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Consensus maximisation (MaxCon), which is widely used for robust fitting in computer vision, aims to find the largest subset of data that fits the model within some tolerance level. In this paper, we outline the connection between MaxCon problem and the abstract problem of finding the maximum upper zero of a Monotone Boolean Function (MBF) defined over the Boolean Cube. Then, we link the concept of influences (in a MBF) to the concept of outlier (in MaxCon) and show that influences of points belonging to the largest structure in data would generally be smaller under certain conditions. Based on this observation, we present an iterative algorithm to perform consensus maximisation. Results for both synthetic and real visual data experiments show that the MBF based algorithm is capable of generating a near optimal solution relatively quickly. This is particularly important where there are large number of outliers (gross or pseudo) in the observed data., To appear in CVPR 2021 as an ORAL paper. arXiv admin note: text overlap with arXiv:2005.05490

Low visibility hazard detection in construction sites is a crucial task for prevention of fatal accidents. Manual monitoring of construction workers to ensure they follow the safety rules (e.g., wear high-visibility vests) is a cumbersome task and practically infeasible in many applications. Therefore, an automated monitoring system is of both fundamental and practical interest. This paper proposes an intelligent solution that uses live camera images to detect workers who breach safety rules by not wearing high-visibility vests. The proposed solution is formulated in the form of an anomaly detection algorithm developed in the random finite set (RFS) framework. The proposed system is comprised of three steps: 1) applying a deep neural network to extract people in the image; 2) extracting particularly engineered features from each blob returned by the deep neural network; and 3) applying the RFS-based anomaly detection algorithm to each set of detected features. The experimental results demonstrate that in terms of F1-score, the proposed solution (as the combination of the newly engineered features and RFS-based anomaly detection algorithm) significantly outperforms various combinations of common and the state-of-the-art features and anomaly detection algorithms employed in machine vision applications.

Three-dimensional point clouds produced by 3D scanners are often noisy and contain outliers. Such data inaccuracies can significantly affect current deep learning-based methods and reduce their ability to classify objects. Most deep neural networks-based object classification methods were targeted to achieve high classification accuracy without considering classification robustness. Thus, despite their great success, they still fail to achieve good classification accuracy with low levels of noise and outliers. This work is carried out to develop a robust network structure that can solidly identify objects. The proposed method uses patches of planar segments, which can robustly capture object appearance. The planar segments information are then fed into a deep neural network for classification. We base our approach on the PointNet deep learning architecture. Our method was tested against several kinds of data inaccuracies such as scattered outliers, clustered outliers, noise and missing points. The proposed method shows excellent performance in the presence of these inaccuracies compared to state-of-the-art techniques. By decomposing objects into planes, the suggested method is simple, fast, provides good classification accuracy and can handle different kinds of point cloud data inaccuracies. The code can be found at https://github.com/AymanMukh/Pl-Net3D.

The accuracy of handwritten text recognition may be affected by the presence of struck-out text in the handwritten manuscript. This paper investigates and improves the performance of a widely used handwritten text recognition approach Convolutional Recurrent Neural Network (CRNN) on handwritten lines containing struck out words. For this purpose, some common types of struck-out strokes were superimposed on words in a text line. A model, trained on the IAM line database was tested on lines containing struck-out words. The Character Error Rate (CER) increased from 0.09 to 0.11. This model was re-trained on dataset containing struck-out text. The model performed well in terms of struck-out text detection. We found that after providing an adequate number of training examples, the model can deal with learning struck-out patterns in a way that does not affect the overall recognition accuracy.

With the continuous process of urbanization in many cities around the globe, construction works are increasingly interrupting traffic flow in urban roads. At construction sites, usually traffic control personnel are employed to instruct the traffic flow at the times obstruction. Employing manual labour flagmen for traffic control is costly and can potentially expose the workers to safety hazards. Alternative attempts such as using remotely controlled signs or timers are not ideal; the former still requires human input and the latter does not optimize traffic control based on traffic density. To tackle this problem, we propose a fully automated traffic controller solution that uses visual sensors and machine vision technology. This system can replace the human traffic controllers during lane closures on the event of a construction. The implementation of this system eliminates the requirement for conventional traffic controllers, reducing the overall health and safety risks to workers and drivers. Our solution can result in substantial cost savings by replacing the traffic controllers. In addition, with manual labour, traffic control performance can be affected by weather conditions or fatigue from long hour shifts. Thus, our solution can lead to improved road safety and robustness of traffic flow as well.

