Statistical Hough Transform

Cascaded Hough Transform-Based Hair Mask Generation and Harmonic Inpainting for Automated Hair Removal from Dermoscopy Images

Restoring information obstructed by hair is one of the main issues for the accurate analysis and segmentation of skin images. For retrieving pixels obstructed by hair, the proposed system converts dermoscopy images into the L*a*b* color space, then principal component analysis (PCA) is applied to produce grayscale images. Afterward, the contrast-limited adaptive histogram equalization (CLAHE) and the average filter are implemented to enhance the grayscale image. Subsequently, the binary image is generated using the iterative thresholding method. After that, the Hough transform (HT) is applied to each image block to generate the hair mask. Finally, the hair pixels are removed by harmonic inpainting. The performance of the proposed automated hair removal was evaluated by applying the proposed system to the International Skin Imaging Collaboration (ISIC) dermoscopy dataset as well as to clinical images. Six performance evaluation metrics were measured, namely the mean squared error (MSE), the peak signal-to-noise ratio (PSNR), the signal-to-noise ratio (SNR), the structural similarity index (SSIM), the universal quality image index (UQI), and the correlation (C). Using the clinical dataset, the system achieved MSE, PSNR, SNR, SSIM, UQI, and C values of 34.7957, 66.98, 42.39, 0.9813, 0.9801, and 0.9985, respectively. The results demonstrated that the proposed system could satisfy the medical diagnostic requirements and achieve the best performance compared to the state-of-art.

Slicing-Tracking-Detection: Simultaneous Multi-Cylinder Detection From Large-Scale and Complex Point Clouds

Multiple cylinders detection from large-scale and complex point clouds is a historical but challenging problem, considering the efficiency and accuracy. We propose a novel framework, named slicing-tracking-detection (STD), that detects multiple cylinders accurately and simultaneously from point clouds of large-scale and complex process plants. In this framework, the 3D cylinder detection problem is reformulated as a cylinder ingredients tracking task based on multi-object tracking (MOT). First, we generate slices from the input point cloud, and render them to slice sequence. Then, the cycle of a cylinder is modeled with a Markov Decision Process (MDP), where the ingredient is tracked with a template and the miss tracking is associated with ingredient proposals through reinforcement learning. Finally, by applying MDP for each cylinder, multiple cylinders can be detected simultaneously and accurately. Extensive experiments show that the proposed STD framework can significantly outperform the state-of-the-art approaches in efficiency, accuracy, and robustness. The source code is available at http://zhiyongsu.github.io.

Hybrid visual natural landmark–based localization for indoor mobile robots

Localization is a crucial part of autonomous moving for the indoor mobile robot. The natural features of the ceiling and surrounding environment can serve for position estimation. Based on these natural features, a hybrid visual natural landmarks–based localization method is proposed. We combine the landmarks-based positioning with ceiling-based visual odometry. During the visual odometry, the orientation is computed from the parallel features between the adjacent frames. The position is calculated from the corresponding point features in the two consecutive images using the perspective-n-point method. During the natural landmarks–based localization, the orientation filter is utilized to obtain the global orientation. Then, the feature points are determined by the Compute Unified Device Architecture–based scale-invariant feature transform algorithm. Finally, the position is estimated based on the computed orientation and point features. Various experiments have been conducted to evaluate the effectiveness of the proposed method. The experimental results show that the proposed localization method outperforms other methods in accuracy and efficiency.

Joint A Contrario Ellipse and Line Detection

We propose a line segment and elliptical arc detector that produces a reduced number of false detections on various types of images without any parameter tuning. For a given region of pixels in a grey-scale image, the detector decides whether a line segment or an elliptical arc is present (model validation). If both interpretations are possible for the same region, the detector chooses the one that best explains the data (model selection ). We describe a statistical criterion based on the a contrario theory, which serves for both validation and model selection. The experimental results highlight the performance of the proposed approach compared to state-of-the-art detectors, when applied on synthetic and real images.

