Search

Modern computer vision techniques have to deal with vast amounts of visual data, which implies a computational effort that has often to be accomplished in broad and challenging scenarios. The interest in efficiently solving these image and video applications has led researchers to develop methods to expertly drive the corresponding processing to conspicuous regions that either depend on the context or are based on specific requirements. In this paper, we propose a general hierarchical probabilistic framework, independent of the application scenario, and relied on the most outstanding psychological studies about attention and eye movements which support that guidance is not based directly on the information provided by early visual processes but on a contextual representation that arose from them. The approach defines the task of context-driven visual attention as a mixture of latent sub-tasks, which are, in turn, modeled as a combination of specific distributions associated to low-, mid-, and high-level spatio-temporal features. Learning from fixations gathered from human observers, we incorporate an intermediate level between feature extraction and visual attention estimation that enables to obtain comprehensively guiding representations. The experiments show how our proposal successfully learns particularly adapted hierarchical explanations of visual attention in diverse video genres, outperforming several leading models in the literature. [ABSTRACT FROM AUTHOR]

License plate detection is a common problem in traffic surveillance applications. Although some solutions have been proposed in the literature, their success is usually restricted to very specific scenarios, with their performance dropping in more demanding conditions. One of the main challenges to be addressed for this kind of systems is the varying scale of the license plates, which depends on the distance between the vehicles and the camera. Traditionally, systems have handled this issue by sequentially running single-scale detectors over a pyramid of images. This approach, although simplifies the training process, requires as many evaluations as considered scales, which leads to running times that grow linearly with the number of scales considered. In this paper, we propose a scale-adaptive deformable part-based model which, based on a well-known boosting algorithm, automatically models scale during the training phase by selecting the most prominent features at each scale and notably reduces the test detection time by avoiding the evaluation at different scales. In addition, our method incorporates an empirically constrained-deformation model that adapts to different levels of deformation shown by distinct local features within license plates. As shown in the experimental section, the proposed detector is robust and scale and perspective independent and can work in quite diverse scenarios. Experiments on two datasets show that the proposed method achieves a significantly better performance in comparison with other methods of the state of the art. [ABSTRACT FROM AUTHOR]

Traditional approaches to automatic diagnosis of skin lesions consisted of classifiers working on sets of hand-crafted features, some of which modeled lesion aspects of special importance for dermatologists. Recently, the broad adoption of convolutional neural networks (CNNs) in most computer vision tasks has brought about a great leap forward in terms of performance. Nevertheless, with this performance leap, the CNN-based computer-aided diagnosis (CAD) systems have also brought a notable reduction of the useful insights provided by hand-crafted features. This paper presents DermaKNet, a CAD system based on CNNs that incorporates specific subsystems modeling properties of skin lesions that are of special interest to dermatologists aiming to improve the interpretability of its diagnosis. Our results prove that the incorporation of these subsystems not only improves the performance, but also enhances the diagnosis by providing more interpretable outputs. [ABSTRACT FROM AUTHOR]

In the last few years, large-scale image retrieval has attracted a lot of attention from the multimedia community. Usual approaches addressing this task first generate an initial ranking of the reference images using fast approximations that do not take into consideration the spatial arrangement of local features in the image (e.g., the bag-of-words paradigm). The top positions of the rankings are then re-estimated with verification methods that deal with more complex information, such as the geometric layout of the image. This verification step allows pruning of many false positives at the expense of an increase in the computational complexity, which may prevent its application to large-scale retrieval problems. This paper describes a geometric method known as neighborhood matching (NM), which revisits the keypoint matching process by considering a neighborhood around each keypoint and improves the efficiency of a geometric verification step in the image search system. Multiple strategies are proposed and compared to incorporate NM into a large-scale image retrieval framework. A detailed analysis and comparison of these strategies and baseline methods have been investigated. The experiments show that the proposed method not only improves the computational efficiency, but also increases the retrieval performance and outperforms state-of-the-art methods in standard datasets, such as the Oxford 5 k and 105 k datasets, for which the spatial verification step has a significant impact on the system performance. [ABSTRACT FROM PUBLISHER]

