Innovative applications of associative morphological memories for image processing and pattern recognition

Morphological Associative Memories have been proposed for some image denoising applications. They can be applied to other less restricted domains, like image retrieval and hyper spectral image unsupervised segmentation. In this paper we present these applications. In both cases the key idea is that Autoassociative Morphological Memories selective sensitivity to erosive and dilative noise can be applied to detect the morphological independence between patterns. Linear unmixing based on the sets of morphological independent patterns define a feature extraction process that is the basis for the image processing applications. We discuss some experimental results on the fish shape data base and on a synthetic hyperspectral image, including the comparison with other linear feature extraction algorithms (ICA and CCA)

Contributions to the analysis and segmentation of remote sensing hyperspectral images

142 p.This PhD Thesis deals with the segmentation of hyperspectral images from the point of view of Lattice Computing. We have introduced the application of Associative Morphological Memories as a tool to detect strong lattice independence, which has been proven equivalent to affine independence. Therefore, sets of strong lattice independent vectors found using our algorithms correspond to the vertices of convex sets that cover most of the data. Unmixing the data relative to these endmembers provides a collection of abundance images which can be assumed either as unsupervised segmentations of the images or as features extracted from the hyperspectral image pixels. Besides, we have applied this feature extraction to propose a content based image retrieval approach based on the image spectral characterization provided by the endmembers. Finally, we extended our ideas to the proposal of Morphological Cellular Automata whose dynamics are guided by the morphological/lattice independence properties of the image pixels. Our works have also explored the applicability of Evolution Strategies to the endmember induction from the hyperspectral image data

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensin

PCE: Piece-wise Convex Endmember Detection

DOI: 10.1109/TGRS.2010.2041062 This item also falls under IEEE copyright. "© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works."A new hyperspectral endmember detection method that represents endmembers as distributions, autonomously partitions the input data set into several convex regions, and simultaneously determines endmember distributions and proportion values for each convex region is presented. Spectral unmixing methods that treat endmembers as distributions or hyperspectral images as piece-wise convex data sets have not been previously developed

INDEXING HYPERSPECTRAL IMAGE USING MORPHOLOGICAL NEURAL NETWORKS

En este artículo se explica el procedimiento para indexar imágenes dereconocimiento remoto utilizando información espectral y espacial. Para obtener características espectrales se aplican redes neuronales morfológicas, obteniendo el conjunto de endmembers de la imagen. Inicialmente se presenta una revisión de conceptos relativos a redes neuronales morfológicas de tipo memorias asociativas. Después se muestran los resultados de segmentación aplicado a un conjunto de imágenes sintéticas. Dichos resultados sirven de apoyo para esta aproximación como caracterización de las imágenes para su uso en la construcción de sistemas CBIR de imágenes hiperespectrales.This paper explains how to index remote sensing images using spectral andspatial information. To obtain spectral features it apply morphological neural network, obtaining the set of endmembers of the image. Initially it presents a review of concepts of morphological associative memories. Following are the results of segmentation of the images compared to some other approaches to calculating the endmember spectra. These results contribute to support this approach as a characterization of images for use in the construction of hyperspectral imaging CBIR system

