Adaptive Scale Selection Research Articles

A novel mathematical morphology spectrum entropy based on scale-adaptive techniques

Mathematical morphology spectrum entropy is a signal feature extraction method based on information entropy and mathematical morphology. The scale of structure element is a critical parameter, whose value determines the accuracy of feature extraction. Existing scale selection methods depend on experiment parameters or external indicators including noise ratio, fault frequencies, etc. In many cases, existing methods obtain fix scale and they are not suitable for quantifying the performance degradation and the fault degree of bearings. There are few researches on scale selection based on the properties of mathematical morphology spectrum. In this study, a scale-adaptive mathematical morphology spectrum entropy (AMMSE) is proposed to improve the scale selection. To support the proposed method, two properties of the mathematical morphology spectrum (MMS), namely non-negativity and monotonic decreasing, are proved. It can be concluded from the two properties that the feature loss of MMS decreases with the increase of scale. Based on the conclusion, two adaptive scale selection strategies are proposed to automatically determine the scale by reducing the feature loss of MMS. AMMSE is the integration of two strategies. Compare to the existing methods, AMMSE is not constrained by the information of the experiment and the signal. The scale of AMMSE changes with the signal characteristics and is no longer fixed by experimental parameters. The parameters of AMMSE are more generalizable as well. The presented method is applied to identify fault degree on CWRU bearing data set and evaluate performance degradation on IMS bearing data set. The experiment result shows that AMMSE has better results in both experiments with the same parameters.

Robust speed prediction of high-speed trains based on improved echo state networks

The accurate and robust speed prediction of high-speed trains (HSTs) is a challenging task in the automatic train operation (ATO) because HSTs operate in an open-air situation with much noise and many uncertainties. This paper contributes to the development of robust speed prediction methods for the ATO of HSTs based on improved echo state networks (ESNs). Firstly, an adaptive temporal scale selection approach is introduced to improve the accuracy and efficiency of ESN modeling, due to the importance of proper temporal scale selection in relation to the sound prediction performance of ESNs. Also, a random weight scaling mechanism is employed to enhance the feasibility and robustness of the proposed method, as the learning ability of ESNs lies in the constrained random connection weights. Furthermore, several conditions for the stability of the closed-loop control system are given. Our experiment results demonstrate that the proposed method successfully achieves sound performance in terms of speed prediction accuracy, efficiency and robustness.

A Narrow Deep Learning Assisted Visual Tracking with Joint Features

A robust tracking method is proposed for complex visual sequences. Different from time-consuming offline training in current deep tracking, we design a simple two-layer online learning network which fuses local convolution features and global handcrafted features together to give the robust representation for visual tracking. The target state estimation is modeled by an adaptive Gaussian mixture. The motion information is used to direct the distribution of the candidate samples effectively. And meanwhile, an adaptive scale selection is addressed to avoid bringing extra background information. A corresponding object template model updating procedure is developed to account for possible occlusion and minor change. Our tracking method has a light structure and performs favorably against several state-of-the-art methods in tracking challenging scenarios on the recent tracking benchmark data set.

Adaptive scale selection in multiscale segmentation based on the segmented object complexity of GF-2 satellite image

Image segmentation is the premise and key step of object-oriented classification, but scale selection remains a challenge in image segmentation. Over the years, scale selection methods of image segmentation have been extensively explored and developed. When the scale is chosen, all the features are generally extracted from images in these methods. In addition, in these methods, an optimal scale is generally selected based on the pre-estimation of the statistical variance of remote sensing images or determined by the post-segmentation evaluation results, rather than during the segmentation. In this study, with the central and eastern parts of Longchang City in Mid-Sichuan Hilly Region as the study area, based on the Gaofen-2 (GF-2) image, an adaptive scale selection method was proposed to determine the optimal scale of each segmentation object during the segmentation. First, the single-scale optimal image segmentation is determined by an unsupervised evaluation method which uses weighted variance and Moran’s I to measure global intra-segment homogeneity and inter-segment heterogeneity, respectively. Then, spectral, texture, and shape features of each segmented object were extracted to construct the complexity index function, and the optimal scale of the segmentation objects was determined through the iterative calculation according to the threshold value. The proposed method was applied to process GF-2 image to obtain the segmentation results and classification map, compared with the methods of determining the optimal scale through estimation local statistic variances of image and post-evaluation of segmentation results. The experimental results showed that the proposed method is of practical helpfulness and effectiveness for generating the optimal scale matching the actual ground objects.

