Object Recognition using 3D SIFT in Complex CT Volumes

Abstract

The automatic detection of objects within complex volumetric imagery is becoming of increased interest due to the use of dual energy Computed Tomography (CT) scanners as an aviation security deterrent. These devices produce a volumetric image akin to that encountered in prior medical CT work but in this case we are dealing with a complex multi-object volumetric environment including significant noise artefacts. In this work we look at the application of the recent extension to the seminal SIFT approach to the 3D volumetric recognition of rigid objects within this complex volumetric environment. A detailed overview of the approach and results when applied to a set of exemplar CT volumetric imagery is presented.

Object classification in 3D baggage security computed tomography imagery using visual codebooks

“…effective atomic number [4,5]) at each voxel location. In turn, this has led to increased research interest in the potential use of object detection and classification techniques to perform automated-analyses tasks on such 3D-baggage imagery [6][7][8][9][10].…”

“…Despite overcoming the inherent problem of occlusion within 2D X-ray, demand for high throughput has often meant that 3D baggage-CT imagery typically contains substantial noise, metal-streaking artefacts and voxel resolutions of significantly poorer quality than the modern medical-CT equivalent [1] (Figure 1). Prior work has considered denoising and metal artefact reduction in baggage-CT imagery [11][12][13][14], although the impact on object classification within this space remains unproven [6][7][8][9][10].…”

Local Shape Similarity Retrieval of Mechanical Parts

“…In this equation, H is the Hessian matrix of DOG. r is a threshold usually being set to an integer in [10,50]. In the experiments, we set r to 30.…”

“…The following equation is used to filter the extremums [10]. In this equation, H is the Hessian matrix of DOG.…”

An efficient registration algorithm based on spin image for LiDAR 3D point cloud models

“…In reference [17], Dinh and Kropac propose to use multiple resolution method to reduce the computational burden of spin image based registration algorithm. While in reference [18], SIFT operator is used to reduce the key-point that needs to be considered, and thus the computational burden of the registration can be decreased greatly. Generally, the computational burden of SIR algorithm highly relates to its robustness and the accuracy, and most of the preceding algorithms improve the time efficiency at the cost of robustness and accuracy.…”