In the two decades since the introduction of the X-ray scanner into radiology, medical imaging techniques have become widely established as essential tools in the diagnosis of disease. As a consequence of recent technological and mathematical advances, the non invasive, three-dimensional imaging of internal organs such as the brain and the heart is now possible, not only for anatomical investigations using X-rays but also for studies which explore the functional status of the body using positron-emitting radioisotopes. This report reviews the historical and physical basis of medical imaging techniques using positron-emitting radioisotopes. Mathematical methods which enable three-dimensional distributions of radioisotopes to be reconstructed from projection data (sinograms) acquired by detectors suitably positioned around the patient are discussed. The extension of conventional twodimensional tomographic reconstruction algorithms to fully three-dimensional reconstruction is described in detail. © Parallel projection Eqn. (3.1), <\> = 0 p(s, 0 = 0) © Filtering Eqn. (3.2) Two disks equal concentrations À © Backprojection Eqn. (3.5)

Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs. Our biologically plausible, wide and deep artificial neural network architectures can. Small (often minimal) receptive fields of convolutional winner-take-all neurons yield large network depth, resulting in roughly as many sparsely connected neural layers as found in mammals between retina and visual cortex. Only winner neurons are trained. Several deep neural columns become experts on inputs preprocessed in different ways; their predictions are averaged. Graphics cards allow for fast training. On the very competitive MNIST handwriting benchmark, our method is the first to achieve near-human performance. On a traffic sign recognition benchmark it outperforms humans by a factor of two. We also improve the state-of-the-art on a plethora of common image classification benchmarks.

Computer vision algorithms are natural candidates for high performance computing systems. Algorithms in computer vision are characterized by complex and repetitive operations on large amounts of data involving a variety of data interactions (e.g., point operations, neighborhood operations, global operations). In this paper, we describe the use of the custom computing approach to meet the computation and communication needs of computer vision algorithms. By customizing hardware architecture at the instruction level for every application, the optimal grain size needed for the problem at hand and the instruction granularity can be matched. A custom computing approach can also reuse the same hardware by reconfiguring at the software level for different levels of the computer vision application. We demonstrate the advantages of our approach using Splash 2-a Xilinx 4010-based custom computer.

A histogram based perceptual quality assessment (HPQA) method for color images is presented in this paper. Basically, the HPQA combines two quality assessment approaches (color image quality assessment and histogram based image quality assessment) and it uses the fourier transform. Its range is between 0 and 1. 1 represents the best quality result, 0 represents the worst quality result for the HPQA. The HPQA results are more suitable than its counterparts in terms of the HVS and they can be obtained faster than the other methods' results. In addition, it can easily differentiate effects of low distortions on color images.

Feature extraction and dimensionality reduction are important tasks in many fields of science dealing with signal processing and analysis. The relevance of these techniques is increasing as current sensory devices are developed with ever higher resolution, and problems involving multimodal data sources become more common. A plethora of feature extraction methods are available in the literature collectively grouped under the field of Multivariate Analysis (MVA). This paper provides a uniform treatment of several methods: Principal Component Analysis (PCA), Partial Least Squares (PLS), Canonical Correlation Analysis (CCA) and Orthonormalized PLS (OPLS), as well as their non-linear extensions derived by means of the theory of reproducing kernel Hilbert spaces. We also review their connections to other methods for classification and statistical dependence estimation, and introduce some recent developments to deal with the extreme cases of large-scale and low-sized problems. To illustrate the wide applicability of these methods in both classification and regression problems, we analyze their performance in a benchmark of publicly available data sets, and pay special attention to specific real applications involving audio processing for music genre prediction and hyperspectral satellite images for Earth and climate monitoring.

The different MPEG standards are well known for their media compression capabilities. MPEG-4 evolved in effort by JVT to develop a codec that maximize quality and minimize bit rates. MPEG-4 Version2 Simple Profile which we choose contain several novel tools for the low-bitrate coding of audio and video. However, in typical conditions, the latency needed to compress/decompress a digital video signal at Standard Definition (SD) resolution is of the order of 15 frames, which corresponds to 0.5 sec. This delay would be more in case of HDTV because of its high resolution and bigger video size.

This paper investigates the accuracy and effectiveness of the face detection and recognition algorithms using OpenCV and C# computer language. The adaboost algorithm [2] is used for face detection and PCA algorithm[1] is used for face recognition. This paper also investigates the robustness of the face recognition system when an unknown person is being detected, wherein the system will send an email to the owner of the system using SMTP [7]. The door lock can also be accessed remotely from any part of the world by using a Dropbox [8] account.

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

Robotics is the future of automated manufacturing. Robotic systems are inherently more useful in undertaking tasks which might be difficult, or where it is necessary for accuracy to be quite high. However, use of robots in industry or other settings, involves complex programming, debugging and subsequent difficulties. Learning from demonstration is a process of automating the programming element of robotics. This report will detail existing literature to do with Learning from demonstration, and will identify the different parts needed to implement such a system, i.e. image processing, analysis, real-world equivalation, generic programming etc. The methodology in this paper will delve slightly into these topics, and provide a basis for implementing these into the Baxter robot by Rethink Robotics.

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.

In this paper, we propose a novel system for beard and mustache detection and segmentation in challenging facial images. Our system first eliminates illumination artifacts using the self-quotient algorithm. A sparse classifier is then used on these self-quotient images to classify a region as either containing skin or facial hair. We conduct experiments on the MBGC and color FERET databases to demonstrate the effectiveness of our proposed system.

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&#39;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.

