A novel food volume measurement technique is proposed in this paper for accurate quantification of the daily dietary intake of the user. The technique is based on simultaneous localisation and mapping (SLAM), a modified version of convex hull algorithm, and a 3D mesh object reconstruction technique. This paper explores the feasibility of applying SLAM techniques for continuous food volume measurement with a monocular wearable camera. A sparse map will be generated by SLAM after capturing the images of the food item with the camera and the multiple convex hull algorithm is applied to form a 3D mesh object. The volume of the target object can then be computed based on the mesh object. Compared to previous volume measurement techniques, the proposed method can measure the food volume continuously with no prior information such as pre-defined food shape model. Experiments have been carried out to evaluate this new technique and showed the feasibility and accuracy of the proposed algorithm in measuring food volume.

In general, synthetic aperture radar (SAR) imaging and image processing are two sequential steps in SAR image processing. Due to the large size of SAR images, most image processing algorithms require image segmentation before processing. However, the existence of speckle noise in SAR images, as well as poor contrast and the uneven distribution of gray values in the same target, make SAR images difficult to segment. In order to facilitate the subsequent processing of SAR images, this paper proposes a new method that combines the back-projection algorithm (BPA) and a first-order gradient operator to enhance the edges of SAR images to overcome image segmentation problems. For complex-valued signals, the gradient operator was applied directly to the imaging process. The experimental results of simulated images and real images validate our proposed method. For the simulated scene, the supervised image segmentation evaluation indexes of our method have more than 1.18%, 11.2% and 11.72% improvement on probabilistic Rand index (PRI), variability index (VI), and global consistency error (GCE). The proposed imaging method will make SAR image segmentation and related applications easier.

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