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Author |
German Barquero; Sergio Escalera; Cristina Palmero |
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Title |
Seamless Human Motion Composition with Blended Positional Encodings |
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Miscellaneous |
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Year |
2024 |
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Arxiv |
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Conditional human motion generation is an important topic with many applications in virtual reality, gaming, and robotics. While prior works have focused on generating motion guided by text, music, or scenes, these typically result in isolated motions confined to short durations. Instead, we address the generation of long, continuous sequences guided by a series of varying textual descriptions. In this context, we introduce FlowMDM, the first diffusion-based model that generates seamless Human Motion Compositions (HMC) without any postprocessing or redundant denoising steps. For this, we introduce the Blended Positional Encodings, a technique that leverages both absolute and relative positional encodings in the denoising chain. More specifically, global motion coherence is recovered at the absolute stage, whereas smooth and realistic transitions are built at the relative stage. As a result, we achieve state-of-the-art results in terms of accuracy, realism, and smoothness on the Babel and HumanML3D datasets. FlowMDM excels when trained with only a single description per motion sequence thanks to its Pose-Centric Cross-ATtention, which makes it robust against varying text descriptions at inference time. Finally, to address the limitations of existing HMC metrics, we propose two new metrics: the Peak Jerk and the Area Under the Jerk, to detect abrupt transitions. |
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Admin @ si @ BEP2024 |
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4022 |
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Author |
Jorge Bernal |
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Title |
Polyp Localization and Segmentation in Colonoscopy Images by Means of a Model of Appearance for Polyps |
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2012 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Colorectal cancer is the fourth most common cause of cancer death worldwide and its survival rate depends on the stage in which it is detected on hence the necessity for an early colon screening. There are several screening techniques but colonoscopy is still nowadays the gold standard, although it has some drawbacks such as the miss rate. Our contribution, in the field of intelligent systems for colonoscopy, aims at providing a polyp localization and a polyp segmentation system based on a model of appearance for polyps. To develop both methods we define a model of appearance for polyps, which describes a polyp as enclosed by intensity valleys. The novelty of our contribution resides on the fact that we include in our model aspects of the image formation and we also consider the presence of other elements from the endoluminal scene such as specular highlights and blood vessels, which have an impact on the performance of our methods. In order to develop our polyp localization method we accumulate valley information in order to generate energy maps, which are also used to guide the polyp segmentation. Our methods achieve promising results in polyp localization and segmentation. As we want to explore the usability of our methods we present a comparative analysis between physicians fixations obtained via an eye tracking device and our polyp localization method. The results show that our method is indistinguishable to novice physicians although it is far from expert physicians. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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F. Javier Sanchez;Fernando Vilariño |
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800 |
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MV |
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Admin @ si @ Ber2012 |
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2211 |
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Author |
Jorge Bernal |
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Title |
Polyp Localization and Segmentation in Colonoscopy Images by Means of a Model of Appearance for Polyps |
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Journal Article |
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2014 |
Publication |
Electronic Letters on Computer Vision and Image Analysis |
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ELCVIA |
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13 |
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2 |
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9-10 |
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Keywords |
Colonoscopy; polyp localization; polyp segmentation; Eye-tracking |
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Abstract |
Colorectal cancer is the fourth most common cause of cancer death worldwide and its survival rate depends on the stage in which it is detected on hence the necessity for an early colon screening. There are several screening techniques but colonoscopy is still nowadays the gold standard, although it has some drawbacks such as the miss rate. Our contribution, in the field of intelligent systems for colonoscopy, aims at providing a polyp localization and a polyp segmentation system based on a model of appearance for polyps. To develop both methods we define a model of appearance for polyps, which describes a polyp as enclosed by intensity valleys. The novelty of our contribution resides on the fact that we include in our model aspects of the image formation and we also consider the presence of other elements from the endoluminal scene such as specular highlights and blood vessels, which have an impact on the performance of our methods. In order to develop our polyp localization method we accumulate valley information in order to generate energy maps, which are also used to guide the polyp segmentation. Our methods achieve promising results in polyp localization and segmentation. As we want to explore the usability of our methods we present a comparative analysis between physicians fixations obtained via an eye tracking device and our polyp localization method. The results show that our method is indistinguishable to novice physicians although it is far from expert physicians. |
