in this thesis, various surface water quality parameters of inland waters have been monitored using hyperspectral images acquired by airborne systems. After processing the images to obtain ground reflectance data, the analysis was performed using similar methods to those of the previous case. Hence, these methods may also find application in future satellite based hyperspectral imaging systems.</p><p>However, the large size of these images raises the need for efficient data reduction. Self organising and learning neural networks, that can follow and preserve the topology of the data, have been shown to be efficient for data reduction. More advanced variants of these neural networks, referred to as the weighted neural networks (WNN), were proposed in this thesis, such as the weighted incremental neural network (WINN), which can be used for efficient reduction, mapping and clustering of large high-dimensional data sets, such as hyperspectral images.</p><p>Finally, the analysis can be reversed to generate spectra from simpler measurements using multiple colour-filter mosaics, as suggested in the thesis. The acquired instantaneous single image, including the mosaic effects, is demosaicked to generate a multi-band image that can finally be transformed into a hyperspectral image.</p>

Hyperspectral reflectance data are utilised in many applications, where measured data are processed and converted into physical, chemical and/or biological properties of the target objects and/or processes being studied. It has been proven that crop reflectance data can be used to detect, characterise and quantify disease severity and plant density. In this thesis, various methods were proposed and used for detection, characterisation and quantification of disease severity and plant density utilising data acquired by hand-held spectrometers. Following this direction, hyperspectral images provide both spatial and spectral information opening for more efficient analysis. Hence, in this thesis, various surface water quality parameters of inland waters have been monitored using hyperspectral images acquired by airborne systems. After processing the images to obtain ground reflectance data, the analysis was performed using similar methods to those of the previous case. Hence, these methods may also find application in future satellite based hyperspectral imaging systems. However, the large size of these images raises the need for efficient data reduction. Self organising and learning neural networks, that can follow and preserve the topology of the data, have been shown to be efficient for data reduction. More advanced variants of these neural networks, referred to as the weighted neural networks (WNN), were proposed in this thesis, such as the weighted incremental neural network (WINN), which can be used for efficient reduction, mapping and clustering of large high-dimensional data sets, such as hyperspectral images. Finally, the analysis can be reversed to generate spectra from simpler measurements using multiple colour-filter mosaics, as suggested in the thesis. The acquired instantaneous single image, including the mosaic effects, is demosaicked to generate a multi-band image that can finally be transformed into a hyperspectral image.