Hand gesture segmentation for identification and prediction analysis has been aprevalent topic in the community of researchers working field image processing andcomputer vision. gestures were used by humans to signify various expressions, moods andactions. The advent intelligent devices made hand recognition even more interesting.Many have worked on interfaces that use its operations.Various st...

Image recognition has been applied to many fields, but it is relatively rarely medical images. Recent significant deep learning progress for image raised strong research interest in recognition. First of all, we found the prediction result using VGG16 model on failed pneumonia X-ray Thus, this paper proposes IVGG13 (Improved Visual Geometry Group-13), a modified classification X-rays Open-sourc...

This thesis deals with biologically-inspired interactive neural networks used for the task of object recognition. Such networks offer an interesting alternative approach to traditional image processing techniques. Although the networks are very powerful classification tools, they are difficult to handle due to their bidirectional interactivity. This is one of the main reasons why these networks...

In these days there is a risk that others can access the same information anywhere and anytime. Currently passwords, personal identification cards are used for personal identification. Now a days Biometric based recognition is the most popular human recognition pattern. Biometrics measures individual’s unique or behavioural characteristics to authenticate personal identity. It provides more rel...

The integration of deep learning methods, especially Convolutional Neural Networks (CNN), and Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has been widely deployed in the field radar signal processing. Nevertheless, these methods are frequently regarded as black-box models due to limited visual interpretation their internal feature representation parameter organization. In th...

My research interest in computer vision includes human activity recognition, human motion analysis, tracking, human identification, gait and gesture recognition, statistical methods for computer vision. I mainly focus on developing robust real-time algorithms based on sound theory for solving realistic computer vision problems for many applications. My research is widely applicable in visual su...

Based on the progress of image recognition, video recognition has been extensively studied recently. However, most of the existing methods are focused on short-term but not long-term video recognition, called contextual video recognition. To address contextual video recognition, we use convolutional recurrent neural networks (ConvRNNs) having a rich spatiotemporal information processing capabil...

Among the vast range of off–line optical character recognition applications is the machine processing of forms. The objective is to imitate the human ability to read but at higher speed. This paper presents a neural network based system to recognise handwritten Arabic characters collected from more than 500 forms. Wavelet coefficients extracted from the character samples are used to tune the ne...

BACKGROUND The variety of ways in which faces are categorized makes face recognition challenging for both synthetic and biological vision systems. Here we focus on two face processing tasks, detection and individuation, and explore whether differences in task demands lead to differences both in the features most effective for automatic recognition and in the featural codes recruited by neural p...

Cells grow and then need a very neat method to create new cells that work properly to maintain the health of the body. When the ability to control the growth of the cells is lost, they are unconsidered and often divided without order. Exemplified cells form a tissue mass called the tumor. In fact, brain tumors are abnormal and uncontrolled cell proliferations. Segmentation methods are used in b...