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Alicia Fornes; Volkmar Frinken |
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MV |
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Admin @ si @ Ber2014 |
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2487 |
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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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Book Whole |
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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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July 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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978-84-948531-8-0 |
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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 |
Albert Berenguel |
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Title |
Analysis of background textures in banknotes and identity documents for counterfeit detection |
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Book Whole |
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Year |
2019 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Counterfeiting and piracy are a form of theft that has been steadily growing in recent years. A counterfeit is an unauthorized reproduction of an authentic/genuine object. Banknotes and identity documents are two common objects of counterfeiting. The former is used by organized criminal groups to finance a variety of illegal activities or even to destabilize entire countries due the inflation effect. Generally, in order to run their illicit businesses, counterfeiters establish companies and bank accounts using fraudulent identity documents. The illegal activities generated by counterfeit banknotes and identity documents has a damaging effect on business, the economy and the general population. To fight against counterfeiters, governments and authorities around the globe cooperate and develop security features to protect their security documents. Many of the security features in identity documents can also be found in banknotes. In this dissertation we focus our efforts in detecting the counterfeit banknotes and identity documents by analyzing the security features at the background printing. Background areas on secure documents contain fine-line patterns and designs that are difficult to reproduce without the manufacturers cutting-edge printing equipment. Our objective is to find the loose of resolution between the genuine security document and the printed counterfeit version with a publicly available commercial printer. We first present the most complete survey to date in identity and banknote security features. The compared algorithms and systems are based on computer vision and machine learning. Then we advance to present the banknote and identity counterfeit dataset we have built and use along all this thesis. Afterwards, we evaluate and adapt algorithms in the literature for the security background texture analysis. We study this problem from the point of view of robustness, computational efficiency and applicability into a real and non-controlled industrial scenario, proposing key insights to use these algorithms. Next, within the industrial environment of this thesis, we build a complete service oriented architecture to detect counterfeit documents. The mobile application and the server framework intends to be used even by non-expert document examiners to spot counterfeits. Later, we re-frame the problem of background texture counterfeit detection as a full-reference game of spotting the differences, by alternating glimpses between a counterfeit and a genuine background using recurrent neural networks. Finally, we deal with the lack of counterfeit samples, studying different approaches based on anomaly detection. |
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November 2019 |
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Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
Oriol Ramos Terrades;Josep Llados |
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978-84-121011-2-6 |
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DAG; 600.140; 600.121 |
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no |
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Admin @ si @ Ber2019 |
Serial |
3395 |
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Author |
Joakim Bruslund Haurum; Sergio Escalera; Graham W. Taylor; Thomas B. |
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Title |
Which Tokens to Use? Investigating Token Reduction in Vision Transformers |
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Conference Article |
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Year |
2023 |
Publication |
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops |
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Since the introduction of the Vision Transformer (ViT), researchers have sought to make ViTs more efficient by removing redundant information in the processed tokens. While different methods have been explored to achieve this goal, we still lack understanding of the resulting reduction patterns and how those patterns differ across token reduction methods and datasets. To close this gap, we set out to understand the reduction patterns of 10 different token reduction methods using four image classification datasets. By systematically comparing these methods on the different classification tasks, we find that the Top-K pruning method is a surprisingly strong baseline. Through in-depth analysis of the different methods, we determine that: the reduction patterns are generally not consistent when varying the capacity of the backbone model, the reduction patterns of pruning-based methods significantly differ from fixed radial patterns, and the reduction patterns of pruning-based methods are correlated across classification datasets. Finally we report that the similarity of reduction patterns is a moderate-to-strong proxy for model performance. Project page at https://vap.aau.dk/tokens. |