Multi-occluded pedestrian real-time detection algorithm based on preprocessing R-FCN

One of main challenges of driver assistance systems is to detect multi-occluded pedestrians in real-time in complicated scenes, to reduce the number of traffic accidents. In order to improve the accuracy and speed of detection system, we proposed a real-time multi-occluded pedestrian detection algorithm based on R-FCN. RoI Align layer was introduced to solve misalignments between the feature map and RoI of original images. A separable convolution was optimized to reduce the dimensions of position-sensitive score maps, to improve the detection speed. For occluded pedestrians, a multi-scale context algorithm is proposed, which adopt a local competition mechanism for adaptive context scale selection. For low visibility of the body occlusion, deformable RoI pooling layers were introduced to expand the pooled area of the body model. Finally, in order to reduce redundant information in the video sequence, Seq-NMS algorithm is used to replace traditional NMS algorithm. The experiments have shown that there is low detection error on the datasets Caltech and ETH, the accuracy of our algorithm is better than that of the detection algorithms in the sets, works particularly well with occluded pedestrians.

Pedestrian Detection via Body Part Semantic and Contextual Information With DNN

Pedestrian detection has achieved great improve-ments in recent years, while complex occlusion handling and high-accurate localization are still the most important problems. To take advantage of the body part semantic information and the contextual information for pedestrian detection, we propose the part and context network (PCN) in this paper. A PCN is composed of three branches: the basic branch; the part branch; and the context branch. It specially utilizes two branches to detect the pedestrians through the body part semantic information and the contextual information, respectively. In the part branch, the semantic information of body parts can communicate with each other via long short-term memory (LSTM). In the context branch, we adopt a local competition mechanism (maxout) for adaptive context scale selection. By combining the outputs of all branches, we develop a strong complementary pedestrian detector with a lower miss rate and higher localization accuracy, especially for the occlusion pedestrian. The combination of the body part semantic information and the contextual information in pedestrian detection is fully explored in this paper. Comprehensive evaluations on three challenging pedestrian detection datasets (i.e., Caltech, INRIA and KITTI) well demonstrate the effectiveness of our proposed PCN. Code for PCN is publicly available on GitHub https://github.com/sunnyxiaohu/pcn_pedestrian .

Edge-Based Defocus Blur Estimation With Adaptive Scale Selection.

Objects that do not lie at the focal distance of a digital camera generate defocused regions in the captured image. This paper presents a new edge-based method for spatially varying defocus blur estimation using a single image based on reblurred gradient magnitudes. The proposed approach initially computes a scale-consistent edge map of the input image and selects a local reblurring scale aiming to cope with noise, edge mis-localization, and interfering edges. An initial blur estimate is computed at the detected scale-consistent edge points and a novel connected edge filter is proposed to smooth the sparse blur map based on pixel connectivity within detected edge contours. Finally, a fast guided filter is used to propagate the sparse blur map through the whole image. Experimental results show that the proposed approach presents a very good compromise between estimation error and running time when compared with the state-of-the-art methods. We also explore our blur estimation method in the context of image deblurring, and show that metrics typically used to evaluate blur estimation may not correlate as expected with the visual quality of the deblurred image.