Classification of 3D shapes into physically meaningful categories is one of the most important tasks in understanding the immediate environment. Methods that leverage the recent advancements in deep learning have shown to outperform the traditional approaches. However, performances of those methods have only been analyzed with relatively clean data. Three-dimensional measurement sets (point clouds) produced by 3D scanners are rarely that accurate and often contain noise, outliers or missing points. This paper presents an extensive analysis of the robustness of the state-of-the-art neural network algorithms to realistic data inaccuracies. Our experiments show that the existence of these inaccuracies can significantly affect the performance of the deep learning-based algorithms.

In this paper we present a novel non-Bayesian filtering method for tracking multiple objects with a particular application in time-lapse cell microscopic video sequence. In our method the heat-map of the frame sequence is extracted and represented as a pseudo-probability hypothesis density of the image. The pseudo-probability hypothesis density is used as measurements and fused with a prior Poisson random finite set density. We employed Cauchy-Schwarz divergence for information fusion. The presented algorithm was tested on a publicly available cell microscopic video sequence.

This paper proposes a novel method in order to obtain voxel-level segmentation for three fluid lesion types (IR-F/SRF/PED) in OCT images provided by the ReTOUCH challenge [1]. The method is based on a deep neural network consisting of encoding and de-coding blocks connected with skip-connections which was trained using a combined cost function comprising of cross-entropy, dice and adversarial loss terms. The segmentation results on a held-out validation set shows that the network architecture and the loss functions used has resulted in improved retinal fluid segmentation. Our method was ranked fourth in the ReTOUCH challenge.

The fovea is one of the most important anatomical landmarks in the eye and its localization is required in automated analysis of retinal diseases due to its role in sharp central vision. In this paper, we propose a two-stage deep learning framework for accurate segmentation of the fovea in retinal colour fundus images. In the first stage, coarse segmentation is performed to localize the fovea in the fundus image. The location information from the first stage is then used to perform fine-grained segmentation of the fovea region in the second stage. The proposed method performs end-to-end pixelwise segmentation by creating a deep learning model based on fully convolutional neural networks, which does not require the prior knowledge of the location of other retinal structures such as optic disc (OD) and vasculature geometry. We demonstrate the effectiveness of our method on a dataset with 400 retinal images with average localization error of 14 ± 7 pixels.

Diabetic Retinopathy (DR) is one of the leading causes of blindness worldwide. Detecting DR and grading its severity is essential for disease treatment. Convolutional neural networks (CNNs) have achieved state-of-the-art performance in many different visual classification tasks. In this paper, we propose to combine CNNs with dictionary based approaches, which incorporates pathology specific image representation into the learning framework, for improved DR severity classification. Specifically, we construct discriminative and generative pathology histograms and combine them with feature representations extracted from fully connected CNN layers. Our experimental results indicate that the proposed method shows improvement in quadratic kappa score (κ2 = 0.86) compared to the state-of-the-art CNN based method (κ2 = 0.81).

This paper approaches the problem of geometric multi-model fitting as a data segmentation problem which is solved by a sequence of sampling, model selection and clustering steps. We propose a sampling method that significantly facilitates solving the segmentation problem using the Normalized cut. The sampler is a novel application of Markov-Chain-Monte-Carlo (MCMC) method to sample from a distribution in the parameter space that is obtained by modifying the Least kth Order Statistics cost function. To sample from this distribution effectively, our proposed Markov Chain includes novel long and short jumps to ensure exploration and exploitation of all structures. It also includes fast local optimization steps to target all, even fairly small, putative structures. This leads to a clustering solution through which final model parameters for each segment are obtained. The method competes favorably with the state-of-the-art both in terms of computation power and segmentation accuracy.

Using splines to model spatio-temporal data is one of the most common methods of data fitting used in a variety of computer vision applications. Despite its ubiquitous applications, particularly for volumetric image registration and interpolation, the existing estimation methods are still sensitive to the existence of noise and outliers. A method of robust data modelling using thin plate splines, based upon the well-known least K-th order statistical model fitting, is proposed and compared with the best available robust spline fitting techniques. Our experiments show that existing methods are not suitable for typical computer vision applications where outliers are structured (pseudo-outliers) while the proposed method performs well even when there are numerous pseudo-outliers.