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Author |
Xim Cerda-Company |
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Title |
Understanding color vision: from psychophysics to computational modeling |
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Year |
2019 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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In this PhD we have approached the human color vision from two different points of view: psychophysics and computational modeling. First, we have evaluated 15 different tone-mapping operators (TMOs). We have conducted two experiments that
consider two different criteria: the first one evaluates the local relationships among intensity levels and the second one evaluates the global appearance of the tonemapped imagesw.r.t. the physical one (presented side by side). We conclude that the rankings depend on the criterion and they are not correlated. Considering both criteria, the best TMOs are KimKautz (Kim and Kautz, 2008) and Krawczyk (Krawczyk, Myszkowski, and Seidel, 2005). Another conclusion is that a more standardized evaluation criteria is needed to do a fair comparison among TMOs.
Secondly, we have conducted several psychophysical experiments to study the
color induction. We have studied two different properties of the visual stimuli: temporal frequency and luminance spatial distribution. To study the temporal frequency we defined equiluminant stimuli composed by both uniform and striped surrounds and we flashed them varying the flash duration. For uniform surrounds, the results show that color induction depends on both the flash duration and inducer’s chromaticity. As expected, in all chromatic conditions color contrast was induced. In contrast, for striped surrounds, we expected to induce color assimilation, but we observed color contrast or no induction. Since similar but not equiluminant striped stimuli induce color assimilation, we concluded that luminance differences could be a key factor to induce color assimilation. Thus, in a subsequent study, we have studied the luminance differences’ effect on color assimilation. We varied the luminance difference between the target region and its inducers and we observed that color assimilation depends on both this difference and the inducer’s chromaticity. For red-green condition (where the first inducer is red and the second one is green), color assimilation occurs in almost all luminance conditions.
Instead, for green-red condition, color assimilation never occurs. Purple-lime
and lime-purple chromatic conditions show that luminance difference is a key factor to induce color assimilation. When the target is darker than its surround, color assimilation is stronger in purple-lime, while when the target is brighter, color assimilation is stronger in lime-purple (’mirroring’ effect). Moreover, we evaluated whether color assimilation is due to luminance or brightness differences. Similarly to equiluminance condition, when the stimuli are equibrightness no color assimilation is induced. Our results support the hypothesis that mutual-inhibition plays a major role in color perception, or at least in color induction.
Finally, we have defined a new firing rate model of color processing in the V1
parvocellular pathway. We have modeled two different layers of this cortical area: layers 4Cb and 2/3. Our model is a recurrent dynamic computational model that considers both excitatory and inhibitory cells and their lateral connections. Moreover, it considers the existent laminar differences and the cells’ variety. Thus, we have modeled both single- and double-opponent simple cells and complex cells, which are a pool of double-opponent simple cells. A set of sinusoidal drifting gratings have been used to test the architecture. In these gratings we have varied several spatial properties such as temporal and spatial frequencies, grating’s area and orientation. To reproduce the electrophysiological observations, the architecture has to consider the existence of non-oriented double-opponent cells in layer 4Cb and the lack of lateral connections between single-opponent cells. Moreover, we have tested our lateral connections simulating the center-surround modulation and we have reproduced physiological measurements where for high contrast stimulus, the
result of the lateral connections is inhibitory, while it is facilitatory for low contrast stimulus. |
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March 2019 |
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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor  |
Xavier Otazu |
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978-84-948531-4-2 |
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NEUROBIT |
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Call Number |
Admin @ si @ Cer2019 |
Serial |
3259 |
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Author |
David Berga |
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Title |
Understanding Eye Movements: Psychophysics and a Model of Primary Visual Cortex |
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Year |
2019 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Humansmove their eyes in order to learn visual representations of the world. These eye movements depend on distinct factors, either by the scene that we perceive or by our own decisions. To select what is relevant to attend is part of our survival mechanisms and the way we build reality, as we constantly react both consciously and unconsciously to all the stimuli that is projected into our eyes. In this thesis we try to explain (1) how we move our eyes, (2) how to build machines that understand visual information and deploy eyemovements, and (3) how to make these machines understand tasks in order to decide for eye movements.
(1) We provided the analysis of eye movement behavior elicited by low-level feature distinctiveness with a dataset of 230 synthetically-generated image patterns. A total of 15 types of stimuli has been generated (e.g. orientation, brightness, color, size, etc.), with 7 feature contrasts for each feature category. Eye-tracking data was collected from 34 participants during the viewing of the dataset, using Free-Viewing and Visual Search task instructions. Results showed that saliency is predominantly and distinctively influenced by: 1. feature type, 2. feature contrast, 3. Temporality of fixations, 4. task difficulty and 5. center bias. From such dataset (SID4VAM), we have computed a benchmark of saliency models by testing performance using psychophysical patterns. Model performance has been evaluated considering model inspiration and consistency with human psychophysics. Our study reveals that state-of-the-art Deep Learning saliency models do not performwell with synthetic pattern images, instead, modelswith Spectral/Fourier inspiration outperform others in saliency metrics and are more consistent with human psychophysical experimentation.
