Registration with a small number of sparse measurements

2019 ◽  
Vol 38 (12-13) ◽  
pp. 1403-1419 ◽  
Author(s):  
Rangaprasad Arun Srivatsan ◽  
Nicolas Zevallos ◽  
Prasad Vagdargi ◽  
Howie Choset
Keyword(s):  
Cost Function ◽  
State Of The Art ◽  
Geometric Model ◽  
Visual Features ◽  
Local Optimum ◽  
Initial Condition ◽  
Critical Importance ◽  
Surface Normal ◽  
Sparse Measurements ◽  
Uncertainty Information

This work introduces a method for performing robust registration given the geometric model of an object and a small number (less than 20) of sparse point and surface normal measurements of the object’s surface. Such a method is of critical importance in applications such as probing-based surgical registration, contact-based localization, manipulating objects devoid of visual features, etc. Our approach for sparse point and normal registration (SPNR) is iterative in nature. In each iteration, the current best pose estimate is perturbed to generate several candidate poses. Among the generated poses, one pose is selected as the best, by evaluating an inexpensive cost function. This pose is used as the initial condition to estimate the locally optimum registration. This process is repeated until the registration estimate converges within a tolerance bound. Two variants are developed: deterministic (dSPNR) and probabilistic (pSPNR). The dSPNR is faster than pSPNR in converging to the local optimum, but the pSPNR requires fewer parameters to be tuned. The pSPNR also provides pose-uncertainty information in addition to the registration estimate. Both approaches were evaluated in simulation using various standard datasets and then compared with results obtained using state-of-the-art methods. Upon comparison with other methods, both dSPNR and pSPNR were found to be robust to initial pose errors as well as noise in measurements. The effectiveness of the approaches are also demonstrated with robot experiments for the application of probing-based registration.

scholarly journals Adapting k-means for graph clustering

2021 ◽  
Author(s):  
Sami Sieranoja ◽  
Pasi Fränti
Keyword(s):  
Cost Function ◽  
State Of The Art ◽  
Real Life ◽  
Local Optimization ◽  
Cost Functions ◽  
Local Optimum ◽  
Local Optima ◽  
Graphs And Networks ◽  
New Algorithms

AbstractWe propose two new algorithms for clustering graphs and networks. The first, called K‑algorithm, is derived directly from the k-means algorithm. It applies similar iterative local optimization but without the need to calculate the means. It inherits the properties of k-means clustering in terms of both good local optimization capability and the tendency to get stuck at a local optimum. The second algorithm, called the M-algorithm, gradually improves on the results of the K-algorithm to find new and potentially better local optima. It repeatedly merges and splits random clusters and tunes the results with the K-algorithm. Both algorithms are general in the sense that they can be used with different cost functions. We consider the conductance cost function and also introduce two new cost functions, called inverse internal weight and mean internal weight. According to our experiments, the M-algorithm outperforms eight other state-of-the-art methods. We also perform a case study by analyzing clustering results of a disease co-occurrence network, which demonstrate the usefulness of the algorithms in an important real-life application.

scholarly journals Efficient Rank-Based Diffusion Process with Assured Convergence

2021 ◽  
Vol 7 (3) ◽  
pp. 49
Author(s):  
Daniel Carlos Guimarães Pedronette ◽  
Lucas Pascotti Valem ◽  
Longin Jan Latecki
Keyword(s):  
Diffusion Process ◽  
Learning Strategies ◽  
State Of The Art ◽  
Representation Learning ◽  
Theoretical Background ◽  
High Dimensional ◽  
Visual Features ◽  
Learning Approaches ◽  
Previous Decade ◽  
Asymptotic Complexity

Visual features and representation learning strategies experienced huge advances in the previous decade, mainly supported by deep learning approaches. However, retrieval tasks are still performed mainly based on traditional pairwise dissimilarity measures, while the learned representations lie on high dimensional manifolds. With the aim of going beyond pairwise analysis, post-processing methods have been proposed to replace pairwise measures by globally defined measures, capable of analyzing collections in terms of the underlying data manifold. The most representative approaches are diffusion and ranked-based methods. While the diffusion approaches can be computationally expensive, the rank-based methods lack theoretical background. In this paper, we propose an efficient Rank-based Diffusion Process which combines both approaches and avoids the drawbacks of each one. The obtained method is capable of efficiently approximating a diffusion process by exploiting rank-based information, while assuring its convergence. The algorithm exhibits very low asymptotic complexity and can be computed regionally, being suitable to outside of dataset queries. An experimental evaluation conducted for image retrieval and person re-ID tasks on diverse datasets demonstrates the effectiveness of the proposed approach with results comparable to the state-of-the-art.

