Protecting private data in smart homes, a popular Internet-of-Things (IoT) application, remains a significant data security and privacy challenge due to the large-scale development and distributed nature of IoT networks. Recently, smart healthcare has leveraged smart home systems, thereby compounding security concerns in terms of the confidentiality of sensitive and private data and by extension the privacy of the data owner. However, PoA-based Blockchain DLT has emerged as a promising solution for protecting private data from indiscriminate use and thereby preserving the privacy of individuals residing in IoT-enabled smart homes. This review elicits some concerns, issues, and problems that have hindered the adoption of blockchain and IoT (BCoT) in some domains and suggests requisite solutions using the aging-in-place scenario. Implementation issues with BCoT were examined as well as the combined challenges BCoT can pose when utilised for security gains. The study discusses recent findings, opportunities, and barriers, and provide recommendations that could facilitate the continuous growth of blockchain application in healthcare. Lastly, the study then explored the potential of using a PoA-based permission blockchain with an applicable consent-based privacy model for decision-making in the information disclosure process, including the use of publisher-subscriber contracts for fine-grained access control to ensure secure data processing and sharing, as well as ethical trust in personal information disclosure, as a solution direction. The proposed authorisation framework could guarantee data ownership, conditional access management, scalable and tamper-proof data storage, and a more resilient system against threat models such as interception and insider attacks.

Abstract We proposed a novel method for 2D image compression-encryption whose quality is demonstrated through accurate 2D image reconstruction at higher compression ratios. The method is based on the DWT-Discrete Wavelet Transform where high frequency sub-bands are connected with a novel Hexadata crypto-compression algorithm at compression stage and a new fast matching search algorithm at decoding stage. The novel crypto-compression method consists of four main steps: 1) A five-level DWT is applied to an image to zoom out the low frequency sub-band and increase the number of high frequency sub-bands to facilitate the compression process; 2) The Hexa data compression algorithm is applied to each high frequency sub-band independently by using five different keys to reduce each sub-band to1/6of its original size; 3) Build a look up table of probability data to enable decoding of the original high frequency sub-bands, and 4) Apply arithmetic coding to the outputs of steps (2) and (3). At decompression stage a fast matching search algorithm is used to reconstruct all high frequency sub-bands. We have tested the technique on 2D images including streaming from videos (YouTube). Results show that the proposed crypto-compression method yields high compression ratios up to 99% with high perceptual quality images.

Structured light is a well-known technique for capturing 3D surface measurements but has yet to achieve satisfactory results for applications demanding high resolution models at frame rate. For these requirements a dense set of uniform uncoded white stripes seems attractive. But the problem of relating projected and recorded stripes, here called the Indexing Problem, has proved to be difficult to overcome reliably for uncoded patterns. We propose a new algorithm that uses the maximum spanning tree of a graph defining potential connectivity and adjacency in recorded stripes. Results are significantly more accurate and reliable than previous attempts. We do however also identify an important limitation of uncoded patterns and claim that, in general, additional stripe coding is necessary. Our algorithm adapts easily to accommodate a minimal coding scheme that increases neither sample size nor acquisition time.

In this paper, we propose a novel algorithm for the registration of two overlapping range images. This algorithm is based on the relative registration error histogram. A comparative study based on both synthetic data and real images has shown that the novel algorithm is accurate and robust.

New technological developments in optics and electronics are rendering laser scanning systems cheaper and more accurate. Such systems can directly capture depth information from objects simplifying the range image analysis and enlarging their applications scope. Accurate image registration algorithms represent a pivotal aspect of range image analysis which still require substantial improvements. This paper presents two novel motion constraints namely proximity and closeness constraints to improve accuracy of image registration and defines the conditions when such constraints are read at specific points of the registration. A number of experiments using real range images demonstrate that the combination of rigid motion constraints with the novel proximity and closeness constraints leads to more accurate evaluation of valid correspondences which, in turn, lead to more accurate image registration.

Accurate registration has proved a difficult task especially when using non-high quality range images. In this paper we investigate and formalise structural relationships existing in the data for registration of free form shapes. We extend an existing ICP-based geometric algorithm by incorporating structural constraints leading to more accurate data correspondences. A number of experiments based on real range images demonstrate that the combination of rigid motion constraints with the novel structural constraint yields more accurate evaluation of possible correspondences preventing the algorithm from converging to local minima leading thus, to more accurate image registration.

