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2D Image Projection in Teleoperation System Using Virtual Reality

2D Image Projection in Teleoperation System Using Virtual Reality

Virtual reality projector acts as a primary system. It tends to visualize the 2D image on an object. It emphasizes the transparency of our predefined device with the operation to perform in the system. The camera located parallel to the projector senses the gesture of our user motion and transmits to the system. Embedded C language is used to define the functions that a user needs to operate in the image. The Virtual reality system is connected to the Graphical User Interface application on the Personal Computer along with a ZigBee controller which acts as a wireless transmitter. At the receiver end, ZigBee controller is located, which helps to receive the signal and sends to a step-down transformer which in turn is connected to a Peripheral Interface Controller(PIC) microcontroller and RELAY circuit composed of in-built components connected in a parallel manner. The integrated circuit IC (MAX 232) used in the embedded board integrates the microcontroller with the application relays. The Liquid Crystal Display notifies the current status of the appliances. The RELAY circuit generates voltage which is used for the devices to work. The relay circuit connected to output system.
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Human Attributes Quantification from A 2D Image Using Hale CANVAS App

Human Attributes Quantification from A 2D Image Using Hale CANVAS App

eing hale and healthy is necessary for leading a happy life. This mantra can be achieved only through being FIT. The whole world is trying to get into better shape, and we don’t want to be left behind. We need have track of our human attributes to be sure of our fitness. The HALE CANVAS app helps to easily figure out your fitness only by using a 2D image. The fitness status of the user is calculated using human attributes. In Today’s Digital World people are extremely practiced to gadgets and mobile devices so the idea of delivering a good product to the market began with iPhone application because iPhone users are drastically increasing in the market.
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A new 2D image compression technique for 3D surface reconstruction

A new 2D image compression technique for 3D surface reconstruction

Our approach is compared with JPEG and JPEG2000; these two techniques are used widely in digital image compression, especially for image transmission and video compression. The JPEG technique is based on the 2D DCT applied on the partitioned image into 8x8 blocks, and then each block encoded by RLE and Huffman encoding [4]. The JPEG2000 is based on the multi-level DWT 9/7-daubaches filter, applied on the partitioned image and then each partition quantized and coded by Arithmetic encoding. Most image compression applications allow the user to specify a quality parameter for the compression. If the image quality is increased the compression ratio is decreased and vice versa [5]. The comparison is based on the 2D image and 3D image for test the quality by Root-Mean-Square-Error (RMSE). Tables: 5 and 6 shows the comparison between three methods for Face1, Face2 respectively.
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A novel point cloud registration using 2D image features

A novel point cloud registration using 2D image features

In order to find the corresponding points between the two shapes of an object efficiently, some approaches ini- tially transform the 3D shapes to 2D images. The depth image of a 3D image is usually adopted for transforming 3D point cloud to a 2D image. The transformation is simple and fast because the gray level of the depth image of a point present the distance from the view point to a point on the object surface, but the depth image discards some important geometric information of the object, e.g., the relation of a point and it neighbors. Depth im- ages are too simple to be used in 3D registration which requires high precision. In [25], a novel approach for transforming a 3D point cloud to a 2D image was pro- posed, namely the bearing angle image. A bearing angle image is the gray level image composed from the angle between the point and the neighbor points, highlighting the edge formed by the angle. This paper presents a novel 3D alignment method based on 2D local feature matching. The proposed method transforms the point clouds into 2D bearing angle images and then uses the 2D image feature matching method, SURF, to find matching point pairs of two images.
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A Novel Hexadata Encoding Method for 2D Image Crypto Compression

