Image Processing

Coordinate metrology is a field of metrology that is becoming increasingly popular in the manufacturing industry. Coordinate metrology enables the three-dimensional measurement to be carried out on complex object in a single setup. The instrument used for this purpose is known as the coordinate measuring machine or CMM. In general, the CMM comprises three frames that move along three orthogonal axes, i.e. X-, Y-and Z-axis. Usually, a contact device known as measuring probe is attached to the end of the Z-axis. The displacement along each axis is measured by a linear measurement system and the readings are sent to an electronic controller. The electronic controller is connected to a computer that also enables various types of data processing to be performed. Repeated measurements on similar objects can be done easily by programming the motion of the axes of the machine. This reduces the time taken for measurement and inspection up to 80% to 90%. These machines are made in various sizes and the methods of operation are based on either manual or computer-aided. 7.2. Types of CMM The basic CMM consists of three axes, each provided with a guide way that enable precise movement along a straight line. Each guide way has a carrier that moves along. The carrier enables the second carrier to move along a straight line based on the first guide way. Each axis is fitted with a precision scale that records the position of the carrier measured from a reference point. The measuring probe is fitted to the carrier on the third axis. When the measuring probe touches the object being measured, the measurement system records the position of all three axes. There are several physical configurations of CMMs. All the configurations have a method of moving the probe along three axes relative to the object. Although there are many designs of CMMs but they can be grouped into five basic types as follows: (a) Cantilever type, (b) Bridge type, (c) Horizontal arm type, (d) Column type and (e) Gantry type 7.2.1 Cantilever type CMM The schematic diagram of the cantilever type CMM is shown in Figure 6.1. In the cantilever type CMM the measuring probe it attached to the Z-axis and moves in the vertical direction. The Z-axis carrier is fitted to the cantilever arm and provides movement in the Y-direction. The Z-axis movement is provided by the table. This type of CMM design provides easy access to the work area and has high workspace volume.

Box filter is simple and well known technique for image resizing. This technical paper describes an algorithm for image downscaling (shrinking) using box filter with subpixel accuracy, based on color space rescaling, which avoids oating point arithmetics. All operations in accompaniment C++ code are completely integer based, which signicantly improves image shrinking speed and quality.

A  base-4  leading  zero  detector  (LZD)  design  is  proposed  in  this  paper.  The  design  is  similar  to  the
approach  originally  proposed  by  V.G.  Oklobdzija  with  a  different  technique.  The  circuit  modules  used  in
the  base-4  LZD  approach  are  designed  and  several  N-bit  LZD  circuits  are  implemented  with  a  standard-
cell  realization  in  the  Taiwan  Semiconductor  Manufacturing  Company  (TSMC)  0.65um  CMOS  process.
The  performance  and  layout  area  of  the  base-4  LZD  realization  is  compared  for  implementations  that
contain only 4-to-1 and 2-to-1 multiplexers

In this paper; we introduce a system of automatic recognition of characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal and of the Zernike moment. These characteristics are linked to pixels' densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the multi-layer perceptron (MLP) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures.

In this paper; we introduce a system of automatic recognition of characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal and of the Zernike moment. These characteristics are linked to pixels' densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the multi-layer perceptron (MLP) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures.

1.1 Suppose the input to an amplifier is x a (t) = sin(2πF 0 t) and the steady-state output is y a (t) = 100 sin(2πF 0 t + φ 1) − 2 sin(4πF 0 t + φ 2) + cos(6πF 0 t + φ 3) (a) Is the amplifier a linear system or is it a nonlinear system? (b) What is the gain of the amplifier? (c) Find the average power of the output signal. (d) What is the total harmonic distortion of the amplifier? Solution (a) The amplifier is nonlinear because the steady-state output contains harmonics. (b) From (1.1.2), the amplifier gain is K = 100. (c) From (1.2.4), the output power is P y = d 2 0 4 + 1 2 d 2 1 + d + 2 2 + d 2 3 = .5(100 2 + 2 2 + 1) = 5002.5 (d) From (1.2.5) THD = 100(P y − d 2 1 /2) P y = 100(5002.5 − 5000) 5002.5 = .05%Solution Manual for Fundamentals of Digital Signal Processing Using MATLAB 2nd Edition by Schilling.      Full file at https://testbanku.eu/

Handwritten character recognition has become a challenging and interesting field in the recent days due to its complex character shapes and huge pragmatic applications. A lot of research are already done and underway on English alphabets and numerals recognition. But in case of Bangla, even being the fifth largest spoken language in the world, has not undergone that much research. Besides, different complex shape of Bangla character makes the recognition more challenging. In this paper, we propose a directional pattern approach for feature extraction of Bangla numeric characters which attains a high accuracy of recognition. We use Local Directional Pattern (LDP) and Gradient Directional Pattern (GDP) for feature extraction and then two well-known machine learning algorithms, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM), to classify the numeric character. We also ensemble the pattern oriented results to enhance the accuracy. Experimental results on the benchmark dataset CMATERdb 3.1.1 demonstrates an astounding recognition rate of accuracy 95.62% without preprocessing the data.