scholarly journals Imbalanced Learning Based on Logistic Discrimination

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Huaping Guo ◽  
Weimei Zhi ◽  
Hongbing Liu ◽  
Mingliang Xu
Keyword(s):  
Statistical Model ◽  
Cost Function ◽  
State Of The Art ◽  
Class Imbalance ◽  
Imbalanced Learning ◽  
Learning Problem ◽  
Logistic Discrimination ◽  
Positive Class ◽  
Negative Class ◽  
Novel Method

In recent years, imbalanced learning problem has attracted more and more attentions from both academia and industry, and the problem is concerned with the performance of learning algorithms in the presence of data with severe class distribution skews. In this paper, we apply the well-known statistical model logistic discrimination to this problem and propose a novel method to improve its performance. To fully consider the class imbalance, we design a new cost function which takes into account the accuracies of both positive class and negative class as well as the precision of positive class. Unlike traditional logistic discrimination, the proposed method learns its parameters by maximizing the proposed cost function. Experimental results show that, compared with other state-of-the-art methods, the proposed one shows significantly better performance on measures of recall,g-mean,f-measure, AUC, and accuracy.

scholarly journals Pay Attention to Devils: A Photometric Stereo Network for Better Details

2020 ◽  
Author(s):  
Yakun Ju ◽  
Kin-Man Lam ◽  
Yang Chen ◽  
Lin Qi ◽  
Junyu Dong
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Photometric Stereo ◽  
Normal Map ◽  
Surface Normal ◽  
3D Surface ◽  
Surface Reconstructions ◽  
Higher Weights

We present an attention-weighted loss in a photometric stereo neural network to improve 3D surface recovery accuracy in complex-structured areas, such as edges and crinkles, where existing learning-based methods often failed. Instead of using a uniform penalty for all pixels, our method employs the attention-weighted loss learned in a self-supervise manner for each pixel, avoiding blurry reconstruction result in such difficult regions. The network first estimates a surface normal map and an adaptive attention map, and then the latter is used to calculate a pixel-wise attention-weighted loss that focuses on complex regions. In these regions, the attention-weighted loss applies higher weights of the detail-preserving gradient loss to produce clear surface reconstructions. Experiments on real datasets show that our approach significantly outperforms traditional photometric stereo algorithms and state-of-the-art learning-based methods.

Convergence of Simulated Annealing with Feedback Temperature Schedules

1997 ◽  
Vol 11 (3) ◽  
pp. 279-304 ◽  
Author(s):  
M. Kolonko ◽  
M. T. Tran
Keyword(s):  
Simulated Annealing ◽  
Cost Function ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Method ◽  
Search Process ◽  
Local Optimum ◽  
Fixed Sequence ◽  
Temperature Parameter ◽  
The Cost

It is well known that the standard simulated annealing optimization method converges in distribution to the minimum of the cost function if the probability a for accepting an increase in costs goes to 0. α is controlled by the “temperature” parameter, which in the standard setup is a fixed sequence of values converging slowly to 0. We study a more general model in which the temperature may depend on the state of the search process. This allows us to adapt the temperature to the landscape of the cost function. The temperature may temporarily rise such that the process can leave a local optimum more easily. We give weak conditions on the temperature schedules such that the process of solutions finally concentrates near the optimal solutions. We also briefly sketch computational results for the job shop scheduling problem.

Fauce

2021 ◽  
Vol 14 (11) ◽  
pp. 1950-1963
Author(s):  
Jie Liu ◽  
Wenqian Dong ◽  
Qingqing Zhou ◽  
Dong Li
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
The State ◽  
Light Weight ◽  
Critical Problem ◽  
Estimation Errors ◽  
Cardinality Estimation ◽  
Complex Queries ◽  
Uncertainty Information ◽  
Deep Learning Model

Cardinality estimation is a fundamental and critical problem in databases. Recently, many estimators based on deep learning have been proposed to solve this problem and they have achieved promising results. However, these estimators struggle to provide accurate results for complex queries, due to not capturing real inter-column and inter-table correlations. Furthermore, none of these estimators contain the uncertainty information about their estimations. In this paper, we present a join cardinality estimator called Fauce. Fauce learns the correlations across all columns and all tables in the database. It also contains the uncertainty information of each estimation. Among all studied learned estimators, our results are promising: (1) Fauce is a light-weight estimator, it has 10× faster inference speed than the state of the art estimator; (2) Fauce is robust to the complex queries, it provides 1.3×--6.7× smaller estimation errors for complex queries compared with the state of the art estimator; (3) To the best of our knowledge, Fauce is the first estimator that incorporates uncertainty information for cardinality estimation into a deep learning model.