A Novel Hexadata Encoding Method for 2D Image Crypto Compression

We proposed a novel method for 2D image compression-encryption whose quality is demon- strated through accurate 2D image reconstruction at higher compression ratios. The method is based on the DWT-Discrete Wavelet Transform where high frequency sub-bands are connect- ed with a novel Hexadata crypto-compression algorithm at compression stage and a new fast matching search algorithm at decoding stage. The novel crypto-compression method consists of four main steps: 1) A five-level DWT is applied to an image to zoom out the low frequency sub-band and increase the number of high frequency sub-bands to facilitate the compression process; 2) The Hexa data compression algorithm is applied to each high frequency sub-band independently by using five different keys to reduce each sub-band to1/6of its original size; 3) Build a look up table of probability data to enable decoding of the original high frequency sub- bands, and 4) Apply arithmetic coding to the outputs of steps (2) and (3). At decompression stage a fast matching search algorithm is used to reconstruct all high frequency sub-bands. We have tested the technique on 2D images including streaming from videos (YouTube). Results show that the proposed crypto-compression method yields high compression ratios up to 99% with high perceptual quality images.
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A Real Time Design and Development of 2d Image Transmission using LIFI Technology

A Real Time Design and Development of 2d Image Transmission using LIFI Technology

the image using LED (Light Emitting Diode). With the increasing popularity of solid state lighting devices, Visible Light Communication (VLC) is globally recognized as an advanced and promising technology to realize short-range, high speed and large capacity wireless data transmission. A LED light change in frequency quicker than the human eye can see, it likewise has higher recurrence than Radio wave which bring about considerably higher speed than Wi-Fi. In this report, a prototype of real-time image broadcast system using inexpensive commercially available light emitting diode (LED) lamps is proposed. The extent of the development is to utilize a LED Bulbs as a medium of information and communication in such a way that can be implemented in home, office, organization and industries. Experimental results show that real time image with the maximum distance of 2ft can be achieved through proper layout of LED sources and improvement of concentration effects. The design and construction of the LI-FI (Light Fidelity) light source enable efficiency, long stable life, as well as full spectrum intensity that is digitally controlled and easy to use.
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Comparative Study: 2D Image Processing In Gray Scale Using Wavelets

Comparative Study: 2D Image Processing In Gray Scale Using Wavelets

Table 4 shows the image 1 and its transformation with the DWT and its reconstruction with the coefficients of the second-last high resolution in its three directional components, in wh ich it can be observed that the resolution Wavelet in this level is composed of ele ments of 4x4 pixels, being lower resolution than the one analyzed in table 2. In the zoo m central section of the image (e) with the reconstruction Db1 not very clear details of the Image, whereas the WD DB7 shows the good definition of diagonal e le ments but less WFB. The analysis with the FFT of the WD Db 1 (b) presents a very clear image, which represents a great dispersion of harmonic frequencies in the image, there are many points very contrasting in that reconstruction. The WD Db4 and Db7 present a scheme of frequencies very simila r to the one presented in table 3. Also, it is notorious the diffe rence between the high WFB and the second -last- high resolution in its simple visualization.
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2D IMAGE SCALING AND CROPPING FOR SECURE DATA SHARING ON CLOUD INFRASTRUCTURE

2D IMAGE SCALING AND CROPPING FOR SECURE DATA SHARING ON CLOUD INFRASTRUCTURE

Cloud computing is the technology that provides different services to the users and these services are made available to the users as per their demand. Users have to pay for the ser-vices depending upon the usage that is pay as you use model. Cloud is fundamentally a bunch of thing PCs composed to-gether in same or distinctive geological regions, cooperating to serve different customers with different need and workload on re-journey preface with the help of virtualization. Circulated processing implies con-trolling, orchestrating, and getting to the gear and programming resources remotely. We can access the data or services from anywhere at any time. Many cloud storage services are available such as Box,DropBox, SkyDrive, Sug-arsync for individual and small to medium business. The confidentiality of outsourced image is protected by using the nave approach. The image is encrypted before it is stored in the cloud. The system has a problem that it is not possibleto perform basic operations on pictures, such as zooming and trimming.
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Implementation of Pi Code using QR Code by embedding 2D Image