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Paris; France; October 2023 |
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ICCVW |
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HUPBA |
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Call Number |
Admin @ si @ BET2023 |
Serial |
3940 |
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Author |
Shida Beigpour; Joost Van de Weijer |
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Title |
Object Recoloring Based on Intrinsic Image Estimation |
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Conference Article |
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Year |
2011 |
Publication |
13th IEEE International Conference in Computer Vision |
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327 - 334 |
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Object recoloring is one of the most popular photo-editing tasks. The problem of object recoloring is highly under-constrained, and existing recoloring methods limit their application to objects lit by a white illuminant. Application of these methods to real-world scenes lit by colored illuminants, multiple illuminants, or interreflections, results in unrealistic recoloring of objects. In this paper, we focus on the recoloring of single-colored objects presegmented from their background. The single-color constraint allows us to fit a more comprehensive physical model to the object. We demonstrate that this permits us to perform realistic recoloring of objects lit by non-white illuminants, and multiple illuminants. Moreover, the model allows for more realistic handling of illuminant alteration of the scene. Recoloring results captured by uncalibrated cameras demonstrate that the proposed framework obtains realistic recoloring for complex natural images. Furthermore we use the model to transfer color between objects and show that the results are more realistic than existing color transfer methods. |
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Barcelona |
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1550-5499 |
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978-1-4577-1101-5 |
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ICCV |
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CIC |
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Admin @ si @ BeW2011 |
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1781 |
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Author |
Arnau Baro; Alicia Fornes; Carles Badal |
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Title |
Handwritten Historical Music Recognition by Sequence-to-Sequence with Attention Mechanism |
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Conference Article |
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2020 |
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17th International Conference on Frontiers in Handwriting Recognition |
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Despite decades of research in Optical Music Recognition (OMR), the recognition of old handwritten music scores remains a challenge because of the variabilities in the handwriting styles, paper degradation, lack of standard notation, etc. Therefore, the research in OMR systems adapted to the particularities of old manuscripts is crucial to accelerate the conversion of music scores existing in archives into digital libraries, fostering the dissemination and preservation of our music heritage. In this paper we explore the adaptation of sequence-to-sequence models with attention mechanism (used in translation and handwritten text recognition) and the generation of specific synthetic data for recognizing old music scores. The experimental validation demonstrates that our approach is promising, especially when compared with long short-term memory neural networks. |
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Virtual ICFHR; September 2020 |
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ICFHR |
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DAG; 600.140; 600.121 |
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Admin @ si @ BFB2020 |
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3448 |
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Author |
Asma Bensalah; Alicia Fornes; Cristina Carmona_Duarte; Josep Llados |
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Title |
Easing Automatic Neurorehabilitation via Classification and Smoothness Analysis |
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Conference Article |
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2022 |
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Intertwining Graphonomics with Human Movements. 20th International Conference of the International Graphonomics Society, IGS 2022 |
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13424 |
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336-348 |
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Neurorehabilitation; Upper-lim; Movement classification; Movement smoothness; Deep learning; Jerk |
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Assessing the quality of movements for post-stroke patients during the rehabilitation phase is vital given that there is no standard stroke rehabilitation plan for all the patients. In fact, it depends basically on the patient’s functional independence and its progress along the rehabilitation sessions. To tackle this challenge and make neurorehabilitation more agile, we propose an automatic assessment pipeline that starts by recognising patients’ movements by means of a shallow deep learning architecture, then measuring the movement quality using jerk measure and related measures. A particularity of this work is that the dataset used is clinically relevant, since it represents movements inspired from Fugl-Meyer a well common upper-limb clinical stroke assessment scale for stroke patients. We show that it is possible to detect the contrast between healthy and patients movements in terms of smoothness, besides achieving conclusions about the patients’ progress during the rehabilitation sessions that correspond to the clinicians’ findings about each case. |