Wood coatings are widely used for aesthetic and protective reasons. Assessment of coating performance during service life is crucial in order to establish a knowledge database for product optimization. A vast amount of techniques is available for analysis of a coating's behavior of which micro-imaging is an important tool. In addition to standard microscopy techniques, high-resolution X-ray tomography is presented as a modality offering non-destructive visualization of a coating and the substrate applied on. Combined with analysis of the 3D volumetric data, surface roughness, structure and thickness of the coating layer, penetration depth and related mechanical anchoring can be studied in relation with the underlying substrate. To provide a clear illustration of the possibilities and limitations of this technique, both an opaque solvent-borne and an opaque water-borne coating applied on two different wood types were scanned and analyzed. Clearly, three-dimensional X-ray imaging at high resolution produces valuable information merely by visualization. Moreover by proper analysis quantitative data is obtained taking into account the limitations of Xray computed tomography and of automated image processing.

In this paper an unsupervised colour image segmentation algorithm is presented. This method combines the advantages of the approaches based on split&amp;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.

At the present time, remote sensing technologies are being improved and day by day, by launching new satellites with new developed techniques, images can be obtained faster with high resolution. The satellites which had been launched recent years for example; Ikonos, Quickbird and Orbview-3 have 1m and better resolution, continue their missions with quite success.

Image registration (or alignment) is a useful preprocessing tool for assisting in manual data extraction from handwritten forms, as well as for preparing documents for batch OCR of specific page regions. A new technique is presented for fast registration of lined tabular document images in the presence of a global affine transformation, using the Discrete Fourier-Mellin Transform (DFMT). Each component of the affine transform is handled separately, which dramatically reduces the total parameter space of the problem. This method is robust and deals with all components of the affine transform in a uniform way by working in the frequency domain. The DFMT is extended to handle shear, which can approximate a small amount of perspective distortion. In order to limit registration to foreground pixels only, and to eliminate Fourier edge effects, a novel, locally adaptive foreground-background segmentation algorithm is introduced, based on the median filter, which eliminates the need for Blackman windowing as usually required by DFMT image registration. A novel information-theoretic optimization of the median filter is presented. An original method is demonstrated for automatically obtaining blank document templates from a set of registered document images.

Automated blood vessel segmentation is an important issue for assessing retinal abnormalities and diagnoses of many diseases. The segmentation of vessels is complicated by huge variations in local contrast, particularly in case of the minor vessels. In this paper, we propose a new method of texture based vessel segmentation to overcome this problem. We use Gaussian and L * a * b * perceptually uniform color spaces with original RGB for texture feature extraction on retinal images. A bank of Gabor energy filters are used to analyze the texture features from which a feature vector is constructed for each pixel. The Fuzzy C-Means (FCM) clustering algorithm is used to classify the feature vectors into vessel or non-vessel based on the texture properties. From the FCM clustering output we attain the final output segmented image after a post processing step. We compare our method with hand-labeled ground truth segmentation of five images and achieve 84.37% sensitivity and 99.61% specificity.

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.

We consider the problem of learning a coefficient vector x 0 ∈ R N from noisy linear observation y = Ax 0 + w ∈ R n . In many contexts (ranging from model selection to image processing) it is desirable to construct a sparse estimator x. In this case, a popular approach consists in solving an ℓ 1 -penalized least squares problem known as the LASSO or Basis Pursuit DeNoising (BPDN).

Magnetic Resonance Cholangiopancreatography (MRCP) is a type of MR imaging which utilises protocols designed to enhance stationary fluids in the imaged volume. In this way it visualises the pancreatobiliary tract by highlighting the bile and pancreatic juices in the system. Current practice sees this data being assessed directly, with little or no processing being performed prior to review. MRCP data presents three main difficulties when it comes to image processing. The first is the relatively noisy nature of the data. Second is its low spatial resolution, especially in the inter-slice direction. And third, the variability observed between MRCP studies, which makes consistent results difficult to attain. This paper describes the initial phase of research which aims to develop assistive image analysis techniques to aid in the interpretation of MRCP data. The first stage in this process is the robust segmentation of the pancreatobiliary system. To this end a segmentation procedure has been developed using an approach based on the tools and techniques of the mathematical morphology. This paper examines the task at hand and presents initial results, describing and assessing the segmentation approach developed.

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.

Although the processing of data streams has been the focus of many research efforts in several areas, the case of remotely sensed streams in scientific contexts has received little attention. We present an extensible architecture to compose streaming image processing pipelines spanning multiple nodes on a network using a scientific workflow approach. This architecture includes (i) a mechanism for stream query dispatching so new streams can be dynamically generated from within individual processing nodes as a result of local or remote requests, and (ii) a mechanism for making the resulting streams externally available. As complete processing image pipelines can be cascaded across multiple interconnected nodes in a dynamic, scientist-driven way, the approach facilitates the reuse of data and the scalability of computations. We demonstrate the advantages of our infrastructure with a toolset of stream operators acting on remotely sensed data streams for realtime change detection.

Image segmentation is a process by which an image is partitioned into regions with similar features. Many approaches have been proposed for color images segmentation, but Fuzzy C-Means has been widely used, because it has a good performance in a wide class of images. However, it is not adequate for noisy images and it takes longer runtimes, as compared to other method like K-means. For this reason, several methods have been proposed to improve these weaknesses. Methods like Fuzzy C-Means with Gustafson-Kessel algorithm (FCM-GK), which improve its performance against the noise, but increase significantly the runtime. In this paper we propose to use the centroids generated by GK-FCM algorithms as seeding for K-means algorithm in order to accelerate the runtime and improve the performance of K-means with random seeding. These segmentation techniques were applied to feature extraction on vineyard images. Segmented images were evaluated using several quality parameters such as the rate of correctly classified area and runtime.

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.