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  1. Anomaly analysis is an important component of any surveillance system. In recent years, it has drawn the attention of the computer vision and machine learning communities. In this article, our overarching goal is thus to provide a coherent and systematic review of state-of-the-art techniques and a comprehensive review of the research works in anomaly analysis. We will provide a broad vision of computational models, datasets, metrics, extensive experiments, and what anomaly analysis can do in images and videos. Intensively covering nearly 200 publications, we review (i) anomaly related surveys, (ii) taxonomy for anomaly problems, (iii) the computational models, (iv) the benchmark datasets for studying abnormalities in images and videos, and (v) the performance of state-of-the-art methods in this research problem. In addition, we provide insightful discussions and pave the way for future work. 
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    Free, publicly-accessible full text available July 31, 2024
  2. In this paper, we investigate the career path prediction of an individual in the future. This benefits a variety of application in the industry including enhancing human resources, career guidance, and keeping track of future trends. To this end, we collected a dataset via LinkedIn network, with the job position and the job domain for each individual. There are many attributes related to historical background for each individual. For the career prediction, we investigate six different multi-class multi-output classification methods. Via the benchmark suite, the best classifier achieves an accuracy rate of 91.21% and 95.97% for the job domain and the job position, respectively. 
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    Free, publicly-accessible full text available March 22, 2024
  3. Portrait cartoonization aims at translating a portrait image to its cartoon version, which guarantees two conditions, namely, reducing textural details and synthesizing cartoon facial features (e.g., big eyes or line-drawing nose). To address this problem, we propose a two-stage training scheme based on GAN, which is powerful for stylization problems. The abstraction stage with a novel abstractive loss is used to reduce textural details. Meanwhile, the perception stage is adopted to synthesize cartoon facial features. To comprehensively evaluate the proposed method and other state-of-the-art methods for portrait cartoonization, we contribute a new challenging large-scale dataset named CartoonFace10K. In addition, we find that the popular metric FID focuses on the target style yet ignores the preservation of the input image content. We thus introduce a novel metric FISI, which compromises FID and SSIM to focus on both target features and retaining input content. Quantitative and qualitative results demonstrate that our proposed method outperforms other state-of-the-art methods. 
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    Free, publicly-accessible full text available March 1, 2024
  4. Free, publicly-accessible full text available March 1, 2024
  5. Image synthesis is a process of converting the input text, sketch, or other sources, i.e., another image or mask, into an image. It is an important problem in the computer vision field, where it has attracted the research community to attempt to solve this challenge at a high level to generate photorealistic images. Different techniques and strategies have been employed to achieve this purpose. Thus, the aim of this paper is to provide a comprehensive review of various image synthesis models covering several aspects. First, the image synthesis concept is introduced. We then review different image synthesis methods divided into three categories: image generation from text, sketch, and other inputs, respectively. Each sub-category is introduced under the proper category based upon the general framework to provide a broad vision of all existing image synthesis methods. Next, brief details of the benchmarked datasets used in image synthesis are discussed along with specifying the image synthesis models that leverage them. Regarding the evaluation, we summarize the metrics used to evaluate the image synthesis models. Moreover, a detailed analysis based on the evaluation metrics of the results of the introduced image synthesis is provided. Finally, we discuss some existing challenges and suggest possible future research directions. 
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  6. The price of a house depends on many factors, such as its size, location, amenities, surrounding establishments, and the season in which the house is being sold, just to name a few of them. As a seller, it is absolutely essential to price the property competitively else it will not attract any buyers. This problem has given rise to multiple companies as well as past research works that try to enhance the predictability of property prices using relevant mathematical models and machine learning techniques. In this research, we investigate the usage of machine learning in predicting the house price based on related estate attributes and visual images. To this end, we collect a dataset of 2,000 houses across different cities in the United States. For each house, we annotate 14 estate attributes and five visual images for exterior, interior-living room, kitchen, bedroom, and bathroom. Following the dataset collection, different features are extracted from the input data. Furthermore, a multi-kernel regression approach is used to predict the house price from both visual cues and estate attributes. The extensive experiments demonstrate the superiority of the proposed method over the baselines. 
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  7. Photobombing occurs very often in photography. This causes inconvenience to the main person(s) in the photos. Therefore, there is a legitimate need to remove the photobombing from taken images to produce a pleasing image. In this paper, the aim is to conduct a benchmark on this aforementioned problem. To this end, we first collect a dataset of images with undesired and distracting elements which requires the removal of photobombing. Then, we annotate the photobombed regions which should be removed. Next, different image inpainting methods are leveraged to remove the photobombed regions and reconstruct the image. We further invited professional photoshoppers to remove the unwanted regions. These photoshopped images are considered as the groundtruth. In our benchmark, several performance metrics are leveraged to compare the results of different methods with the groundtruth. The experiments provide insightful results which demonstrate the effectiveness of inpainting methods in this particular problem. 
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  8. In developing countries, high schoolers rarely have opportunities to conduct chemical experiments due to the lack of facilities. There-fore, chemistry experiment simulation is an alternative environment for students to do the chemistry lab assignments. Despite the need of creating virtual simulations to expand the application usability, it is challenging to synthesize a realistic environment given the limited computing resources. In this paper, we propose Chemisim, a highly realistic web-based VR laboratory simulation for students with high quality and usability. In particular, we make use of the fluid simulation system to mimic real chemical reactions. The imple-mented simulation was based on the chemistry assignments in the national education system, consulted by chemical teachers. Then we deployed the simulator on the web to promote a wide range of students usage. The system was evaluated by collecting and analyzing feedback from chemical teachers based on four criteria, namely, convenience, realism, functionality, and preferences. Our experimental findings address educational challenges and produce innovative technical solutions to solve them in developing countries. 
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  9. Big cities are well-known for their traffic congestion and high density of vehicles such as cars, buses, trucks, and even a swarm of motorbikes that overwhelm city streets. Large-scale development projects have exacerbated urban conditions, making traffic congestion more severe. In this paper, we proposed a data-driven city traffic planning simulator. In particular, we make use of the city camera system for traffic analysis. It seeks to recognize the traffic vehicles and traffic flows, with reduced intervention from monitoring staff. Then, we develop a city traffic planning simulator upon the analyzed traffic data. The simulator is used to support metropolitan transportation planning. Our experimental findings address traffic planning challenges and the innovative technical solutions needed to solve them in big cities. 
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  10. Public speaking is one of the most important ways to share ideas with many people in different domains such as education, training, marketing, or healthcare. Being able to master this skill allows the speaker to clearly advocate for their subject and greatly influence others. However, most of the population reported having public speaking anxiety or glossophobia, which prevents them from effectively conveying their messages to others. One of the best solutions is to have a safe and private space to practice speaking in front of others. As a result, this research work is proposed with the overarching goal of providing people with virtual environments to practice in front of simulated audiences. In addition, the proposed work will aim to have live audience feedback and speech analysis details which could be useful for the users. The experiments via a user study provide insights into the proposed public speaking simulator. 
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