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In this paper, we present a novel pixel-based airplane segmentation method from remote sensing images by combining Single Shot MultiBox Detector (SSD) and Single-layer Cellular Automata (SCA). SSD is a kind of deep ConvNet for object detection while SCA is a saliency detection method via Cellular Automata. First, we obtain detection result where every airplane is boxed by a rectangle through the SSD...
Novelty detection is the task of recognising events the differ from a model of normality. This paper proposes an acoustic novelty detector based on neural networks trained with an adversarial training strategy. The proposed approach is composed of a feature extraction stage that calculates Log-Mel spectral features from the input signal. Then, an autoencoder network, trained on a corpus of “normal”...
Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing — data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are...
With the big success of deep convolutional neural networks (CNN) in image classification task, many proposal based networks are proposed to detect given objects in an image. Faster R-CNN is such a network that uses a region proposal network (RPN) to generate nearly cost-free region proposals, which has shown excellent performance in ILSVRC and MS COCO datasets. However, Faster R-CNN does not behave...
The problem of object localization in image appear ubiquitously in computer vision applications including image classification, object detection and visual tracking. Recently, it is shown that multiple-instance learning(MIL) which is regarded as the fourth machine learning framework compared with supervised learning, unsupervised learning and reinforce learning has been verified that will get good...
In this paper, we address the problem of signal detection in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system by using auto-encoder (AE) network and extreme learning machine (ELM). The existing signal detection algorithms, such as zero-forcing successive-interference-cancellation (ZF-SIC), minimum-mean-square-error successive-interference-cancellation...
A real-time Deep Learning based method for Pedestrian Detection (PD) is applied to the Human-Aware robot navigation problem. The pedestrian detector combines the Aggregate Channel Features (ACF) detector with a deep Convolutional Neural Network (CNN) in order to obtain fast and accurate performance. Our solution is firstly evaluated using a set of real images taken from onboard and offboard cameras...
Intrusion Detection Systems (IDS) are security technologies. In this regard, Artificial Immune System (AIS) which provides distributed detection through its lymphocytes is an appealing approach for designing IDSs. In this paper, an AIS based intrusion detection is proposed in which two sets of antibodies — positive and negative — are generated for normal and attack samples respectively using negative...
Drivers fatigue is the major cause of traffic accidents all over the world. Advanced image processing technology processing the stream obtained from infrared cameras is able to supervise blinking rate and at the same time drowsiness of the vehicle driver. Such a system may warn not only the tired person, but also the passengers, whom the driver takes responsibility for. In this article we present...
Recently a new set consisting of six information theory features was proposed for on-line signature verification by Rosso, Ospina and Frery. The proposed features were evaluated on the MCYT-100 on-line signature database resulting in the best performance ever measured on that dataset. In this paper we repeat their measurements and show that their result is erroneous. In addition, we evaluate the performance...
Our objective is to count objects using a single frame from a surveillance camera. We focus on the area where individual object detectors fail, mostly due to clutter, occlusion, or variations in scene due to perspective change. For tackling the counting problem, first the object density is estimated by using ridge regression. Object counts are then estimated by integrating the density over the region...
In crowded scene abnormal event detection is a major issue. Many existing methods are there. Abnormal events are those which cannot be well represented. For example, if a flight is hijacked or it is damaged, it is due to some abnormal activities. Abnormal activities may occur due to human intervention or due to some weather conditions. So in this system we are using abnormal detector to detect the...
The process of mining includes various methodologies and data classification is one of the advantageous methods involved in it. It not only eases the process of machine learning but also gives a platform for proper functioning of the process. There are cases wherein the data which is important or unidentified is missed during the process of classification. The process of mining is highly affected...
Here, evaluate the abasement in execution of well known and effective face detector when human captured picture quality is corrupted by additive gaussian noise and blur. It is observed that, inside a specific scope of recognized picture quality, an adequate increase in picture quality can improve face detection performance. These results can be utilized to guide data transfer capacity which regards...
Splicing, cutting and insertion are the most common operations imposed on audio files when the adversary intends to modify or fabricate the content. The detection of such kinds of tampering is still challenging in real-world applications. In this paper, a generic approach for the detection of audio tampering is proposed via the analysis of electric network frequency (ENF). Based on the fact that tampering...
This paper investigates practical strategies for distributing payload across images with content-adaptive steganography and for pooling outputs of a single-image detector for steganalysis. Adopting a statistical model for the detector's output, the steganographer minimizes the power of the most powerful detector of an omniscient Warden, while the Warden, informed by the payload spreading strategy,...
I-vector training and extraction assume that a speech file is spoken by a single speaker. This work considers the effects of violating that assumption with the presence of cross-talk or multi-speaker conversations. First, it is demonstrated that these problematic speech files can be detected using the i-vector representation itself. The impact of these violations of the single-speaker assumption are...
Accurately recognizing speaker emotion and age/gender from speech can provide better user experience for many spoken dialogue systems. In this study, we propose to use deep neural networks (DNNs) to encode each utterance into a fixed-length vector by pooling the activations of the last hidden layer over time. The feature encoding process is designed to be jointly trained with the utterance-level classifier...
In this paper, we consider the problem of falls risk prediction in elderly adults using smartphone-based inertial gait measurements. We begin by collecting a parallel data set from a pressure sensitive walkway and smartphones. The walk-way data is used to calculate the falls risk ground truth using well-established biomechanical norms. The smartphone data and falls risk labels are then used to train...
Occlusion handling is one of the most challenging issues for pedestrian detection, and no satisfactory achievement has been found in this issue yet. Using human body parts has been considered as a reasonable way to overcome such an issue. In this paper, we propose a brand new approach based on the fusion of Mid-level body part mining and Convolutional Neural Network (CNN) to solve this problem, named...
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