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Multi-object model-free tracking is challenging because the tracker is not aware of the objects' type (not allowed to use object detectors), and needs to distinguish one object from background as well as other similar objects. Most existing methods keep updating their appearance model individually for each target, and their performance is hampered by sudden appearance change and/or occlusion. We propose...
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...
We propose “Areas of Attention”, a novel attentionbased model for automatic image captioning. Our approach models the dependencies between image regions, caption words, and the state of an RNN language model, using three pairwise interactions. In contrast to previous attentionbased approaches that associate image regions only to the RNN state, our method allows a direct association between caption...
The computing systems used by LHC experiments has historically consisted of the federation of hundreds to thousands of distributed resources, ranging from small to mid-size re-source. In spite of the impressive scale of the existing distributed computing solutions, the federation of small to mid-size resources will be insufficient to meet projected future demands. This paper is a case study of how...
Modern scientific experimental facilities such as x-ray light sources increasingly require on-demand access to large-scale computing for data analysis, for example to detect experimental errors or to select the next experiment. As the number of such facilities, the number of instruments at each facility, and the scale of computational demands all grow, the question arises as to how to meet these demands...
In this paper, we present a technique that places 3D bounding boxes around objects in an RGB-D scene. Our approach makes best use of the 2D information to quickly reduce the search space in 3D, benefiting from state-of-the-art 2D object detection techniques. We then use the 3D information to orient, place, and score bounding boxes around objects. We independently estimate the orientation for every...
Imagery texts are usually organized as a hierarchy of several visual elements, i.e. characters, words, text lines and text blocks. Among these elements, character is the most basic one for various languages such as Western, Chinese, Japanese, mathematical expression and etc. It is natural and convenient to construct a common text detection engine based on character detectors. However, training character...
In this paper, we present a non-parametric dataanalytic soft-error detector. Our detector uses the key properties of Gaussian process regression. First, because Gaussian process regression provides confidence on the prediction, this confidence can be used to automatize construction of the detection range. Second, because the correlation model of a Gaussian process captures the similarity among neighboring...
Characteristics of semiconductor diodes as power indicators are studied. Rigorous electromagnetic model of direct problem of average detection is derived. Approximate models of the inverse problem for detector characteristic are proposed. Maximum errors of approximation for different orders of approximating polynomial and source resistance are calculated.
With the increasing use of unmanned aerial vehicles (UAVs) by consumers, automatic UAV detection systems have become increasingly important for security services. In such a system, video imagery is a core modality for the detection task, because it can cover large areas and is very cost-effective to acquire. Many detection systems consist of two parts: flying object detection and subsequent object...
Car Recognition is a part of Intelligent Transportation System. This research proposes the manufacture of ITS-based system to identify car model from Its frontal image using Binary Robust Invariant Scalable method. The BRISK method is used to detect image keypoint, and it uses Hamming Distance for keypoint matching. As for matching error, this research depends on RANSAC. BRISK method excellence lies...
Projections and measurements of error rates in near-exascale and exascale systems suggest a dramatic growth, due to extreme scale (10^9 cores), concurrency, software complexity, and deep submicron transistor scaling. Such a growth makes resilience a critical concern, and may increase the incidence of errors that "escape", silently corrupting application state. Such errors can often be revealed...
We have proposed a concept for classification interesting points in images by means of a machine learning approach. The basic idea is that each interesting point detected in an image is classified either as a point belonging to some trained model (e.g. corner of a license plate) or not. During the first stage, we detected interesting points in a set of images by the well-known SURF method. Then we...
Most of the recent successful methods in accurate object detection and localization used some variants of R-CNN style two stage Convolutional Neural Networks (CNN) where plausible regions were proposed in the first stage then followed by a second stage for decision refinement. Despite the simplicity of training and the efficiency in deployment, the single stage detection methods have not been as competitive...
Training object class detectors typically requires a large set of images with objects annotated by bounding boxes. However, manually drawing bounding boxes is very time consuming. In this paper we greatly reduce annotation time by proposing center-click annotations: we ask annotators to click on the center of an imaginary bounding box which tightly encloses the object instance. We then incorporate...
Deep Neural Networks (DNNs) have substantially improved the state-of-the-art in salient object detection. However, training DNNs requires costly pixel-level annotations. In this paper, we leverage the observation that image-level tags provide important cues of foreground salient objects, and develop a weakly supervised learning method for saliency detection using image-level tags only. The Foreground...
Fractal analysis has been widely used in computer vision, especially in texture image processing and texture analysis. The key concept of fractal-based image model is the fractal dimension, which is invariant to bi-Lipschitz transformation of image, and thus capable of representing intrinsic structural information of image robustly. However, the invariance of fractal dimension generally does not hold...
Constrained Local Models (CLMs) are a well-established family of methods for facial landmark detection. However, they have recently fallen out of favor to cascaded regressionbased approaches. This is in part due to the inability of existing CLM local detectors to model the very complex individual landmark appearance that is affected by expression, illumination, facial hair, makeup, and accessories...
Object detection is a crucial task for autonomous driving. In addition to requiring high accuracy to ensure safety, object detection for autonomous driving also requires realtime inference speed to guarantee prompt vehicle control, as well as small model size and energy efficiency to enable embedded system deployment.,,,,,, In this work, we propose SqueezeDet, a fully convolutional neural network...
With the rise of end-to-end learning through deep learning, person detectors and re-identification (ReID) models have recently become very strong. Multi-target multicamera (MTMC) tracking has not fully gone through this transformation yet. We intend to take another step in this direction by presenting a theoretically principled way of integrating ReID with tracking formulated as an optimal Bayes filter...
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