Browsing by Author "Nicolls, F"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
- ItemOpen AccessTexture measures for improved watershed segmentation of froth images(2004) Nicolls, F (Editor); Nicolls, FLuc Vincent’s fast watershed algorithm has been successfully applied to determine the bubble size distribution in an image of froth when the bubbles are all of similar size [1]. This technique fails to work successfully when the image contains both tiny and large bubbles. A new technique is proposed which combines the use of a texture measure, the output of an initial watershed and a further watershed stage to successfully segment both tiny and large bubbles.
- ItemOpen AccessUse of a general imaging model to achieve predictive autofocus in the scanning electron microscope.(Elsevier, 1997) Nicolls, F; de Jager, G; Sewell, BThis work outlines the development of a general imaging model for use in autofocus, astigmatism correction, and resolution analysis. The model is based on the modulation transfer function of the system in the presence of aberrations, in particular, defocus. The signals used are related to the ratios of the Fourier transforms of images captured under different operating conditions. Methods are developed for working with these signals in a consistent manner. The model described is then applied to the problem of autofocus. A fairly general autofocus algorithm is presented and results given which reflect the predictive properties of this model. The imaging system used for the generation of results was a scanning electron microscope (SEM), although the conclusions should be valid across a far wider range of instruments. It is, however, the specific requirements of the SEM that make the generalisation presented here particularly useful. We have, therefore, confined our investigation to SEM.
- ItemOpen AccessUsing regions of interest to track landmarks for RGBD simultaneous localisation and mapping(2019) Harribhai, Jatin I; Nicolls, F; Verrinder, RobynThe simultaneous localisation and mapping (SLAM) algorithm have been widely used for autonomous navigation of robots. A type of visual SLAM that is popular among the researchers is RGBD SLAM. However processing immense image data to identify and track landmarks in RGBD SLAM can be computationally expensive for smaller robots. This dissertation presents an alternate method to reduce the computational time. The proposed algorithm extracts features from a region of interest (ROI) to track landmarks for RGBD SLAM. This strategy is compared to the traditional method of extracting features from an entire image. The ROI algorithm is implemented via a pre-processing algorithm, which is then integrated into the RGBD SLAM framework. The pre-processing pipeline implements image processing algorithms in three stages to process the data. Stage one uses a ROI algorithm to detect ROIs in an image. For visual SLAM such as RGBD SLAM, objects that are highly detailed are used as landmarks. Hence the ROI algorithm is designed to detect ROIs containing highly detailed objects. Stage two extracts features from the image and stage three uses feature matching algorithms to re-identify a ROI. Once a ROI has been successfully re-identified, it is stored and categorised as a landmark for RGBD SLAM. Scale invariant feature transform (SIFT), speeded up robust features (SURF) and orientated FAST and rotated BRIEF (ORB) are three feature extraction algorithms that are used in stage two. The outcomes from this study revealed that the pipeline was able to successfully create a database of landmarks which can be re-identified in subsequent frames. In addition, the results showed that when the pipeline is configured such that SURF features are used with a bigger ROI, RGBD SLAM produced more accurate results in determining the position of the robot compared to the traditional method of extracting features from an entire image. However, this strategy requires more computational time. The findings further revealed that this strategy still out performs the traditional method when the number of features extracted is reduced. This indicated that this strategy performs more robustly compared to the traditional method in environments that can contain few features. The method presented in this study did not improve the computational time of RGBD SLAM but did improve the accuracy in localizing the robot.