Abel Gonzalez-Garcia, Robert Benavente, Olivier Penacchio, Javier Vazquez, Maria Vanrell, & C. Alejandro Parraga. (2013). Coloresia: An Interactive Colour Perception Device for the Visually Impaired. In Multimodal Interaction in Image and Video Applications (Vol. 48, pp. 47–66). Springer Berlin Heidelberg.
Abstract: A significative percentage of the human population suffer from impairments in their capacity to distinguish or even see colours. For them, everyday tasks like navigating through a train or metro network map becomes demanding. We present a novel technique for extracting colour information from everyday natural stimuli and presenting it to visually impaired users as pleasant, non-invasive sound. This technique was implemented inside a Personal Digital Assistant (PDA) portable device. In this implementation, colour information is extracted from the input image and categorised according to how human observers segment the colour space. This information is subsequently converted into sound and sent to the user via speakers or headphones. In the original implementation, it is possible for the user to send its feedback to reconfigure the system, however several features such as these were not implemented because the current technology is limited.We are confident that the full implementation will be possible in the near future as PDA technology improves.
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Shida Beigpour. (2013). Illumination and object reflectance modeling (Joost Van de Weijer, & Ernest Valveny, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: More realistic and accurate models of the scene illumination and object reflectance can greatly improve the quality of many computer vision and computer graphics tasks. Using such model, a more profound knowledge about the interaction of light with object surfaces can be established which proves crucial to a variety of computer vision applications. In the current work, we investigate the various existing approaches to illumination and reflectance modeling and form an analysis on their shortcomings in capturing the complexity of real-world scenes. Based on this analysis we propose improvements to different aspects of reflectance and illumination estimation in order to more realistically model the real-world scenes in the presence of complex lighting phenomena (i.e, multiple illuminants, interreflections and shadows). Moreover, we captured our own multi-illuminant dataset which consists of complex scenes and illumination conditions both outdoor and in laboratory conditions. In addition we investigate the use of synthetic data to facilitate the construction of datasets and improve the process of obtaining ground-truth information.
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Joost Van de Weijer, & Fahad Shahbaz Khan. (2013). Fusing Color and Shape for Bag-of-Words Based Object Recognition. In 4th Computational Color Imaging Workshop (Vol. 7786, pp. 25–34). Springer Berlin Heidelberg.
Abstract: In this article we provide an analysis of existing methods for the incorporation of color in bag-of-words based image representations. We propose a list of desired properties on which bases fusing methods can be compared. We discuss existing methods and indicate shortcomings of the two well-known fusing methods, namely early and late fusion. Several recent works have addressed these shortcomings by exploiting top-down information in the bag-of-words pipeline: color attention which is motivated from human vision, and Portmanteau vocabularies which are based on information theoretic compression of product vocabularies. We point out several remaining challenges in cue fusion and provide directions for future research.
Keywords: Object Recognition; color features; bag-of-words; image classification
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Joost Van de Weijer, Fahad Shahbaz Khan, & Marc Masana. (2013). Interactive Visual and Semantic Image Retrieval. In Angel Sappa, & Jordi Vitria (Eds.), Multimodal Interaction in Image and Video Applications (Vol. 48, pp. 31–35). Springer Berlin Heidelberg.
Abstract: One direct consequence of recent advances in digital visual data generation and the direct availability of this information through the World-Wide Web, is a urgent demand for efficient image retrieval systems. The objective of image retrieval is to allow users to efficiently browse through this abundance of images. Due to the non-expert nature of the majority of the internet users, such systems should be user friendly, and therefore avoid complex user interfaces. In this chapter we investigate how high-level information provided by recently developed object recognition techniques can improve interactive image retrieval. Wel apply a bagof- word based image representation method to automatically classify images in a number of categories. These additional labels are then applied to improve the image retrieval system. Next to these high-level semantic labels, we also apply a low-level image description to describe the composition and color scheme of the scene. Both descriptions are incorporated in a user feedback image retrieval setting. The main objective is to show that automatic labeling of images with semantic labels can improve image retrieval results.
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Fahad Shahbaz Khan, Muhammad Anwer Rao, Joost Van de Weijer, Andrew Bagdanov, Antonio Lopez, & Michael Felsberg. (2013). Coloring Action Recognition in Still Images. IJCV - International Journal of Computer Vision, 105(3), 205–221.
Abstract: In this article we investigate the problem of human action recognition in static images. By action recognition we intend a class of problems which includes both action classification and action detection (i.e. simultaneous localization and classification). Bag-of-words image representations yield promising results for action classification, and deformable part models perform very well object detection. The representations for action recognition typically use only shape cues and ignore color information. Inspired by the recent success of color in image classification and object detection, we investigate the potential of color for action classification and detection in static images. We perform a comprehensive evaluation of color descriptors and fusion approaches for action recognition. Experiments were conducted on the three datasets most used for benchmarking action recognition in still images: Willow, PASCAL VOC 2010 and Stanford-40. Our experiments demonstrate that incorporating color information considerably improves recognition performance, and that a descriptor based on color names outperforms pure color descriptors. Our experiments demonstrate that late fusion of color and shape information outperforms other approaches on action recognition. Finally, we show that the different color–shape fusion approaches result in complementary information and combining them yields state-of-the-art performance for action classification.
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Jordi Roca, C. Alejandro Parraga, & Maria Vanrell. (2013). Chromatic settings and the structural color constancy index. JV - Journal of Vision, 13(4-3), 1–26.
