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Aura Hernandez-Sabate, Debora Gil, David Roche, Monica M. S. Matsumoto, & Sergio S. Furuie. (2011). Inferring the Performance of Medical Imaging Algorithms. In Pedro Real, Daniel Diaz-Pernil, Helena Molina-Abril, Ainhoa Berciano, & Walter Kropatsch (Eds.), 14th International Conference on Computer Analysis of Images and Patterns (Vol. 6854, pp. 520–528). LNCS. Berlin: Springer-Verlag Berlin Heidelberg.
Abstract: Evaluation of the performance and limitations of medical imaging algorithms is essential to estimate their impact in social, economic or clinical aspects. However, validation of medical imaging techniques is a challenging task due to the variety of imaging and clinical problems involved, as well as, the difficulties for systematically extracting a reliable solely ground truth. Although specific validation protocols are reported in any medical imaging paper, there are still two major concerns: definition of standardized methodologies transversal to all problems and generalization of conclusions to the whole clinical data set.
We claim that both issues would be fully solved if we had a statistical model relating ground truth and the output of computational imaging techniques. Such a statistical model could conclude to what extent the algorithm behaves like the ground truth from the analysis of a sampling of the validation data set. We present a statistical inference framework reporting the agreement and describing the relationship of two quantities. We show its transversality by applying it to validation of two different tasks: contour segmentation and landmark correspondence.
Keywords: Validation, Statistical Inference, Medical Imaging Algorithms.
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Naveen Onkarappa, & Angel Sappa. (2011). Space Variant Representations for Mobile Platform Vision Applications. In W. Kropatsch A. Berciano H. Molina D. D. P. Real (Ed.), 14th International Conference on Computer Analysis of Images and Patterns (Vol. 6855, pp. 146–154). Springer Berlin Heidelberg.
Abstract: The log-polar space variant representation, motivated by biological vision, has been widely studied in the literature. Its data reduction and invariance properties made it useful in many vision applications. However, due to its nature, it fails in preserving features in the periphery. In the current work, as an attempt to overcome this problem, we propose a novel space-variant representation. It is evaluated and proved to be better than the log-polar representation in preserving the peripheral information, crucial for on-board mobile vision applications. The evaluation is performed by comparing log-polar and the proposed representation once they are used for estimating dense optical flow.
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Fahad Shahbaz Khan, Joost Van de Weijer, Sadiq Ali, & Michael Felsberg. (2013). Evaluating the impact of color on texture recognition. In 15th International Conference on Computer Analysis of Images and Patterns (Vol. 8047, pp. 154–162). Springer Berlin Heidelberg.
Abstract: State-of-the-art texture descriptors typically operate on grey scale images while ignoring color information. A common way to obtain a joint color-texture representation is to combine the two visual cues at the pixel level. However, such an approach provides sub-optimal results for texture categorisation task.
In this paper we investigate how to optimally exploit color information for texture recognition. We evaluate a variety of color descriptors, popular in image classification, for texture categorisation. In addition we analyze different fusion approaches to combine color and texture cues. Experiments are conducted on the challenging scenes and 10 class texture datasets. Our experiments clearly suggest that in all cases color names provide the best performance. Late fusion is the best strategy to combine color and texture. By selecting the best color descriptor with optimal fusion strategy provides a gain of 5% to 8% compared to texture alone on scenes and texture datasets.
Keywords: Color; Texture; image representation
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Naveen Onkarappa, & Angel Sappa. (2013). Laplacian Derivative based Regularization for Optical Flow Estimation in Driving Scenario. In 15th International Conference on Computer Analysis of Images and Patterns (Vol. 8048, pp. 483–490). LNCS. Springer Berlin Heidelberg.
Abstract: Existing state of the art optical flow approaches, which are evaluated on standard datasets such as Middlebury, not necessarily have a similar performance when evaluated on driving scenarios. This drop on performance is due to several challenges arising on real scenarios during driving. Towards this direction, in this paper, we propose a modification to the regularization term in a variational optical flow formulation, that notably improves the results, specially in driving scenarios. The proposed modification consists on using the Laplacian derivatives of flow components in the regularization term instead of gradients of flow components. We show the improvements in results on a standard real image sequences dataset (KITTI).