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June 7-9, 2022, Las Palmas de Gran Canaria, Spain |
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LNCS |
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IGS |
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DAG; 600.121; 600.162; 602.230; 600.140 |
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Admin @ si @ BFC2022 |
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3738 |
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Author |
David Berga; Xose R. Fernandez-Vidal; Xavier Otazu; V. Leboran; Xose M. Pardo |
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Title |
Psychophysical evaluation of individual low-level feature influences on visual attention |
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Journal Article |
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2019 |
Publication |
Vision Research |
Abbreviated Journal |
VR |
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154 |
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60-79 |
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Visual attention; Psychophysics; Saliency; Task; Context; Contrast; Center bias; Low-level; Synthetic; Dataset |
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In this study we provide the analysis of eye movement behavior elicited by low-level feature distinctiveness with a dataset of synthetically-generated image patterns. Design of visual stimuli was inspired by the ones used in previous psychophysical experiments, namely in free-viewing and visual searching tasks, to provide a total of 15 types of stimuli, divided according to the task and feature to be analyzed. Our interest is to analyze the influences of low-level feature contrast between a salient region and the rest of distractors, providing fixation localization characteristics and reaction time of landing inside the salient region. Eye-tracking data was collected from 34 participants during the viewing of a 230 images dataset. Results show 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. This experimentation proposes a new psychophysical basis for saliency model evaluation using synthetic images. |
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NEUROBIT; 600.128; 600.120 |
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Admin @ si @ BFO2019a |
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3274 |
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Author |
David Berga; Xose R. Fernandez-Vidal; Xavier Otazu; Xose M. Pardo |
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Title |
SID4VAM: A Benchmark Dataset with Synthetic Images for Visual Attention Modeling |
Type |
Conference Article |
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Year |
2019 |
Publication |
18th IEEE International Conference on Computer Vision |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
8788-8797 |
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Abstract |
A benchmark of saliency models performance with a synthetic image dataset is provided. Model performance is evaluated through saliency metrics as well as the influence of model inspiration and consistency with human psychophysics. SID4VAM is composed of 230 synthetic images, with known salient regions. Images were generated with 15 distinct types of low-level features (e.g. orientation, brightness, color, size...) with a target-distractor popout type of synthetic patterns. We have used Free-Viewing and Visual Search task instructions and 7 feature contrasts for each feature category. Our study reveals that state-ofthe-art Deep Learning saliency models do not perform well with synthetic pattern images, instead, models with Spectral/Fourier inspiration outperform others in saliency metrics and are more consistent with human psychophysical experimentation. This study proposes a new way to evaluate saliency models in the forthcoming literature, accounting for synthetic images with uniquely low-level feature contexts, distinct from previous eye tracking image datasets. |
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Address |
Seul; Corea; October 2019 |
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ICCV |
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Notes |
NEUROBIT; 600.128 |
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no |
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Call Number |
Admin @ si @ BFO2019b |
Serial |
3372 |
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Author |
David Berga; Xose R. Fernandez-Vidal; Xavier Otazu; Victor Leboran; Xose M. Pardo |
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Title |
Measuring bottom-up visual attention in eye tracking experimentation with synthetic images |
Type |
Conference Article |
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Year |
2019 |
Publication |
8th Iberian Conference on Perception |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
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Abstract |
A benchmark of saliency models performance with a synthetic image dataset is provided. Model performance is evaluated through saliency metrics as well as the influence of model inspiration and consistency with human psychophysics. SID4VAM is composed of 230 synthetic images, with known salient regions. Images were generated with 15 distinct types of low-level features (e.g. orientation, brightness, color, size...) with a target-distractor pop-out type of synthetic patterns. We have used Free-Viewing and Visual Search task instructions and 7 feature contrasts for each feature category. Our study reveals that state-of-the-art Deep Learning saliency models do not perform well with synthetic pattern images, instead, models with Spectral/Fourier inspiration outperform others in saliency metrics and are more consistent with human psychophysical experimentation. This study proposes a new way to evaluate saliency models in the forthcoming literature, accounting for synthetic images with uniquely low-level feature contexts, distinct from previous eye tracking image datasets. |
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Address |
San Lorenzo El Escorial; July 2019 |
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CIP |
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Notes |
NEUROBIT; 600.128 |