Implementation of Pi Code using QR Code by embedding 2D Image

In 21st century, 2D barcodes are used very commercial. It has been mostly used to product advertisement contents and many other purposes. However, it can appears a 2D barcode pattern is often too obtrusive for integrating into an aesthetically designed advertisement. In this way, before the barcode is decoded, no human readable information is provided. This paper proposes a new picture-embedding 2D barcode, called PiCode, which overcome these two limitations by equipping a scannable 2D barcode with a picturesque appearance. PiCode is designed with mannerly both the perceptual quality of the embedded image and the decoding robustness of the encoded message. It compared with the existing beautified 2D barcodes show that PiCode achieves one of the best perceptual qualities for the embedded image, and maintains a better tradeoff between image quality and decoding robustness in various application conditions. PiCode has been implemented in the Android based on a system and some key building blocks have also been provided to Android and iOS platforms. It
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2D IMAGE COMPRESSION USING DCT + DWT TRANSFORM AND COMPARISON WITH OTHER TRANSFORMS

2D IMAGE COMPRESSION USING DCT + DWT TRANSFORM AND COMPARISON WITH OTHER TRANSFORMS

Wavelets are tool for decomposing signals such as images, into a hierarchy of increasing resolutions. The more resolution layers, the more detailed features of the image are shown. They are localized waves that drop to zero. They come from iteration of filters together with rescaling. Wavelet produces a natural multi resolution of every image, including the all-important edges. The output from the low pass channel is useful compression. Wavelet has an unconditional basis as a result the size of the wavelet coefficients drop off rapidly. The wavelet expansion coefficients represent a local component thereby making it easier to interpret. Wavelets are adjustable and hence can be designed to suit the individual applications. Its generation and calculation of DWT is well suited to the digital computer [3]. They are only multiplications and additions in the calculations of wavelets, which are basic to a digital computer.
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Edge-texture 2D image quality metrics suitable for evaluation of image interpolation algorithms

Edge-texture 2D image quality metrics suitable for evaluation of image interpolation algorithms

Background and the related work are given in the second section. A new objective, full-reference method for image quality assessment, which is proposed in this paper, should indicate important aspects for a better match with MOS. The proposed method does not have any adjustable parameters. The main scientific contributions are: the new 2D measures based on a soft mask separation between the edges and texture areas and separation factor related to the content. They are explained in the third section. The proof of the concept is given in the fourth section through two case studies: quality assessment for blurred images with known blurring kernel and comparison of four interpolation algorithms. Results are benchmarked with two previous assessment methods for objective image quality measure: the VQM method [3] and the FSIM method [18]. Finally, conclusions and outlook are given in the fifth section.
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2D to 3D Image Conversion Using Machine Learning Approach

2D to 3D Image Conversion Using Machine Learning Approach

Stereoscopy is the process of producing the illusion of depth in the two-dimensional image by means of stereopsis for binocular vision. For normal 2D images, we can add additional dimension 'DEPTH' to perceive them as 3D. Stereoscopy process creates stereo-pair of a 2D image. Stereo-pair contains two images one of the views is intended for the left eye and the other for the right eye.

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Image Denoising using SWT 2D Wavelet Transform

Image Denoising using SWT 2D Wavelet Transform

sequence of different spatial resolution images using DWT. In case of a 2D image, an N level decomposition can be performed resulting in 3N+1 different frequency bands namely, LL, LH, HL and HH as shown in figure 1. These bands provide specific information of coefficients which are horizontal, vertical and diagonal coefficient detail and the overall average part of the image coefficients. Computing is done row wise and then column wise. The Gaussian noise will nearly be averaged out in low frequency wavelet coefficients. Therefore, only the wavelet coefficients in the high frequency levels need to be thresholded.
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Learning Based, Automatic 2D To 3D Image And Video Conversion

