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Welding is a process recognized by the laborious work and hazardous work environment it takes place, but it is an important process in different industrial scenarios, like the shipbuilding industry. The use of robots has been increasing in recent years, reducing the human interference necessary for the process. This paper proposes a system for automated seam tracking and a geometric welding bead analysis...
Detecting fraudulent users in online social networks is a fundamental and urgent research problem as adversaries can use them to perform various malicious activities. Global social structure based methods, which are known as guilt-by-association, have been shown to be promising at detecting fraudulent users. However, existing guilt-by-association methods either assume symmetric (i.e., undirected)...
Stereo matching is a fundamental task in vision applications. we propose an adaptive cross-scale aggregation method for stereo matching, which is introduced by solving an optimization problem. Unlike the original approach which introduces the same regularization term based on the inter-scale regularizer parameter to control the cost consistency among the multi-scales for all regions of the input images...
Convolutional neural networks showed the ability in stereo matching cost learning. Recent approaches learned parameters from public datasets that have ground truth disparity maps. Due to the difficulty of labeling ground truth depth, usable data for system training is rather limited, making it difficult to apply the system to real applications. In this paper, we present a framework for learning stereo...
Anomaly detection is the process of identifying unusual signals in a set of observations. This is a vital task in a variety of fields including cybersecurity and the battlefield. In many scenarios, observations are gathered from a set of distributed mobile or small form factor devices. Traditionally, the observations are sent to centralized servers where large-scale systems perform analytics on the...
There is a large demand in the area of video-surveillance, especially in people detection, which has caused a large increase in the number of researches and resources in this field. As training images and annotations are not always available, it is important to consider the cost involved in creating the detector models. For example, for elderly people detection, the detector must have into account...
With millions of people suffering from dementia worldwide, the global prevalence of dementia has a significant impact on the patients' lives, their caregivers' physical and emotional states, and the global economy. Early diagnosis of dementia helps in finding suitable therapies that reduce or even prevent further deterioration of patients' cognitive abilities. MRI scans are shown to be the most effective...
In free viewpoint television (FTV) application scenario, views that synthesized with depth image-based rendering (DIBR) techniques mainly contain special artifacts like geometric distortions. These artifacts may affect the structure of images/videos by changing the global contour characteristics and thus are annoying for human observers. Context tree based contour coding scheme can be a good tool...
Weakly supervised object localization remains challenging, where only image labels instead of bounding boxes are available during training. Object proposal is an effective component in localization, but often computationally expensive and incapable of joint optimization with some of the remaining modules. In this paper, to the best of our knowledge, we for the first time integrate weakly supervised...
In this paper we propose a new solution to the text detection problem via border learning. Specifically, we make four major contributions: 1) We analyze the insufficiencies of the classic non-text and text settings for text detection. 2) We introduce the border class to the text detection problem for the first time, and validate that the decoding process is largely simplified with the help of text...
Learned boundary maps are known to outperform handcrafted ones as a basis for the watershed algorithm. We show, for the first time, how to train watershed computation jointly with boundary map prediction. The estimator for the merging priorities is cast as a neural network that is convolutional (over space) and recurrent (over iterations). The latter allows learning of complex shape priors. The method...
Motivated by product detection in supermarkets, this paper studies the problem of object proposal generation in supermarket images and other natural images. We argue that estimation of object scales in images is helpful for generating object proposals, especially for supermarket images where object scales are usually within a small range. Therefore, we propose to estimate object scales of images before...
This paper proposes a deep neural network structure that exploits edge information in addressing representative low-level vision tasks such as layer separation and image filtering. Unlike most other deep learning strategies applied in this context, our approach tackles these challenging problems by estimating edges and reconstructing images using only cascaded convolutional layers arranged such that...
A lot of tools are developed for AI (Artificial Intelligent) development. These tools are easy to use and the number of kinds of the tools are increasing quickly with new research results, therefore they are widely utilized for AI development in nowadays. A research issue here we need to solve is to provide methods for reducing training samples for AI development. The research issue comes from the...
The demand of High Definition Maps (HD-Maps) has been increasing, especially for autonomous vehicle application. Usually, HD-Map is created by scanning the road using LiDAR sensor and reconstructing the road on 3D world to capture all aspects of road properties. One of the important properties of a road is its edge or boundary. In this paper, we propose end-to-end 3D Encoder-Decoder Convolutional...
In this study, we propose a novel shape-based traffic sign detection method which consists of two stages. First, a rotational symmetry voting scheme is proposed to detect the centers and boundary sets of the candidate polygons in the image. Second, a Link Distribution (LD) model, which considers a polygon as the collection of links between center and boundary points, is proposed to refine the detection...
We present a new deep learning-based approach for dense stereo matching. Compared to previous works, our approach does not use deep learning of pixel appearance descriptors, employing very fast classical matching scores instead. At the same time, our approach uses a deep convolutional network to predict the local parameters of cost volume aggregation process, which in this paper we implement using...
Camera tamper detection is the ability to detect faults and operational failures in video surveillance cameras by analyzing the video. Researchers have increasingly focused on such techniques attributing to the ubiquitous deployment of large scale surveillance systems. In this paper, a signal detection theory approach is proposed to quantitatively analyze the information being captured by the camera...
Currently, the American Sign Language (ASL), expressed through the use of body gestures (hand, face, torso) and perceived through the eyes, is the standard language of communication used by the Deaf community. Our main objective is to implement an automated translation system that is capable of translating ASL to English text using common computing environments such as a computer and a generic webcam...
This paper proposes an insulator defect detection algorithm based on computer vision for helicopter aerial insulator imaging in complex backgrounds. The algorithm runs fast with high detection accuracy, which meets the requirements for detecting missing insulators. However, because the background of the insulator image acquired by aerial photography is complicated and there is more than one insulator...
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