Anthropomorphic model observers are mathe- matical algorithms which are applied to images with the ultimate goal of predicting human signal detection and classification accuracy across varieties of backgrounds, image acquisitions and display conditions. A limitation of current channelized model observers is their inability to handle irregularly-shaped signals, which are common in clinical images, without a high number of directional channels. Here, we derive a new linear model observer based on convolution channels which we refer to as the “Filtered Channel observer” (FCO), as an extension of the channelized Hotelling observer (CHO) and the nonprewhitening with an eye filter (NPWE) observer. In analogy to the CHO, this linear model observer can take the form of a single template with an external noise term. To compare with human observers, we tested signals with irregular and asymmetrical shapes spanning the size of lesions down to those of microcalfications in 4-AFC breast tomosynthesis detection tasks, with three different contrasts for each case. Whereas humans uniformly outperformed conventional CHOs, the FCO observer outperformed humans for every signal with only one exception. Additive internal noise in the models allowed us to degrade model performance and match human performance. We could not match all the human performances with a model with a single internal noise component for all signal shape, size and contrast conditions. This suggests that either the internal noise might vary across signals or that the model cannot entirely capture the human detection strategy. However, the FCO model offers an efficient way to apprehend human observer performance for a non-symmetric signal. [ABSTRACT FROM AUTHOR]

This paper proposes a probabilistic generative model that concurrently tackles the problems of image retrieval and region-of-interest (ROI) segmentation. Specifically, the proposed model takes into account several properties of the matching process between two objects in different images, namely: objects undergoing a geometric transformation, typical spatial location of the region of interest, and visual similarity. In this manner, our approach improves the reliability of detected true matches between any pair of images. Furthermore, by taking advantage of the links to the ROI provided by the true matches, the proposed method is able to perform a suitable ROI segmentation. Finally, the proposed method is able to work when there is more than one ROI in the query image. Our experiments on two challenging image retrieval datasets proved that our approach clearly outperforms the most prevalent approach for geometrically constrained matching and compares favorably to most of the state-of-the-art methods. Furthermore, the proposed technique concurrently provided very good segmentations of the ROI. Furthermore, the capability of the proposed method to take into account several objects-of-interest was also tested on three experiments: two of them concerning image segmentation and object detection in multi-object image retrieval tasks, and another concerning multiview image retrieval. These experiments proved the ability of our approach to handle scenarios in which more than one object of interest is present in the query. [ABSTRACT FROM PUBLISHER]

The problem of blind source separation of complex-valued sources from a linear mixture is addressed. We propose a deflationary algorithm for the sequential recovery of a set of communication signals, where each source is extracted by performing a Bounded Component Analysis of the linear mixture. The contribution of each recovered source to the observations is removed by minimizing its convex perimeter, without using second-order statistics. This implies to run a gradient descent algorithm several times. In order to accelerate the convergence, we have derived a fast step size that exploits the second-order information of the cost function by means of the augmented Hessian matrix. Computer simulations show that the proposed method is able to blindly separate even dependent sources, as long as they satisfy the BCA separability conditions. Also, the speed of convergence of this novel step size is compared with other classical approaches. [ABSTRACT FROM AUTHOR]

In this paper we propose a complete preprocessing system for restoring low-quality Quick Response code images. The target application for the system is to restore and decode a code image in a cell phone when the image is shown in another cell phone display. Therefore, the system design focuses on low complexity solutions that are effective for a variety of capturing and displaying devices. The system has been designed to deal with low contrast, blurred, rotated, and deformed code images, while keeping complexity low enough to run in real-time. The proposed system takes advantage of intrinsic Quick Response code characteristics in order to restore the code properly and efficiently. The system has been assessed on a realistic database composed of 533 images captured by a cell a phone from several different cell phone displays. After the proposed preprocessing, 97.4 % of the images in the database were correctly decoded. [ABSTRACT FROM AUTHOR]