Learning Based, Automatic 2D To 3D Image And Video Conversion

The system architecture of the system is as shown below the procedure is as follows The availability of 3D-capable hardware today, such as TVs, Blu -Ray players, gaming consoles, and smart phones, is not yet matched by 3D content production. Although constantly growing in numbers, 3D movies are still an exception rather than a rule, and 3D broadcasting (mostly sports) is still minuscule compared to 2D broadcasting. The gap between 3D hardware and 3D content availability is likely to close in the future, but today there exists an urgent need to convert the existing 2D content to 3D. A typical 2D-to-3D conversion process consists of two steps: depth estimation for a given 2D image and depth based rendering of a new image in order to form a stereo pair. While the rendering step is well understood and algorithms exist that produce good quality images, the challenge is in estimating depth from a single image (video). Therefore, throughout this paper the focus is on depth recovery and not on depth-based rendering, although we will briefly discuss our approach to this problem later. There are two basic approaches to 2D-to-3D conversion: one that requires a human operator’s intervention and one that does not. In the former case, the so-called semiautomatic methods have been proposed where a skilled operator assigns depth to various parts of an image or video.
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2D-3D Enhanced Image Conversion Using NLS Method

2D-3D Enhanced Image Conversion Using NLS Method

ABSTRACT: Image processing has completely different forms to perform the popularity, identification and enhancement of pictures. One in all the such associated growing field is that the transformation of the image from one kind to different. This transformation will be done from one image format to different or one structural facet to different. one in all such transformation sort is to generate the 3D image by mistreatment the series of 2nd pictures. As we know, in world objects are obtainable in 3D kind. If the objects area unit conferred in same approach in pictures, the recognition or the identification rate are high. however just in case of existing dataset available in 2D kind, there's the necessity of some machine-controlled system, which will get the 3D image from 2D image set. This image set is that the real time set of pictures that covers the various structural views of 2D face. The bestowed thesis work is on the brink of supported identical conception. The work is about to use the prevailing 2D real time image dataset and kind a 3D image from it. The presented work is targeted on facial dataset wherever a series of 2nd pictures in hand. The work relies on the structural analysis of the prevailing image and to derive the structural feature like depth of those feature points. To perform the facial feature analysis, the improved least sq. methodology is employed during this work. supported this analysis, the structural information is collected and also the correlation analysis is performed on these feature points to obtain the article similarities and also the feature analysis. Finally, the similar and distinctive options area unit separated and determine the meaningful options mistreatment that the face reconstruction is performed. The work is enforced in mat-lab atmosphere. The results obtained from the system shows the effective generation of 3D facial image from 2D image set.
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Recent Trends in 2d to 3d Image Conversion: Algorithms at a glance

Recent Trends in 2d to 3d Image Conversion: Algorithms at a glance

from motion or depth from defocus. , have not yet achieved the same level of quality for they rely on assumptions that are often violated in practice. Methods involving human operators have been most successful but also time- consuming and costly. The main difference between 2D and 3D images is clearly the presence of depth in 3D images which makes the calculation of depth the most important factor in the conversion of images from 2D to 3D. There are two steps in 2D to 3D conversion process: depth estimation for a given 2D image and depth-based rendering of a new image in order to form a stereo pair. While the rendering step is well understood and algorithms exist that produce good quality images, the main problem is in estimating depth from a single image. Several methods have been proposed for the same. Out of these, we shall study mainly two methods. First, calculating the depth using monocular depth cues and then by learning depth via a simplified algorithm that learns the scene depth from a large database which is having an image and depth pairs. To compare these two methods, we use the generation of a depth map. A depth map is a 2D function that gives the depth (with respect to the viewpoint) of an object point as a function of the image coordinates. The depth map is a kind of image which is composed of the gray pixels defined by 0 ~ 255 values. The "0" value of gray pixels stand for that "3D" pixels are located at the most distant place in the 3D scene while the "255" value of gray pixels stand for that "3D" pixels are located at the most near the place. In-depth map, each depth pixel would define the position in Z-axis where its corresponding 2D pixel will be located. It is called as pixel-by-pixel which produces a reasonably good 3D image, it is now widely used for producing 3D contents, especially the multi-view 3D contents for 3D digital signage.
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Learning-Based, Automatic 2D-to-3D Image and Video change: A Survey