Keywords: Optical flow; regularization; Driver Assistance Systems; Performance Evaluation
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Marcelo D. Pistarelli, Angel Sappa, & Ricardo Toledo. (2013). Multispectral Stereo Image Correspondence. In 15th International Conference on Computer Analysis of Images and Patterns (Vol. 8048, pp. 217–224). LNCS. Springer Berlin Heidelberg.
Abstract: This paper presents a novel multispectral stereo image correspondence approach. It is evaluated using a stereo rig constructed with a visible spectrum camera and a long wave infrared spectrum camera. The novelty of the proposed approach lies on the usage of Hough space as a correspondence search domain. In this way it avoids searching for correspondence in the original multispectral image domains, where information is low correlated, and a common domain is used. The proposed approach is intended to be used in outdoor urban scenarios, where images contain large amount of edges. These edges are used as distinctive characteristics for the matching in the Hough space. Experimental results are provided showing the validity of the proposed approach.
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Eduardo Aguilar, & Petia Radeva. (2019). Class-Conditional Data Augmentation Applied to Image Classification. In 18th International Conference on Computer Analysis of Images and Patterns (Vol. 11679, pp. 182–192). LNCS.
Abstract: Image classification is widely researched in the literature, where models based on Convolutional Neural Networks (CNNs) have provided better results. When data is not enough, CNN models tend to be overfitted. To deal with this, often, traditional techniques of data augmentation are applied, such as: affine transformations, adjusting the color balance, among others. However, we argue that some techniques of data augmentation may be more appropriate for some of the classes. In order to select the techniques that work best for particular class, we propose to explore the epistemic uncertainty for the samples within each class. From our experiments, we can observe that when the data augmentation is applied class-conditionally, we improve the results in terms of accuracy and also reduce the overall epistemic uncertainty. To summarize, in this paper we propose a class-conditional data augmentation procedure that allows us to obtain better results and improve robustness of the classification in the face of model uncertainty.
Keywords: CNNs; Data augmentation; Deep learning; Epistemic uncertainty; Image classification; Food recognition
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Estefania Talavera, Nicolai Petkov, & Petia Radeva. (2019). Unsupervised Routine Discovery in Egocentric Photo-Streams. In 18th International Conference on Computer Analysis of Images and Patterns (Vol. 11678, pp. 576–588). LNCS.
Abstract: The routine of a person is defined by the occurrence of activities throughout different days, and can directly affect the person’s health. In this work, we address the recognition of routine related days. To do so, we rely on egocentric images, which are recorded by a wearable camera and allow to monitor the life of the user from a first-person view perspective. We propose an unsupervised model that identifies routine related days, following an outlier detection approach. We test the proposed framework over a total of 72 days in the form of photo-streams covering around 2 weeks of the life of 5 different camera wearers. Our model achieves an average of 76% Accuracy and 68% Weighted F-Score for all the users. Thus, we show that our framework is able to recognise routine related days and opens the door to the understanding of the behaviour of people.
Keywords: Routine discovery; Lifestyle; Egocentric vision; Behaviour analysis
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Javad Zolfaghari Bengar, Bogdan Raducanu, & Joost Van de Weijer. (2021). When Deep Learners Change Their Mind: Learning Dynamics for Active Learning. In 19th International Conference on Computer Analysis of Images and Patterns (Vol. 13052, pp. 403–413).
Abstract: Active learning aims to select samples to be annotated that yield the largest performance improvement for the learning algorithm. Many methods approach this problem by measuring the informativeness of samples and do this based on the certainty of the network predictions for samples. However, it is well-known that neural networks are overly confident about their prediction and are therefore an untrustworthy source to assess sample informativeness. In this paper, we propose a new informativeness-based active learning method. Our measure is derived from the learning dynamics of a neural network. More precisely we track the label assignment of the unlabeled data pool during the training of the algorithm. We capture the learning dynamics with a metric called label-dispersion, which is low when the network consistently assigns the same label to the sample during the training of the network and high when the assigned label changes frequently. We show that label-dispersion is a promising predictor of the uncertainty of the network, and show on two benchmark datasets that an active learning algorithm based on label-dispersion obtains excellent results.
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Bogdan Raducanu, & Jordi Vitria. (2007). Incremental Subspace Learning for Cognitive Visual Processes. In Advances in Brain, Vision and Artificial Intelligence, 2nd International Symposium (Vol. 4729, 214–223). LNCS.
