In proteomics, two dimensional gel electrophoresis (2–DE) is a separation technique for proteins.
Gel electrophoresis is registered and the final digital image is computer analyzed for protein spots finding; the protein spots can be detected by visual inspection of a digital gel image or by image processing algorithm. On computer image analysis, difficulties arise from image noise, spot saturation and irregular geometric distortions.
Aiming at the automated analysis of large series of 2–DE images, the bottleneck is to solve the two most basic algorithmic problems: identifying protein spots and computing the protein spots map in order to compare it to database or different image.
We developed a robust Analysis of Variance (ANOVA) based algorithm able to excite spot in order to be easy found and separated by classic algorithm as edge detection or watershed. The implementation is done in a client standalone application called VisualBio.
Chiari: Lezione su Particle Induced Gamma-ray Emission, PIGE (2012)Massimo Chiari
Slide delle lezioni sulla tecnica PIGE (Particle Induced Gamma-ray Emission) nell'ambito del corso "Tecniche di analisi con fasci di ioni", corso di Laurea Magistrale in Fisica e Astrofisica, Univ. Firenze AA 2011-2012 (Massimo Chiari, P.A. Mandò)
Chiari: Introduzione alle tecniche di Ion Beam Analysis, IBA (2012)Massimo Chiari
Slide della lezione introduttiva sulle tecniche IBA (Ion Beam Analysis) nell'ambito del corso "Tecniche di analisi con fasci di ioni", corso di Laurea Magistrale in Fisica e Astrofisica, Univ. Firenze AA 2011-2012 (Massimo Chiari, P.A. Mandò)
Giacomo Veneri Thesis 1999 University of SienaGiacomo Veneri
In proteomics, two dimensional gel electrophoresis (2–DE) is a separation technique for proteins.
Gel electrophoresis is registered and the final digital image is computer analyzed for protein spots finding; the protein spots can be detected by visual inspection of a digital gel image or by image processing algorithm. On computer image analysis, difficulties arise from image noise, spot saturation and irregular geometric distortions.
Aiming at the automated analysis of large series of 2–DE images, the bottleneck is to solve the two most basic algorithmic problems: identifying protein spots and computing the protein spots map in order to compare it to database or different image.
We developed a robust Analysis of Variance (ANOVA) based algorithm able to excite spot in order to be easy found and separated by classic algorithm as edge detection or watershed.
Study the influence of (eye) motor control on selective attention
Develop a method to extract motor control parameters during visual search
Develop a method to extract selective attention features during visual search
Chiari: Lezione su Particle Induced Gamma-ray Emission, PIGE (2012)Massimo Chiari
Slide delle lezioni sulla tecnica PIGE (Particle Induced Gamma-ray Emission) nell'ambito del corso "Tecniche di analisi con fasci di ioni", corso di Laurea Magistrale in Fisica e Astrofisica, Univ. Firenze AA 2011-2012 (Massimo Chiari, P.A. Mandò)
Chiari: Introduzione alle tecniche di Ion Beam Analysis, IBA (2012)Massimo Chiari
Slide della lezione introduttiva sulle tecniche IBA (Ion Beam Analysis) nell'ambito del corso "Tecniche di analisi con fasci di ioni", corso di Laurea Magistrale in Fisica e Astrofisica, Univ. Firenze AA 2011-2012 (Massimo Chiari, P.A. Mandò)
Giacomo Veneri Thesis 1999 University of SienaGiacomo Veneri
In proteomics, two dimensional gel electrophoresis (2–DE) is a separation technique for proteins.
Gel electrophoresis is registered and the final digital image is computer analyzed for protein spots finding; the protein spots can be detected by visual inspection of a digital gel image or by image processing algorithm. On computer image analysis, difficulties arise from image noise, spot saturation and irregular geometric distortions.
Aiming at the automated analysis of large series of 2–DE images, the bottleneck is to solve the two most basic algorithmic problems: identifying protein spots and computing the protein spots map in order to compare it to database or different image.
We developed a robust Analysis of Variance (ANOVA) based algorithm able to excite spot in order to be easy found and separated by classic algorithm as edge detection or watershed.
