Optimized Hybrid DCT-SVD Computation over Extremely Large Images

High performance computing (HPC) is required for image processing especially for picture element (pixel) with huge size. To avoid dependence to HPC equipment which is very expensive to be provided, the soft approach has been performed in this work. Actually, both hard and soft methods offer similar goal which are to reach time computation as short as possible. The discrete cosine transformation (DCT) and singular values decomposition (SVD) are conventionally performed to original image by consider it as a single matrix. This will result in computational burden for images with huge pixel. To overcome this problem, the second order matrix has been performed as block matrix to be applied on the original image which delivers the DCT-SVD hybrid formula. Hybrid here means the only required parameter shown in formula is intensity of the original pixel as the DCT and SVD formula has been merged in derivation. Result shows that when using Lena as original image, time computation of the singular values using the hybrid formula is almost two seconds faster than the conventional. Instead of pushing hard to provide the equipment, it is possible to overcome computational problem due to the size simply by using the proposed formula.

Combined with Local Neighborhood Characteristics and Remote Sensing Image Fusion Method of C-BEMD

For the sake of ameliorate the high resolution recognition capacity building remote sensing images, a remote sensing image fusion method based on local neighborhood characteristics and C-BEMD is advanced. The building remote sensing image acquisition model and the building remote sensing image picture element edge feature detection model are designed. The wavelet multi-scale denoising method is used to suppress the fuzzy spread of picture element feature points between image residual units, extract the geometric feature points of image sequence, and process the building remote sensing image block by block. The global residual learning and message fusion of building remote sensing image are implemented. The local neighborhood feature matching method is used to reconstruct the building remote sensing image region. Combined with the C-BEMD empirical mode decomposition method, the building remote sensing image fusion and feature point matching in affine region are implemented, and the block image template matching method is used to realize the automatic fusion and recognition of building remote sensing image. Simulation results show that this method has high precision in constructing remote sensing image fusion and good positioning performance in constructing remote sensing image feature points.

Techniques for Image Segmentation: A Critical Review

In today’s world there is requirement of some techniques or methods that will be helpful for retrieval of the information from the images. Information those are important for finding solution to the problems in the present time are needed. In this review we will study the processing involved in the digitalization of the image. The set or proper array of the pixels that is also called as picture element is known as image. The positioning of these pixels is in matrix which is formed in columns and rows. The image undergoes the process of digitalization by which a digital image is formed. This process of digitalization is called digital image processing of the image (D.I.P). Electronic devices as such computers are used for the processing of the image into digital image. There are various techniques that are used for image segmentation process. In this review we will also try to understand the involvement of data mining for the extraction of the information from the image. The process of the identifying patterns in the large stored data with the help of statistic and mathematical algorithms is data mining. The pixel wise classification of the image segmentation uses data mining technique.

