A survey of recent advances in visual feature detection

This paper proposes a corner detector and classifier using anisotropic directional derivative (ANDD) representations. The ANDD representation at a pixel is a function of the oriented angle and characterizes the local directional grayscale variation around the pixel. The proposed corner detector fuses the ideas of the contour- and intensity-based detection. It consists of three cascaded blocks. First, the edge map of an image is obtained by the Canny detector and from which contours are extracted and patched. Next, the ANDD representation at each pixel on contours is calculated and normalized by its maximal magnitude. The area surrounded by the normalized ANDD representation forms a new corner measure. Finally, the nonmaximum suppression and thresholding are operated on each contour to find corners in terms of the corner measure. Moreover, a corner classifier based on the peak number of the ANDD representation is given. Experiments are made to evaluate the proposed detector and classifier. The proposed detector is competitive with the two recent state-of-the-art corner detectors, the He & Yung detector and CPDA detector, in detection capability and attains higher repeatability under affine transforms. The proposed classifier can discriminate effectively simple corners, Y-type corners, and higher order corners.

Corner Detection and Classification Using Anisotropic Directional Derivative Representations

Noise robust image edge detection based upon the automatic anisotropic Gaussian kernels.

In intelligent transportation systems, there are many tasks that rely on the detection of road congestion, such as traffic signal scheduling and traffic accident detection. As traditional methods for traffic congestion detection are difficult to use, expensive, and may cause damage to the road surface, this paper presents a method for road congestion detection that is based on multidimensional visual features and a convolutional neural network (CNN). This method first detects the density of foreground objects by using a gray-level co-occurrence matrix; second, the speed of moving objects is detected by using the Lucas–Kanade optical flow with pyramid implementation. Third, a Gaussian mixture model is used to model the background, and the CNN is then used to accurately detect the final foreground from the candidate foregrounds. Finally, the proposed method performs road congestion detection in terms of a multidimensional feature space, including traffic density, traffic velocity, road occupancy, and traffic flow. Furthermore, we propose an information entropy method using a histogram of optical flow to enhance the accuracy and reliability of road congestion detection. Simulation results via quantitative and qualitative assessment indicate that the proposed method is able to significantly outperform the state-of-the-art road-traffic congestion detection methods due to the fusion of multidimensional features using the CNN.

Multi-Dimensional Traffic Congestion Detection Based on Fusion of Visual Features and Convolutional Neural Network

Defect in honeycomb has been paid close attention in recent years, as it significantly threats on its performance. In this study, detailed behavior and influence were extensively investigated in terms of defect distribution, influence on the mechanical performance, as well as the improvement methodology. Firstly, quasi-static experiments were extensively conducted to directly evaluate the inhomogeneity with 12 sub-blokes divided from a given conventional man-made honeycomb core. Variances in the resultant compression history as well as strength of these sub-blocks were determined. From the experimental observation, it can be seen that the regularity and adhesive failure of cells happened in expansion process are two main reasons for strength inhomogeneity. Afterwards, parametric studies on the extensive influence on mechanical performance were discussed by employing the specimens with structural defects in terms of the irregularity, under and over stretch states and adhesive failure. As the findings cofirmed that, the structural defects hugely cut down the plateau force and energy absorption capacity. The honeycomb with regular hexagonal cells without any adhesive failure performs the best. To improve the irregularity, a Machine Vision Methodology was put forward for cells expansion supervision. Detail algorithm in vertex identifying of honeycomb cell was discussed. All these achievements shed a light on the consistency, reliability and homogeneity of high standard honeycomb structure.

On the influence of structural defects for honeycomb structure

Noise-robust edge detector combining isotropic and anisotropic Gaussian kernels

Contour-based corner detection via angle difference of principal directions of anisotropic Gaussian directional derivatives

Corners are important features for image analysis and computer vision tasks. Local structure tensors with multiple scales are widely used in intensity-based corner detectors. In this paper, the properties of intensity variations of a step edge, L-type corner, Y- or T-type corner, X-type corner, and star-type corner are investigated. The properties that we obtained indicate that the image intensity variations of a corner are not always large in all directions. The properties also demonstrate that existing structure tensor-based corner detection methods cannot depict the differences of intensity variations well between edges and corners which result in wrong corner detections. We present a new technique to extract the intensity variations from input images using anisotropic Gaussian directional derivative filters with multiple scales. We prove that the new extraction technique on image intensity variation has the ability to accurately depict the characteristics of edges and corners in the continuous domain. Furthermore, the properties of the intensity variations of step edges and corners enable us to derive a new multi-directional structure tensor with multiple scales, which has the ability to depict the intensity variation differences well between edges and corners in the discrete domain. The eigenvalues of the multi-directional structure tensor with multiple scales are used to develop a new corner detection method. Finally, the criteria on average repeatability (under affine image transformation, JPEG compression, and noise degradation), region repeatability based on the Oxford dataset, repeatability metric based on the DTU dataset, detection accuracy, and localization accuracy are used to evaluate the proposed detector against ten state-of-the-art methods. The experimental results show that our proposed detector outperforms all the other tested detectors.

Weichuan Zhang

Papers

Corner Detection and Classification Using Anisotropic Directional Derivative Representations

Noise-robust edge detector combining isotropic and anisotropic Gaussian kernels

Noise robust image edge detection based upon the automatic anisotropic Gaussian kernels.

Contour-based corner detection via angle difference of principal directions of anisotropic Gaussian directional derivatives

Corner Detection Using Multi-directional Structure Tensor with Multiple Scales