The Radon Cumulative Distribution Transform and Its Application to Image Classification

Invertible image representation methods (transforms) are routinely employed as low-level image processing operations based on which feature extraction and recognition algorithms are developed. Most transforms in current use (e.g., Fourier, wavelet, and so on) are linear transforms and, by themselves, are unable to substantially simplify the representation of image classes for classification. Here, we describe a nonlinear, invertible, low-level image processing transform based on combining the well-known Radon transform for image data, and the 1D cumulative distribution transform proposed earlier. We describe a few of the properties of this new transform, and with both theoretical and experimental results show that it can often render certain problems linearly separable in a transform space.

Analytical analysis of motion separability

Motion segmentation is an important task in computer vision and several practical approaches have already been developed. A common approach to motion segmentation is to use the optical flow and formulate the segmentation problem using a linear approximation of the brightness constancy constraints. Although there are numerous solutions to solve this problem and their accuracies and reliabilities have been studied, the exact definition of the segmentation problem, its theoretical feasibility and the conditions for successful motion segmentation are yet to be derived. This paper presents a simplified theoretical framework for the prediction of feasibility, of segmentation of a two-dimensional linear equation system. A statistical definition of a separable motion (structure) is presented and a relatively straightforward criterion for predicting the separability of two different motions in this framework is derived. The applicability of the proposed criterion for prediction of the existence of multiple motions in practice is examined using both synthetic and real image sequences. The prescribed separability criterion is useful in designing computer vision applications as it is solely based on the amount of relative motion and the scale of measurement noise.

Cylinder detection in large-scale point cloud of pipeline plant

The huge number of points scanned from pipeline plants make the plant reconstruction very difficult. Traditional cylinder detection methods cannot be applied directly due to the high computational complexity. In this paper, we explore the structural characteristics of point cloud in pipeline plants and define a structure feature. Based on the structure feature, we propose a hierarchical structure detection and decomposition method that reduces the difficult pipeline-plant reconstruction problem in IR³ into a set of simple circle detection problems in IR². Experiments with industrial applications are presented, which demonstrate the efficiency of the proposed structure detection method.

Connectivity-enforcing Hough transform for the robust extraction of line segments

Global voting schemes based on the Hough transform (HT) have been widely used to robustly detect lines in images. However, since the votes do not take line connectivity into account, these methods do not deal well with cluttered images. On the other hand, the so-called local methods enforce connectivity but lack robustness to deal with challenging situations that occur in many realistic scenarios, e.g., when line segments cross or when long segments are corrupted. We address the critical limitations of the HT as a line segment extractor by incorporating connectivity in the voting process. This is done by only accounting for the contributions of edge points lying in increasingly larger neighborhoods and whose position and directional information agree with potential line segments. As a result, our method, which we call segment extraction by connectivity-enforcing HT (STRAIGHT), extracts the longest connected segments in each location of the image, thus also integrating into the HT voting process the usually separate step of individual segment extraction. The usage of the Hough space mapping and a corresponding hierarchical implementation make our approach computationally feasible. We present experiments that illustrate, with synthetic and real images, how STRAIGHT succeeds in extracting complete segments in situations where current methods fail.

A closed-form solution to tensor voting: theory and applications

We prove a closed-form solution to tensor voting (CFTV): Given a point set in any dimensions, our closed-form solution provides an exact, continuous, and efficient algorithm for computing a structure-aware tensor that simultaneously achieves salient structure detection and outlier attenuation. Using CFTV, we prove the convergence of tensor voting on a Markov random field (MRF), thus termed as MRFTV, where the structure-aware tensor at each input site reaches a stationary state upon convergence in structure propagation. We then embed structure-aware tensor into expectation maximization (EM) for optimizing a single linear structure to achieve efficient and robust parameter estimation. Specifically, our EMTV algorithm optimizes both the tensor and fitting parameters and does not require random sampling consensus typically used in existing robust statistical techniques. We performed quantitative evaluation on its accuracy and robustness, showing that EMTV performs better than the original TV and other state-of-the-art techniques in fundamental matrix estimation for multiview stereo matching. The extensions of CFTV and EMTV for extracting multiple and nonlinear structures are underway.