Geometric model fitting is a fundamental research topic in computer vision and it aims to fit and segment multiple-structure data. In this paper, we propose a novel superpixel-guided two-view geometric model fitting method (called SDF), which can obtain reliable and consistent results for real images. Specifically, SDF includes three main parts: a deterministic sampling algorithm, a model hypothesis updating strategy and a novel model selection algorithm. The proposed deterministic sampling algorithm generates a set of initial model hypotheses according to the prior information of superpixels. Then the proposed updating strategy further improves the quality of model hypotheses. After that, by analyzing the properties of the updated model hypotheses, the proposed model selection algorithm extends the conventional "fit-and-remove" framework to estimate model instances in multiple-structure data. The three parts are tightly coupled to boost the performance of SDF in both speed and accuracy, and SDF has the deterministic nature. Experimental results show that the proposed SDF has significant advantages over several state-of-the-art fitting methods when it is applied to real images with single-structure and multiple-structure data.
Generally, a good keypoint correspondence between an image pair has a high matching score, while a bad keypoint correspondence has a low matching score. Based on this observation, some fitting methods, e.g., @cite_9 @cite_14 @cite_0 @cite_21 @cite_1 , were proposed to guide sampling minimal subsets, by which they can effectively accelerate the process of promising model hypothesis generation. Most of these fitting methods can work well on single-structure data, but they may fail on multiple-structure data. This is because the keypoint correspondences with high matching scores may belong to different model instances in multiple-structure data, and thus it is hard to distinguish them from the global perspective.
@cite_14 @cite_9 @cite_21 @cite_1 @cite_0
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A sampling method EGHG is proposed for multi-structure geometry estimation.EGHG combines the benefits of a global and a local sampling strategy.The global sampling strategy is designed to rapidly obtain promising solutions.The local sampling strategy is designed to efficiently achieve accurate solutions.Experimental results show the effectiveness of EGHG on public real image pairs. We propose an Efficient Guided Hypothesis Generation (EGHG) method for multi-structure epipolar geometry estimation. Based on the Markov Chain Monte Carlo process, EGHG combines two guided sampling strategies: a global sampling strategy and a local sampling strategy. The global sampling strategy, guided by using both spatial sampling probabilities and keypoint matching scores, rapidly obtains promising solutions. The spatial sampling probabilities are computed by using a normalized exponential loss function. The local sampling strategy, guided by using both Joint Feature Distributions (JFDs) and keypoint matching scores, efficiently achieves accurate solutions. In the local sampling strategy, EGHG updates a set of current best hypothesis candidates on the fly, and then computes JFDs between the input data and these candidates. Experimental results on public real image pairs show that EGHG significantly outperforms several state-of-the-art sampling methods on multi-structure data. Random hypothesis generation underpins many geometric model fitting techniques. Unfortunately it is also computationally expensive. We propose a fundamentally new approach to accelerate hypothesis sampling by guiding it with information derived from residual sorting. We show that residual sorting innately encodes the probability of two points to have arisen from the same model and is obtained without recourse to domain knowledge (e.g. keypoint matching scores) typically used in previous sampling enhancement methods. More crucially our approach is naturally capable of handling data with multiple model instances and excels in applications (e.g. multi-homography fitting) which easily frustrate other techniques. Experiments show that our method provides superior efficiency on various geometric model estimation tasks. Implementation of our algorithm is available on the authors, homepage. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. Finding correspondences between images or 3D scans is at the heart of many computer vision and image retrieval applications and is often enabled by matching local keypoint descriptors. Various learning approaches have been applied in the past to different stages of the matching pipeline, considering detection, description, or metric learning objectives. These objectives were typically addressed separately and most previous work has focused on image data. This paper proposes an end-to-end learning framework for keypoint detection and its representation (descriptor) for 3D depth maps or 3D scans, where the two can be jointly optimized towards task-specific objectives without a need for separate annotations. We employ a Siamese architecture augmented by a sampling layer and a novel score loss function which in turn affects the selection of region proposals. The positive and negative examples are obtained automatically by sampling corresponding region proposals based on their consistency with known 3D pose labels. Matching experiments with depth data on multiple benchmark datasets demonstrate the efficacy of the proposed approach, showing significant improvements over state-of-the-art methods. We develop a robust multi-scale structure-aware neural network for human pose estimation. This method improves the recent deep conv-deconv hourglass models with four key improvements: (1) multi-scale supervision to strengthen contextual feature learning in matching body keypoints by combining feature heatmaps across scales, (2) multi-scale regression network at the end to globally optimize the structural matching of the multi-scale features, (3) structure-aware loss used in the intermediate supervision and at the regression to improve the matching of keypoints and respective neighbors to infer a higher-order matching configurations, and (4) a keypoint masking training scheme that can effectively fine-tune our network to robustly localize occluded keypoints via adjacent matches. Our method can effectively improve state-of-the-art pose estimation methods that suffer from difficulties in scale varieties, occlusions, and complex multi-person scenarios. This multi-scale supervision tightly integrates with the regression network to effectively (i) localize keypoints using the ensemble of multi-scale features, and (ii) infer global pose configuration by maximizing structural consistencies across multiple keypoints and scales. The keypoint masking training enhances these advantages to focus learning on hard occlusion samples. Our method achieves the leading position in the MPII challenge leaderboard among the state-of-the-art methods.