PENGOLAHAN CITRA DIGITAL DAN HISTOGRAM DENGAN PHYTON DAN TEXT EDITOR PHYCHARM

Citra merupakan salah satu bentuk informasi yang diperlukan manusia selain teks, suara dan video.Informasi yang terkandung dalam sebuah citra dapat diinterpretasikan berbeda-beda oleh manusia satu dengan yang lain.Citra analog dihasilkan dari alat akuisisi citra analog, contohnya adalah mata manusia dan kamera analog. Gambaran yang tertangkap oleh mata manusia dan foto atau film yang tertangkap oleh kamera analog merupakan contoh dari citra analog.. Citra digital merupakan representasi dari fungsi intensitas cahaya dalam bentuk diskrit pada bidang dua dimensi. Citra tersusun oleh sekumpulan piksel (picture element) yang memiliki koordinat (x,y) dan amplitudo f(x,y). Koordinat (x,y) menunjukkan letak/posisi piksel dalam suatu citra, sedangkan amplitudo f(x,y) menunjukkan nilai intensitas warna citra. Pengolahan citra (image Processing) adalah proses mengolah piksel-piksel di dalam citra digital yang digunakan untuk tujuan tertentu. Awalnya pengolahan citra dilakukan untuk memperbaiki kualitas citra, dengan berkembangnya dunia komputasi yang ditandai dengan semakin meningkatnya kapasitas dan kemampuan komputer memungkinkan manusia dapat Mengambil informasi dari suatu citra.Pengolahan Citra Digital (Digital Image Processing) merupakan bidang ilmu yang mempelajari tentang bagaimana suatu citra itu dibentuk, diolah, dan dianalisis sehingga menghasilkan informasi yang dapat dipahami oleh manusia, sedangkan Histogram citra adalah representasi grafik yang menyatakan distribusi nilai-nilai warna atau intensitas piksel-piksel di dalam citra. Frekuensi kemunculan nilai intensitas piksel pada suatu citra dapat diketahui melalui 50 Pengolahan Citra histogram citra tersebut. Pengolahan Citra Digital ini menggunakan bahasa pemrograman Phyton dan Phycharm , yang fungsinya melihat hasil perubahan citra, Histogram pada gambar dengan menggunakan Phyton dan phycharmKata kunci: Citra, Image Processing ,Digital Image Processing, Histogram

LEARNING INVARIANT COLOUR FEATURES FOR PERSON REIDENTIFICATION

In this examination we have proposed Learning invariant shading highlights for individual recognizable proof utilizing human face for high proficient flag exchange framework applications. In this paper, we have a tendency to propose an information driven approach for taking in shading designs from pixels examined from pictures crosswise over to camera sees. The instinct behind this work is that, even assuming picture element values of same colour would wander across views, they thought to be encoded with indistinguishable qualities. We tend to model colour feature age as a learning drawback by together learning a direct transformation and a wordbook to write in code picture component esteems. We have a tendency to conjointly dissect entirely unexpected estimating invariant shading zones. Abuse shading in light of the fact that the exclusively prompt, we tend to contrast our approach and all the estimating invariant shading zones and show better execution over every one of them. Overwhelming pivoted nearby double example is anticipated yields higher execution. This paper proposes a totally exceptional strategy of characterizing the outer body part misuse Convolutional Neural Network.

Region-Based Segmentation and Object Detection

Object identification and multi-object picture separation are two firmly related processes and it can be enhanced when understood jointly by supporting data from one assignment to the next. Be that as it may, current best in object models are different portrayal for each space creation joint objects and leaving the categorization of numerous part of the scene uncertain. Picture element appearance highlights enable us to do well on classifying formless foundation classes, while the express portrayal of districts encourage the calculation of increasingly complex highlights essential for object detection. Vitally, our model gives a solitary bound together portrayal of the scene we clarify each picture elements of image and authorize it contains in the web between every random variable in our model.

Electron-Excited X-ray Microanalysis by Energy Dispersive Spectrometry at 50: Analytical Accuracy, Precision, Trace Sensitivity, and Quantitative Compositional Mapping

Abstract2018 marked the 50th anniversary of the introduction of energy dispersive X-ray spectrometry (EDS) with semiconductor detectors to electron-excited X-ray microanalysis. Initially useful for qualitative analysis, EDS has developed into a fully quantitative analytical tool that can match wavelength dispersive spectrometry for accuracy in the determination of major (mass concentration C > 0.1) and minor (0.01 ≤ C ≤ 0.1) constituents, and useful accuracy can extend well into the trace (0.001 < C < 0.01) constituent range even when severe peak interference occurs. Accurate analysis is possible for low atomic number elements (B, C, N, O, and F), and at low beam energy, which can optimize lateral and depth spatial resolution. By recording a full EDS spectrum at each picture element of a scan, comprehensive quantitative compositional mapping can also be performed.

De pixel a voxel. Arquitectura entre identidad líquida y imagen física.