Body patterning and cognition in cephalopods - a literature review

Cephalopods are a valuable model for studying the evolution of cognition due to their distinctive brain structure, organisation, and connectivity patterns compared to vertebrates. The development of large brains and behavioural complexities are believed to be triggered by evolutionary pressures stemming from factors like heightened predation, more demanding foraging conditions, and intense mating competition. While the differences between corvid and mammals are less pronounced, the cephalopod brain is closer to the vertebrate brain in terms of encephalisation of ganglionic masses observed by nerve cell clusters. The cerebral ganglion in cephalopods is similar to the vertebrate forebrain and midbrain, while the vertical lobe is similar to the vertebrate cerebral cortex and hippocampus formation, which are involved in learning and memory. These brain regions function in a hierarchical system and are intimately connected with their eyes and optic lobes where visual inputs are processed, motor commands are transmitted to the lower motor centre. Chromatophores are skin elements and the physiological control of body patterning and are visually driven and light sensitive. This sets cephalopods apart from their molluscan families such as gastropods and bivalves. Recent studies have revealed that the opsins present in the skin are like those occurring in the retina. This infers that the connection between visual processing and body patterns is not exclusively innate. Expanding on Macphail's Null Hypothesis which posits no significant qualitative or quantitative differences in intelligence across vertebrates, this study seeks to explore the link between body patterning and cognitive abilities across cephalopod species. By comparing patterns of similarities and differences in cognitive abilities, this study aims to investigate whether body patterning can serve as an indicator of cognitive capacity. In conclusion, the study finds the presence of interindividual variations within species and disparities across different species in both body patterning and cognitive abilities. There are associations between cognitive capacity and body patterns. However, establishing a direct and conclusive connection between high-level cognitive abilities and the expression of body patterns remains elusive, as concrete evidence supporting such a relationship is lacking.Cephalopoda utgör en värdefull modell för att studera den kognitiva evolutionen på grund av deras distinkta hjärnstruktur, organisation och nervernas kontaktmönster jämfört med ryggradsdjur. Utvecklingen av stora hjärnor och komplexa beteenden tros vara resultatet av evolutionär press från faktorer som ökad predation, mer krävande födosökningsförhållanden och intensiv parningskonkurrens. Medan skillnaderna mellan kråkfåglar och däggdjur är mindre uttalade, är bläckfiskhjärnan närmare ryggradsdjurshjärnan när det gäller encefalisering av nervcellkluster. Det cerebrala ganglie hos bläckfiskar liknar ryggradsdjurens främre hjärna och mellanhjärna, medan den vertikala loben liknar ryggradsdjurens hjärnbark och hippocampusformation, som är involverade i inlärning och minne. Dessa hjärnregioner fungerar inom ett hierarkiskt system och är intimt kopplade till deras ögon och optiska lober där visuell information bearbetas och motoriska kommandon överförs till de nedre motoriska centrarna. Kromatoforer är hudstrukturer som fysiologiskt kontrollerar kroppsmönster och är visuellt styrda och ljuskänsliga. Detta skiljer cephalopoder från andra molluskfamiljer som gastropoder och musslor. Studier har nyligenavslöjat att de opsin som finns i huden liknar de som förekommer i näthinnan. Detta antyder att sambandet mellan visuell bearbetning och kroppsmönster inte är uteslutande medfödd. Utöver Macphails nollhypotes, som hävdar att det inte finns några signifikanta kvalitativa eller kvantitativa skillnader i intelligens mellan ryggradsdjur, ämnar denna studie utforska kopplingen mellan kroppsmönster och kognitiva förmågor hos cephalopoda. Genom att jämföra likheter och skillnader i kognitiva förmågor syftar denna studie till att undersöka om kroppsmönster kan fungera som en indikator på kognitiv kapacitet. Resultaten visar på förekomst av variationer mellan individer inom arter och skillnader mellan olika arter både vad gäller kroppsmönster och kognitiva förmågor. Det finns samband mellan kognitiv kapacitet och funktioner samt kroppsmönster. Dock är det fortfarande svårt att fastställa en direkt och definitiv koppling mellan hög kognitiva förmågor och uttrycket av kroppsmönster, eftersom konkret bevis som stöder ett sådant samband saknas

