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Convolutional neural network (CNN) has been successfully used in many fields including image recognition. CNN is composed of input, convolution, pooling, hidden and output layers, and the weights and biases between layers except the ones between convolution and pooling layers are acquired by learning. In comparison to the conventional neural networks, the learning cost of CNN is higher, and the learning...
This paper deals with the field of computer vision, mainly for the application of deep learning in object detection task. On the one hand, there is a simple summary of the datasets and deep learning algorithms commonly used in computer vision. On the other hand, a new dataset is built according to those commonly used datasets, and choose one of the network called faster r-cnn to work on this new dataset...
Eye state recognition is still challenging in the field of computer vision. Many researchers have reported that their methods can work well with frontal face views, but not with variations of head poses. Some have described that their methods deal effectively with head pose problems, but the systems are complex to implement and consume a lot of processing time. In this paper, a novel method of eye...
Object detection and recognition are crucial elements of any high level image analysis system. Convolutional Neural Networks (CNNs) or ConvNets have been applied for recognizing the category of the principal entity in an image for several years. One major benefit of convolutional networks is the use of shared weights in the intermediate convolutional layers, which reduces the required memory size...
The Eigenface method is a classic face recognition method. This article is based on the method of Eigen face to recognize the facial expression. The aim of this method is to recognize the facial expression stored in a database. It uses a set of single static image with different expression labels as the training database, projected the training image to subspaces. The similar face of the tested expression...
Recently, we introduced a robust and adaptive method for constructing sparse graphs. This method was termed Two Phase Weighted Regularized Least Square (TPWRLS) [6]. In this framework, the graph structure and its affinity matrix are simultaneously computed through a two phase sample coding. The second phase of coding utilizes adaptive sample pruning and re-weighting. In the context of graph-based...
The manual process for privacy setting could be very time-consuming and challenging for common users. By assuming that there are hidden correlations between the visual properties of images (i.e., visual features) or object classes and the privacy settings for image sharing, an effective algorithm is developed in this paper to achieve automatic prediction of image privacy, so that the best-matching...
Feature extraction and classifier is crucial for content-based image retrieve and analysis. In this paper, a novel method for handwritten numeral image extraction is proposed based on Random Forest(RF) and Histogram of Oriented Gradient(HOG). The main contribution of the proposed method is to consider the advantage of HOG and RF. Further, our method extract the impactful information of image, and...
Nowadays, image processing is getting more popular due to the daily increase of diverse data acquisition methods such as digital scanners and cameras. Due to the high volume of archived documents, automatic document classification methods can help to save the time and space in digital document organization. Logos in official and business documents are used to identify document identities. Different...
This paper describes the different classifier methods with minimum means of clusters to achieve face recognition rate of humans from the feature extracted of training face image data for many sets of images as a data base. Principal Component Analysis (PCA) is a robust method used as feature extraction techniques for face recognition but the recognition decreases with the variation of person's actions...
Difficulty on collecting annotated medical images leads to lack of enough supervision and makes discrimination tasks challenging. However, raw data, e.g., spatial context information from 3D CT images, even without annotation, may contain rich useful information. In this paper, we exploit spatial context information as a source of supervision to solve discrimination tasks for fine-grained body part...
To improve the performance of the convolutional neural networks, it is normally done by increase the deepness or put more layers to the network. By doing such, the number of parameters is increased. In this paper, NU-InNet, which was developed from GoogLeNet, is modified by adding more layers to the network in order to improve the accuracy of the network while keeping the number of the parameters...
In nowadays, the image recognition is widely applying to various fields. With the construction of informatization and intellectualization highly developing, it will inevitably becomes investigative trend that apply image recognition to drawings and literal data of power system. This paper mainly studies the Zernike moment that applied to feature extraction of electrical engineering drawings, which...
Hand gesture has been used in different applications and implemented on different platforms. Hence, a real-time and robust approach with high recognition accuracy is important in smart devices. This paper describes a novel method of hand gesture recognition using Principle Component Analysis (PCA) implemented in Android phone. Area features are adopted to do the gesture recognition. It solves these...
Recently, there has been an explosion of cloud-based services that enable developers to include a spectrum of recognition services, such as emotion recognition, in their applications. The recognition of emotions is a challenging problem, and research has been done on building classifiers to recognize emotion in the open world. Often, learned emotion models are trained on data sets that may not sufficiently...
Optical Character Recognition is the process of converting an input text image into a machine encoded format. Different methods are used in OCR for different languages. The main steps of optical character recognition are pre-processing, segmentation and recognition. Recognizing handwritten text is harder than recognizing printed text. Convolutional Neural Network has shown remarkable improvement in...
Plants play an important role in Earth's ecology by providing sustenance, shelter and maintaining a healthy atmosphere. Some of these plants have important medicinal properties. Automatic recognition of plant leaf is a challenging problem in the area of computer vision. An efficient Ayurvedic plant leaf recognition system will beneficial to many sectors of society which include medicinal field botanic...
This paper presents an image representation approach which is based on matrix factorization in the complex domain and called exemplar-embed complex matrix factorization (EE-CMF). The proposed EE-CMF approach can very effectively improve the performance of facial expression recognition. Moreover, Wirtinger's calculus was employed to determine derivatives. The gradient descent method was utilized to...
This paper presents an image recognition technique based on discriminative models using features generated from separable lattice hidden Markov models (SL-HMMs). A major problem in image recognition is that the recognition performance is degraded by geometric variations such as that in position and size of the object to be recognized. SL-HMMs have been proposed to solve this problem. SL-HMMs are an...
Face recognition systems are designed to handle well-aligned images captured under controlled situations. However real-world images present varying orientations, expressions, and illumination conditions. Traditional face recognition algorithms perform poorly on such images. In this paper we present a method for face recognition adapted to real-world conditions that can be trained using very few training...
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