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Since the design of an inspection system typically requires a lot of application-dependent work, the provision of systematic methods and tools to assist in the design process could significantly reduce the system development and installation time. With this in view, a step-by-step design procedure for image acquisition systems is suggested, consisting of measurements of certain important optical parameters for the surfaces to be inspected, modelling of the measurements and arrangement of the imaging in a form that a computer can understand, simulation of the imaging process in a computer using optical analysis tools, and verification of the results through a pilot system. The procedure is exemplified by describing its application to the design of a steel sheet inspection system and its capacity for optimising the detection of various defects is demonstrated. For comparison, measurements made on some other materials are shown and the implications discussed. The results of the simulation and the pilot system for steel are compared and the usefulness of the computer-based design method is evaluated.

In the process of image acquisition and transmission, data can be corrupted by impulse noise. This paper presented the removal of impulse noise in medical image by using Very Large Scale Integrated circuit (VLSI) implementation. The Low Cost Reduced Simple Edge Preserved De-noising (LCRSEPD) technique is introduced using Low Area Carry Select Adder (CSLA) to remove the salt and pepper noise instead of normal adder. Thus, LCRSEPD technique preserves visual performance and edge features in terms of quality and quantitative evaluation. By optimizing the architecture, low cost RSEPD can achieve low computational complexity that will reflect in area, power and delay. Compared to the previous VLSI implementations, the LCRSEPD implementation with CSLA adder has achieved good medical image quality and less hardware cost due to the reduction of area, power and delay.

In this paper, a computer vision-based cashew nut grading system has been designed and implemented for classifying different grades of cashew nuts using combined features and machine learning approaches. The important task in the cashew nut grading system is to classify the whole and split down cashew nuts. Since these cashew nuts look very similar from the top view, it is a challenging task to classify the whole cashew nut and split down cashew nuts. Hence, a single-view image of cashew nut has been captured by placing a camera with a distance of 17cm (from the right side of the conveyor belt). The captured red, blue and green images are normalized and converted into hue, saturation and value color space. S channel from HSV image is used for segmentation process using Otsu threshold technique. The total numbers of features extracted are 275 and the features are texture (180), color (90), and shape (5). The constrained optimization-based feature selection method is used and 30 features are selected for further process. The Support Vector Machine (SVM) classifier is used for the classification, and the results obtained from different kernel functions are computed and compared. The 8-layer convolutional neural network (CNN) has been developed in this work for classification and to analyze the performance and accuracy. The accuracy of different machine learning classifiers like SVM 1-1, SVM 1-All and CNN model is also evaluated and compared. The overall accuracy obtained by SVM 1-All with kernel function radial basis for classification is 98.93%.

This is a study on measuring the effect of stress that stimulated by the different difficulty of game level on pupil dilation. Playing games are claimed a method to distress in the community, however, this activity is enhancing stress level? The effect of the game playing on the changes of pupil dilation is unknown. An investigation was conducted on relating the changes of pupil diameter measurement at the different difficulty levels of the game. A simple tool was developed by using a webcam as image acquisition device together with MATLAB Image Processing Tool to analyze the acquired images. Here, experiments on the pupil detection algorithm, which consisted of four different methods are to work alternatively with one another and to provide higher chances of pupil detection. 10 subjects were involved in the experiments, which they were requested to play a selected game with four different ascending increased in difficulties, meanwhile, pupil size changes were measured simultaneously. The selected game served as a stress stimulus to the subjects. The result showed a positive proportional relationship between increased in game difficulty levels and pupil dilation. The investigation reported that the pupil dilated when the difficulty of game level increased. Limitation of this study is the sample size on the prototype testing should be increased to concrete the outcome.

The need for automation in the food industry is growing. Some industries such as the poultry industry are now highly automated whereas others such as the fishing industry are still highly dependent on human operators. At the same time consumers are demanding increased quality of the products. In the food industry the objects are often of varying size and shape, and often flexible and randomly oriented when presented to the automation system. To automate handling of these objects, an intelligent system such as a vision system is needed to control the mechanical operations to ensure optimum performance and quality.

This chapter describes vision techniques that can be used to detect and measure shape and quality of food products. It stresses the specific implementation context, needed performance, sensors, optics, illumination as well as vision algorithms. Algorithms include those for the size measurement of flexible objects and for the colour measurement of objects with nonuniform colour. Some results are given.

Single-shot fast MRI acquisition techniques are described in this chapter, with an emphasis on echo-planar imaging and spiral imaging. Advantages and potential shortcomings of these imaging schemes are discussed. Applications of the fast acquisition techniques in advanced MRI techniques such as functional neuroimaging, perfusion imaging with arterial spin labeling, diffusion tensor imaging and perfusion imaging with injection of susceptibility contrast agents are demonstrated.

Since the design of an inspection system typically requires a lot of application-dependent work, the provision of systematic methods and tools to assist in the design process could significantly reduce the system development and installation time. With this in view, a step-by-step design procedure for image acquisition systems is suggested, consisting of measurements of certain important optical parameters for the surfaces to be inspected, modelling of the measurements and arrangement of the imaging in a form that a computer can understand, simulation of the imaging process in a computer using optical analysis tools, and verification of the results through a pilot system. The procedure is exemplified by describing its application to the design of a steel sheet inspection system and its capacity for optimising the detection of various defects is demonstrated. For comparison, measurements made on some other materials are shown and the implications discussed. The results of the simulation and the pilot system for steel are compared and the usefulness of the computer-based design method is evaluated.

This chapter describes image processing methods for document image analysis. The methods are grouped into four categories, namely, image acquisition, image transformation, image segmentation, and feature extraction. In image acquisition, we describe the process of converting a document into its numerical representation, including image coding as a means to reduce the storage requirement. Image transformation addresses image-to-image operations, which comprise a large spectrum of techniques ranging from geometrical correction, filtering and figure-background separation to boundary detection and thinning. In image segmentation, we describe four popular techniques, namely, connected component labeling, X-Y-tree decomposition, run-length smearing, and Hough transform. Finally, a number of feature extraction methods, which constitute the basis of image classification, are presented.

The need for automation in the food industry is growing. Some industries such as the poultry industry are now highly automated whereas others such as the fishing industry are still highly dependent on human operators. At the same time consumers are demanding increased quality of the products. In the food industry the objects are often of varying size and shape, and often flexible and randomly oriented when presented to the automation system. To automate handling of these objects, an intelligent system such as a vision system is needed to control the mechanical operations to ensure optimum performance and quality.

This chapter describes vision techniques that can be used to detect and measure shape and quality of food products. It stresses the specific implementation context, needed performance, sensors, optics, illumination as well as vision algorithms. Algorithms include those for the size measurement of flexible objects and for the colour measurement of objects with nonuniform colour. Some results are given.