Study the influence of (eye) motor control on selective attention
Develop a method to extract motor control parameters during visual search
Develop a method to extract selective attention features during visual search
Industrial IoT - build your industry 4.0 @techitalyGiacomo Veneri
Explore industrial processes, devices, and protocols
Design and implement the I-IoT network flow
Gather and transfer industrial data in a secure way
Get to grips with popular cloud-based platforms
Understand diagnostic analytics to answer critical workforce questions
Discover the Edge device and understand Edge and Fog computing
Implement equipment and process management to achieve business-specific goals
The eye gaze analysis represents a challenging field of
research, since it offers a reproducible method to study the mechanisms of the brain. Eye movements are arguably the most frequent of all human movements and an essential part of human vision: they drive the fovea and consequently, the attention towards regions of interest in space. This enables the visual system to fixate and to process an image or its details with high resolution: act of fixation. This chapter investigates some common techniques and algorithms to study human vision.
Bayesain Hypothesis of Selective Attention - Raw 2011 posterGiacomo Veneri
The aim of the study is to understand the process of target averaging during the selection process. We analyzed the probability to select the target after a fixation outside ROIs from the duration of fixations and the distance to the target. We aimed to respond to the question “is it possible to predict the selected area?” . In this study we tested the presence of information in non-ROI fixation data about the occurrence of a target at the next saccade. A classification algorithm was trained to predict the target vs. non-target outcome (dependent variable) of a saccade from summary statistics of fixation data (covariates). We claim that significantly accurate predictions are substantial evidence to support the hypothesis of "presence of information".
Raw 2009 -THE ROLE OF LATEST FIXATIONS ON ONGOING VISUAL SEARCH A MODEL TO E...Giacomo Veneri
The aim of the study is to understand the selection process, that modulates the exploration mechanism, during the execution of a high cognitively demanding task. The main purpose is to identify the mechanism competition mechanism between top-down and bottom-up. We developed an adaptive system trying to emulate this mechanism.
EVA – EYE TRACKING - STIMULUS INTEGRATED SEMI AUTOMATIC CASE BASE SYSTEMGiacomo Veneri
In a real world visual search is a common task depending from
sensory, perceptual and cognitive processes. Different classes
of eye movements are necessary to hold an image on the retina
during head rotation or movement of the image, and to move
the eye suddenly to a new point of interest in space. From a
functional point of view, two major classes of eye movements
are described in humans: those stabilizing gaze (optokinetic
nystagmus, oculovestibular reflex) and those movinggaze
(saccades, pursuits and vergence). Under natural conditions,
however, a mix of all kinds of eye movements permit
continuous scanning of the visual scene. The sequence of
fixations and saccades during visual exploration isan
expression of a number of cognitive processes; the use of
standardized tasks with pre-defined spatial-temporal variables
allows us to assess specific cognitive domains, such as
perception, attention, memory, preference and motivation.
Manipulating the search task can vary the demands on brain. In
turn, brain modulates visual search by selecting and limiting
the information available at various levels of processing.
The EVA software is a complete system based on a set of
stimulus and patient’s case able to stress brain functionalities in
order to assess some cognitive functions.
THE ROLE OF LATEST FIXATIONS ON ONGOING VISUAL SEARCH Giacomo Veneri
The aim of the study is to understand the selection process, that modulates the exploration mechanism, during the execution of a high cognitively demanding task. The main purpose is to identify the mechanism competition mechanism between top-down and bottom-up. We developed an adaptive system trying to emulate this mechanism.
Evaluating Human Visual Search Performance by Monte Carlo methods and Heurist...Giacomo Veneri
Visual search is an everyday activity that enables
humans to explore the real world. Given the visual input,
during a visual search, it’s required to select some aspects of the input in order to move to the next location. Exploration is guided by two factors: saliency of image (bottom-up) and endogenous mechanism (top-down). These two mechanisms interact to perform an efficient visual search. We developed a stochastic model, the ”break away from fixations” (BAF), to emulate the visual search on a high cognitively demanding task such as a trail making test (TMT). The paper reports a case study providing evidence that human exploration performs an efficient visual search based also on an internal model of regions already explored.