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no |
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Call Number |
Admin @ si @ BFO2019c |
Serial |
3375 |
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Author |
Pau Baiget; Carles Fernandez; Xavier Roca; Jordi Gonzalez |
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Title |
Trajectory-Based Abnormality Categorization for Learning Route Patterns in Surveillance |
Type |
Book Chapter |
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Year |
2012 |
Publication |
Detection and Identification of Rare Audiovisual Cues, Studies in Computational Intelligence |
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Volume |
384 |
Issue |
3 |
Pages |
87-95 |
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Abstract |
The recognition of abnormal behaviors in video sequences has raised as a hot topic in video understanding research. Particularly, an important challenge resides on automatically detecting abnormality. However, there is no convention about the types of anomalies that training data should derive. In surveillance, these are typically detected when new observations differ substantially from observed, previously learned behavior models, which represent normality. This paper focuses on properly defining anomalies within trajectory analysis: we propose a hierarchical representation conformed by Soft, Intermediate, and Hard Anomaly, which are identified from the extent and nature of deviation from learned models. Towards this end, a novel Gaussian Mixture Model representation of learned route patterns creates a probabilistic map of the image plane, which is applied to detect and classify anomalies in real-time. Our method overcomes limitations of similar existing approaches, and performs correctly even when the tracking is affected by different sources of noise. The reliability of our approach is demonstrated experimentally. |
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Springer Berlin Heidelberg |
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1860-949X |
ISBN |
978-3-642-24033-1 |
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Notes |
ISE |
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no |
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Call Number |
Admin @ si @ BFR2012 |
Serial |
2062 |
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Author |
Simone Balocco; Mauricio Gonzalez; Ricardo Ñancule; Petia Radeva; Gabriel Thomas |
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Title |
Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets |
Type |
Conference Article |
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Year |
2018 |
Publication |
International Workshop on Artificial Intelligence and Pattern Recognition |
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Volume |
11047 |
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Pages |
34-42 |
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Keywords |
Intravascular ultrasound images; Convolutional nets; Deep learning; Medical image analysis |
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Abstract |
The manual inspection of intravascular ultrasound (IVUS) images to detect clinically relevant patterns is a difficult and laborious task performed routinely by physicians. In this paper, we present a framework based on convolutional nets for the quick selection of IVUS frames containing arterial calcification, a pattern whose detection plays a vital role in the diagnosis of atherosclerosis. Preliminary experiments on a dataset acquired from eighty patients show that convolutional architectures improve detections of a shallow classifier in terms of 𝐹1-measure, precision and recall. |
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Address |
Cuba; September 2018 |
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IWAIPR |
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Notes |
MILAB; no menciona |
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no |
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Call Number |
Admin @ si @ BGÑ2018 |
Serial |
3237 |
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Author |
Francesco Brughi; Debora Gil; Llorenç Badiella; Eva Jove Casabella; Oriol Ramos Terrades |
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Title |
Exploring the impact of inter-query variability on the performance of retrieval systems |
Type |
Conference Article |
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Year |
2014 |
Publication |
11th International Conference on Image Analysis and Recognition |
Abbreviated Journal |
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Volume |
8814 |
Issue |
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Pages |
413–420 |
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Abstract |
This paper introduces a framework for evaluating the performance of information retrieval systems. Current evaluation metrics provide an average score that does not consider performance variability across the query set. In this manner, conclusions lack of any statistical significance, yielding poor inference to cases outside the query set and possibly unfair comparisons. We propose to apply statistical methods in order to obtain a more informative measure for problems in which different query classes can be identified. In this context, we assess the performance variability on two levels: overall variability across the whole query set and specific query class-related variability. To this end, we estimate confidence bands for precision-recall curves, and we apply ANOVA in order to assess the significance of the performance across different query classes. |
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Address |
Algarve; Portugal; October 2014 |
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Springer International Publishing |
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0302-9743 |
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978-3-319-11757-7 |
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ICIAR |
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Notes |
IAM; DAG; 600.060; 600.061; 600.077; 600.075 |
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Call Number |
Admin @ si @ BGB2014 |
Serial |
2559 |
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