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Automatically detecting violence in videos is paramount for enforcing the law and providing the society with better policies for safer public places. In addition, it may be essential for protecting minors from accessing inappropriate contents on-line, and for helping parents choose suitable movie titles for their children. However, this is an open problem as the very definition of violence is subjective...
This paper introduces a new detector for digital watermarking based on dual-tree complex wavelet transform (DT-CWT). The DT-CWT benefits from the high directionality and shift invariance, which ensure the high efficiency of proposed method. The watermark is additively embedded in the magnitude components of DT-CWT coefficients. Also, the watermark detection is formulated as a binary hypothesis test...
This paper considers the recovery of group sparse signals over a multi-agent network, where the measurements are subject to sparse errors. We first investigate the robust group LASSO model and its centralized algorithm based on the alternating direction method of multipliers (ADMM), which requires a central fusion center to compute a global row-support detector. To implement it in a decentralized...
Numerous trackers have been proposed in recent years with considerable success. But few trackers can cope with all scenarios without failures. It is very difficult to design a tracker robust enough to keep off tracking failure. As failure is inevitable, we propose a framework to correct tracker, verify failure, predict object position and re-detect object. The original model of the first frame is...
In this paper, we address the problem of visual tracking in videos without using a pre-learned model of the object. This type of model-free tracking is a hard problem because of limited information about the object, abrupt object motion, and shape deformation. We propose to integrate an object-agnostic prior, called objectness, which is designed to measure the likelihood of a given location to contain...
We propose a multiscale extension of a well-known line segment detector, LSD. We show that its multiscale nature makes it much less prone to over-segmentation, more robust to low contrast and less sensitive to noise, while keeping the parameterless advantage of LSD and still being fast. Moreover, we show that in scenes with little or no feature points, but where it is however possible to perform structure...
Simultaneous Localization and Mapping (SLAM) requires both rotation and scale invariant features. Few algorithms have been developed with rotation and scale invariant features with few limitations. Thus, an algorithm has been proposed to address rotation and scaling invariance. Proposed algorithm Harris-FAST interest point detector is a fusion of Harris and FAST interest point detectors. The detector...
Outlier detection algorithms are often computationally intensive because of their need to score each point in the data. Even simple distance-based algorithms have quadratic complexity. High-dimensional outlier detection algorithms such as subspace methods are often even more computationally intensive because of their need to explore different subspaces of the data. In this paper, we propose an exceedingly...
Multi-label learning is widely applied in many tasks, where an object possesses multiple concepts with each represented by a class label. Previous studies on multi-label learning have focused on a fixed set of class labels, i.e., the class label set of test data is the same as that in the training set. In many applications, however, the environment is open and new concepts may emerge with previously...
To solve the problem that there is few invariant features, which can be extracted from both images, to be matched for large changes of view, an efficient invariant image matching approach is presented. The proposed approach consists of two main steps. In the first step, we use the multi-resolution strategy to detect maximally stable extremal regions (MSERs) and obtain the geometric transformation...
Finding correspondences between two images of the same scene or object, taken from different viewpoints and in different conditions, is a challenging task. Furthermore, in the analysis of scientific imagery, it must be possible in terms of human perception to appreciate detected local features, thus making the task even more complex. A renowned generic feature detector, Maximally Stable Extremal Regions...
Speed Up Robust Features SURF is one of the most popular and efficient methods used for image registration task. In order to achieve a correct registration, a good matching of feature point is required. However in the case of multimodal images, the high and non-linear intensity changes between different modalities led to many outliers (mismatching of detected points) and consequently a fail in the...
Adaptive detection of range-spread target is discussed in the extremely training-deficient scenarios. The performance analysis of the detector without training data is conducted by Monte Carlo simulation. It implies that, the detector performs robustly for different correlations of clutter and for different target scatterer models. Furthermore, the detection performance improves as the number of channels...
Image matching plays an essential role in various computer vision applications. Recent researches found that relative positions among a feature point and its local neighbors can be utilized to build a K Nearest Neighbors (KNN) graph to eliminate the matches with geometric inconsistency. However, the existing KNN graph construction method is unstable under viewpoint changes, as the used Euclidean metric...
This paper presents a study on the exploitation of visual information from two points of view radically different. Computer vision is a branch of artificial intelligence that focuses on the extraction of useful information in an image. Image matching is a fundamental aspect of many problems in computer vision. Several algorithms have been developed for this purpose. Based on this research, this paper...
There exists a range of feature detecting and feature matching algorithms; many of which have been included in the Open Computer Vision (OpenCV) library. However, given these different tools, which one should be used? This paper discusses the implementation and comparison of a range of the library's feature detectors and feature matchers. It shows that the Speeded-Up Robust Features (SURF) detector...
With the growing of available large datasets for evaluation, face detection in recent literature has progressed rapidly. However, little research has been dedicated to develop a face detector robust to all possible variations. To address this problem, novel unconstrained datasets containing faces with more challenging variations are proposed. We notice that some recent face detectors have not been...
AprilTags and other passive fiducial markers require specialized algorithms to detect markers among other features in a natural scene. The vision processing steps generally dominate the computation time of a tag detection pipeline, so even small improvements in marker detection can translate to a faster tag detection system. We incorporated lessons learned from implementing and supporting the AprilTag...
The existing pedestrian counting methods use the various keypoint detectors but there is no attempt to find a suitable keypoint detector for counting pedestrians. Therefore, in this paper, we compare the various keypoint detectors using a public dataset. Our evaluation framework uses the processing time of keypoint detection and matching as a performance measure. Also, we use the accuracy of moving...
Matching 3D point clouds, a critical operation in map building and localization, is difficult with Velodyne-type sensors due to the sparse and non-uniform point clouds that they produce. Standard methods from dense 3D point clouds are generally not effective. In this paper, we describe a feature-based approach using Principal Components Analysis (PCA) of neighborhoods of points, which results in mathematically...
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