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    Color Image Encryption Algorithm Based on Hyper-Chaos and DNA Computing
    (IEEE, 2020-04-24) Malik MGA; Bashir Z; Iqbal N; Imtiaz MA; Gambino O
    In this study, a novel technique using a hyper chaotic dynamical system and DNA computing has been designed with high plaintext sensitivity. In order to reduce cost, a selection procedure using tent map has been employed for generating different key streams from the same chaotic data obtained from the iterations of chaotic dynamical system. After separating the three channels from the input color image, they are both confused and diffused. First of all, these channels are diffused on a decimal level. Then they are permuted. Further, DNA encoding is performed upon these channels. Moreover, DNA level diffusion is performed to further increase the degree of randomness in the image. Lastly, the DNA encoded image is converted into decimal to get the final cipher image. Both the experimental results and security analysis strongly demonstrate the robustness of the proposed scheme. A comparison of the proposed scheme has also been made with other recently developed schemes to show that this scheme outperforms the others in terms of computational cost, time and memory efficiency. Additionally, with the large key space, the proposed scheme can resist any brute force, plaintext and statistical attacks, therefore it is a good fit for the real world applications of the image security.
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    Mineral prospecting via biogeochemical signals and surface indicators using hyperspectral remote sensing : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Earth Science at Massey University, Palmerston North, New Zealand
    (Massey University, 2022) Chakraborty, Rupsa
    Preliminary steps of mineral exploration have traditionally included drilling and other destructive, expensive, and time-consuming techniques. To meet the ever-increasing demand for mineral resources pertaining to the increase in population and technological demand, it is very important to develop environmentally friendly, faster, and cheaper prospecting methods. In this study, we have targeted three known regions of mesothermal gold mineralisation in the South Island, New Zealand to develop hyperspectral remote sensing-based prospect models combined with biogeochemical data. The three study sites have geological similarities around the gold mineralisation including the major pathfinder elements. On the contrary the environmental settings, and other surficial and near-surface processes including the soil and groundwater interactions with the host rock, are vastly different. This led to a wide variation in the physico-chemical properties of the soil cover and the subsequent uptake by the overlying vegetation. The Pinus radiata plantation at the Hyde-Macraes Shear Zone was the first study site to test the feasibility of using biogeochemical responses overlying the gold mineralisation through hyperspectral remote sensing for gold prospecting. Pinus radiata is known to be an accumulator of metals and metalloids with roots reaching as deep as the shear zone beneath it. The data showed a good spatial elemental trend along the shear zone for both the bark and the needle samples although the regression models performed much better with R2CV >0.7 for the bark samples. After confirming the feasibility of utilising the vegetation cover as a medium, the second site in the Rise and Shine Shear Zone was examined to assess the limits of the airborne hyperspectral data over variably exposed soil. The potentially high As anomalies indicating the gold mineralisation were classified coupled with a thorough understanding of the soil cover and its relation to the lithology. The orthogonal total variation component analysis transformed data produced the best-performing models using random forest classification with an accuracy ~50% for the high concentration As zonation. Finally, the third study site in Reefton exhibited a multi-species natural forest overlying the gold mineralisation. Apart from varying elemental responses among the different species the Reefton study area also manifested regions contaminated by previous mining activity which likely impacted the elemental uptake in the overlying vegetation. The regression models performed poorly but the spatial predictions rendered some valid correlations based on ground knowledge from previous studies.
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    Deep learning-based approaches for plant disease and weed detection : a thesis by publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering, Massey University, Auckland, New Zealand
    (Massey University, 2022) Saleem, Muhammad Hammad
    To match the ever-growing food demand, the scientific community has been actively focusing on addressing the various challenges faced by the agricultural sector. The major challenges are soil infertility, abrupt changes in climatic conditions, scarcity of water, untrained labor, emission of greenhouse gases, and many others. Moreover, plant diseases and weeds are two of the most important agricultural problems that reduce crop yield. Therefore, accurate detection of plant diseases and weeds is one of the essential operations to apply targeted and timely control measures. As a result, this can improve crop productivity, reduce the environmental effects and financial losses resulting from the excessive application of fungicide/herbicide spray on diseased plants/weeds. Among various ways of plant disease and weed detection, image-based methods are significantly effective for the interpretation of the distinct features. In recent years, image-based deep learning (DL) techniques have been reported in literature for the recognition of weeds and plant diseases. However, the full potential of DL has not yet been explored as most of the methods rely on modifications of the DL models for well-known and readily available datasets. The current studies lack in several ways, such as addressing various complex agricultural conditions, exploring several aspects of DL, and providing a systematic DL-based approach. To address these research gaps, this thesis presents various DL-based methodologies and aims to improve the mean average precision (mAP) for the identification of diseases and weeds in several plant species. The research on plant disease recognition starts with a publicly available dataset called PlantVillage and comparative analyses are conducted on various DL feature extractors, meta-architectures, and optimization algorithms. Later, new datasets are generated from various local New Zealand horticultural farms, named NZDLPlantDisease-v1 & v2. The proposed datasets consist of healthy and diseased plant organs of 13 economically important horticultural crops of New Zealand, divided into 48 classes. A performance-optimized DL model and a transfer learning-based approach are proposed for the detection of plant diseases using curated datasets. The weed identification has been performed on an open-source dataset called DeepWeeds. A two-step weed detection pipeline is presented to show the performance improvement of the deep learning model with a significant margin. The results for both agricultural tasks achieve superior performance compared to the existing method/default settings. The research outcomes elaborate the practical aspects and extended potential of DL for selected agricultural applications. Therefore, this thesis is a benchmark step for cost-effective crop protection and site-specific weed management systems (SSWM).
