Abstract-A Pareto-based evolutionary multi-objective approach is adopted to optimize the functionals in the Trace Transform for extracting image features that are robust to noise and invariant to geometric deformations such as rotation, scale and translation (RST). To this end, sample images with noise and with RST distortion are employed in the evolutionary optimization of the Trace Transform, which is termed evolutionary Trace Transform with noise (ETTN). Experimental studies on a fish image database and the Columbia COIL-20 image database show that the ETTN optimized on a few low-resolution images from the fish database can extract robust and RST invariant features from the standard images in the fish database as well as in the COIL-20 database. These results demonstrate that the proposed ETTN is very promising in that it is computationally efficient, invariant to RST deformation, robust to noise and generalizable.

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Abstract-Trace transform is one representation of images that uses different functionals applied on the image function. When the functional is integral, it becomes identical to the well-known Radon transform, which is a useful tool in computed tomography medical imaging. The key question in Trace transform is to select the best combination of the Trace functionals to produce the optimal triple feature, which is a challenging task. In this paper, we adopt a multi-objective evolutionary algorithm adapted from the elitist nondominated sorting genetic algorithm (NSGA-II), an evolutionary algorithm that has shown to be very efficient for multi-objective optimization, to select the best functionals as well as the optimal number of projections used in Trace transform to achieve invariant image identification. This is achieved by minimizing the within-class variance and maximizing the between-class variance. To enhance the computational efficiency, the Trace parameters are calculated offline and stored, which are then used to calculate the triple features in the evolutionary optimization. The proposed Evolutionary Trace Transform (ETT) is empirically evaluated on various images from fish database. It is shown that the proposed algorithm is very promising in that it is computationally efficient and considerably outperforms existing methods in literatures.

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Abstract. Soft continuum robots are highly deformable and manoeuvrable manipulators, capable of navigating through confined space and interacting safely with their surrounding environment, making them ideal for minimally invasive surgical applications. A crucial requirement of a soft robot is to control its overall stiffness efficiently, in order to execute the necessary surgical task in an unstructured environment. This paper presents a comparative study detailing the stiffness characterization of two soft manipulator designs and the formulation of a dynamic stiffness matrix for the purpose of disturbance rejection and stiffness control for precise tip positioning. An empirical approach is used to accurately describe the stiffness characteristics along the length of the manipulator and the derived stiffness matrix is applied in real-time control to reject disturbances. Further, the capability of the two types of soft robots to reject disturbances using the dynamic control technique is tested and compared. The results presented in this paper provide new insights into controlling the stiffness of soft continuum robots for minimally invasive surgical applications.