SIFT Saliency Analysis for Matching Repetitive Structures

The ambiguity resulting from repetitive structures in a scene presents a major challenge for image matching. This paper proposes a matching method based on SIFT feature saliency analysis to achieve robust feature matching between images with repetitive structures. The feature saliency within the reference image is estimated by analyzing feature stability and dissimilarity via Monte-Carlo simulation. In the proposed method, feature matching is performed only within the region of interest to reduce the ambiguity caused by repetitive structures. The experimental results demonstrate the efficiency and robustness of the proposed method, especially in the presence of respective structures

Nonlinear vibration of rectangular plate under the parametric excitation

In this paper, the dynamic behavior of rectangular plate under the in-plane load is studied. The partial differential equation based on the mechanical model is established, which will be deduced into two ordinary differential equations by use of Galerkin method. The existence of 1/2 harmonic solutions of the dynamical system applying the harmonic balance method is analyzed. The amplitude-frequency relationship is found, and the stability of solutions is investigated. The stable zone of dynamical system is determined

M-Tuning: Prompt Tuning with Mitigated Label Bias in Open-Set Scenarios

In realistic open-set scenarios where labels of a part of testing data are totally unknown, when vision-language (VL) prompt learning methods encounter inputs related to unknown classes (i.e., not seen during training), they always predict them as one of the training classes. The exhibited label bias causes difficulty in open set recognition (OSR), in which an image should be correctly predicted as one of the known classes or the unknown one. To achieve this goal, we propose a vision-language prompt tuning method with mitigated label bias (M-Tuning). It introduces open words from the WordNet to extend the range of words forming the prompt texts from only closed-set label words to more, and thus prompts are tuned in a simulated open-set scenario. Besides, inspired by the observation that classifying directly on large datasets causes a much higher false positive rate than on small datasets, we propose a Combinatorial Tuning and Testing (CTT) strategy for improving performance. CTT decomposes M-Tuning on large datasets as multiple independent group-wise tuning on fewer classes, then makes accurate and comprehensive predictions by selecting the optimal sub-prompt. Finally, given the lack of VL-based OSR baselines in the literature, especially for prompt methods, we contribute new baselines for fair comparisons. Our method achieves the best performance on datasets with various scales, and extensive ablation studies also validate its effectiveness

SimLocator: robust locator of similar objects in images

International audienceSimilar objects commonly appear in natural images, and locating and cutting out these objects can be tedious when using classical interactive image segmentation methods. In this paper, we propose SimLocator, a robust method oriented to locate and cut out similar objects with minimum user interaction. After extracting an arbitrary object template from the input image, candidate locations of similar objects are roughly detected by distinguishing the shape and color features of each image. A novel optimization method is then introduced to select accurate locations from the two sets of candidates. Additionally, a mattingbased method is used to improve the results and to ensure that all similar objects are located in the image. Finally, a method based on alpha matting is utilized to extract the precise object contours. To ensure the performance of the matting operation, this work has developed a new method for foreground extraction. Experiments show that SimLocator is more robust and more convenient to use compared to other more advanced repetition detection and interactive image segmentation methods, in terms of locating similar objects in images

Liquid phase blockage in micro-nano capillary pores of tight condensate reservoirs

The development of tight condensate gas reservoirs faces complex formation damage mechanisms, seepage characteristics and hydrocarbon phase changes, which are common challenges for both tight gas reservoirs and condensate gas reservoirs. In the near-well area, the liquid phase blockage problem due to water phase retention formed by capillary spontaneous imbibition of invasive water and oil phase accumulation due to retrograde condensation precipitation has become a key obstacle to the efficient development of tight condensate gas reservoirs. Experiments were conducted to evaluate the damage of liquid phase blockage under different conditions near the wellbore area. The results show that when the liquid phase saturation in the near-wellbore area increased to 80.12%, the relative permeability of the gas phase decreased to 0. It is concluded that the mixed wettability of formation rocks, ultra-low water saturation, abundant hydrophilic clay minerals and high capillary resistance of micro-nano pores are the main causes for the easy adsorption and retention of liquid phase. Reduced pressure transmission capacity and irreversible formation damage induced by liquid-phase blockage are the two major controlling factors for the low liquid phase flowback rate. It is suggested that developing a flowback system based on the formation physical properties differentiation to control water phase invasion, and changing wettability or injecting thermochemical fluid to control condensate blocking are feasible methods to relieve liquid phase blockage damage in tight condensate reservoirs.Cited as: Wang, Y., Kang, Y., Wang, D., You, L., Chen, M., Yan, X. Liquid phase blockage in micro-nano capillary pores of tight condensate reservoirs. Capillarity, 2022, 5(1): 12-22. https://doi.org/10.46690/capi.2022.01.0