The Esns-Based Traction Electric Quantity Prediction of High-Speed Train with Adaptive Model Inputs Temporal Scale

The precise and reliable speed prediction provides a basis for predicting the traction electric quantity of high-speed train (HST). However, various existing mathematical speed control modes of HST may be insufficiently applicable because of the complex nonlinear and uncertain disturbance. To address this issue, in this paper, a speed predictive model of HST is established by taking the advantages of the efficient and robust prediction capability of echo state networks (ESNs) in dealing with nonlinear time-series. In consideration of the uncertainties of the temporal scale of the model inputs, an adaptive temporal scale selection criterion is set up to further enhance the model prediction accuracy. Then, based on the given traction characteristic curve of HST, the ESNs-based traction electric quantity prediction framework is established. Some experimental results illustrate that the proposed predictive framework can achieve a good performance in terms of predicting accuracy and reliability.

The Southampton-York Natural Scenes (SYNS) dataset: Statistics of surface attitude.

Recovering 3D scenes from 2D images is an under-constrained task; optimal estimation depends upon knowledge of the underlying scene statistics. Here we introduce the Southampton-York Natural Scenes dataset (SYNS: https://syns.soton.ac.uk), which provides comprehensive scene statistics useful for understanding biological vision and for improving machine vision systems. In order to capture the diversity of environments that humans encounter, scenes were surveyed at random locations within 25 indoor and outdoor categories. Each survey includes (i) spherical LiDAR range data (ii) high-dynamic range spherical imagery and (iii) a panorama of stereo image pairs. We envisage many uses for the dataset and present one example: an analysis of surface attitude statistics, conditioned on scene category and viewing elevation. Surface normals were estimated using a novel adaptive scale selection algorithm. Across categories, surface attitude below the horizon is dominated by the ground plane (0° tilt). Near the horizon, probability density is elevated at 90°/270° tilt due to vertical surfaces (trees, walls). Above the horizon, probability density is elevated near 0° slant due to overhead structure such as ceilings and leaf canopies. These structural regularities represent potentially useful prior assumptions for human and machine observers, and may predict human biases in perceived surface attitude.

Classification of Ultra-High Resolution Orthophotos Combined with DSM Using a Dual Morphological Top Hat Profile

New aerial sensors and platforms (e.g., unmanned aerial vehicles (UAVs)) are capable of providing ultra-high resolution remote sensing data (less than a 30-cm ground sampling distance (GSD)). This type of data is an important source for interpreting sub-building level objects; however, it has not yet been explored. The large-scale differences of urban objects, the high spectral variability and the large perspective effect bring difficulties to the design of descriptive features. Therefore, features representing the spatial information of the objects are essential for dealing with the spectral ambiguity. In this paper, we proposed a dual morphology top-hat profile (DMTHP) using both morphology reconstruction and erosion with different granularities. Due to the high dimensional feature space, we have proposed an adaptive scale selection procedure to reduce the feature dimension according to the training samples. The DMTHP is extracted from both images and Digital Surface Models (DSM) to obtain complimentary information. The random forest classifier is used to classify the features hierarchically. Quantitative experimental results on aerial images with 9-cm and UAV images with 5-cm GSD are performed. Under our experiments, improvements of 10% and 2% in overall accuracy are obtained in comparison with the well-known differential morphological profile (DMP) feature, and superior performance is observed over other tested features. Large format data with 20,000 × 20,000 pixels are used to perform a qualitative experiment using the proposed method, which shows its promising potential. The experiments also demonstrate that the DSM information has greatly enhanced the classification accuracy. In the best case in our experiment, it gives rise to a classification accuracy from 63.93% (spectral information only) to 94.48% (the proposed method).

A nonparametric shape prior constrained active contour model for segmentation of coronaries in CTA images.

We present a nonparametric shape constrained algorithm for segmentation of coronary arteries in computed tomography images within the framework of active contours. An adaptive scale selection scheme, based on the global histogram information of the image data, is employed to determine the appropriate window size for each point on the active contour, which improves the performance of the active contour model in the low contrast local image regions. The possible leakage, which cannot be identified by using intensity features alone, is reduced through the application of the proposed shape constraint, where the shape of circular sampled intensity profile is used to evaluate the likelihood of current segmentation being considered vascular structures. Experiments on both synthetic and clinical datasets have demonstrated the efficiency and robustness of the proposed method. The results on clinical datasets have shown that the proposed approach is capable of extracting more detailed coronary vessels with subvoxel accuracy.

