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Carles Fernandez; Pau Baiget; Xavier Roca; Jordi Gonzalez |
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Title |
Augmenting Video Surveillance Footage with Virtual Agents for Incremental Event Evaluation |
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2011 |
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Pattern Recognition Letters |
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PRL |
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32 |
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6 |
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878–889 |
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The fields of segmentation, tracking and behavior analysis demand for challenging video resources to test, in a scalable manner, complex scenarios like crowded environments or scenes with high semantics. Nevertheless, existing public databases cannot scale the presence of appearing agents, which would be useful to study long-term occlusions and crowds. Moreover, creating these resources is expensive and often too particularized to specific needs. We propose an augmented reality framework to increase the complexity of image sequences in terms of occlusions and crowds, in a scalable and controllable manner. Existing datasets can be increased with augmented sequences containing virtual agents. Such sequences are automatically annotated, thus facilitating evaluation in terms of segmentation, tracking, and behavior recognition. In order to easily specify the desired contents, we propose a natural language interface to convert input sentences into virtual agent behaviors. Experimental tests and validation in indoor, street, and soccer environments are provided to show the feasibility of the proposed approach in terms of robustness, scalability, and semantics. |
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Admin @ si @ FBR2011b |
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1723 |
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Author |
Marcel P. Lucassen; Theo Gevers; Arjan Gijsenij |
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Title |
Texture Affects Color Emotion |
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2011 |
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Color Research & Applications |
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CRA |
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36 |
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6 |
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426–436 |
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color;texture;color emotion;observer variability;ranking |
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Several studies have recorded color emotions in subjects viewing uniform color (UC) samples. We conduct an experiment to measure and model how these color emotions change when texture is added to the color samples. Using a computer monitor, our subjects arrange samples along four scales: warm–cool, masculine–feminine, hard–soft, and heavy–light. Three sample types of increasing visual complexity are used: UC, grayscale textures, and color textures (CTs). To assess the intraobserver variability, the experiment is repeated after 1 week. Our results show that texture fully determines the responses on the Hard-Soft scale, and plays a role of decreasing weight for the masculine–feminine, heavy–light, and warm–cool scales. Using some 25,000 observer responses, we derive color emotion functions that predict the group-averaged scale responses from the samples' color and texture parameters. For UC samples, the accuracy of our functions is significantly higher (average R2 = 0.88) than that of previously reported functions applied to our data. The functions derived for CT samples have an accuracy of R2 = 0.80. We conclude that when textured samples are used in color emotion studies, the psychological responses may be strongly affected by texture. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2010 |
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ALTRES;ISE |
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Admin @ si @ LGG2011 |
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1844 |
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Mikhail Mozerov; Joost Van de Weijer |
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Title |
One-view occlusion detection for stereo matching with a fully connected CRF model |
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2019 |
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IEEE Transactions on Image Processing |
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TIP |
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28 |
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6 |
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2936-2947 |
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Stereo matching; energy minimization; fully connected MRF model; geodesic distance filter |
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In this paper, we extend the standard belief propagation (BP) sequential technique proposed in the tree-reweighted sequential method [15] to the fully connected CRF models with the geodesic distance affinity. The proposed method has been applied to the stereo matching problem. Also a new approach to the BP marginal solution is proposed that we call one-view occlusion detection (OVOD). In contrast to the standard winner takes all (WTA) estimation, the proposed OVOD solution allows to find occluded regions in the disparity map and simultaneously improve the matching result. As a result we can perform only
one energy minimization process and avoid the cost calculation for the second view and the left-right check procedure. We show that the OVOD approach considerably improves results for cost augmentation and energy minimization techniques in comparison with the standard one-view affinity space implementation. We apply our method to the Middlebury data set and reach state-ofthe-art especially for median, average and mean squared error metrics. |
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LAMP; 600.098; 600.109; 602.133; 600.120;ISE |
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Admin @ si @ MoW2019 |
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3221 |
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Author |
Mikhail Mozerov; Fei Yang; Joost Van de Weijer |
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Title |
Sparse Data Interpolation Using the Geodesic Distance Affinity Space |
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2019 |
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IEEE Signal Processing Letters |
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SPL |
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26 |
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6 |
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943 - 947 |
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In this letter, we adapt the geodesic distance-based recursive filter to the sparse data interpolation problem. The proposed technique is general and can be easily applied to any kind of sparse data. We demonstrate its superiority over other interpolation techniques in three experiments for qualitative and quantitative evaluation. In addition, we compare our method with the popular interpolation algorithm presented in the paper on EpicFlow optical flow, which is intuitively motivated by a similar geodesic distance principle. The comparison shows that our algorithm is more accurate and considerably faster than the EpicFlow interpolation technique. |
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LAMP; 600.120;ISE |
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Admin @ si @ MYW2019 |
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3261 |
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AN Ruchai; VI Kober; KA Dorofeev; VN Karnaukhov; Mikhail Mozerov |
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Classification of breast abnormalities using a deep convolutional neural network and transfer learning |
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2021 |
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Journal of Communications Technology and Electronics |
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66 |
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6 |
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778–783 |
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A new algorithm for classification of breast pathologies in digital mammography using a convolutional neural network and transfer learning is proposed. The following pretrained neural networks were chosen: MobileNetV2, InceptionResNetV2, Xception, and ResNetV2. All mammographic images were pre-processed to improve classification reliability. Transfer training was carried out using additional data augmentation and fine-tuning. The performance of the proposed algorithm for classification of breast pathologies in terms of accuracy on real data is discussed and compared with that of state-of-the-art algorithms on the available MIAS database. |
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Admin @ si @ RKD2022 |
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3680 |
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