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Deep machine learning is an emerging framework for dealing with complex high-dimensionality data in a hierarchical fashion which draws some inspiration from biological sources. Despite the notable progress made in the field, there remains a need for an architecture that can represent temporal information with the same ease that spatial information is discovered. In this work, we present new results...
In this paper, we propose a manifold-based methodology for color constancy. It is observed that the center surround information of an image creates a manifold in color space. The relationship between the points in the manifold is modeled as a line. The human visual system is capable of learning these relationships. This is the basis of color constancy. In illumination correction, the image in the...
Path loss prediction is an essential building block for planning and optimization of cellular radio networks. Semi-empirical land use based models yield accurate and efficient path loss prediction results in rural areas. Consequently, land use information serves as a key input for those models. In this paper, we present a new C × K-Nearest-Mean classification method operating on landscape images to...
Because of ultrasound images' low quality, fully automated segmentation of breast ultrasound (BUS) image is a challenging task. In this paper, a novel segmentation method for BUS images which is fully automatic without any human intervention is proposed. By incorporating empirical knowledge and characteristics of breast structure, a ROI is generated automatically. Then two newly proposed lesion features:...
This paper shows a methodology for on-line recognition and classification of pieces in robotic assembly tasks and its application into an intelligent manufacturing cell. The performance of industrial robots working in unstructured environments can be improved using visual perception and learning techniques The object recognition is accomplished using a neuronal network with FuzzyARTMAP architecture...
Clustered micro calcifications (MCs) are one of the early signs of breast cancer. In this paper, we propose a new computer aided diagnosis (CAD) system for automatic detection of MCs in two steps. First, pixels corresponding to potential micro calcifications are found using a multilayer feed-forward neural network. The input of this network consists of 4 wavelet and 2 gray-level features. The output...
Answering to a query like when a particular document was printed is quite helpful in practice especially forensic purposes. This study attempts to develop a general framework that makes use of image processing and pattern recognition principles for ink age determination in printed documents. The approach, at first, computationally extracts a set of suitable color features and then analyzes them to...
In the field of electronics device assembly, miniaturization of components, denser packing of boards, surface mounting technology, and highly automated assembly equipment make the task of inspecting the defects of soldering joints in the electronics products more critical and more difficult for humans. The automated inspection systems are required for the stable inspection of products. One of the...
We propose a multiclass hierarchical abductive learning classifier and apply it to improve the recognition rate of handwritten numerals while reduce the dimensionality of the feature space. For handwritten recognition, there are ten classes. Using 9 binary GMDH-based neural network models structured in a hierarchy has led to improving balance factor of the dataset for each classifier and improving...
Human posture recognition is gaining increasing attention in the fields of artificial intelligence and computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computer-interaction and surveillance systems. Human posture recognition in video sequences is a challenging task which is part of the more comprehensive problem of video sequence...
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.
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...
Video artificial text detection is a challenging problem of pattern recognition. Current methods which are usually based on edge, texture, connected domain, feature or learning are always limited by size, location, language of artificial text in video. To solve the problems mentioned above, this paper applied SOM (Self-Organizing Map) based on supervised learning to video artificial text detection...
In this paper, we propose a face detection framework that combines both feature, and skin pixel approaches, while making the framework self adaptive which is important for non controlled environmental conditions. The framework uses skin color information to reduce the search space for faces by localizing the probable skin regions using a mixture of multivariate Gaussians whose parameters are first...
It is fundamental work to translate the historical characters called "kuzushi-ji" into the contemporary characters in Japanese historical studies. In this paper, we develop the Japanese historical character recognition system using the directional element features and modular neural networks. Modular neural networks consist of two kinds of classifiers: a rough classifier to find the several...
The use of neural networks as a nonlinear predictor in many applications including predictive image coding has been successfully presented by many researchers. However, almost all of the research papers have focused on the architecture of the neural network and very little attention has been given to the design of the training and testing data. This paper demonstrates how the choice of the training...
This paper proposed a new motion detection algorithm based on neural network (NN). Video background was modeled by combing probabilistic neural network (PNN) and winner take all (WTA) network, which is called adaptive background PNN (ABPNN). Every pixel in a video frame was classified to be foreground or background by conditional probability of being a background. Foreground was further classified...
In this paper we propose a framework to learn and predict saliency in videos using human eye movements. In our approach, we record the eye-gaze of users as they are watching videos, and then learn the low level features of regions that are of visual interest. The learnt classifier is then used to predict salient regions in videos belonging to the same application. So far, predicting saliency in images...
We propose a technique to classify characters by two different forms of their symmetry features. The generalized symmetry transform is applied to digits from the USPS data set. These features are then used to train probabilistic neural networks and their performances are compared to the traditional method.
The detection of texts in video images is an important task towards automatic content-based information indexing and retrieval system. In this paper, we propose a texture-based method for text detection in complex video images. Taking advantage of the desirable characteristic of gray-scale invariance of local binary patterns (LBP), we apply a modified LBP operator to extract feature of texts. A polynomial...
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