Face recognition based smart attendance system using IoT is a tool for recognizing the students face while taking attendance by using face biometrics based on monitor camera image capturing. In our face recognition based smart attendance project, a raspberry pi system will be able to find and recognize human faces fast and precisely in images. The long-established method of calling name of each student is tedious and there is always a chance of proxy attendance. The proposed system is based on face recognition to maintain the attendance record of students. As the process of attendance taking starts the system takes pictures of the attendees and then applies face detection and recognition technique to the given image and the recognized students are marked as present and their attendance is updated with corresponding time, student name and register number. We have used deep learning techniques to develop this project.

Object recognition is a very well known problem domain in the field of computer vision and robot vision. In earlier years in neuro science field CNN has played a key role in solving many problems related to identification and recognition of object. As visual system of our brain shares many features with CNN's properties it is very easy to model and test the problem domain of classification and identification of object. Basically CNN is typically a feed forward architecture; on the other hand visual system is based upon recurrent CNN (RCNN) for incorporating recurrent connections to each convolutional layer. In middle layers each unit is modulated by the activities of its neighboring units. Here Various RCNN techniques (RCNN,FAST RCNN,FASTER RCNN)are implemented for identifying bikes using CALTECH-101 database and alter their performances are compared.

In this paper an unsupervised colour image segmentation algorithm is presented. This method combines the advantages of the approaches based on split&merge and region growing, and the use of the RGB and HSV colour representation models. The effectiveness of the proposed method has been verified by the implementation of the algorithm using three different testing images with homogeneous regions, spatially compact and continuous. It was observed that the proposed algorithm outperforms the other analysed techniques requiring shorter processing time when compared with the other analysed methods.

This paper presents a non-intrusive system that gives the illusion of a 3D immersive and interactive environment with 2D projectors. The user does not need to wear glasses, nor to watch a (limited) screen. The virtual world is all around him, drawn on the floor. As the user is himself immersed in the virtual world, there is no need for a proxy like an avatar; he can move inside the virtual environment freely. Moreover, the I-see-3D system allows a user to manipulate virtual objects with his own body, making interactions with the virtual world very intuitive. Giving the illusion of 3D requires to render images in such a way that the deformation of the image projected on the floor is taken into account, as well as the position of the user’s “eye” in its virtual world. The resulting projection is neither perspective nor orthographic. Nevertheless, we describe how this can be implemented with the standard OpenGL pipeline, without any shader. Our experiments demonstrate that our system i...

Fault diagnosis of industrial machineries become very much important for improving the quality of the manufacturing as well as for reducing the cost for product testing. In modern manufacturing scenario, a fast and reliable diagnosis system has turned into a challenging issue in the complex industrial environment. In this work, the diagnosis of gearbox is considered as a mean of health monitoring system by used lubricant. The proposed methodology has been performed on the basis of wear particle analysis in gearbox at offline stage. Possible wear characterization has been done by image vision system to interpret into soft computing techniques like fuzzy inference and neural network mechanisms. Basically, the maintenance policy has been taken with the help of fuzzy expert system, which has been described in the present work.

In this paper we propose to develop a device that can be used by the visually challenged to read normal English books. Here we focus on letter-by-letter segmentation, recognition and transliteration to the Braille format. The device would use on board software to do the recognition and conversion. The recognized characters are transmitted to the interface which converts the characters to the Braille format which can be felt-read by the visually challenged. The device would be cheaper among its counterparts.

In this review, the application of in-depth learning for medical diagnosis will be corrected. A thorough analysis of various scientific articles in the domain of deep neural network applications in the medical field has been implemented. Has received more than 300 research articles and after several steps of selection, 46 articles have been presented in more detail The research found that the neural network (CNN) is the most prevalent agent when talking about deep learning and medical image analysis. In addition, from the findings of this article, it can be observed that the application of widespread learning technology is widespread. But most of the applications that focus on bioinformatics, medical diagnostics and other similar fields. In this work, we examine the strength of the deep learning method for pathological examination in chest radiography. Convolutional neural networks (CNN) The method of deep architectural classification is popular due to the ability to learn to represent medium and high level images. We explore CNN's ability to identify different types of diseases in chest X-ray images. Moreover, because of the very large training sets that are not available in the medical domain, we therefore explore the possibility of using deep learning methods based on non-medical learning. We tested our algorithm on 93 datasets. We use CNN that is trained with ImageNet, which is a well-known non-animated large image database. The best performance is due to the use of features pulled from CNN and low-level features.

This paper describes person identity by fingerprint , Face recognition, and voice information using Bio-metrics tool. The person is modeled by their features using Gaussian Mixture Model (GMM). Bio metrics is frequently used in signal processing applications. Thus, we concentrated on the methodology of biometrics for person identification which is useful in industrial and military security systems. The statistical values are measured by GMM in pattern recognition, Face recognition, and voice Recognition. These statistical values will helps for modeling of person using Bio-metric technique. The voice features are mapped into a Mel-Frequency-Cepstral-Coefficients (MFCC) form. The process of indentifying a person using MFCC data is described in this paper.

ABSTRACT In this paper we discuss issues in real-time image processing, including applications, approaches and hardware. In particular, we discuss the failure of existing programming languages to support these considerations and present requirements for any language that can support real-time image processing.