Efficient and robust fragments-based multiple kernels tracking

Representing an object with multiple image fragments or patches for target tracking in a video has proved to be able to maintain the spatial information. The major challenges in visual tracking are effectiveness and robustness. In this paper, we propose an efficient and robust fragments-based multiple kernels tracking algorithm. Fusing the log-likelihood ratio image and morphological operation divides the object into some fragments, which can maintain the spatial information. By assigning each fragment to different weight, more robust target and candidate models are built. Applying adaptive scale selection and updating schema for the target model and the weighting factors of each fragment can improve tracking robustness. Upon these advantages, the novel tracking algorithm can provide more accurate performance and can be directly extended to a multiple object tracking system.

Kernel-based object tracking using asymmetric kernels with adaptive scale and orientation selection

Kernel-based object tracking refers to computing the translation of an isotropic object kernel from one video frame to the next. The kernel is commonly chosen as a primitive geometric shape and its translation is computed by maximizing the likelihood between the current and past object observations. In the case when the object does not have an isotropic shape, kernel includes non-object regions which biases the motion estimation and results in loss of the tracked object. In this paper, we propose to use an asymmetric object kernel for improving the tracking performance. An important advantage of an asymmetric kernel over an isotropic kernel is its precise representation of the object shape. This property enhances tracking performance due to discarding the non-object regions. The second contribution of our paper is the introduction of a new adaptive kernel scale and orientation selection method which is currently achieved by greedy algorithms. In our approach, the scale and orientation are introduced as additional dimensions to the spatial image coordinates, in which the mode seeking, hence tracking, is achieved simultaneously in all coordinates. Demonstrated in a set of experiments, the proposed method has better tracking performance with comparable execution time then kernel tracking methods used in practice.

Improved Automatic Detection and Segmentation of Cell Nuclei in Histopathology Images

Automatic segmentation of cell nuclei is an essential step in image cytometry and histometry. Despite substantial progress, there is a need to improve accuracy, speed, level of automation, and adaptability to new applications. This paper presents a robust and accurate novel method for segmenting cell nuclei using a combination of ideas. The image foreground is extracted automatically using a graph-cuts-based binarization. Next, nuclear seed points are detected by a novel method combining multiscale Laplacian-of-Gaussian filtering constrained by distance-map-based adaptive scale selection. These points are used to perform an initial segmentation that is refined using a second graph-cuts-based algorithm incorporating the method of alpha expansions and graph coloring to reduce computational complexity. Nuclear segmentation results were manually validated over 25 representative images (15 in vitro images and 10 in vivo images, containing more than 7400 nuclei) drawn from diverse cancer histopathology studies, and four types of segmentation errors were investigated. The overall accuracy of the proposed segmentation algorithm exceeded 86%. The accuracy was found to exceed 94% when only over- and undersegmentation errors were considered. The confounding image characteristics that led to most detection/segmentation errors were high cell density, high degree of clustering, poor image contrast and noisy background, damaged/irregular nuclei, and poor edge information. We present an efficient semiautomated approach to editing automated segmentation results that requires two mouse clicks per operation.

Adaptive Behavior and Low Incidence Handicaps

This article presents a practitioner's guide to adaptive behavior scale selection for the low incidence populations of deaf, blind, and deafblind/multihandicapped sensory impaired students and/or clients. Revisions for the Vineland Adaptive Behavior Scales (Sparrow, Balla, & Cicchetti, 1984) are discussed with respect to use with the sensory impaired population. Revisions for other scales are also identified. Notable for its suitability for sensory impaired populations is the National Independent Living Skills project (Iceman, Woosley, Windham, & Sanders, 1985a, 1985b), normed on a national sample.