(2) Computations in the primary visual cortex (area V1 or striate cortex) have long been hypothesized to be responsible, among several visual processing mechanisms, of bottom-up visual attention (also named saliency). In order to validate this hypothesis, images from eye tracking datasets have been processed with a biologically plausible model of V1 (named Neurodynamic SaliencyWaveletModel or NSWAM). Following Li’s neurodynamic model, we define V1’s lateral connections with a network of firing rate neurons, sensitive to visual features such as brightness, color, orientation and scale. Early subcortical processes (i.e. retinal and thalamic) are functionally simulated. The resulting saliency maps are generated from the model output, representing the neuronal activity of V1 projections towards brain areas involved in eye movement control. We want to pinpoint that our unified computational architecture is able to reproduce several visual processes (i.e. brightness, chromatic induction and visual discomfort) without applying any type of training or optimization and keeping the same parametrization. The model has been extended (NSWAM-CM) with an implementation of the cortical magnification function to define the retinotopical projections towards V1, processing neuronal activity for each distinct view during scene observation. Novel computational definitions of top-down inhibition (in terms of inhibition of return and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search conditions. Results show that our model outperforms other biologically-inpired models of saliency prediction as well as to predict visual saccade sequences, specifically for nature and synthetic images. We also show how temporal and spatial characteristics of inhibition of return can improve prediction of saccades, as well as how distinct search strategies (in terms of feature-selective or category-specific inhibition) predict attention at distinct image contexts.
(3) Although previous scanpath models have been able to efficiently predict saccades during Free-Viewing, it is well known that stimulus and task instructions can strongly affect eye movement patterns. In particular, task priming has been shown to be crucial to the deployment of eye movements, involving interactions between brain areas related to goal-directed behavior, working and long-termmemory in combination with stimulus-driven eyemovement neuronal correlates. In our latest study we proposed an extension of the Selective Tuning Attentive Reference Fixation ControllerModel based on task demands (STAR-FCT), describing novel computational definitions of Long-TermMemory, Visual Task Executive and Task Working Memory. With these modules we are able to use textual instructions in order to guide the model to attend to specific categories of objects and/or places in the scene. We have designed our memorymodel by processing a visual hierarchy of low- and high-level features. The relationship between the executive task instructions and the memory representations has been specified using a tree of semantic similarities between the learned features and the object category labels. Results reveal that by using this model, the resulting object localizationmaps and predicted saccades have a higher probability to fall inside the salient regions depending on the distinct task instructions compared to saliency. |
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Address |
July 2019 |
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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
Place of Publication |
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Editor |
Xavier Otazu |
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978-84-948531-8-0 |
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NEUROBIT |
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no |
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Call Number |
Admin @ si @ Ber2019 |
Serial |
3390 |
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Author |
Naila Murray |
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Title |
Predicting Saliency and Aesthetics in Images: A Bottom-up Perspective |
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Year |
2012 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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In Part 1 of the thesis, we hypothesize that salient and non-salient image regions can be estimated to be the regions which are enhanced or assimilated in standard low-level color image representations. We prove this hypothesis by adapting a low-level model of color perception into a saliency estimation model. This model shares the three main steps found in many successful models for predicting attention in a scene: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. For such models, integrating spatial information and justifying the choice of various parameter values remain open problems. Our saliency model inherits a principled selection of parameters as well as an innate spatial pooling mechanism from the perception model on which it is based. This pooling mechanism has been fitted using psychophysical data acquired in color-luminance setting experiments. The proposed model outperforms the state-of-the-art at the task of predicting eye-fixations from two datasets. After demonstrating the effectiveness of our basic saliency model, we introduce an improved image representation, based on geometrical grouplets, that enhances complex low-level visual features such as corners and terminations, and suppresses relatively simpler features such as edges. With this improved image representation, the performance of our saliency model in predicting eye-fixations increases for both datasets.
In Part 2 of the thesis, we investigate the problem of aesthetic visual analysis. While a great deal of research has been conducted on hand-crafting image descriptors for aesthetics, little attention so far has been dedicated to the collection, annotation and distribution of ground truth data. Because image aesthetics is complex and subjective, existing datasets, which have few images and few annotations, have significant limitations. To address these limitations, we have introduced a new large-scale database for conducting Aesthetic Visual Analysis, which we call AVA. AVA contains more than 250,000 images, along with a rich variety of annotations. We investigate how the wealth of data in AVA can be used to tackle the challenge of understanding and assessing visual aesthetics by looking into several problems relevant for aesthetic analysis. We demonstrate that by leveraging the data in AVA, and using generic low-level features such as SIFT and color histograms, we can exceed state-of-the-art performance in aesthetic quality prediction tasks.
Finally, we entertain the hypothesis that low-level visual information in our saliency model can also be used to predict visual aesthetics by capturing local image characteristics such as feature contrast, grouping and isolation, characteristics thought to be related to universal aesthetic laws. We use the weighted center-surround responses that form the basis of our saliency model to create a feature vector that describes aesthetics. We also introduce a novel color space for fine-grained color representation. We then demonstrate that the resultant features achieve state-of-the-art performance on aesthetic quality classification.
As such, a promising contribution of this thesis is to show that several vision experiences – low-level color perception, visual saliency and visual aesthetics estimation – may be successfully modeled using a unified framework. This suggests a similar architecture in area V1 for both color perception and saliency and adds evidence to the hypothesis that visual aesthetics appreciation is driven in part by low-level cues. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Xavier Otazu;Maria Vanrell |
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CIC |
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Admin @ si @ Mur2012 |
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2212 |
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Author |
Jordi Gonzalez |
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Title |
Human Sequence Evaluation: the Key-frame Approach |
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2004 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Ph.D. thesis |
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Editor |
Xavier Roca;Javier Varona |
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ISE @ ise @ Gon2004 |
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362 |
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