We propose a novel geometric method for the alignment of two overlapping range images. The method first employs the traditional iterative closest point (ICP) criterion to establish a set of possible correspondences and then refine these correspondences using geometric constraints derived from properties of reflected correspondence vectors. In this way, the method overcomes a major limitation of the traditional ICP criterion which is the introduction of false matches in almost every iteration of the alignment. For an accurate estimation of the geometric parameters of interest, the Monte Carlo method is used in conjunction with a median filter. Finally, the quaternion method is used to estimate the motion parameters based on the refined correspondences. Experimental results based on both synthetic data and real images show that the proposed method can effectively align two overlapping range images with a small motion.

We present a novel algorithm to cope with appearing and disappearing points for the projective registration of free-form surfaces. The novel method combines the ICP algorithm with FOE theory for the effective elimination of false matches. Experimental results based on both synthetic data and real images show that the proposed algorithm is accurate and robust.

We propose a method to deal with false matches that occur in almost every iteration of iterative closest point (ICP) based registration algorithms. First, a set of correspondences between the two images to be registered are established using the standard ICP criterion. From this set of correspondences, the algorithm as described in Liu et al. (2000) is employed to estimate the essential point defined by geometric properties of reflected correspondence vectors. After a rigid motion, the essential point must be equidistant from reflected correspondences and thus, relative differences between motion equations can be computed and geometric histograms are then constructed at each step of the iteration. False matches are eliminated by only selecting correspondences that show a small relative difference between the two sides of the motion equation. A number of experiments based on both synthetic data and real images demonstrate that the proposed method is accurate, robust, and efficient for the registration of free-form shapes with large motions.

Motion estimation from outlier corrupted data is a fundamental and difficult problem acknowledged in the machine vision literature. In this paper a robust motion estimation method is presented. First, the Monte Carlo resampling technique is used for an initial estimation of motion parameters, then a geometric histogram method is proposed to synthesize possible solutions to motion parameters based on geometric properties of reflected correspondence vectors. A number of experiments using both synthetic data and real images demonstrate the robustness of the proposed motion estimation method leading to more accurate registration of free form shapes.

Many methods to estimate rigid body motion parameters from range images have been put forward in the last decade. Such methods work well for range image data corrupted by Gaussian random noise without outliers. In particular, the constraint least squares (CLS) is the most accurate, robust, stable, and efficient motion estimation algorithm. However, the CLS and none of the current methods are very robust in the presence of outliers. Therefore, in this paper, we focus on the problem of estimating motion parameters from noise and outlier corrupted range image data. We propose a novel motion estimation geometric algorithm with fuzzy reasoning (GAFR). The algorithm is based on the geometric properties of correspondence vectors to synthesise motion parameter candidates and employs a robust fuzzy reasoning method based on computing deviations and selecting estimates from membership function values. The GAFR is validated through experimentation using synthetic and real range image data.

In this paper we present a novel image registration algorithm combining the iterative closest point algorithm with focus of expansion theory for 3D-2D projective registration of free-form surfaces. A pure translational camera configuration is used, which is a widely adopted constraint to structural estimation. Experimental results based on both synthetic and real images have shown that the proposed algorithm provides accurate and efficient registration and effective elimination of false matches.

This paper presents a novel method for structural and pose estimation from 3D-2D correspondence. Structural data are obtained by first estimating the depths of some reference points using the Newton-Raphson method with a distance constraint followed by depth estimation of the remaining points in closed form solution. Pose estimation is obtained through the constraint least squares method which has been proven accurate and robust for noisy image data. Experimental results based on both synthetic data and real images have shown that the proposed algorithm is accurate and robust in the presence of noise.

The registration of free-form shapes by the iterative closest point algorithm (ICP) has attracted much attention from the computer vision and image processing community since it was first proposed in 1992. Many methods, mainly based on incorporating invariants described in a single coordinate frame have been devised to improve the accuracy and efficiency of the algorithm. In this paper, a novel method to improve image registration is proposed based on rigid constraints derived from geometric properties of correspondence vectors synthesised into a singe coordinate frame. False matches, which occur in almost every iteration of the ICP algorithm are eliminated through properties of the motion. For an accurate estimation of the geometric parameters of the motion, the Monte Carlo method is used in conjunction with a median filter. Experimental results based on both synthetic data and real images show that the improved method can effectively eliminate false matches, is accurate, robust, and efficient for the registration of free-form shapes with small motions.

We present a novel two-stage algorithm for structural and pose estimation from a single image. Our method is a significant improvement on the work described by Quan et al. (1999) because: our algorithm is developed in similarity space rather than in camera centred coordinate frame; our algorithm is directly based on a fourth order polynomial rather than on an eighth order polynomial resulting in an easier and more efficient implementation; and finally our algorithm is a unified linear algorithm for structural estimation. The algorithm has been extensively validated by experimentation using both synthetic and real images and compared with a classical linear algorithm and an algorithm based on epipolar geometry. Experimental results show that the proposed algorithm is generally accurate, robust, and efficient for the calibration of all parameters of interest.

