Simulation of greyscale image colouring using blob detection

Automatic colouring of greyscale images using computer is one of the important fields in digital image processing. It helps to produce more appealing visuals to human eye when one have to deal with medical images, night vision cameras or scientific illustrations. However, to produce images that are at par with the ability of human eyes, computerised colouring process takes a lot of time and ample calculation. Recent years, blob detection has shown a good development for finding features in an image. This method not only can run on low memory devices but also provides users with faster calculation. Encouraged by these advantages – work on low memory devices and enable faster calculation, two models of untrained colouring of greyscale images are proposed in this study. The maximum number of blob features is examined using Centre Surround Extremas (CenSurE) and Binary Robust Independent Elementary Features (BRIEF). The result of this study proves that the images coloured by these models look better with increment features of the key point if the minimum matching distance is as low as possible. In addition, when comparing feature descriptors using Fast Retina Keypoint (FREAK) solely and FREAK together with Speeded-Up Robust Features (SURF), it is concluded that the result is getting better with the decrement of minimum Hessian in the image. This experiment leads to the discovery that the selection of feature descriptors will influence the result of colouring

Robotic Visual Tracking of Relevant Cues in Underwater Environments with Poor Visibility Conditions

Using visual sensors for detecting regions of interest in underwater environments is fundamental for many robotic applications. Particularly, for an autonomous exploration task, an underwater vehicle must be guided towards features that are of interest. If the relevant features can be seen from the distance, then smooth control movements of the vehicle are feasible in order to position itself close enough with the final goal of gathering visual quality images. However, it is a challenging task for a robotic system to achieve stable tracking of the same regions since marine environments are unstructured and highly dynamic and usually have poor visibility. In this paper, a framework that robustly detects and tracks regions of interest in real time is presented. We use the chromatic channels of a perceptual uniform color space to detect relevant regions and adapt a visual attention scheme to underwater scenes. For the tracking, we associate with each relevant point superpixel descriptors which are invariant to changes in illumination and shape. The field experiment results have demonstrated that our approach is robust when tested on different visibility conditions and depths in underwater explorations

Connected Attribute Filtering Based on Contour Smoothness

A new attribute measuring the contour smoothness of 2-D objects is presented in the context of morphological attribute filtering. The attribute is based on the ratio of the circularity and non-compactness, and has a maximum of 1 for a perfect circle. It decreases as the object boundary becomes irregular. Computation on hierarchical image representation structures relies on five auxiliary data members and is rapid. Contour smoothness is a suitable descriptor for detecting and discriminating man-made structures from other image features. An example is demonstrated on a very-high-resolution satellite image using connected pattern spectra and the switchboard platform