Abstract: Color constancy is usually measured by achromatic setting, asymmetric matching, or color naming paradigms, whose results are interpreted in terms of indexes and models that arguably do not capture the full complexity of the phenomenon. Here we propose a new paradigm, chromatic setting, which allows a more comprehensive characterization of color constancy through the measurement of multiple points in color space under immersive adaptation. We demonstrated its feasibility by assessing the consistency of subjects' responses over time. The paradigm was applied to two-dimensional (2-D) Mondrian stimuli under three different illuminants, and the results were used to fit a set of linear color constancy models. The use of multiple colors improved the precision of more complex linear models compared to the popular diagonal model computed from gray. Our results show that a diagonal plus translation matrix that models mechanisms other than cone gain might be best suited to explain the phenomenon. Additionally, we calculated a number of color constancy indices for several points in color space, and our results suggest that interrelations among colors are not as uniform as previously believed. To account for this variability, we developed a new structural color constancy index that takes into account the magnitude and orientation of the chromatic shift in addition to the interrelations among colors and memory effects.
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Naila Murray, Maria Vanrell, Xavier Otazu, & C. Alejandro Parraga. (2013). Low-level SpatioChromatic Grouping for Saliency Estimation. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(11), 2810–2816.
Abstract: We propose a saliency model termed SIM (saliency by induction mechanisms), which is based on a low-level spatiochromatic model that has successfully predicted chromatic induction phenomena. In so doing, we hypothesize that the low-level visual mechanisms that enhance or suppress image detail are also responsible for making some image regions more salient. Moreover, SIM adds geometrical grouplets to enhance complex low-level features such as corners, and suppress relatively simpler features such as edges. Since our model has been fitted on psychophysical chromatic induction data, it is largely nonparametric. SIM outperforms state-of-the-art methods in predicting eye fixations on two datasets and using two metrics.
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Albert Gordo. (2009). A Cyclic Page Layout Descriptor for Document Classification & Retrieval (Vol. 128). Master's thesis, , Bellaterra, Barcelona.
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Naila Murray. (2009). Perceptual Feature Detection (Vol. 131). Master's thesis, , Bellaterra, Barcelona.
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David Augusto Rojas. (2009). Colouring Local Feature Detection for Matching (Vol. 133). Master's thesis, , Bellaterra, Barcelona.
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Olivier Penacchio. (2009). Relative Density of L, M, S photoreceptors in the Human Retina (Vol. 135). Master's thesis, , Bellaterra, Barcelona.
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Xavier Boix. (2009). Learning Conditional Random Fields for Stereo (Vol. 136). Master's thesis, , Bellaterra, Barcelona.
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Shida Beigpour. (2009). Physics-based Reflectance Estimation Applied to Recoloring (Vol. 137). Master's thesis, , Bellaterra, Barcelona.
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Jose Carlos Rubio. (2009). Graph matching based on graphical models with application to vehicle tracking and classification at night (Vol. 144). Master's thesis, , Bellaterra, Barcelona.
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Ivet Rafegas. (2013). Exploring Low-Level Vision Models. Case Study: Saliency Prediction (Vol. 175). Master's thesis, , .
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Fahad Shahbaz Khan, Joost Van de Weijer, Andrew Bagdanov, & Michael Felsberg. (2014). Scale Coding Bag-of-Words for Action Recognition. In 22nd International Conference on Pattern Recognition (pp. 1514–1519).
Abstract: Recognizing human actions in still images is a challenging problem in computer vision due to significant amount of scale, illumination and pose variation. Given the bounding box of a person both at training and test time, the task is to classify the action associated with each bounding box in an image.
Most state-of-the-art methods use the bag-of-words paradigm for action recognition. The bag-of-words framework employing a dense multi-scale grid sampling strategy is the de facto standard for feature detection. This results in a scale invariant image representation where all the features at multiple-scales are binned in a single histogram. We argue that such a scale invariant
strategy is sub-optimal since it ignores the multi-scale information
available with each bounding box of a person.
This paper investigates alternative approaches to scale coding for action recognition in still images. We encode multi-scale information explicitly in three different histograms for small, medium and large scale visual-words. Our first approach exploits multi-scale information with respect to the image size. In our second approach, we encode multi-scale information relative to the size of the bounding box of a person instance. In each approach, the multi-scale histograms are then concatenated into a single representation for action classification. We validate our approaches on the Willow dataset which contains seven action categories: interacting with computer, photography, playing music,
riding bike, riding horse, running and walking. Our results clearly suggest that the proposed scale coding approaches outperform the conventional scale invariant technique. Moreover, we show that our approach obtains promising results compared to more complex state-of-the-art methods.
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Shida Beigpour, Christian Riess, Joost Van de Weijer, & Elli Angelopoulou. (2014). Multi-Illuminant Estimation with Conditional Random Fields. TIP - IEEE Transactions on Image Processing, 23(1), 83–95.
Abstract: Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach.
Keywords: color constancy; CRF; multi-illuminant
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Carlo Gatta, Adriana Romero, & Joost Van de Weijer. (2014). Unrolling loopy top-down semantic feedback in convolutional deep networks. In Workshop on Deep Vision: Deep Learning for Computer Vision (pp. 498–505).
Abstract: In this paper, we propose a novel way to perform top-down semantic feedback in convolutional deep networks for efficient and accurate image parsing. We also show how to add global appearance/semantic features, which have shown to improve image parsing performance in state-of-the-art methods, and was not present in previous convolutional approaches. The proposed method is characterised by an efficient training and a sufficiently fast testing. We use the well known SIFTflow dataset to numerically show the advantages provided by our contributions, and to compare with state-of-the-art image parsing convolutional based approaches.
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