16 research outputs found
Detection of curved lines with B-COSFIRE filters: A case study on crack delineation
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
Multimodal image alignment through a multiscale chain of neural networks with application to remote sensing
International audienceWe tackle here the problem of multimodal image non-rigid registration, which is of prime importance in remote sensing and medical imaging. The difficulties encountered by classical registration approaches include feature design and slow optimization by gradient descent. By analyzing these methods, we note the significance of the notion of scale. We design easy-to-train, fully-convolutional neural networks able to learn scale-specific features. Once chained appropriately, they perform global registration in linear time, getting rid of gradient descent schemes by predicting directly the deformation. We show their performance in terms of quality and speed through various tasks of remote sensing multimodal image alignment. In particular, we are able to register correctly cadastral maps of buildings as well as road polylines onto RGB images, and outperform current keypoint matching methods
Marked Point Process Model for Curvilinear Structures Extraction
International audienceIn this paper, we propose a new marked point process (MPP) model and the associated optimization technique to extract curvilinear structures. Given an image, we compute the intensity variance and rotated gradient magnitude along the line segment. We constrain high level shape priors of the line segments to obtain smoothly connected line configuration. The optimization technique consists of two steps to reduce the significance of the parameter selection in our MPP model. We employ Monte Carlo sampler with delayed rejection to collect line hypotheses over different parameter spaces. Then, we maximize the consensus among line detection results to reconstruct the most plausible curvilinear structures without parameter estimation process. Experimental results show that the algorithm effectively localizes curvilinear structures on a wide range of datasets
Brain-inspired robust delineation operator
In this paper we present a novel filter, based on the existing COSFIRE
filter, for the delineation of patterns of interest. It includes a mechanism of
push-pull inhibition that improves robustness to noise in terms of spurious
texture. Push-pull inhibition is a phenomenon that is observed in neurons in
area V1 of the visual cortex, which suppresses the response of certain simple
cells for stimuli of preferred orientation but of non-preferred contrast. This
type of inhibition allows for sharper detection of the patterns of interest and
improves the quality of delineation especially in images with spurious texture.
We performed experiments on images from different applications, namely the
detection of rose stems for automatic gardening, the delineation of cracks in
pavements and road surfaces, and the segmentation of blood vessels in retinal
images. Push-pull inhibition helped to improve results considerably in all
applications.Comment: Accepted at Brain-driven Computer Vision workshop at ECCV 201