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    Sketch recognition of digital ink diagrams : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Palmerston North, New Zealand
    (Massey University, 2020) Ghodrati, Amirhossein
    Sketch recognition of digital ink diagrams is the process of automatically identifying hand-drawn elements in a diagram. This research focuses on the simultaneous grouping and recognition of shapes in digital ink diagrams. In order to recognise a shape, we need to group strokes belonging to a shape, however, strokes cannot be grouped until the shape is identified. Therefore, we treat grouping and recognition as a simultaneous task. Our grouping technique uses spatial proximity to hypothesise shape candidates. Many of the hypothesised shape candidates are invalid, therefore we need a way to reject them. We present a novel rejection technique based on novelty detection. The rejection method uses proximity measures to validate a shape candidate. In addition, we investigate on improving the accuracy of the current shape recogniser by adding extra features. We also present a novel connector recognition system that localises connector heads around recognised shapes. We perform a full comparative study on two datasets. The results show that our approach is significantly more accurate in finding shapes and faster on process diagram compared to Stahovich et al. (2014), which the results show the superiority of our approach in terms of computation time and accuracy. Furthermore, we evaluate our system on two public datasets and compare our results with other approaches reported in the literature that have used these dataset. The results show that our approach is more accurate in finding and recognising the shapes in the FC dataset (by finding and recognising 91.7% of the shapes) compared to the reported results in the literature.
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    Lens distortion correction by analysing the shape of patterns in Hough transform space : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Electronics and Computer Engineering at Massey University, Manawatu, New Zealand
    (Massey University, 2018) Chang, Yuan
    Many low cost, wide angle lenses suffer from lens distortion, resulting from a radial variation in the lens magnification. As a result, straight lines, particularly those in the periphery, appear curved. The Hough transform is a commonly used linear feature detection technique within an image. In Hough transform space, straight lines and curved lines have different shapes of peaks. This thesis proposes a lens distortion correction method named SLDC based on analysing the shape of patterns in the Hough transform space. It works by reconstructing the distorted line from significant points on the smile-shaped Hough pattern. It then optimises the distortion parameter by mapping the reconstructed curved line into a straight line and minimising the RMSE. From both simulation and correcting real world images, the SLDC provides encouraging results.
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    Tracking vertebrae in cinematic fluoroscopic X-rays : a thesis presented in fulfilment of the requirements for the degree of Master of Technology at Massey University
    (Massey University, 1992) Long, Samantha Robyn
    This thesis concerns the evaluation of an image subtraction statistic that is used by a prototype of a chiropractic image processing package to track spinal movement. The image subtraction statistic is calculated by summing the absolute differences in pixel intensity of two images. Also included in the thesis is a brief discussion of different methods of tracking and a literature search of alternative statistics that may be appropriate for the image type (low contrast and noisy). In summary the experimental work concluded that inter frame rotation does not have a significant effect on the performance of the image subtraction statistic when tracking inter-frame but when tracking from a particular frame to one which is significantly later in the sequence rotation must be included in the algorithm. It was also found that discretisation of the image had a detrimental effect on performance. This can be compensated for by adding a sub-pixel location calculation into the algorithm. In the original prototype a median filter (rank 5) was used to smooth the noise in the image to be searched. This was found to have marginal affect on the performance of the statistic. Many of the algorithms presently defined in the literature were found to be unsuitable for this application as they tracked clearly defined lines or searched for a two-dimensional shape that matched a predefined three-dimensional model. An algorithm that may prove to be a suitable alternative compared the rate of change in intensity across a window so is based on locating a change of intensity pattern rather than a pixel to pixel comparison. There are some features that could be included in the tracking procedure to make the algorithm more efficient (the two-dimensional logarithmic search) and provide checks to safeguard against points incrementally deviating from the correct location as tracking progresses (referencing a moused frame, using the vertebra rigid body property). The benefit of incorporating the safeguard features would have to be weighed against the cost of extra computational time. In conclusion, the image subtraction technique can be improved from, in some cases, total tracking loss to accuracy within two pixels of the correct location. This is achieved by tracking inter frame, that is from one frame to the next in the video sequence, and including a sub-pixel location calculation.
