Quality inspection of engraved image using based matching approach

The role of machine vision system as a vital component for quality control mainly in manufacturing process cannot be denied. The system is developed to overcome the discrepancy from human vision and illumination changes. This paper proposes shape-based vision algorithm, a hierarchical template-matching approach that implemented in flexible manufacturing system to verify the quality of engraved image. Color and gray scale charged couple device (CCD) cameras are used to acquire engraved image for different kind of environment. The engraved image is preprocessed using image processing technique. Region of interest (ROI) is then selected and digitized into gray level to extract the contour of the object using segmentation technique. The extracted contour is used as template for object recognition during matching process. Several objects are engraved on the acrylic souvenir bases with different color background to test the algorithm. This experiment result shows that the algorithm works better with detection rate of 100% and matching accuracy of more than 98%. The approach can be applied in packaging, pharmacy, education, medical or any other areas which apply shape in their application

A New Algorithm For Difference Image Analysis

In the context of difference image analysis (DIA), we present a new method for determining the convolution kernel matching a pair of images of the same field. Unlike the standard DIA technique which involves modelling the kernel as a linear combination of basis functions, we consider the kernel as a discrete pixel array and solve for the kernel pixel values directly using linear least-squares. The removal of basis functions from the kernel model is advantageous for a number of compelling reasons. Firstly, it removes the need for the user to specify such functions, which makes for a much simpler user application and avoids the risk of an inappropriate choice. Secondly, basis functions are constructed around the origin of the kernel coordinate system, which requires that the two images are perfectly aligned for an optimal result. The pixel kernel model is sufficiently flexible to correct for image misalignments, and in the case of a simple translation between images, image resampling becomes unnecessary. Our new algorithm can be extended to spatially varying kernels by solving for individual pixel kernels in a grid of image sub-regions and interpolating the solutions to obtain the kernel at any one pixel.Comment: MNRAS Letters Accepte

Multiscale Point Correspondence Using Feature Distribution and Frequency Domain Alignment

In this paper, a hybrid scheme is proposed to find the reliable point-correspondences between two images, which combines the distribution of invariant spatial feature description and frequency domain alignment based on two-stage coarse to fine refinement strategy. Firstly, the source and the target images are both down-sampled by the image pyramid algorithm in a hierarchical multi-scale way. The Fourier-Mellin transform is applied to obtain the transformation parameters at the coarse level between the image pairs; then, the parameters can serve as the initial coarse guess, to guide the following feature matching step at the original scale, where the correspondences are restricted in a search window determined by the deformation between the reference image and the current image; Finally, a novel matching strategy is developed to reject the false matches by validating geometrical relationships between candidate matching points. By doing so, the alignment parameters are refined, which is more accurate and more flexible than a robust fitting technique. This in return can provide a more accurate result for feature correspondence. Experiments on real and synthetic image-pairs show that our approach provides satisfactory feature matching performance

Compressive MRI quantification using convex spatiotemporal priors and deep encoder-decoder networks

We propose a dictionary-matching-free pipeline for multi-parametric quantitative MRI image computing. Our approach has two stages based on compressed sensing reconstruction and deep learned quantitative inference. The reconstruction phase is convex and incorporates efficient spatiotemporal regularisations within an accelerated iterative shrinkage algorithm. This minimises the under-sampling (aliasing) artefacts from aggressively short scan times. The learned quantitative inference phase is purely trained on physical simulations (Bloch equations) that are flexible for producing rich training samples. We propose a deep and compact encoder-decoder network with residual blocks in order to embed Bloch manifold projections through multi-scale piecewise affine approximations, and to replace the non-scalable dictionary-matching baseline. Tested on a number of datasets we demonstrate effectiveness of the proposed scheme for recovering accurate and consistent quantitative information from novel and aggressively subsampled 2D/3D quantitative MRI acquisition protocols

Determination of Elevations for Excavation Operations Using Drone Technologies

Using deep learning technology to rapidly estimate depth information from a single image has been studied in many situations, but it is new in construction site elevation determinations, and challenges are not limited to the lack of datasets. This dissertation presents the research results of utilizing drone ortho-imaging and deep learning to estimate construction site elevations for excavation operations. It provides two flexible options of fast elevation determination including a low-high-ortho-image-pair-based method and a single-frame-ortho-image-based method. The success of this research project advanced the ortho-imaging utilization in construction surveying, strengthened CNNs (convolutional neural networks) to work with large scale images, and contributed dense image pixel matching with different scales.This research project has three major tasks. First, the high-resolution ortho-image and elevation-map datasets were acquired using the low-high ortho-image pair-based 3D-reconstruction method. In detail, a vertical drone path is designed first to capture a 2:1 scale ortho-image pair of a construction site at two different altitudes. Then, to simultaneously match the pixel pairs and determine elevations, the developed pixel matching and virtual elevation algorithm provides the candidate pixel pairs in each virtual plane for matching, and the four-scaling patch feature descriptors are used to match them. Experimental results show that 92% of pixels in the pixel grid were strongly matched, where the accuracy of elevations was within ±5 cm.Second, the acquired high-resolution datasets were applied to train and test the ortho-image encoder and elevation-map decoder, where the max-pooling and up-sampling layers link the ortho-image and elevation-map in the same pixel coordinate. This convolutional encoder-decoder was supplemented with an input ortho-image overlapping disassembling and output elevation-map assembling algorithm to crop the high-resolution datasets into multiple small-patch datasets for model training and testing. Experimental results indicated 128×128-pixel small-patch had the best elevation estimation performance, where 21.22% of the selected points were exactly matched with “ground truth,” 31.21% points were accurately matched within ±5 cm. Finally, vegetation was identified in high-resolution ortho-images and removed from corresponding elevation-maps using the developed CNN-based image classification model and the vegetation removing algorithm. Experimental results concluded that the developed CNN model using 32×32-pixel ortho-image and class-label small-patch datasets had 93% accuracy in identifying objects and localizing objects’ edges

Handwritten and machine-printed text discrimination using a template matching approach

We propose a novel template matching approach for the discrimination of handwritten and machine-printed text. We first pre-process the scanned document images by performing denoising, circles/lines exclusion and word-block level segmentation. We then align and match characters in a flexible sized gallery with the segmented regions, using parallelised normalised cross-correlation. The experimental results over the Pattern Recognition & Image Analysis Research Lab-Natural History Museum (PRImA-NHM) dataset show remarkably high robustness of the algorithm in classifying cluttered, occluded and noisy samples, in addition to those with significant high missing data. The algorithm, which gives 84.0% classification rate with false positive rate 0.16 over the dataset, does not require training samples and generates compelling results as opposed to the training-based approaches, which have used the same benchmark