Transgenic mouse models enabling photolabeling of individual neurons in vivo

Viele grundlegende Prinzipien der Funktion neuronaler Netzwerke sind noch immer unbekannt. Eine Theorie besagt, dass die gleichzeitige, geordnete Aktivität vieler Nervenzellen im Cortex das grundlegende Substrat höherer Gedaechtnissfunktionen ist. Allerdings ist der Mechanismus wie und warum diese Aktivitätsmuster entstehen unbekannt. Ein grundlegendes Problem dies genauer zu untersuchen ist, dass die experimentellen Werkzeuge dazu noch immer fehlen. Darum ist es nicht möglich die spezifische Funktion einer Nervenzelle in einem neuronalen Netzwerk festzustellen. Deshalb wäre es ein großer Fortschritt Nervenzellen anhand ihrer Aktivität im Netzwerk zu markieren. Dies würde uns erlauben diese Zelle wieder zu identifizieren und weiteren Analysen zu unterziehen. Dadurch wäre es uns möglich mehr über die Verbindungen zu anderen Nervenzellen, die Proteinexpression oder die Morphologie dieser Zelle zu erfahren. In dieser Arbeit testeten wir zwei verschiedene Strategien um Nervenzellen zu markieren. Zuerst untersuchten wir die Spezifität der Genexpression früher Gene im auditorischen Cortex. Wir verwendeten ein vom auditorischen Cortex abhängiges Lernprotokoll und testeten die Genexpression der zwei Gene c-fos und Arc. Weiters testeten wir auch andere Verhaltensprotokolle um lerninduzierte Genexpression von unspezifisch induzierter Genexpression zu unterscheiden. Wir fanden einen starken Anstieg beider Gene bei allen Protokollen die einen Schock beinhalteten. Dies ist ein Hinweis darauf, dass auch andere unspezifische Faktoren die Expression von c-fos und Arc aktvieren können. Weiters verwendeten wir eine mRNA Mikroarray Analyse um weitere lernspezifisch aktivierte Genen zu finden. Auch in diesem Experiment fanden wir einen starken Anstieg bekannter früher Gene konnten aber keinen neuen Genen die spezifisch während des Lernens aktiviert werden finden. Die zweite Strategie beruht auf photoaktivierbaren fluoreszierenden Proteinen. Wir testeten ob es mit diesen Proteinen möglich ist Zellen im lebenden Gehirn konditional zu markieren. Dafür testeten wir sechs verschiedenen photoactivierbare Proteine und generierten drei Mäuselinien die photoactivierbares GFP expremieren. Mit diesen Mäusen ist es möglich einzelne Zellen in vivo für viele Stunden zu markieren. Durch die Markierung können diese Zellen auch in Hirnschnitten wiedergefunden und dadurch weiter charakterisiert werden. Weiteres kann Photolabeling auch mit funktionellem Calicum Imaging kombiniert werden. Dadurch ermöglichen es diese Mäuse eine direkte Verbindung zwischen der Aktivität einzelner Zellen in einem Netzwerk mit einer weiteren Charakterisierung dieser Zellen herzustellen. Wir testeten diese Mäuse und korrelierten die spontane in vivo Aktivität einzelner Zellen mit der Genexpression des frühen Genes c-fos. Wir fanden, dass die c-fos Genexpression sehr variable in stark aktive Nervenzellen war und konnten keine Korrelation zwischen hoher spontaner Aktivität und hoher c-fos Genexpression finden. Unsere Ergebnisse deuten darauf hin, dass auch andere, unspezifische Faktoren die c-fos Genexpression beeinflussen können. Diese Ergebnisse zeigen wie wichtig es ist Neuronen unabhängig von frühen Genen markieren zu können. Wir erreichten das durch die PA-GFP expremierenden Mäuselinien. Weiters kann die photolabeling Methode auch auf andere Zelltypen angewandt werden.It is believed, that activity patterns in the cortex are correlates of higher brain functions such as perception or decision making. However the mechanisms how and why these activity patterns emerge are not known. One major experimental obstacle to understand the function of brain circuits is the lack of tools to determine both the precise function of a neuron and its position in the connectivity diagram of the circuit. Therefore, it would be necessary to tag neurons in vivo at single cell resolution, based on functional criteria, and to re-identify these neurons in vitro. This would allow us to obtain more information about the connectivity, the gene expression or the morphology of these neurons and thus to gain information that goes beyond its in vivo activity pattern. This information is crucial to gain a mechanistic understanding of the neurons function. We conceived two strategies to label neurons in the auditory cortex in vivo. First we tested if immediate early genes can be reliable markers to tag active neurons in the auditory cortex during associative learning. We established an auditory cued fear conditioning protocol that depends on the auditory cortex and tested the expression of c-fos and Arc using quantitative PCR following the acquisition of this fear memory. We also compared paired conditioning to several control paradigms to disassociate gene expression that arises from the learning event from gene expression that arises from other factors unrelated to associative learning. We found that c-fos and Arc show strong induction after paired fear conditioning but also in all other paradigms that involved shocks indicating that other unspecific factors or cross-modal inputs can activate immediate early gene expression in the auditory cortex. We further used a near genome wide mRNA microarray analysis to screen for genes other than c-fos and Arc that could be specifically upregulated during learning. We again observed upregulation of known immediate early genes but could not detect genes specifically induced by associative learning to sound. The second strategy is based on photoactivatable fluorescence proteins. We explored the ability of photoactivatable fluorescence proteins to serve as conditional markers in the living brain. We tested six PA-FPs and generated mouse lines expressing PA-GFP::NLS either under the Thy1.2 promoter or conditionally as a knock in construct from the Rosa26 locus. Using two-photon excitation we were able to conditionally label neurons at single cell resolution in vivo for many hours. Labeling greatly facilitated re-identification of these neurons in vitro in acute brain slices and in fixed samples and can therefore be combined with targeted patch clamp recordings and histology. Furthermore, photolabeling can also be combined with functional in vivo calcium imaging which allows us to link in vivo neuronal activity with a further analysis of these neurons. We demonstrate this by correlating spontaneous activity levels in the auditory cortex with a histological analysis of c-fos expression. We observed that particularly active neurons can have highly variable c-fos expression levels and we could not find a direct correlation between neuronal activity and c-fos expression. Furthermore, the results from both studies highlight that IEG expression is also controlled by other factors than neuronal activity. This again emphasizes the value of a methode to label neurons independent of IEG expression. In this study we achieved this by generating PA-GFP expressing mice to photolabel neurons. This photolabeling approach can also be applied to other cell types and fields of biology

