In 2019, the outbreak of the novel coronavirus swept across the globe. With the increase in the number of infections, the demand for automated pneumonia detection gradually increased. This study mainly focused on the ...
详细信息
作者：
Qin, MinboLi, LinAutomation Division
Department of Control Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China
Addressing the challenges of wild facial expression datasets being affected by illumination and pose variations, and the expression features being dispersed across multiple easily overlooked facial regions, this paper...
详细信息
An important stage in recognizing Land Cover (LC) on satellite images is the determination of a set of features that can best describe it and help infer between types of covers. This article analyzes and selects textu...
详细信息
Biometric is learning of human features and behavior. The Face Recognition system is used for security. Its purpose is to identify someone's face with his 2D image, which involves the extraction of his facial feat...
详细信息
In this paper, a new texture descriptor named "Fractional local Neighborhood Intensity pattern" (FLNIP) has been proposed for content-based image retrieval (CBIR). It is an extension of an earlier work invol...
详细信息
In this paper, a new texture descriptor named "Fractional local Neighborhood Intensity pattern" (FLNIP) has been proposed for content-based image retrieval (CBIR). It is an extension of an earlier work involving adjacent neighbors (local neighborhood intensity pattern). However, instead of considering two separate patterns for representing sign and magnitude information, one single pattern is generated. FLNIP calculates the relative intensity difference between a particular pixel and the center pixel of a 3 x 3 window by considering the relationship with adjacent neighbors. In this work, the fractional change in the local neighborhood involving the adjacent neighbors has been calculated first with respect to one of the eight neighbors of the center pixel of a 3 x 3 window. Next, the fractional change has been calculated with respect to the center itself. The two values of fractional change are next compared to generate a binary bit pattern. The descriptor is applied on four images- one being the raw image and the other three being filtered gaussian images obtained by applying gaussian filters of different standard deviations on the raw image to signify the importance of exploring texture information at different resolutions in an image. The four sets of distances obtained between the query and the target image are then combined with a genetic algorithm based approach to improve the retrieval performance by minimizing the distance between similar class images. The performance of the method has been tested for image retrieval on four databases and the proposed method has shown a significant improvement over many other existing methods.
Convolutional neural network (CNN) is a very important method in deep learning, which solves many complex pattern recognition problems. Fruitful results have been achieved in image recognition, speech recognition, and...
详细信息
Convolutional neural network (CNN) is a very important method in deep learning, which solves many complex pattern recognition problems. Fruitful results have been achieved in image recognition, speech recognition, and natural language processing. Compared with traditional neural network, convolutional weight sharing, sparse connection, and pooling operations in convolutional neural network greatly reduce the number of training parameters, reduce size of feature map, simplify network model, and improve training efficiency. Based on convolution operation, pooling operation, softmax classifier, and network optimization algorithm in improved convolutional neural network of LeNet-5, this paper conducts image recognition experiments on handwritten digits and face datasets, respectively. A method combining local binary pattern and convolutional neural network is proposed for face recognition research. Through experiments, it is found that adding LBP image information to improved convolutional neural network of LeNet-5 can improve accuracy of face recognition to 99.8%, which has important theoretical and practical significance.
Many problems change or appear quickly over time, and it is necessary to obtain label examples for performing classification tasks with supervised learning. In agricultural classification problems where there can be v...
详细信息
In educational institutions, attendance used to be manually recorded on attendance sheets. This method doesn't work since substitute pupils can easily use them. This study introduces a face recognition-based autom...
详细信息
Traditional locking system like key locks are easily duplicated, thus security of the possessions remains to be a threat. With Artificial Intelligence (AI) proving itself to be robust and accurate, implementing the re...
详细信息
In order to improve the accuracy of facial expression recognition, a novel method integrating attention mechanism on LBP and CNN networks is proposed, and verified on public datasets. The self-built attention mechanis...
详细信息
暂无评论