The growing role of structural mass spectrometry in the discovery and development of therapeutic antibodies

The Analyst ◽  
2018 ◽  
Vol 143 (11) ◽  
pp. 2459-2468 ◽  
Author(s):  
Yuwei Tian ◽  
Brandon T. Ruotolo
Keyword(s):  
Mass Spectrometry ◽  
State Of The Art ◽  
Therapeutic Antibodies ◽  
Critical Importance ◽  
Current State ◽  
Measurement Science ◽  
Structural Mass Spectrometry ◽  
Structural Mass

The comprehensive structural characterization of therapeutic antibodies is of critical importance for the successful discovery and development of such biopharmaceuticals, yet poses many challenges to modern measurement science. Here, we review the current state-of-the-art mass spectrometry technologies focusing on the characterization of antibody-based therapeutics.

scholarly journals Outlier Detection Based on Residual Histogram Preference for Geometric Multi-Model Fitting

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3037
Author(s):  
Xi Zhao ◽  
Yun Zhang ◽  
Shoulie Xie ◽  
Qianqing Qin ◽  
Shiqian Wu ◽  
...  
Keyword(s):  
Outlier Detection ◽  
State Of The Art ◽  
Geometric Model ◽  
Model Fitting ◽  
Distribution Model ◽  
Data Sets ◽  
Analysis Method ◽  
Detection Scheme ◽  
The Impact ◽  
Better Than

Geometric model fitting is a fundamental issue in computer vision, and the fitting accuracy is affected by outliers. In order to eliminate the impact of the outliers, the inlier threshold or scale estimator is usually adopted. However, a single inlier threshold cannot satisfy multiple models in the data, and scale estimators with a certain noise distribution model work poorly in geometric model fitting. It can be observed that the residuals of outliers are big for all true models in the data, which makes the consensus of the outliers. Based on this observation, we propose a preference analysis method based on residual histograms to study the outlier consensus for outlier detection in this paper. We have found that the outlier consensus makes the outliers gather away from the inliers on the designed residual histogram preference space, which is quite convenient to separate outliers from inliers through linkage clustering. After the outliers are detected and removed, a linkage clustering with permutation preference is introduced to segment the inliers. In addition, in order to make the linkage clustering process stable and robust, an alternative sampling and clustering framework is proposed in both the outlier detection and inlier segmentation processes. The experimental results also show that the outlier detection scheme based on residual histogram preference can detect most of the outliers in the data sets, and the fitting results are better than most of the state-of-the-art methods in geometric multi-model fitting.

scholarly journals Zero-Shot Learning from Adversarial Feature Residual to Compact Visual Feature

2020 ◽  
Vol 34 (07) ◽  
pp. 11547-11554
Author(s):  
Bo Liu ◽  
Qiulei Dong ◽  
Zhanyi Hu
Keyword(s):  
State Of The Art ◽  
Feature Space ◽  
Visual Features ◽  
Selection Strategy ◽  
Semantic Features ◽  
Visual Feature ◽  
Adversarial Network ◽  
Benchmark Datasets ◽  
Residual Generator ◽  
Object Features

Recently, many zero-shot learning (ZSL) methods focused on learning discriminative object features in an embedding feature space, however, the distributions of the unseen-class features learned by these methods are prone to be partly overlapped, resulting in inaccurate object recognition. Addressing this problem, we propose a novel adversarial network to synthesize compact semantic visual features for ZSL, consisting of a residual generator, a prototype predictor, and a discriminator. The residual generator is to generate the visual feature residual, which is integrated with a visual prototype predicted via the prototype predictor for synthesizing the visual feature. The discriminator is to distinguish the synthetic visual features from the real ones extracted from an existing categorization CNN. Since the generated residuals are generally numerically much smaller than the distances among all the prototypes, the distributions of the unseen-class features synthesized by the proposed network are less overlapped. In addition, considering that the visual features from categorization CNNs are generally inconsistent with their semantic features, a simple feature selection strategy is introduced for extracting more compact semantic visual features. Extensive experimental results on six benchmark datasets demonstrate that our method could achieve a significantly better performance than existing state-of-the-art methods by ∼1.2-13.2% in most cases.

scholarly journals Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning

2019 ◽  
Vol 33 ◽  
pp. 6722-6729 ◽  
Author(s):  
Ziming Li ◽  
Julia Kiseleva ◽  
Maarten De Rijke
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Imitation Learning ◽  
Local Optimum ◽  
Inverse Reinforcement Learning ◽  
High Quality ◽  
Overall Performance

The performance of adversarial dialogue generation models relies on the quality of the reward signal produced by the discriminator. The reward signal from a poor discriminator can be very sparse and unstable, which may lead the generator to fall into a local optimum or to produce nonsense replies. To alleviate the first problem, we first extend a recently proposed adversarial dialogue generation method to an adversarial imitation learning solution. Then, in the framework of adversarial inverse reinforcement learning, we propose a new reward model for dialogue generation that can provide a more accurate and precise reward signal for generator training. We evaluate the performance of the resulting model with automatic metrics and human evaluations in two annotation settings. Our experimental results demonstrate that our model can generate more high-quality responses and achieve higher overall performance than the state-of-the-art.

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