This work was performed under the following financial assistance award: 60NANB18D279 from U.S. Department of Commerce, National Institute of Standards and Technology, and also funding from SAP SE
[width=.17 ] img_orig_002081.jpg & [width=.17 ] mask_reg_patch_002081_orig_019_target_010_pred_010.JPEG & [width=.17 ] occ_reg_patch_adv_img_002081_10.png & [width=.17 ] mask_our_patch_002081_orig_019_target_010_pred_010.png & [width=.17 ] occ_our_patch_adv_img_002081_10.png TV Monitor & Dining Table & Dining Table & Dining Table & Dining Table [width=.17 ] img_orig_002245.jpg & [width=.17 ] mask_reg_patch_002245_orig_005_target_019_pred_019.JPEG & [width=.17 ] occ_reg_patch_adv_img_002245_19.png & [width=.17 ] mask_our_patch_002245_orig_005_target_019_pred_019.png & [width=.17 ] occ_our_patch_adv_img_002245_19.png Bus & TV Monitor & TV Monitor & TV Monitor & TV Monitor [width=.17 ] img_orig_003019.jpg & [width=.17 ] mask_reg_patch_003019_orig_007_target_001_pred_001.JPEG & [width=.17 ] occ_reg_patch_adv_img_003019_1.png & [width=.17 ] mask_our_patch_003019_orig_007_target_001_pred_001.png & [width=.17 ] occ_our_patch_adv_img_003019_1.png Cat & Bicycle & Bicycle & Bicycle & Bicycle [width=.17 ] img_orig_003069.jpg & [width=.17 ] mask_reg_patch_003069_orig_016_target_015_pred_015.JPEG & [width=.17 ] occ_reg_patch_adv_img_003069_15.png & [width=.17 ] mask_our_patch_003069_orig_016_target_015_pred_015.png & [width=.17 ] occ_our_patch_adv_img_003069_15.png Sheep & Potted Plant & Potted Plant & Potted Plant & Potted Plant @cite_6 network for VOC dataset. The predicted label is written under each image, the attack was successful for all images, and Grad-CAM and Occluding Patch visualizations are always computed for the target category. Note that the patch is hidden in both visualizations in columns 4 and 5.
@cite_6
1975020933 2207282238 2400779783 2344129934
In this paper, we propose a new classification method for five categories of lung tissues in high-resolution computed tomography (HRCT) images, with feature-based image patch approximation. We design two new feature descriptors for higher feature descriptiveness, namely the rotation-invariant Gabor-local binary patterns (RGLBP) texture descriptor and multi-coordinate histogram of oriented gradients (MCHOG) gradient descriptor. Together with intensity features, each image patch is then labeled based on its feature approximation from reference image patches. And a new patch-adaptive sparse approximation (PASA) method is designed with the following main components: minimum discrepancy criteria for sparse-based classification, patch-specific adaptation for discriminative approximation, and feature-space weighting for distance computation. The patch-wise labelings are then accumulated as probabilistic estimations for region-level classification. The proposed method is evaluated on a publicly available ILD database, showing encouraging performance improvements over the state-of-the-arts. Patch based image modeling has achieved a great success in low level vision such as image denoising. In particular, the use of image nonlocal self-similarity (NSS) prior, which refers to the fact that a local patch often has many nonlocal similar patches to it across the image, has significantly enhanced the denoising performance. However, in most existing methods only the NSS of input degraded image is exploited, while how to utilize the NSS of clean natural images is still an open problem. In this paper, we propose a patch group (PG) based NSS prior learning scheme to learn explicit NSS models from natural images for high performance denoising. PGs are extracted from training images by putting nonlocal similar patches into groups, and a PG based Gaussian Mixture Model (PG-GMM) learning algorithm is developed to learn the NSS prior. We demonstrate that, owe to the learned PG-GMM, a simple weighted sparse coding model, which has a closed-form solution, can be used to perform image denoising effectively, resulting in high PSNR measure, fast speed, and particularly the best visual quality among all competing methods. Enhanced particle filter tracker by latent occlusion flag to handle full occlusion.Handled persistent and or complex occlusions in RGBD sequences.Developed data-driven occlusion mask to evaluate various parts of observation.Fused multiple feature from color and depth domains to gain occlusion robustness. Although appearance-based trackers have been greatly improved in the last decade, they still struggle with challenges that are not fully resolved. Of these challenges, occlusions, which can be long lasting and of a wide variety, are often ignored or only partly addressed due to the difficulty in their treatments. To address this problem, in this study, we propose an occlusion-aware particle filter framework that employs a probabilistic model with a latent variable representing an occlusion flag. The proposed framework prevents losing the target by prediction of emerging occlusions, updates the target template by shifting relevant information, expands the search area for an occluded target, and grants quick recovery of the target after occlusion. Furthermore, the algorithm employs multiple features from the color and depth domains to achieve robustness against illumination changes and clutter, so that the probabilistic framework accommodates the fusion of those features. This method was applied to the Princeton RGBD Tracking Dataset, and the performance of our method with different sets of features was compared with those of the state-of-the-art trackers. The results revealed that our method outperformed the existing RGB and RGBD trackers by successfully dealing with different types of occlusions. The purpose of this paper is the detection of salient areas in natural video by using the new deep learning techniques. Salient patches in video frames are predicted first. Then the predicted visual fixation maps are built upon them. We design the deep architecture on the basis of CaffeNet implemented with Caffe toolkit. We show that changing the way of data selection for optimisation of network parameters, we can save computation cost up to @math times. We extend deep learning approaches for saliency prediction in still images with RGB values to specificity of video using the sensitivity of the human visual system to residual motion. Furthermore, we complete primary colour pixel values by contrast features proposed in classical visual attention prediction models. The experiments are conducted on two publicly available datasets. The first is IRCCYN video database containing @math videos with an overall amount of @math frames and eye fixations of @math subjects. The second one is HOLLYWOOD2 provided @math movie clips with the eye fixations of @math subjects. On IRCYYN dataset, the accuracy obtained is of @math . On HOLLYWOOD2 dataset, results in prediction of saliency of patches show the improvement up to @math with regard to RGB use only. The resulting accuracy of @math is obtained. The AUC metric in comparison of predicted saliency maps with visual fixation maps shows the increase up to @math on a sample of video clips from this dataset.