Learning-Based, Automatic 2D-to-3D Image and Video change: A Survey

Today there is an improvement in 3D proficient equipment as like TVs Blu- Ray-players, gaming consoles and advanced cells, tablets and some more. In image processing 2D image comprises with two measurements which are height and width, it doesn't have depth. At the point when consider 3D image alongside the height and width it is having profundity subsequently known as 3D dimensional image. The 3D gives feel of exact viewer experience. Be that as it may, the 3D content accessibility is not coordinating with its creation rate. Really there are two routines for create 3D substance, First is catch the image with numerous cameras and second is taken2D ordinarily footage of image and changes over into 3D. The main strategy is giving the best results yet it can be troublesome, costly and it additionally requires unique hardware and solid generation framework. The second system is troublesome however it is financially savvy. Here we mentioned the two methods: In one technique 2D to 3D conversion by utilizing taking in a nearby point change. Second depends on worldwide closest neighbor profundity learning. A image or video outlines have its own qualities at pixel level that is educated by a point change. The point change is connected to image and profundity allocated to pixel in view of its properties. The key component is point change utilizing for calculation of profundity from image or video casing qualities. Evaluating the change preparing on a ground truth database methodology is utilized.
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Design of Pipelined architecture for jpeg image compression with 2D-DCT and Huffman Encoding

Design of Pipelined architecture for jpeg image compression with 2D-DCT and Huffman Encoding

Abstract-Image and video compression is one of the major components used in video-telephony, videoconferencing and multimedia-related applications where digital pixel information can comprise considerably large amounts of data. Management of such data can involve significant overhead in computational complexity and data processing. Compression allows efficient utilization of channel bandwidth and storage size. In this paper we describe the design and implementation of a fully pipelined architecture for implementing the JPEG image compression standard. The architecture exploits the principles of pipelining and parallelism in order to obtain high speed and throughput. The design was synthesized using Xilinx9.2i and Spartan 3 FPGAs, and simulation was carried out using ModelSim environment. It has been estimated that the entire architecture can be implemented on a single FPGA to yield a clock rate of about 100 MHz which allow an input rate of 24 bit input RBG.
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Car recognition on a static image using 2d 
		basic shape geometry

Car recognition on a static image using 2d basic shape geometry

The previous similar research about shape recognition using geometrical shape approach was done to detect and recognize road sign in USA [1]. Another researches about shape, object and image recognition also were done by using fuzzy approach [2], simple string matching method [3], shape masks (segmentation masks) [4], multi features (shape feature and text feature) [5], bottom-up image structures [6], boundary structure segmentation [7]. In this research, we did shape recognition of car parts which extracted from the digital car image. The common composition of basic shapes in every car part and their structure would be the interesting point as the background in this research.
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FPGA Implementation of 2D and 3D Image Enhancement Chip in HDL Environment

FPGA Implementation of 2D and 3D Image Enhancement Chip in HDL Environment

Frequency domain is straightforward method for image enhancement. Fourier Transform is used to compute the Fourier values of the image to be enhanced, after that multiplication is done with the result by a filter instead of convoluting in the spatial domain, and take the inverse transform to produce the enhanced image. Blurring of an image can be done by reducing its high frequency components or sharpening an image by increasing the magnitude of its high frequency components. However, computationally, it is often more efficient to implement these operations as convolutions by small spatial filters in the spatial domain. Understand the frequency domain concepts are very important, leads to enhancement techniques that might not have been thought of by restricting attention to the spatial domain.
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