Study the influence of (eye) motor control on selective attention
Develop a method to extract motor control parameters during visual search
Develop a method to extract selective attention features during visual search
Industrial IoT - build your industry 4.0 @techitalyGiacomo Veneri
Explore industrial processes, devices, and protocols
Design and implement the I-IoT network flow
Gather and transfer industrial data in a secure way
Get to grips with popular cloud-based platforms
Understand diagnostic analytics to answer critical workforce questions
Discover the Edge device and understand Edge and Fog computing
Implement equipment and process management to achieve business-specific goals
The eye gaze analysis represents a challenging field of
research, since it offers a reproducible method to study the mechanisms of the brain. Eye movements are arguably the most frequent of all human movements and an essential part of human vision: they drive the fovea and consequently, the attention towards regions of interest in space. This enables the visual system to fixate and to process an image or its details with high resolution: act of fixation. This chapter investigates some common techniques and algorithms to study human vision.
Bayesain Hypothesis of Selective Attention - Raw 2011 posterGiacomo Veneri
The aim of the study is to understand the process of target averaging during the selection process. We analyzed the probability to select the target after a fixation outside ROIs from the duration of fixations and the distance to the target. We aimed to respond to the question “is it possible to predict the selected area?” . In this study we tested the presence of information in non-ROI fixation data about the occurrence of a target at the next saccade. A classification algorithm was trained to predict the target vs. non-target outcome (dependent variable) of a saccade from summary statistics of fixation data (covariates). We claim that significantly accurate predictions are substantial evidence to support the hypothesis of "presence of information".
Raw 2009 -THE ROLE OF LATEST FIXATIONS ON ONGOING VISUAL SEARCH A MODEL TO E...Giacomo Veneri
The aim of the study is to understand the selection process, that modulates the exploration mechanism, during the execution of a high cognitively demanding task. The main purpose is to identify the mechanism competition mechanism between top-down and bottom-up. We developed an adaptive system trying to emulate this mechanism.
EVA – EYE TRACKING - STIMULUS INTEGRATED SEMI AUTOMATIC CASE BASE SYSTEMGiacomo Veneri
In a real world visual search is a common task depending from
sensory, perceptual and cognitive processes. Different classes
of eye movements are necessary to hold an image on the retina
during head rotation or movement of the image, and to move
the eye suddenly to a new point of interest in space. From a
functional point of view, two major classes of eye movements
are described in humans: those stabilizing gaze (optokinetic
nystagmus, oculovestibular reflex) and those movinggaze
(saccades, pursuits and vergence). Under natural conditions,
however, a mix of all kinds of eye movements permit
continuous scanning of the visual scene. The sequence of
fixations and saccades during visual exploration isan
expression of a number of cognitive processes; the use of
standardized tasks with pre-defined spatial-temporal variables
allows us to assess specific cognitive domains, such as
perception, attention, memory, preference and motivation.
Manipulating the search task can vary the demands on brain. In
turn, brain modulates visual search by selecting and limiting
the information available at various levels of processing.
The EVA software is a complete system based on a set of
stimulus and patient’s case able to stress brain functionalities in
order to assess some cognitive functions.
THE ROLE OF LATEST FIXATIONS ON ONGOING VISUAL SEARCH Giacomo Veneri
The aim of the study is to understand the selection process, that modulates the exploration mechanism, during the execution of a high cognitively demanding task. The main purpose is to identify the mechanism competition mechanism between top-down and bottom-up. We developed an adaptive system trying to emulate this mechanism.
Evaluating Human Visual Search Performance by Monte Carlo methods and Heurist...Giacomo Veneri
Visual search is an everyday activity that enables
humans to explore the real world. Given the visual input,
during a visual search, it’s required to select some aspects of the input in order to move to the next location. Exploration is guided by two factors: saliency of image (bottom-up) and endogenous mechanism (top-down). These two mechanisms interact to perform an efficient visual search. We developed a stochastic model, the ”break away from fixations” (BAF), to emulate the visual search on a high cognitively demanding task such as a trail making test (TMT). The paper reports a case study providing evidence that human exploration performs an efficient visual search based also on an internal model of regions already explored.
