Object Edge Contour Localisation Based on HexBinary Feature Matching

This paper addresses the issue of localising object edge contours in cluttered backgrounds to support robotics tasks such as grasping and manipulation and also to improve the potential perceptual capabilities of robot vision systems. Our approach is based on coarse-to-fine matching of a new recursively constructed hierarchical, dense, edge-localised descriptor, the HexBinary, based on the HexHog descriptor structure first proposed in [1]. Since Binary String image descriptors [2]– [5] require much lower computational resources, but provide similar or even better matching performance than Histogram of Orientated Gradient (HoG) descriptors, we have replaced the HoG base descriptor fields used in HexHog with Binary Strings generated from first and second order polar derivative approximations. The ALOI [6] dataset is used to evaluate the HexBinary descriptors which we demonstrate to achieve a superior performance to that of HexHoG [1] for pose refinement. The validation of our object contour localisation system shows promising results with correctly labelling ~86% of edgel positions and mis-labelling ~3%

Hierarchically grouped 2D local features applied to edge contour localisation

One of the most significant research topics in computer vision is object detection. Most of the reported object detection results localise the detected object within a bounding box, but do not explicitly label the edge contours of the object. Since object contours provide a fundamental diagnostic of object shape, some researchers have initiated work on linear contour feature representations for object detection and localisation. However, linear contour feature-based localisation is highly dependent on the performance of linear contour detection within natural images, and this can be perturbed significantly by a cluttered background. In addition, the conventional approach to achieving rotation-invariant features is to rotate the feature receptive field to align with the local dominant orientation before computing the feature representation. Grid resampling after rotation adds extra computational cost and increases the total time consumption for computing the feature descriptor. Though it is not an expensive process if using current computers, it is appreciated that if each step of the implementation is faster to compute especially when the number of local features is increasing and the application is implemented on resource limited ”smart devices”, such as mobile phones, in real-time. Motivated by the above issues, a 2D object localisation system is proposed in this thesis that matches features of edge contour points, which is an alternative method that takes advantage of the shape information for object localisation. This is inspired by edge contour points comprising the basic components of shape contours. In addition, edge point detection is usually simpler to achieve than linear edge contour detection. Therefore, the proposed localization system could avoid the need for linear contour detection and reduce the pathological disruption from the image background. Moreover, since natural images usually comprise many more edge contour points than interest points (i.e. corner points), we also propose new methods to generate rotation-invariant local feature descriptors without pre-rotating the feature receptive field to improve the computational efficiency of the whole system. In detail, the 2D object localisation system is achieved by matching edge contour points features in a constrained search area based on the initial pose-estimate produced by a prior object detection process. The local feature descriptor obtains rotation invariance by making use of rotational symmetry of the hexagonal structure. Therefore, a set of local feature descriptors is proposed based on the hierarchically hexagonal grouping structure. Ultimately, the 2D object localisation system achieves a very promising performance based on matching the proposed features of edge contour points with the mean correct labelling rate of the edge contour points 0.8654 and the mean false labelling rate 0.0314 applied on the data from Amsterdam Library of Object Images (ALOI). Furthermore, the proposed descriptors are evaluated by comparing to the state-of-the-art descriptors and achieve competitive performances in terms of pose estimate with around half-pixel pose error

Contour localization based on matching dense HexHoG descriptors

The ability to detect and localize an object of interest from a captured image containing a cluttered background is an essential function for an autonomous robot operating in an unconstrained environment. In this paper, we present a novel approach to refining the pose estimate of an object and directly labelling its contours by dense local feature matching. We perform this task using a new image descriptor we have developed called the Hex-HoG. Our key novel contribution is the formulation of HexHoG descriptors comprising hierarchical groupings of rotationally invariant (S)HoG fields, sampled on a hexagonal grid. These HexHoG groups are centred on detected edges and therefore sample the image relatively densely. This formulation allows arbitrary levels of rotation-invariant HexHoG grouped descriptors to be implemented efficiently by recursion. We present the results of an evaluation based on the ALOI image dataset which demonstrates that our proposed approach can significantly improve an initial pose estimation based on image matching using standard SIFT descriptors. In addition, this investigation presents promising contour labelling results based on processing 5000 images derived from the 1000 image ALOI dataset

Contour localization based on matching dense HexHoG descriptors

The ability to detect and localize an object of interest from a captured image containing a cluttered background is an essential function for an autonomous robot operating in an unconstrained environment. In this paper, we present a novel approach to refining the pose estimate of an object and directly labelling its contours by dense local feature matching. We perform this task using a new image descriptor we have developed called the Hex-HoG. Our key novel contribution is the formulation of HexHoG descriptors comprising hierarchical groupings of rotationally invariant (S)HoG fields, sampled on a hexagonal grid. These HexHoG groups are centred on detected edges and therefore sample the image relatively densely. This formulation allows arbitrary levels of rotation-invariant HexHoG grouped descriptors to be implemented efficiently by recursion. We present the results of an evaluation based on the ALOI image dataset which demonstrates that our proposed approach can significantly improve an initial pose estimation based on image matching using standard SIFT descriptors. In addition, this investigation presents promising contour labelling results based on processing 5000 images derived from the 1000 image ALOI dataset