Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection

Recently, many researchers have directed their attention to methods of predicting shorelines by the use of multispectral images. Thus, a simple and optimised method using image enhancements is proposed to improve the low contrast of the Satellite pour l'Observation de la Terre-5 (SPOT-5) images in the detection of shorelines. The near-infrared (NIR) channel is important in this study to ensure the contrast of the vegetated area and sea classification, due to the high reflectance of leaves in the near infrared wavelength region. This study used five scenes of interest to show the different results in shoreline detection. The results demonstrated that the proposed method performed in an enhanced manner as compared to current methods when dealing with the low contrast ratio of SPOT-5 images. As a result, by utilising the near-infrared histogram equalization (NIR-HE), the contrast of all datasets was efficiently restored, producing a higher efficiency in edge detection, and achieving higher overall accuracy. The improved filtering method showed significantly better shoreline detection results than the other filter methods. It was concluded that this method would be useful for detecting and monitoring the shoreline edge in Tanjung Piai.

Eye Tracking Interaction on Unmodified Mobile VR Headsets Using the Selfie Camera

Input methods for interaction in smartphone-based virtual and mixed reality (VR/MR) are currently based on uncomfortable head tracking controlling a pointer on the screen. User fixations are a fast and natural input method for VR/MR interaction. Previously, eye tracking in mobile VR suffered from low accuracy, long processing time, and the need for hardware add-ons such as anti-reflective lens coating and infrared emitters. We present an innovative mobile VR eye tracking methodology utilizing only the eye images from the front-facing (selfie) camera through the headset’s lens, without any modifications. Our system first enhances the low-contrast, poorly lit eye images by applying a pipeline of customised low-level image enhancements suppressing obtrusive lens reflections. We then propose an iris region-of-interest detection algorithm that is run only once. This increases the iris tracking speed by reducing the iris search space in mobile devices. We iteratively fit a customised geometric model to the iris to refine its coordinates. We display a thin bezel of light at the top edge of the screen for constant illumination. A confidence metric calculates the probability of successful iris detection. Calibration and linear gaze mapping between the estimated iris centroid and physical pixels on the screen results in low latency, real-time iris tracking. A formal study confirmed that our system’s accuracy is similar to eye trackers in commercial VR headsets in the central part of the headset’s field-of-view. In a VR game, gaze-driven user completion time was as fast as with head-tracked interaction, without the need for consecutive head motions. In a VR panorama viewer, users could successfully switch between panoramas using gaze.

Algorithms for Vein Image Enhancement and Matching in the Cloud IoT-Based M-Health Environment

In this chapter, the authors have described the methodologies to achieve the objectives of veins image enhancement, feature extractions, and matching with other veins images in the cloud IoT-based m-health environment. The initial steps to propose the algorithms for veins image enhance and feature extractions will have five parts. Once the proposed algorithm is written, the hardware architecture designs of the proposed veins image enhancements and feature extraction algorithm will be described by the authors. The hardware designs are presented in subsequent sections of this chapter. Further, the hardware designs are elaborated in detail for each of the techniques. The presented algorithms are implemented in MATLAB 11.0 software, and these algorithms are simulated and integrated with different veins sample images. The hardware designs of veins image enhancements and feature extractions are implemented using Verilog Hardware Language Description (VHLD), and these implemented results are simulated using MSA (Model-Sim-Altera) for sample images of different types of veins.

High-Resolution Mapping of Tile Drainage in Agricultural Fields Using Unmanned Aerial System (UAS)-Based Radiometric Thermal and Optical Sensors

With the growing concerns of water quality related to tile drainage in agricultural lands, developing an efficient and cost-effective method of mapping tile drainage is essential. This research aimed to establish mapping of tile drainage systems in agricultural fields using optical and radiometric thermal sensors mounted on Unmanned Aerial System (UAS). The overarching hypothesis is that in a tile-drained land, spatial distribution of soil water content is affected by tile lines, therefore, contrasting soil temperature signals exist between areas along the tile lines and between the tile lines. Designated flights were conducted to assess the effectiveness of the UAS under various conditions such as rainfall, crop cover, crop maturity and time of the day. Image correction, mosaicking, image enhancements and map production were conducted using Agisoft and ENVI image analysis software. The results showed intermediate growth stage of soybean plants and rainfall helped delineating tile lines. In-situ soil temperature measurements revealed appropriate time of the day (14:00 to 18:00 h) for thermal image detection of the tile lines. The role of soil moisture and plant cover is not resolved, thus, further refinement of the approach considering these factors is necessary to develop efficient mapping techniques of tile drainage using UAS thermal and optical sensors.

Accident Detection and Number Plate Recognition using Image Processing and Machine Learning

The purpose of this project is to detect the accident before it happens along with theextraction the number plate. Different image processing techniques along with morphological operators and Canny Edge Detection are used for image enhancements and object outline detections. With analysis of continuous frames, the relative velocity and the distance from which the leading vehicles are moving could be computed which is further helpful in accident detection and thus prevention too. Histogram of Oriented Gradients (HOG features) are used for feature extraction. Different machine learning classification algorithms like SVM, MLP, and XGBoost are used for classification of the object. Different standard OCR tools like Pytesseract, PyOCR, TesserOCR are used for the retrieval of the vehicle number from the extracted licence plate sub-image.

Enhancement of Underwater Images using Pixel Arrangements and Image Enhancement Algorithm

Underwater image processing is faced with a number of challenges from distinct resolutions, format variations, scattering, absorption of light etc. It is also affected by contrast difference and orientation. Conveying different resolved underwater images using enhanced pixel arrangement and image algorithm. This can be proceeded by converting the multi scale fused resolution images to high resolution images. Image enhancements has some in-built pixels properties having low intensities in at least one-color channel. Such kind of images are then converted to high resolution imaging by using the tools of enhanced pixel arrangement algorithm and then the output images are clarified.