<p>La actividad de investigación dedicada a la gestión y comunicación de formas arquitectónicas complejas favorece al voxel, volumetric pixel (picture element), como principal herramienta tecnológica que caracteriza la producción física y virtual de la segunda era digital. El proceso de voxelización distorsiona y fragmenta la imagen fluida y abstracta de la arquitectura líquida que caracteriza la primera era digital. Desde la arquitectura calculada a la construida, el proceso de voxelización manipula la imagen del proyecto para encontrar una solución tecnológicamente más accesible. Las imágenes de los experimentos artísticos y arquitectónicos más recientes, resultado de herramientas digitales generativas y computacionales consolidadas, testifican un enfoque de diseño caracterizado por un tecnicismo avanzado.Por lo tanto, esta comunicación nos invita a reflexionar sobre el impacto visual de la necesidad actual de inmediatez resolutiva y optimización económica constructiva, expresiones de la sociedad contemporánea, que son determinantes en la figuración del concepto de complejidad arquitectónica.</p>

Research on the best visual search effect of logo elements in internet advertising layout

Purpose The purpose of this paper is to control the size of online advertising by the use of the single factor experiment design using the eight matching methods of logo and commodity picture elements as independent variables, under the premise of background color and content complexity and to investigate the best visual search law of logo elements in online advertising format. The result shows that when the picture element is fixed in the center of the advertisement, it is suggested that the logo element should be placed in the middle position parallel to the picture element (left middle and upper left), placing the logo element at the bottom of the picture element, especially at the bottom left should be avoided. The designer can determine the best online advertising format based on the visual search effect of the logo element and the actual marketing purpose. Design/methodology/approach In this experiment, the repeated measurement experiment design was used in a single factor test. According to the criteria of different types of commodities and eight matching methods, 20 advertisements were randomly selected from 50 original advertisements as experimental stimulation materials, as shown in Section 2.3. The eight matching methods were processed to obtain a total of 20×8=160 experimental stimuli. At the same time, in order to minimize the memory effect of the repeated appearance of the same product, all pictures, etc., the probability was randomly presented. In addition, in order to avoid the pre-judgment of the test for the purpose of the experiment, 80 additional filler online advertisements were added. Therefore, each testee was required to watch 160+80=240 pieces of stimulation materials. Findings On one hand, when the image elements are fixed for an advertisement, the advertiser should first try to place the logo element in the right middle position parallel to the picture element, because the commodity logo in this matching mode can get the longest average time of consumers’ attention, and the duration of attention is the most. Danaher and Mullarkey (2003) clearly pointed out that as consumers look at online advertising, the length of fixation time increases, the degree of memory of online advertisement is also improved accordingly. Second, you can consider placing the logo element in the left or upper left of the picture element. In contrast, advertisers should try to avoid placing the logo element at the bottom of the picture element (lower left and lower right), especially at the lower left, because, at this area, the logo attracts less attention, resulting in shortest duration of consumer attention, less than a quarter of consumers’ total attention. This conclusion is consistent with the related research results. Originality/value Advertising owners in the logo and picture elements for typesetting, if advertisers want to highlight the elements of the commodity logo, the logo should be arranged in the first point of view more locations, which cause consumers more unconscious processing, to achieve good memory and communication effects. Therefore, based on the above conclusions, it is also recommended that the logo elements should be placed on the right side of the picture elements in the advertising layout, and the sixth form of matching should be avoided as much as possible.

Color detection in RGB-modeled images using MAT LAB

This research work introduces a method of using color thresholds to identify two-dimensional images in MATLAB using the RGB Color model to recognize the Color preferred by the user in the picture. Methodologies including image color detection convert a 3-D RGB Image into a Gray-scale Image, at that point subtract the two pictures to obtain a 2-D black-and-white picture, filtering the noise picture elements using a median filter, detecting with a connected component mark digital pictures in the connected area and utilize the bounding box and its properties to calculate the metric for every marking area. In addition, the shade of the picture element is identified by examining the RGB value of every picture element present in the picture. Color Detection algorithm is executed utilizing the MATLAB Picture handling Toolkit. The result of this implementation can be used in as a bit of security applications such as spy robots, object tracking, Color-based object isolation, and intrusion detection.