We propose a novel method to deal with sphere ambiguity, occlusion, appearance and disappearance of points in image registration. We have developed a number of rigid motion constraints through analysis of geometrical properties of reflected correspondence vectors synthesised into a single coordinate frame. The properties are used as further constraints to eliminate false matches obtained by the iterative closest point criterion. A number of experiments based on both synthetic data and real images demonstrate that the proposed method is accurate, robust, and efficient for the registration of free-form shapes.

The paper presents a novel algorithm for the registration of free-form shapes represented by two sets of points before and after a rigid body motion. First, the geometric properties of reflected correspondence vectors are analysed, yielding a number of rigid motion constraints bridging the points described in different coordinate frames before and after the rigid motion. Then, a novel algorithm is developed for the registration of free-form shapes, making full use of the rigid motion constraints to cope with false matches that occur in almost every iteration of the ICP algorithm. In order to improve the accuracy of the motion parameter estimation and computational efficiency, the Monte Carlo resampling technique and a median filter are employed. A number of experiments based on both synthetic data and real images demonstrate that the proposed algorithm is accurate and robust.

Estimation of rigid-body motion parameters in computer vision is normally performed from image correspondences between two coordinate frames. A large number of methods and algorithms have been proposed based on that such correspondences are known. Unfortunately, the establishment of correspondences is often time-consuming and, in many cases, impossible. In this paper, we propose a novel correspondenceless motion estimation algorithm based on the cross matrix. For a comparative study, we also implemented a correspondenceless motion estimation algorithm based on the scatter matrix. Experimental results have demonstrated that our method is more accurate and robust than the scatter matrix-based algorithm.

Motion parameter estimation is a fundamental problem in image processing and image understanding. A large number of algorithms have been proposed based on a number of different geometrical considerations, such as perspective or epipolar geometries. However, proposed motion estimation algorithms do not explicitly use the distance between feature points and angle information as rigid constraints to calibration. In this paper, we present a new geometric analysis of correspondence data and derive explicit expressions for rigid constraints that are then used to estimate motion parameters. We then present a novel, efficient motion parameter estimation algorithm based on a coarse to fine strategy from a single image data. For a comparative study of performance, we also extended to the 3D-2D case a well known 2D-2D motion estimation algorithm based on the epipolar geometry. Experimental results demonstrate that the coarse to fine strategy is appropriate for the problem and that the algorithm generally performs better than the extended epipolar geometry based algorithm.

It has been demonstrated by Chasles over a century ago that any given displacement of a rigid body can be effected by a single rotation around an axis combined with a translation parallel to that axis. In this paper, we first describe geometric properties of image correspondence vectors. We then formalise such properties by extending Chasles' work and put forward a new general theoretical framework for the analysis of rigid body transformations in 2D and in 3D. We then propose two novel algorithms to calibrate rigid body transformation parameters which are validated through experiments. Our analysis addresses central issues to computer vision applications as it provides closed form solutions for all calibrated parameters and an accurate insight into the number of calibration solutions.

Given two sets of image correspondence data, the analysis of the corresponding rigid body transformation that accurately describes the object's motion in 3D space is a fundamental problem in image understanding. While many methods have been put forward to analyse 3D transformations, such methods do not make use of the vector distance between feature points and angular information as constraints to analyse transformation parameters. Current methods and algorithms have also suffered from the problems of lack of efficiency, sensitivity to noise, and multiplicity of solutions. In this paper we present a novel geometrical analysis of rigid body transformations and a novel algorithm to calibrate transformation parameters based on image correspondence. The algorithm is validated through experimental calibration and a comparison is made with calibration implemented by the least squares method. We demonstrate that the proposed algorithm works well in the presence of noise and that its performance is in general superior to algorithms based on the least squares method.

Accurate motion estimation algorithms are based on a number of invariant properties that can be inferred from the motion. A large number of calibration algorithms have been proposed over the last two decades mainly based on analytic, perspective, or epipolar geometries. Extending Chasles' screw motion concept to the estimation of motion parameters in computer vision, we have presented an analysis of geometric properties of image correspondence vectors synthesized into a single coordinate frame and developed calibration algorithms using both simulated and real range image data.15,16 In this paper, we extend that work by defining the relevant geometric properties of image correspondence vectors from the point of view of invariants and by developing two calibration algorithms using the Monte Carlo method and median filtering. The algorithms are applied to real and synthetic image data corrupted by noise and outliers. Experimental results demonstrate that the median filter based algorithm is generally more robust and accurate than the Monte Carlo based algorithm and that the geometric analysis of invariant properties of correspondence vectors is a useful framework to motion parameter estimation.