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The first-person-shooter Quake II is used as a platform to test neuro-visual control and retina input layouts. Agents are trained to shoot a moving enemy as quickly as possible in a visually simple environment, using a neural network controller with evolved weights. Two retina layouts are tested, each with the same number of inputs: first, a graduated density retina which focuses near the center of...
In recent years, facial expressions of pain have been the focus of considerable behavioral research. Such work has documented that pain expressions, like other affective facial expressions, play an important role in social communication. Enabling computer systems to recognize pain expression from facial images is a challenging research topic. In this paper, we present two systems for pain recognition...
A novel method for the detection of multiple moving objects is proposed in this paper. In order to get the detailed information of the objects, fast level set method which is only based on the evolution of single link list is mainly used to detect the boundaries of moving objects. The whole process consists of two main procedures: the coarse detection and the fine localization. During the coarse detection...
The incremental learning system for a feature extraction unit in the character recognition system is described and experimental results are shown. The relationship between this learning system and neural networks (NN) are explained and the specifications of this method are described as an NN application. The improved version of this system which is related to the Gabor filter was tested and an accuracy...
An improved classified image interpolation algorithm is presented. The algorithm obtains high-resolution pixels by filtering with parameters that are optimal for the selected class. By applying the highly flexible neural network model in the proposed algorithms, classified image data is extended into a nonlinear model in each class while enhancing the sharpness and edge characteristic. Meantime the...
We present an empirical study of gender classification of human faces, using new learning methodology called inference through contradictions, introduced in . This approach allows to incorporate a priori knowledge in the form of additional (unlabeled) samples, called the Universum, into the supervised learning process. Application of this methodology to gender classification shows that using this...
Most of the thyroid nodules are heterogeneous with various internal components, which confuse many radiologists and physicians with their various echo patterns in thyroid nodules. A lot of texture extraction methods were used to characterize the thyroid nodules. Accordingly, the thyroid nodules could be classified by the corresponding textural features. In this paper, five support vector machines...
The process of segmenting images is one of the most critical ones in automatic image analysis whose goal can be regarded as to find what objects are presented in images. Artificial neural networks have been well developed. First two generations of neural networks have a lot of successful applications. Spiking neuron networks (SNNs) are often referred to as the 3rd generation of neural networks which...
This paper proposes a novel face representation approach, local Gabor binary mapping pattern (LGBMP), for multi-view gender classification. In this approach, a face image is first represented as a series of Gabor magnitude pictures (GMP) by applying multi-scale and multi-orientation Gabor filters. Each GMP is then encoded as a LGBP image where a uniform local binary pattern (LBP) operator is used...
In this paper, a vision-based on-line traffic information system is discussed. The system objectives are to detect levels of traffic congestion on certain roads in Dhaka City and to make this information available to the travelers. To achieve this task, multiple Web cams will be installed on designated roads. The system will capture digital images of the passing by traffic, analyze these images, and...
In this paper, we propose a method for distinction between handwritten and machine-printed characters with no need to locate positions of characters or text lines. We call the proposed method psilaspectrum-based local fluctuation detection method. The method transforms local regions in document images into power spectrum to extract feature values which represent fluctuations caused by handwriting...
In the paper, we present an approach of image processing analysis to extract flowchart information from digital imagery. Firstly, flowchart imagery is processed to extract the text components and then extract the geometrical shapes components. We analyze text, and various geometrical shapes present in flowchart and carry out a variety of processes such as image segmentation, shape description, text...
This paper describes a neural network based multiscale image restoration approach using multilayer perceptron neural networks trained with artificially degraded images of gray level co-centered circles. The main goal of the approach is to make the neural network learn inherent space relations of the degraded pixels in restoring the image. In the conducted experiment, the degradation is simulated by...
This paper presents a novel segmentation algorithm for offline cursive handwriting recognition. An over-segmentation algorithm is introduced to dissect the words from handwritten text based on the pixel density between upper and lower baselines. Each segment from the over-segmentation is passed to a multiple expert-based validation process. First expert compares the total foreground pixel of the segmentation...
Separate processing of local luminance and contrast in biological visual systems has been argued to be due to the independence of these two properties in natural image data. In this paper we examine spatial, retinotopic channels formed by these two quantities and use Independent Component Analysis to study the possible dependencies between the channels. As a result, oriented, localized bandpass filter...
Listening to music, as per clinical neuro science, involves many cognitive components with distinct brain substrates and its study has advanced greatly in the last three decades. But the studies of Indian music and its influence in the brain have not yet been studied. This article presents sequence of image processing steps using statistical parametric mapping for the analysis of fMRI brain structures...
In this paper, we present an artificial neural network (ANN) architecture for segmenting unconstrained handwritten sentences in the English language into single words. Feature extraction is performed on a line of text to feed an ANN that classifies each column image as belonging to a word or gap between words. Thus, a sequence of columns of the same class represents words and inter-word gaps. Through...
Currently, associative neural networks (AsNNs) are among the most extensively studied and understood neural paradigms. In this paper, we use a hybrid model of neural network for associative recall of analog and digital patterns. This hybrid model which consists of self-feedback neural network structures (SFNN) parallel with generalized regression neural network (GRNN) were first proposed by authors...
Autonomous systems for surveillance, security, patrol, search and rescue are the focal point of extensive research and interest from defense and the security related industry, traffic control and other institutions. A range of sensors can be used to detect and track objects, but optical cameras or camcorders are often considered due to their convenience and passive nature. Tracking based on color...
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