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In this paper, a simultaneous sparsity representation-based binary hypothesis (S-SRBBH) model for target detection in hyperspectral image (HSI) is proposed. The S-SRBBH exploits the interpixel correlation within neighboring pixels in HSI, and then, each test pixel is represented by only the background dictionary (Ab) under null hypothesis or from the union of Ab and target dictionary (At) under alternative...
Most approaches for scene parsing, recognition or retrieval use detectors that are either (i) independently trained or (ii) jointly trained for conjunctions of object-object or object-attribute phrases. We posit that neither of these two extremes is uniformly optimal, in terms of performance, across all categories and conjunctions. The choice of whether one should train an independent or composite...
A boosted convolutional neural network (BCNN) system is proposed to enhance the pedestrian detection performance in this work. Being inspired by the classic boosting idea, we develop a weighted loss function that emphasizes challenging samples in training a convolutional neural network (CNN). Two types of samples are considered challenging: 1) samples with detection scores falling in the decision...
This paper presents a novel technique of image classification using BOVW model. The entire process first involves feature detection of images using FAST, the choice made in order to speed up the process of detection. Then comes the stage of feature extraction for which FREAK, a binary feature descriptor is employed. K-means clustering is then applied in order to make the bag of visual words. Every...
Although the LPDDR4 interface has enabled industry requirements, such as low power consumption and high bandwidth, additional evolution of the current LPDDR4 performance is expected. To respond to the need for more power efficient devices with higher bandwidth, a 2nd generation LPDDR4 (referred to as LPDDR4X), with extreme low power and extended performance, has been developed in this work. In the...
The identification of small icebergs in SAR images is challenging especially when these are embedded in sea ice. In this work, a new detector is proposed based on dual-polarized incoherent SAR images. Small icebergs have a stronger cross polarization accompanied by a higher cross- over co-polarization ratio compared to sea ice in many cases. This is the rational at the base of the detector.
This paper describes EM-Based Detection of Deviations in Program Execution (EDDIE), a new method for detecting anomalies in program execution, such as malware and other code injections, without introducing any overheads, adding any hardware support, changing any software, or using any resources on the monitored system itself. Monitoring with EDDIE involves receiving electromagnetic (EM) emanations...
In this paper, the filtering characteristics of Empirical Mode Decomposition (EMD) are used to create a blind and adaptive energy detector for single or multi-channel spectrum sensing. EMD is an adaptive tool that decomposes time-series signals into a set of modes called Intrinsic Mode Functions (IMF). Due to the EMD filtering behavior, the first IMF is mostly contaminated by noise from the received...
Detecting actions or verbs in still images is a challenging problem for a variety of reasons such as the absence of temporal information and polysemy of verbs which lead to difficulty in generating large verb datasets. In this paper, we propose to first detect the prominent objects in the image and then infer the relevant actions or verbs using Natural Language Processing (NLP)-based techniques. The...
In advanced semiconductor-process technology, the ability to detect and repair lithography hotspots, which can affect printability, is essential. In this paper, we propose a two-stage cascade classifier for accurate hotspot detection. Our classifier uses a novel layout feature based on the propagation of light passing through a photomask. We performed experiments to evaluate our cascade classifier...
Nowadays, many pedestrians are victims of road accidents. Several artificial vision solutions, based on pedestrian detection, have therefore been developed to assist drivers and reduce the accident rate. But most of the proposed pedestrian databases make it possible to test detection only in favorable conditions. The main goal of this research is to provide a learning and testing environment for the...
Deep learning methods are powerful approaches but often require expensive computations and lead to models of high complexity which need to be trained with large amounts of data. In this paper, we consider the problem of face detection and we propose a light-weight deep convolutional neural network that achieves a state-of-the-art recall rate at the challenging FDDB dataset. Our model is designed with...
We model dyadic (two-person) interactions by discriminatively training a spatio-temporal deformable part model of fine-grained human interactions. All interactions involve at most two persons. Our models are capable of localizing human interactions in unsegmented videos, marking the interactions of interest in space and time. Our contributions are as follows: First, we create a model that localizes...
Face detection is challenging as faces in images could be present at arbitrary locations and in different scales. We propose a three-stage cascade structure based on fully convolutional neural networks (FCNs). It first proposes the approximate locations where the faces may be, then aims to find the accurate location by zooming on to the faces. Each level of the FCN cascade is a multi-scale fully-convolutional...
Extreme learning machine (ELM) and support vector machine (SVM) classifiers are developed to detect rales (a gurgling sound that is a symptom of respiratory diseases in poultry). These classifiers operate on Mel-scaled spectral features calculated from recordings of healthy and sick chickens during a vaccine trial. Twenty minutes of labeled data were used to train and test the classifiers, then they...
Tattoos have been increasingly used as a discriminative soft biometric for people identification, such as criminal and victim identification in forensics investigation and law enforcement. However, automatic detection of tattoo images and accurate localization of the regions of interest are challenged by the large variations in artistic composition, color, shape, texture, location on the body, local...
We present a novel approach towards web video classification and recounting that uses video segments to model an event. This approach overcomes the limitations faced by the classical video-level models such as modeling semantics, identifying informative segments in a video and background segment suppression. We posit that segment-based models are able to identify both the frequently-occurring and...
Appearance-based action recognition can be considered as a natural extension of appearance-based object detection from the spatial to the spatio-temporal domain. Although this step seems natural, most action recognition approaches are evaluated in isolation. Towards this end the contribution of this paper is twofold. First, a view-independent approach to action recognition is proposed and second the...
We present an approach for detecting application level protocols over a wireless communications link, without the need for demodulation or decryption. Our detector is suitable for diverse radio types, since only simple external signal features are used as inputs. We show that the Profile Hidden Markov Model (PHMM) is well suited to this task, due to the probabilistic nature of the wireless channel...
Face detection is a vital step in the process of extracting semantic information about the driver's state, such as distraction and fatigue, from pixel values in images looking at the driver. Therefore, in the context of time and safety critical situation like driving, efficient use of time and reliable detection of faces is essential. While challenges like lighting and occlusion are prevalent in the...
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