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Remote sensing image registration is still a challenging task because of diverse image types and the lack of a consistent transformation. To improve image registration in remote sensing, this paper develops a robust and accurate feature point matching framework. A modified scale-invariant feature transform (SIFT) method is first introduced for feature detection and pair matching. Based on the properties...
In 3D object recognition, local feature-based recognition is known to be robust against occlusion and clutter. Local feature estimation requires feature correspondences, including feature extraction and matching. Feature extraction is normally a two-stage process that estimates keypoints and keypoint descriptors, and existing studies show repeatability to be a good indicator of keypoint feature detector...
Different types of traffic signs has different colors and shapes located in uncontrolled traffic environments. The detection of different types of traffic signs is a difficult problem in pattern recognition and computer vision. In our study, a region of interest (ROI) extraction method is proposed to extract ROI using color contrast in local regions. We utilize the high contrast in local regions to...
The requiring of large amounts of annotated training data has become a common constraint on various deep learning systems. In this paper, we propose a weakly supervised scene text detection method (WeText) that trains robust and accurate scene text detection models by learning from unannotated or weakly annotated data. With a "light" supervised model trained on a small fully annotated dataset,...
Instead of using HOG feature on cells or blocks, the extraction of HOG features on corner points is proposed for multiple object visual tracking system in which single or multiple moving objects could be classified. Background subtraction and extraction of corner feature are applied to track and classify the moving objects. Firstly, moving objects will be detected in the form of regions from background...
We describe a method to produce a network where current methods such as DeepFool have great difficulty producing adversarial samples. Our construction suggests some insights into how deep networks work. We provide a reasonable analyses that our construction is difficult to defeat, and show experimentally that our method is hard to defeat with both Type I and Type II attacks using several standard...
The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far. In...
This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S3FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces. Specifically, we try to solve the common problem that anchorbased detectors deteriorate dramatically as the objects become smaller. We make contributions in the following three aspects:...
Robust detection of the smallest circulating cerebral microemboli is an efficient way of preventing cerebrovascular accidents (CVA). Transcranial Doppler ultrasound is widely considered as the most convenient system for the detection of microemboli. Standard detection used in commercial device is achieved through the whole Doppler energy spectrum where constant empirical thresholds are implemented...
Paper is focused on problem of robust feature detector stability and efficiency in case when intensity distribution is uneven over image. Results of feature detector simulation and equations for estimation rate of false alarms and probability of correct detection are presented in paper.
As the usage areas of the images increase, the functions of various image editing software are increasing. Easy-to-use software has caused the images to be tampered with easily. Many Copy-Move Forgery Detection (CMFD) algorithms have been developed against these attacks. In literature CMFD methods are divided into block based and keypoint based methods. In this paper, recent works in keypoint based...
In this paper we propose a novel efficient method of characteristic image point detection based on the fractional order derivative. The concept of this approach called (FSIFT: Fractional-SIFT) is inspired by the Scale-Invariant Feature Transform (SIFT) proposed by Lowe and can be viewed as a certain generalization of this formula. The classical SIFT detector is implemented efficiently by using a difference...
In this paper, we present a novel approach for real-time object identification on a mobile platform. First, our system detects keypoints within a scaled pyramid-based FAST detector and then descriptors of the object of interest are computed using an Analytical Fourier-Mellin transform. The Fourier-Mellin is used in similarity studies due to its invariance property and discrimination power. In this...
We present a method to improve the accuracy of a foot-mounted, zero-velocity-aided inertial navigation system (INS) by varying estimator parameters based on a real-time classification of motion type. We train a support vector machine (SVM) classifier using inertial data recorded by a single foot-mounted sensor to differentiate between six motion types (walking, jogging, running, sprinting, crouch-walking,...
License Plate Detection (LPD) is the pivotal step for License Plate Recognition. In this work, we explore and customize state-of-the-art detection approaches for exclusively handling the LPD in the wild. In-the-wild LPD considers license plates captured in challenging conditions caused by bad weathers, lighting, traffics, and other factors. As conventional methods failed to handle these inevitable...
Efficient crowd counting is an essential task in crowd monitoring, and significant advances have been made in this field recently by counting-by-regression techniques. We propose in this work a learning-to-count strategy with a generic detection algorithm which benefits from a counting regressor in order to identify crowded subregions with inadequate head detection performance, and to improve their...
Nowadays, transmission of data via Internet has made illegal data distribution a major problem in digital world. Watermarking is known as a possible solution to protect digital data. In this work, we propose a blind detector for multiplicative watermarking of images in the wavelet domain. To this end, the vector-based hidden Markov model (HMM) is employed as a prior model for the wavelet coefficients...
In this paper, we are proposing Bag of Feature (BoF) approach for vehicle classification using Speeded Up Robust Features (SURF). First, monocular video taken using a stationary camera is given as the input to Gaussian Mixture Model (GMM) based foreground detector. Then a grid is used to measure the number of foreground pixels. If the pixels inside the grid is greater than a pre-assigned threshold,...
By injecting false data through compromised sensors, an adversary can drive the probability of detection in a sensor network-based spatial field surveillance system to arbitrarily low values. As a countermeasure, a small subset of sensors may be secured. Leveraging the theory of Matched Subspace Detection, we propose and evaluate several detectors that add robustness to attacks when such trusted nodes...
In object tracking, a novel tracking framework which is called “Tracking-Leaning-Detection” was proposed by Zdenka Kalal. This framework decomposes the object tracking task into tracking, learning and detection. In every frame that follows, the tracker and the detector work simultaneously to obtain the location of the object independently, and the learning acts as an information exchanging center...
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