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    Parallel processor implementation in computerized tomography using transputers : a thesis presented in partial fulfilment of the requirement for the degree of Master of Technology in Production Technology at Massey University
    (Massey University, 1993) Wu, Wenju
    Image reconstruction by computerized tomography provides a nonintrusive method of imaging the internal structure of objects. From measurements of radiation (e.g. X-rays or gamma rays) passed through an object, it is possible to observe the internal structure. The reconstruction process is computationally intensive and requires imaginative parallel processing algorithms to attain 'real­ time' performance. The Inmos transputer makes parallel processing algorithms both feasible and relatively straight forward. In this thesis, a modification to the backprojection algorithm is introduced in order to improve the speed of the implementation. Work carried out has involved evaluating how these algorithms ( convolution, backprojection and interpolation ) can be used in multiprocessor concurrent architecture to obtain rapid image reconstruction. Several suitable transputer network structures have been advanced to simulate the image reconstruction. The reconstruction time is decreased very greatly and the image reconstruction result is good.
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    DNA sequence reading by image processing : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science at Massey University
    (Massey University, 1993) Fan, Baozhen
    The research described in this thesis is the development of the DNA sequence reading system. Macromolecular sequences of DNA are the encoded form of the genetic information of all living organisms. DNA sequencing has therefore played a significant role in the elucidation of biological systems. DNA sequence reading is a part of DNA sequencing. This project is for reading DNA sequences directly from DNA sequencing gel autoradiographs within a general purpose image processing system. The DNA sequence reading software is developed based on the waterfall software development approach combined with exploratory programming. Requirement analysis, software design, detailed design, implementation, system testing and maintenance are the basic development stages. The feedback from implementation and system testing to detailed design is much stronger in image processing than a lot of other software development. After an image is captured from a gel autoradiograph, the background of the image is normalised and the contrast is enhanced. The captured image consists several lane sets of bands. Each of the lane set represents one part of a DNA sequence. The lane sets are separated automatically into subimages to be read individually. The gap lines between the lane sets are detected for separation. The geometric distortions are corrected by finding the boundaries of the lane set in the subimage. The left boundary of the lane set is used to straighten lane set and the right boundary is used to warp the lane set into a standard width. If separation of the lane sets or geometry correction is unsuccessful by automatical processing, manual selection is used. After the band features are enhanced, the individual bands are extracted and the positions of the bands are determined. The band positions are then converted into the order of the DNA sequence. Different part of a sequence from subsequences are merged into a longer sequence. In most of the cases, the individual lane sets in a captured image are able to be separated automatically. Manual processing is necessary to handle the cases where the lane sets are too close. The system may reach an accuracy of 98% if the bands are clear. Manual checking and correcting the detected bands helps to obtain a reliable sequence. If a lane set on the autoradiograph is indistinct or bands are too close it may reduce the accuracy, in extreme cases to the point where it is unreadable. For a 512x512 image captured from a gel autoradiograph, preprocessing takes 90 seconds, processing each subimage takes 40 seconds on a 33Hz 486 PC. If processing a 430x350 mm autoradiograph with 16 lane sets, assuming 6 images are required, it takes about 40 minutes.
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    Fuzzy neural network interface : development and application : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Information Engineering at Massey University, Palmerston North, New Zealand
    (Massey University, 2003) Todd, Greg
    This project is concerned with the development and application of an interface for a fuzzy neural network (FuNN). The original program, for which the interface was written, is a tool to research the mapping of problem knowledge to initialize the weights of a FuNN. The interface concentrates on allowing the user to efficiently manipulate network settings and to be able to easily perform large numbers of experiments. After the interface was completed, the new integrated application was used to investigate the use of problem knowledge on FuNN training in specific image processing problems.
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    A systematic algorithm development for image processing feature extraction in automatic visual inspection : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in the Department of Production Technology, Massey University
    (Massey University, 1990) Xing, G. X. (Guo Xin)
    Image processing techniques applied to modern quality control are described together with the development of feature extraction algorithms for automatic visual inspection. A real-time image processing hardware system already available in the Department of Production Technology is described and has been tested systematically for establishing an optimal threshold function. This systematic testing has been concerned with edge strength and system noise information. With the a priori information of system signal and noise, non-linear threshold functions have been established for real time edge detection. The performance of adaptive thresholding is described and the usefulness of this nonlinear approach is demonstrated from results using machined test samples. Examination and comparisons of thresholding techniques applied to several edge detection operators are presented. It is concluded that, the Roberts' operator with a non-linear thresholding function has the advantages of being simple, fast, accurate and cost effective in automatic visual inspection.