Contour integration is believed to be a fundamental process in object recognition and image segmentation. However, its neuronal mechanisms are still not well understood. Psychophysical experiments showed that humans are remarkably efficient in integrating contours even if these are jittered or partially occluded. Therefore the brain requires a reliable algorithm for extracting contours from sti...

We test the proposition that the appearance and detection of visual contours is based on an increase in the perceived contrast of contour elements. First we show that detection of contours is quite possible in the presence of very high levels of variability in contrast. Second we show that inclusion in a contour does not induce Gabor patches to appear to be of higher contrast than patches outsi...

In this work a new robust color and contour based object detection method in images with varying shadows is presented. The method relies on a physics-based contour detector that emphasizes material changes and a contourbased boosted classifier. The method has been tested in a sequence of outdoor color images presenting varying shadows using two classifiers, one that learnt contour object featur...

We investigate the contour detection task in complex natural images. We propose a novel contour detection algorithm which locally tracks small pieces of edges called edgelets. The combination of the Bayesian modeling and the edgelets enables the use of semi-local prior information and image-dependent likelihoods. We use a mixed offline and online learning strategy to detect the most relevant ed...

1 Recursive Context Reasoning for Human Detection and Parts Identi cation Liang Zhao and Chuck Thorpe The Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 Email: [email protected] Abstract Human detection and body parts identi cation are important and challenging problems in computer vision. High performance human detection depends on reliable contour extraction, but contour ex...

The fast growing deep learning technologies have become the main solution of many machine learning problems for medical image analysis. Deep convolution neural networks (CNNs), as one of the most important branch of the deep learning family, have been widely investigated for various computer-aided diagnosis tasks including long-term problems and continuously emerging new problems. Image contour...

The human visual system groups local elements into global objects seemingly without effort. Using a contour integration task and EEG source level analyses, we tested the hypothesis that perceptual grouping requires a top-down selection, rather than a passive pooling, of neural information that codes local elements in the visual image. The participants were presented visual displays with or with...

After doing research on fiber image with low quality and the Grey prediction model, the Grey correlation degree, directed graph and existing edge detection algorithm is addressed and this article proposed a new edge detection algorithm to obtain complete and continuous edge and to improve the defects in traditional operators profiled fiber contour extraction, such as the discontinuous edge, the...

An original method for an automatic detection of contours in difficult images is proposed. This method is based on a tight cooperation between a multi-resolution neural network and a hidden Markov model-enhanced dynamic programming procedure. This new method is able to overcome the three major drawbacks of the “standard” active contours: initialization dependancy, exclusive use of local informa...