938 research outputs found

    A shape descriptor based on trainable COSFIRE filters for the recognition of handwritten digits

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    The recognition of handwritten digits is an application which has been used as a benchmark for comparing shape recognition methods. We train COSFIRE filters to be selective for different parts of handwritten digits. In analogy with the neurophysiological concept of population coding we use the responses of multiple COSFIRE filters as a shape descriptor of a handwritten digit. We demonstrate the effectiveness of the proposed approach on two data sets of handwritten digits: Western Arabic (MNIST) and Farsi for which we achieve high recognition rates of 99.52% and 99.33%, respectively. COSFIRE filters are conceptually simple, easy to implement and they are versatile trainable feature detectors. The shape descriptor that we propose is highly effective to the automatic recognition of handwritten digits.peer-reviewe

    Vectorisation of sketched drawings using co-occurring sample circles

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    This paper presents a drawing vectorisation algorithm which uses multiple concentric families of circles placed in a dense grid on the image space. We show that any off-centered junction within the family of circles can be located and hence show how these junction points may be linked to neighbouring junction points, thereby creating a vector representation of the drawing geometry. The proposed algorithm identified 98%98% of the junctions in the drawings on which it was evaluated, each within a localisation error of 4.7±2.34.7±2.3 pixels, resulting in straight line vectors which are well placed with respect to the drawn edges.peer-reviewe

    Model-free head pose estimation based on shape factorisation and particle filtering

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    This work forms part of the project Eye-Communicate funded by the Malta Council for Science and Technology through the National Research & Innovation Programme (2012) under Research Grant No. R&I-2012-057.Head pose estimation is essential for several applications and is particularly required for head pose-free eye-gaze tracking where estimation of head rotation permits free head movement during tracking. While the literature is broad, the accuracy of recent vision-based head pose estimation methods is contingent upon the availability of training data or accurate initialisation and tracking of specific facial landmarks. In this paper, we propose a method to estimate the head pose in real time from the trajectories of a set of feature points spread randomly over the face region, without requiring a training phase or model-fitting of specific facial features. Conversely, without seeking specific facial landmarks, our method exploits the sparse 3-dimensional shape of the surface of interest, recovered via shape and motion factorisation, in combination with particle filtering to correct mistracked feature points and improve upon an initial estimation of the 3-dimensional shape during tracking. In comparison with two additional methods, quantitative results obtained through our model- and landmark-free method yield a reduction in the head pose estimation error for a wide range of head rotation angles.peer-reviewe

    Multiscale blood vessel delineation using B-COSFIRE filters

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    We propose a delineation algorithm that deals with bar-like structures of different thickness. Detection of linear structures is applicable to several fields ranging from medical images for segmentation of vessels to aerial images for delineation of roads or rivers. The proposed method is suited for any delineation problem and employs a set of B-COSFIRE filters selective for lines and line-endings of different thickness. We determine the most effective filters for the application at hand by Generalized Matrix Learning Vector Quantization (GMLVQ) algorithm. We demonstrate the effectiveness of the proposed method by applying it to the task of vessel segmentation in retinal images. We perform experiments on two benchmark data sets, namely DRIVE and STARE. The experimental results show that the proposed delineation algorithm is highly effective and efficient. It can be considered as a general framework for a delineation task in various applications.peer-reviewe

    Detection of curved lines with B-COSFIRE filters: A case study on crack delineation

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    The detection of curvilinear structures is an important step for various computer vision applications, ranging from medical image analysis for segmentation of blood vessels, to remote sensing for the identification of roads and rivers, and to biometrics and robotics, among others. %The visual system of the brain has remarkable abilities to detect curvilinear structures in noisy images. This is a nontrivial task especially for the detection of thin or incomplete curvilinear structures surrounded with noise. We propose a general purpose curvilinear structure detector that uses the brain-inspired trainable B-COSFIRE filters. It consists of four main steps, namely nonlinear filtering with B-COSFIRE, thinning with non-maximum suppression, hysteresis thresholding and morphological closing. We demonstrate its effectiveness on a data set of noisy images with cracked pavements, where we achieve state-of-the-art results (F-measure=0.865). The proposed method can be employed in any computer vision methodology that requires the delineation of curvilinear and elongated structures.Comment: Accepted at Computer Analysis of Images and Patterns (CAIP) 201

    Finger-Knuckle-Print Verification Based on Band-Limited Phase-Only Correlation

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    13th International Conference on Computer Analysis of Images and Patterns, CAIP 2009, Munster, 2-4 September 2009This paper investigates a new automated personal authentication technique using finger-knuckle-print (FKP) imaging. First, a specific data acquisition device is developed to capture the FKP images. The local convex direction map of the FKP image is then extracted, based on which a coordinate system is defined to align the images and a region of interest (ROI) is cropped for feature extraction and matching. To match two FKPs, we present a Band-Limited Phase-Only Correlation (BLPOC) based method to register the images and further to evaluate their similarity. An FKP database is established to examine the performance of the proposed method, and the promising experimental results demonstrated its advantage over the existing finger-back surface based biometric systems.Department of ComputingRefereed conference pape

    Deterministic and stochastic methods for gaze tracking in real-time

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    Presentado al 12th International Conference on Computer Analysis of Images and Patterns (CAIP-2007) celebrado en Viena (Austria) del 27 al 29 de agosto.Psychological evidence demonstrates how eye gaze analysis is requested for human computer interaction endowed with emotion recognition capabilities. The existing proposals analyse eyelid and iris motion by using colour information and edge detectors, but eye movements are quite fast and difficult for precise and robust tracking. Instead, we propose to reduce the dimensionality of the image-data by using multi-Gaussian modelling and transition estimations by applying partial differences. The tracking system can handle illumination changes, low-image resolution and occlusions while estimating eyelid and iris movements as continuous variables. Therefore, this is an accurate and robust tracking system for eyelids and irises in 3D for standard image quality.This work is supported by EC grants IST-027110 for the HERMES project, IST-045547 for the VIDI-Video project, by the Spanish MEC under projects TIN2006-14606 and DPI-2004-5414. Jordi Gonzàlez also acknowledges the support of a Juan de la Cierva Postdoctoral fellowship from the Spanish MEC.Peer Reviewe

    Fast and easy blind deblurring using an inverse filter and PROBE

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    PROBE (Progressive Removal of Blur Residual) is a recursive framework for blind deblurring. Using the elementary modified inverse filter at its core, PROBE's experimental performance meets or exceeds the state of the art, both visually and quantitatively. Remarkably, PROBE lends itself to analysis that reveals its convergence properties. PROBE is motivated by recent ideas on progressive blind deblurring, but breaks away from previous research by its simplicity, speed, performance and potential for analysis. PROBE is neither a functional minimization approach, nor an open-loop sequential method (blur kernel estimation followed by non-blind deblurring). PROBE is a feedback scheme, deriving its unique strength from the closed-loop architecture rather than from the accuracy of its algorithmic components

    On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

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    Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise
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