Illuminating cAMP dynamics at the synapse with multiphoton FLIM-FRET Imaging

The study of signalling pathways within mammalian physiology has long been hindered by the size of the players involved, being far beyond the realms of the conventional light microscope. The advent of advanced fluorescent imaging techniques has revolutionised our capabilities to probe biological processes. The work in this thesis particularly utilised Förster resonance energy transfer (FRET), a fluorescence-based technique that can provide functional readouts of the processes underlying cellular function. Specifically I worked to develop and optimise a fluorescence imaging system for investigating the dynamics and function of cyclic adenosine monophosphate (cAMP), a ubiquitous second messenger. The neuroscientific study of how the brain can learn and recall memories is a rapidly advancing field. The current challenges of tackling dementias, such as Alzheimer’s disease, and preventing memory loss can only be addressed through better understanding of how memories can be stored. It is now believed that neurons retain memories within their synapses, the femtolitre structures that determine the strength of these connections. cAMP has been shown to play a distinctive role in orchestrating the retention of long term memory at the synaptic level. However, its spatial and temporal activation profiles are still not fully understood. To address this, my PhD project combined FRET readouts with cutting edge imaging techniques applied to synapses in neuronal cultures that provide reasonably convenient optical access. By examining the structure of these synapses, along with the measurement of cAMP concentration in different neuronal regions, this project uncovered the highly compartmentalised nature of this signalling molecule, seen to act directly at the sites of strengthening synapses. Through the optimisation of a FRET imaging system for studying activity in neuronal tissues, this project establishes a method for the future investigation of a plethora of pathways underlying the healthy functioning of the mammalian brain.Open Acces

Accélération algorithmique et matérielle des méthodes d’estimation de cartes d’abondances en imagerie hyperspectrale

Hyperspectral imaging consists in collecting the reflectance spectrum for each pixel of an image. This measurement technique is used in airborne remote sensing, astrophysics, or microscopy. Processing the large data volume of a hyperspectral image requires a method with both restrained computational cost and limited memory usage. The method proposed in this thesis aims at estimating the abundance maps (component's proportions in each image pixel) by constrained least squares criterion minimization with the addition of a penalization term to ensure the maps spatial regularity. The work done intends to reduce the computing time of an interior point optimization method. Algorithmic modifications based on separable majorization are proposed. It results in a method both faster and more adapted to parallel computing tools. An implementation on Graphics Processing Units (GPU) is achieved and applied in a large scale experiment where a high number of hyperspectral images from Mars Express exploration mission are processed. The developed method is also used in a vegetation monitoring project on the french atlantic coast.L'imagerie hyperspectrale consiste en une mesure du spectre de réflectance en chacun des pixels d'une image. Cette technique de mesure est utilisée pour la télédétection aéroportée, en astrophysique ou encore en microscopie. Le traitement du grand volume de données que représente une image hyperspectrale nécessite à la fois des méthodes présentant un coût de calcul maîtrisé et un besoin mémoire raisonnable. Le traitement proposé dans cette thèse a pour objectif l'estimation de cartes d'abondances (proportions de plusieurs constituants dans chaque pixel de l'image) par minimisation d'un critère de type moindres carrés sous des contraintes de positivité et de somme à un, additionné d'un terme de pénalisation pour assurer une régularité spatiale des cartes. Les travaux réalisés ont pour objectif la réduction du temps de calcul d'une méthode d'optimisation de type points-intérieurs. Des modifications algorithmiques basées sur la notion d'approximation majorante séparable sont proposées. Il en résulte une méthode à la fois plus rapide et plus adaptée aux outils de calcul parallèle. Une implémentation sur processeurs de cartes graphiques (GPU) est réalisée et appliquée à grande échelle pour traiter un grand nombre d'images hyperspectrales issues de la mission d'exploration spatiale Mars Express. La méthode développée est également utilisée dans un projet de suivi de la végétation sur la côte atlantique française