ECE345, Visual-to-Audio Electronic Travel Aid Code for TM320C54x (v2a.asm) download This project involves the design and implementation of a audio synthesis device that converts moving imAGEs into audio signals. The system is built on a TM320C54x DSP with interface to an IMAQ camera module via the serial port on a PC. Brief description: A LabVIEW VI acquires an image from the IMAQ camera module. It quantizes the image into a 5x5, 3-bit image, and sends the data to the TM320C54x DSP via a serial port. The TM320C54x DSP constructs a 64-tap FIR by combining a series of 64-tap head related transfer functions (HRTF) according to the incoming data, and then filters an input audio signal with this FIR filter, in effect creating a correspondence between the filtered signal and the original image.
標(biāo)簽： Visual-to-Audio Electronic download project
上傳時(shí)間： 2017-02-01
上傳用戶：笨小孩
本文以華北水利水電學(xué)院學(xué)生宿舍管理信息系統(tǒng)的研發(fā)為課題，就如何開發(fā)一個(gè)基于URP系統(tǒng)構(gòu)架的學(xué)生宿舍信息管理系統(tǒng)展開研究。首先以華北水利水電管理學(xué)生宿舍管理及其相關(guān)的業(yè)務(wù)體系的調(diào)查報(bào)告為基礎(chǔ)，分析了一般高校的學(xué)生宿舍管理業(yè)務(wù)流程；其次，將業(yè)務(wù)體系抽象成若干個(gè)用例，對(duì)用例模型進(jìn)行了粒度適當(dāng)?shù)募?xì)化和規(guī)范及詳盡的描述，在此基礎(chǔ)上進(jìn)行了功能模塊設(shè)計(jì)和數(shù)據(jù)庫(kù)結(jié)構(gòu)的設(shè)計(jì)；最后結(jié)合URP開發(fā)平臺(tái)和面向?qū)ο蟮能浖O(shè)計(jì)思想將系統(tǒng)各功能實(shí)現(xiàn)。 如下載的全文無圖片顯示時(shí)，請(qǐng)下載圖片,解壓后放在同盤根目錄下(例如學(xué)位全文下載至d:\xxxx,則圖片放到d:\imAGEs)
標(biāo)簽： 水利水電 管理信息系統(tǒng)
上傳時(shí)間： 2017-03-07
上傳用戶：zxc23456789
The existence of numerous imaging modalities makes it possible to present different data present in different modalities together thus forming multimodal imAGEs. Component imAGEs forming multimodal imAGEs should be aligned, or registered so that all the data, coming from the different modalities, are displayed in proper locations. The term image registration is most commonly used to denote the process of alignment of imAGEs , that is of transforming them to the common coordinate system. This is done by optimizing a similarity measure between the two imAGEs. A widely used measure is Mutual Information (MI). This method requires estimating joint histogram of the two imAGEs. Experiments are presented that demonstrate the approach. The technique is intensity-based rather than feature-based. As a comparative assessment the performance based on normalized mutual information and cross correlation as metric have also been presented.
標(biāo)簽： present modalities existence different
上傳時(shí)間： 2017-04-03
上傳用戶：qunquan
-The existence of numerous imaging modalities makes it possible to present different data present in different modalities together thus forming multimodal imAGEs. Component imAGEs forming multimodal imAGEs should be aligned, or registered so that all the data, coming from the different modalities, are displayed in proper locations. Mutual Information is the similarity measure used in this case for optimizing the two imAGEs. This method requires estimating joint histogram of the two imAGEs. The fusion of imAGEs is the process of combining two or more imAGEs into a single image retaining important features from each. The Discrete Wavelet Transform (DWT) has become an attractive tool for fusing multimodal imAGEs. In this work it has been used to segment the features of the input imAGEs to produce a region map. Features of each region are calculated and a region based approach is used to fuse the imAGEs in the wavelet domain.
標(biāo)簽： present modalities existence different
上傳時(shí)間： 2014-03-04
上傳用戶：15736969615
In computer vision, sets of data acquired by sampling the same scene or object at different times, or from different perspectives, will be in different coordinate systems. Image registration is the process of transforming the different sets of data into one coordinate system. Registration is necessary in order to be able to compare or integrate the data obtained from different measurements. Image registration is the process of transforming the different sets of data into one coordinate system. To be precise it involves finding transformations that relate spatial information conveyed in one image to that in another or in physical space. Image registration is performed on a series of at least two imAGEs, where one of these imAGEs is the reference image to which all the others will be registered. The other imAGEs are referred to as target imAGEs.
標(biāo)簽： different computer acquired sampling
上傳時(shí)間： 2013-12-28
上傳用戶：來茴
Java Media APIs: Cross-Platform Imaging, Media, and Visualization presents integrated Java media solutions that demonstrate the best practices for using this diverse collection. According to Sun MicroSystems, "This set of APIs supports the integration of audio and video clips, animated presentations, 2D fonts, graphics, and imAGEs, as well as speech input/output and 3D models." By presenting each API in the context of its appropriate use within an integrated media application, the authors both illustrate the potential of the APIs and offer the architectural guidance necessary to build compelling programs.
標(biāo)簽： Media Java Cross-Platform Visualization
上傳時(shí)間： 2013-12-04
上傳用戶：hanli8870
Chinese Remainder theorem implementation. Its done in php. Its implemented with interactive gui.index.php contain all the code.Rest folders contain the css imAGEs and js scripts that enhance the gui
標(biāo)簽： implementation implemented interactive Remainder
上傳時(shí)間： 2017-04-14
上傳用戶：lanwei
This project features a complete JPEG Hardware Compressor (standard Baseline DCT, JFIF header) with 2:1:1 subsampling, able to compress at a rate of up to 24 imAGEs per second (on XC2V1000-4 @ 40 MHz with resolution 352x288). Image resolution is not limited. It takes an RGB input (row-wise) and outputs to a memory the compressed JPEG image. Its quality is comparable to software solutions.
標(biāo)簽： Compressor Hardware Baseline features
上傳時(shí)間： 2017-04-21
上傳用戶：wyc199288
The GIFT (the GNU Image-Finding Tool) is a Content Based Image Retrieval System (CBIRS: http://en.wikipedia.org/wiki/CBIR). It enables you to do Query By Example (QBE: http://en.wikipedia.org/wiki/QBE) on imAGEs, giving you the opportunity to improve query results by relevance feedback. For processing your queries the program relies entirely on the content of the imAGEs, freeing you from the need to annotate all imAGEs before querying the collection.
標(biāo)簽： Image-Finding Retrieval Content System
上傳時(shí)間： 2014-01-11
上傳用戶：咔樂塢
OTSU Gray-level image segmentation using Otsu s method. Iseg = OTSU(I,n) computes a segmented image (Iseg) containing n classes by means of Otsu s n-thresholding method (Otsu N, A Threshold Selection Method from Gray-Level Histograms, IEEE Trans. Syst. Man Cybern. 9:62-66 1979). Thresholds are computed to maximize a separability criterion of the resultant classes in gray levels. OTSU(I) is equivalent to OTSU(I,2). By default, n=2 and the corresponding Iseg is therefore a binary image. The pixel values for Iseg are [0 1] if n=2, [0 0.5 1] if n=3, [0 0.333 0.666 1] if n=4, ... [Iseg,sep] = OTSU(I,n) returns the value (sep) of the separability criterion within the range [0 1]. Zero is obtained only with imAGEs having less than n gray level, whereas one (optimal value) is obtained only with n-valued imAGEs.
標(biāo)簽： OTSU segmentation Gray-level segmented
上傳時(shí)間： 2017-04-24
上傳用戶：yuzsu
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