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Jaume Amores, N. Sebe, & Petia Radeva. (2007). Class-Specific Binaryy Correlograms for Object Recognition. In British Machine Vision Conference.
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Marçal Rusiñol, Lluis Gomez, A. Landman, M. Silva Constenla, & Dimosthenis Karatzas. (2019). Automatic Structured Text Reading for License Plates and Utility Meters. In BMVC Workshop on Visual Artificial Intelligence and Entrepreneurship.
Abstract: Reading text in images has attracted interest from computer vision researchers for
many years. Our technology focuses on the extraction of structured text – such as serial
numbers, machine readings, product codes, etc. – so that it is able to center its attention just on the relevant textual elements. It is conceived to work in an end-to-end fashion, bypassing any explicit text segmentation stage. In this paper we present two different industrial use cases where we have applied our automatic structured text reading technology. In the first one, we demonstrate an outstanding performance when reading license plates compared to the current state of the art. In the second one, we present results on our solution for reading utility meters. The technology is commercialized by a recently created spin-off company, and both solutions are at different stages of integration with final clients.
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German Ros, J. Guerrero, Angel Sappa, Daniel Ponsa, & Antonio Lopez. (2013). Fast and Robust l1-averaging-based Pose Estimation for Driving Scenarios. In 24th British Machine Vision Conference.
Abstract: Robust visual pose estimation is at the core of many computer vision applications, being fundamental for Visual SLAM and Visual Odometry problems. During the last decades, many approaches have been proposed to solve these problems, being RANSAC one of the most accepted and used. However, with the arrival of new challenges, such as large driving scenarios for autonomous vehicles, along with the improvements in the data gathering frameworks, new issues must be considered. One of these issues is the capability of a technique to deal with very large amounts of data while meeting the realtime
constraint. With this purpose in mind, we present a novel technique for the problem of robust camera-pose estimation that is more suitable for dealing with large amount of data, which additionally, helps improving the results. The method is based on a combination of a very fast coarse-evaluation function and a robust ℓ1-averaging procedure. Such scheme leads to high-quality results while taking considerably less time than RANSAC.
Experimental results on the challenging KITTI Vision Benchmark Suite are provided, showing the validity of the proposed approach.
Keywords: SLAM
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Jon Almazan, Albert Gordo, Alicia Fornes, & Ernest Valveny. (2012). Efficient Exemplar Word Spotting. In 23rd British Machine Vision Conference (67.pp. 1–67.11).
Abstract: In this paper we propose an unsupervised segmentation-free method for word spotting in document images.
Documents are represented with a grid of HOG descriptors, and a sliding window approach is used to locate the document regions that are most similar to the query. We use the exemplar SVM framework to produce a better representation of the query in an unsupervised way. Finally, the document descriptors are precomputed and compressed with Product Quantization. This offers two advantages: first, a large number of documents can be kept in RAM memory at the same time. Second, the sliding window becomes significantly faster since distances between quantized HOG descriptors can be precomputed. Our results significantly outperform other segmentation-free methods in the literature, both in accuracy and in speed and memory usage.
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Naila Murray, Luca Marchesotti, & Florent Perronnin. (2012). Learning to Rank Images using Semantic and Aesthetic Labels. In 23rd British Machine Vision Conference (110.pp. 1–110.10).
Abstract: Most works on image retrieval from text queries have addressed the problem of retrieving semantically relevant images. However, the ability to assess the aesthetic quality of an image is an increasingly important differentiating factor for search engines. In this work, given a semantic query, we are interested in retrieving images which are semantically relevant and score highly in terms of aesthetics/visual quality. We use large-margin classifiers and rankers to learn statistical models capable of ordering images based on the aesthetic and semantic information. In particular, we compare two families of approaches: while the first one attempts to learn a single ranker which takes into account both semantic and aesthetic information, the second one learns separate semantic and aesthetic models. We carry out a quantitative and qualitative evaluation on a recently-published large-scale dataset and we show that the second family of techniques significantly outperforms the first one.
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Pedro Martins, Paulo Carvalho, & Carlo Gatta. (2012). Context Aware Keypoint Extraction for Robust Image Representation. In 23rd British Machine Vision Conference (100.pp. 1–100.12).
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