Study the influence of (eye) motor control on selective attention
Develop a method to extract motor control parameters during visual search
Develop a method to extract selective attention features during visual search
1. Sistema per l’identificazione
automatica di gruppi proteici
nelle immagini
elettroforetiche
bidimensionali
Prof. Alessandro Mecocci
Ing. Paolo Bussotti Giacomo Veneri
2. Processo elettroforetico
(1)
(2)
Spot matching
Gel preparation
(4)
Person
Person
A11
Classification Spot classification
A17
A11
A11
Z1X
(3)
AQS Mel1
AqR
3. Gel Elettroforetico
Punto isoelettrico Eterogeneità
Eterogeneità
Affidabilità
Affidabilità
Problemi nel gel
Problemi nel gel
••Streaks :rumore correlato
Streaks :rumore correlato
••Saturazionegrigi
Saturazione grigi
••Spot overlapping
Spot overlapping
Peso Molecolare
4. Informazioni note a priori
Spot = Distribuzione Gaussiana
Spot = Distribuzione Gaussiana
Spot Grandi = Spot Marcati
Spot Grandi = Spot Marcati
Streaks costanti lungo le Y
Streaks costanti lungo le Y
5. Scopo
FFT
1. Preelaborazione: Rimozione delle streaks
Minimum
1.a Massima Rimozione
1.b Minima perdita
Watershed
Geodesic
2. Spot Matching: individuazione degli spots
Anova CW
2.a Risoluzione dello spot overlapping
Snake
2.b Estrazione degli spots dalle streaks
2.c Massima sensibilità verso gli spots più piccoli
12. Riconoscimento degli Spot
Watershed
Diga
spots
Spot overlapping a sopraffazione
••Thresholdingai vari livelli
Thresholding ai vari livelli
••Se il livello superiore contiene
Se il livello superiore contiene
due insiemi viene eratta una diga
due insiemi viene eratta una diga
13. Segmentazione
Watershed delle streaks
Risolve lo spot overlapping
Non risolve lo spot overlapping a sopraffazione
Segmenta le streaks e descrive male i contorni
15. Geodesic
Risolve lo spot overlapping
Non risolve lo spot overlapping a sopraffazione
Sensitività
Descrive efficientemente i contorni estraendo gli
massima
spots dalle streaks
16. Riconoscimento degli Spot
Anova CW m
n∑ ( y. j + y.. ) 2 /( m − 1)
yij = µ + β j + eij F=
j =1
n m
H0 : β j ≠ 0 j = 1..m ∑∑ ( yij + y. j ) 2 /(n(m − 1))
i =1 j =1
Cartesiane
Polari
Ogni pixel
Ogni pixel Probabiltà che in (x,y) sia
Probabiltà che in (x,y) sia
(x,y)
(x,y) centrato uno spot
centrato uno spot
17. Riconoscimento degli Spot
Anova CW con Maschera adattiva
Come scegliere R?
Adattivo con il livello di grigio del centro (x,y)
Adattivo con il livello di grigio del centro (x,y)
R=50 log(0.31 I(x,y))
R=50 log(0.31 I(x,y))
19. Anova CW + watershed
Risolve lo spot overlapping
Risolve lo spot overlapping a sopraffazione
Descrive efficientemente i contorni
Tempi più lunghi
20. Confronto con altro software
Phoretix VComput
Migliore descrizione dei contorni
Migliore descrizione dei contorni
Nessuna perdita (99.6%)
Nessuna perdita (99.6%)
Maggior tempo di elaborazione
Maggior tempo di elaborazione
Melanie Gellab II +
22. Sviluppi Futuri
• Incremento velocità ottimizzando il codice
• Creazione di un software professionale
•Classificazione degli spots
• Comunicazione di nuovi spots mai classificato
• Classificazione del gel
• Ricerca di nuove configurazioni sfruttando
algoritmi genetici