It is well accepted that convolutional neural networks play an important role in learning excellent features for image classification and recognition. However, in tradition they only allow adjacent layers connected, limiting integration of multi-scale information. To further improve their performance, we present a concatenating framework of shortcut convolutional neural networks. This framework...

using image processing and artificial intelligence systems in agriculture and food industry is increasing daily. the purpose of this research is to study the feasibility of using image processing technique in predicting process of moisture content changes on green tea sheets during the drying using predictive artificial intelligence systems such as: artificial neural networks and adaptive neuro...

Recent breakthroughs in computational capabilities and optimization algorithms have enabled a new class of signal processing approaches based on deep neural networks (DNNs). These algorithms have been extremely successful in the classification of natural images, audio, and text data. In particular, a special type of DNNs, called convolutional neural networks (CNNs) have recently shown superior ...

The neural-like growing networks used in the intelligent system of recognition of images are under consideration in this paper. All operations made over the image on a pre-design stage and also classification and storage of the information about the images and their further identification are made extremely by mechanisms of neural-like networks without usage of complex algorithms requiring cons...

The physiological studies since the Hubel and Wiesel’s experimentation of cat’s visual cortex have confirmed the consensus about the brain’s intelligence of visual perception. A new way of enhancing the safety of vehicle is proposed by employing the neuromorphic VLSI or processing for mimicking the robust and natural intelligence of visual recognition, inspired by both the Hubel and Wiesel’s ex...

Neural networks have been reported to be succesful in all kinds of pattern recognition and image processing applications. In this paper a neural network is proposed to solve the problem of spot counting in bio-imaging. Currently we have a microscope system coupled to a computer which performs the image acquisition and classiication task. Neural networks ooer a exibility which might be used to b...

This paper presents an approach to perform neutral facial image recognition through facial expression image. The differential of this work is the previous images processing. First, the dimension is reduced. Next, the image is sliced into gray levels, from the most significant bits to the less significant bits. Finally, eight parallel hopfield neural networks are applied, one for each gray level...

To remove artifacts, two post processing techniques that carry out optimization in the Fourier domain are developed. Decompressed iris images obtained from two public iris image databases are evaluated by visual comparison, two objective image quality assessment metrics, and eight iris recognition methods. To improve the efficiency, sensitivity and reduce the complexity. In existing system use ...

This paper proposed an efficient, intelligent and less complex method of face recognition. A hybrid approach of face recognition is proposed here using the neural networks and Bezier curves. Detection of structural facial features such as eyes, eyebrows, nose, lips and mouth boundaries is an essential process for various image processing task such as face recognition. In this paper we introduce...

In the near future, digitization and processing of current paper documents describe efficient role in creation a paperless environment. Deep learning techniques for handwritten recognition have been extensively studied by various researchers. neural networks can be trained quickly thanks to lot data other algorithmic advancements. Various methods extracting text from manuscripts developed liter...