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This paper tackles anomaly detection in videos, which is an extremely challenging task because anomaly is unbounded. We approach this task by leveraging a Convolutional Neural Network (CNN or ConvNet) for appearance encoding for each frame, and leveraging a Convolutional Long Short Term Memory (ConvLSTM) for memorizing all past frames which corresponds to the motion information. Then we integrate...
Recently, visual features extracted by convolutional neural networks (CNNs) have been widely used in computer vision. Most state-of-the-art CNNs adopt a convolutional layer to map the high dimensional features into the number of the output classes. However, it is not good enough for feature similarity comparison. So we propose a new layer, Euclidean output layer, for extracting discriminative features...
Ground Penetrating Radar (GPR) is a remote sensing modality that has been researched extensively for buried threat detection. For this purpose, algorithms can be developed to automatically determine the presence of such threats. To train such algorithms, small 2-dimensional images can be extracted from the larger image, or volume, of GPR data. One thread of research in the buried threat detection...
Pedestrian recognition is a key problem for a number of application domains namely autonomous driving, search and rescue, surveillance and robotics. Real-time pedestrian recognition entails determining if a pedestrian is in an image frame. State-of-art pedestrian detection convolution neural networks(CNN) such as Fast R-CNN depend on computationally expensive region detection algorithms to hypothesize...
The system security has turned into an extremely critical worry as system assaults have been extending with the ascent of hacking devices, inconvenience of systems and interruptions in number and brutality. This paper is centered around interruption identification by utilizing Multilayer Perceptron (MLP) with various calculation of backpropagation neural network. In this paper, performance of various...
Many applications require both the location and identity of objects in images and video. Most existing solutions, like QR codes, AprilTags, and ARTags use complex machine-readable fiducial markers with heuristically derived methods for detection and classification. However, in applications where humans are integral to the system and need to be capable of locating objects in the environment, fiducial...
Describes the universal approach to the intellectual automated system development of digital signal processing for acoustic testing devices with free vibrations method and the usage of artificial neural networks. The system solves the problem of defects recognition and classification, and enhances performance testing in comparison with traditional instruments.
This paper presents two different implementations for recognition of handwritten numerals using a high performance autoencoder and Principal Component Analysis (PCA) by making use of neural networks. Different from other approaches, the non-linear mapping capability of neural networks is used extensively here. The implementation involves the deployment of a neural network, and the use of an auto encoder...
Rail health monitoring plays an important role in the railway system, and how to accurately obtain the rail state is very significant for the railway safety. This paper proposes an improved method of rail health monitoring based on convolutional neural network (CNN) and probability analysis of multiple acoustic emission (AE) events. By tensile testing machine, AE signals with safe and unsafe states...
In order to improve the accuracy of Indoor Human Activity Recognition based on the spatial location information, we proposed a recognition method using the convolutional neural network(CNN). We pre-process the raw spatial location data and transfer them into motion feature, frequency feature and statistic feature. These features are input into the CNN to do local feature analysis. After that, we got...
The aim of this work is to increase reliability of personal biometric authentication systems deployed on mobile devices. Particularly considered is authentication via handwriting. Proposed approach is based on usage of devices with multilevel pressure sensor considering pressure power while handwriting. Implemented algorithms apply neural networks. As a result obtained reliability of signatures online...
Recognizing human faces is one of the most popular problems in the field of pattern recognition. Many approaches and methods have been tested and applied on the topic, especially neural networks. This paper proposed a new loss layer that can be replaced at the bottom of a neural network architecture in terms of face recognition, called constrained triplet loss layer (CTLL). In order to make more confident...
Optimization is important in neural networks to iteratively update weights for pattern classification. Existing optimization techniques suffer from suboptimal local minima and slow convergence rate. In this paper, stochastic diagonal Approximate Greatest Descent (SDAGD) algorithm is proposed to optimize neural network weights using multi-stage backpropagation manner. SDAGD is derived from the operation...
Good speaker recognition systems should identify the speaker irrespective of what is spoken, including non-speech sounds that are often produced during natural conversations. In this work, the inclusion of breath sounds in the training phase of the speaker recognition is analyzed using the popular Gaussian mixture model-universal background model (GMM-UBM) and deep neural network (DNN) based systems...
This paper presents the implementation of a system to classify muscular intent. A neural network is used for this purpose. After skin preparation, feature extraction, network training and real-time testing, an average overall classification accuracy of 93.3% over three possible gestures was obtained. Ultimately, the results obtained speak to the suitability of an Arduino-based system for the acquisition...
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...
One of the most important prognostic markers to assess proliferation activity of breast tumors is estimating the number of mitotic figures in H&E stained tissue. We propose the use of a recently published convolutional neural network architecture, Wide Residual Networks, for mitosis detection in breast histology images. The model is trained to classify each pixel of on an image using as context...
Automatic recognition of surgical workflow is an unresolved problem among the community of computer-assisted interventions. Among all the features used for surgical workflow recognition, one important feature is the presence of the surgical tools. Extracting this feature leads to the surgical tool presence detection problem to detect what tools are used at each time in surgery. This paper proposes...
Three-dimensional head pose estimation has been an important and challenging task in computer vision partly because of its diverse applications. In this paper, we propose a new method to estimate head pose for the faces in the wild using deep neural network based on classification, rather than conventional regression. The network consists of three CNNs, corresponding to three head pose components,...
To improve the accuracy and reduce the computational complexity of neural networks for vehicle type recognition, this paper proposes a novel method based on Multi-branch and Multi-layer features. First of all, each car-face image is divided into multiple sub-images according to texture features' characteristic. Secondly, global and local features are extracted using several convolutional neural networks...
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