The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Segmentation of microcalcifications (MCs) significantly influences the performance of shape-based method for the diagnosis of MCs, which continues to be a challenge as it tends to have high false positive results. Texture based characterization of MCs represents a possible alternative that does not require prior segmentation of MCs and may improve the positive predictive value of automated diagnosis...
Autonomous robot navigation in unstructured outdoor environments is a challenging and largely unsolved area of active research. The navigation task requires identifying safe, traversable paths that allow the robot to progress towards a goal while avoiding obstacles. Machine learning techniques are well adapted to this task, accomplishing near-to-far learning by training appearance-based models using...
Most large-scale public environments provide direction signs to facilitate the orientation for humans and to find their way to a goal location in the environment. Thus, for a robot operating in the same environment, it would be beneficial to interpret such signs correctly for a safe and efficient navigation. In this work, we propose a novel approach to infer the meaning of direction signs and to use...
High-quality mammography is the most effective technology presently available for breast cancer screening. High resolution mammograms usually lead to more accurate diagnoses; however, they require large doses of radiation, which may have harmful effects. In this paper, we present a method to synthesize high-resolution mammograms from low-resolution inputs, which offers the potential of allowing accurate...
Detecting the boundaries of objects is a key step in separating foreground objects from the background, which is useful for robotics and computer vision applications, such as object detection, recognition, and tracking. We propose a new method for detecting object boundaries using planar laser scanners (LIDARs) and, optionally, co-registered imagery. We formulate boundary detection as a classification...
We consider the problem of grasping novel objects and its application to cleaning a desk. A recent successful approach applies machine learning to learn one grasp point in an image and a point cloud. Although those methods are able to generalize to novel objects, they yield suboptimal results because they rely on motion planner for finger placements. In this paper, we extend their method to accommodate...
Topographic maps contain a small amount of text compared to other forms of printed documents. Furthermore, the text and graphical components typically intersect with one another thus making the extraction of text a very difficult task. Creating training sets with a suitable size from the actual characters in maps would therefore require the laborious processing of many maps with similar features and...
In this paper, we propose a novel Adaboost template to recognize human upper body poses from disparity images for natural human robot interaction (HRI). First, the upper body poses of standing persons are classified into seven categories of views. For each category, a mean template, variance template, and percentage template are generated. Then, the template region is divided into positive and negative...
An algorithm of segmentation by using feature extraction techniques of Synthetic Aperture Radar (SAR) images in this paper. The segmentation processor are shown to be of interest for analysing SAR image data. The extracted and selected features are then used to train different neural-network based classifiers. Segmentation makes use of wavelet decomposition and unsupervised clustering based on PCA...
Fluroscopic imaging provides means to assess the motion of the internal structures and therefore is of great use during surgery. In this paper we propose a novel approach for the segmentation of curvilinear structures in these images. The main challenge to be addressed is the lack of visual support due to the low SNR where traditional edge-based methods fail. Our approach combines machine learning...
Thai characters are one of the most complex characters because of many reasons. For example, they can be aligned into different levels, they are composed of a number of small components, and there are no word or sentence separating symbols. Noise reduction algorithms which are successfully applied to English documents might yield a poor result from Thai documents. This paper thus proposes a novel...
As the training of Adaboost is complicated or does not work well in the multi-view face detection with large plane-rotated angle, this paper proposes a rotation invariant multi-view color face detection method combining skin color segmentation and multi-view Adaboost algorithm. First the possible face region is fast detected by skin color table, and the skin-color background adhesion of face is separated...
Inspired by the idea of co-training algorithm, in this paper we propose a novel semi-supervised learning algorithm, co-Gaussian Process (co-GP), under a Bayesian framework. Image data are characterized in two distinct views, i.e. two disjoint feature sets. A latent function with a GP prior is employed for each view. In learning process of co-GP, knowledge acquired in each view is transferred by probabilistic...
This paper investigates the inconvenience of using huge number of features, enormous training dataset and lengthy training session to achieve a good performance frontal face detector. The proposed face detector is based on a novel idea which proposes using joint decision from two parallel different features trained detectors, one detector is trained with Local Binary Patterns (LBP) features and the...
The goal of semi-supervised image segmentation is to obtain the segmentation from a partially labeled image. By utilizing the image manifold structure in labeled and unlabeled pixels, semi-supervised methods propagate the user labeling to the unlabeled data, thus minimizing the need for user labeling. Several semi-supervised learning methods have been proposed in the literature. Although results have...
As nanoscale devices such as OG-CNTFETs are under studies and may be used in a near futur, we choose to investigate in wich application domain such components may be of the most interest. In this paper we present how neural networks can be used to implement functions on nano-scale components. This method has been tested in the image processing application field.
Typical digital cameras use a single-chip image sensor covered with a mosaic of red, green, and blue color filters for capturing color information. At each pixel location, only one of the three color values is known. The interpolation of the two missing color values at each pixel in a color filter array image (CFA) is called demosaicing. In this paper, we propose a novel training-based approach for...
In precision agriculture, crop/weed discrimination is often based on image analysis but though several algorithms using spatial information have been proposed, not any has been tested on relevant databases. A simple model that simulates virtual fields is developed to evaluate these algorithms. Virtual fields are made of crops, arranged according to agricultural practices and represented by simple...
Target discrimination is the key step of automatic target detection in synthetic aperture radar (SAR) images. Aiming at the issue of aircraft discrimination in high resolution SAR images, a novel discrimination method is proposed with using texture features. First of all the method of gray level co-occurrence matrix is used to generate eight discrimination texture features: mean, variance, deficit...
Cellular Neural Network (CNN) chips containing a thousand times as many processors as conventional programmable chips can offer a huge improvement in computational throughput, for those applications they are able to address. The artificial neural network (ANN) community has developed new learning designs and topologies, consistent with CNN, which can provide very general capabilities, especially for...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.