image buffer

与image buffer相关的网络例句 [注：此内容来源于网络，仅供参考]

In this paper we combined three chromatographic separation and purification technique such as affinity chromatography, ion exchanger chromatography and hydrophobic interaction chromatography to develope a new technology of stimutaneous extraction of three enzyme from pancreatin. We optimized the technology by studying the methods of purification and assured the technology as: The crude extraction from the dissolution of Pancreatin is directly absorbed on the DEAE gelose fast flow columnEquilibrating buffer is 0.01mol/L NaoAc-HoAc buffer(pH4.5; eluting buffer is 0.2～0.35mol/LNaCl in 0.01mol/LNaoAc-HoAc buffer (pH4.5), and then be eluted by two steps to acquire the peak of kallikrein.The solution which can"t be adsorbed by DEAE gelose fast flow column is adsorbed on affinity chromatographic column Equilibrating buffer is 0.01mol/LTris-HCl buffer(pH7.5, eluting buffer is 0.5mol/LNaCl in 0.01mol/Ltris-HCl buffer(pH7.5)and then be eluted by one step to acquire the peak of trypsin.The solution which can"t be adsorbed by is pretreated with 30%～80%(NH_4)_2SO_4 fractional precipitation, the deposition of the precipitation is dissolved to beabsorbed on phenyl gelose fast flow columnhydrophobic interaction chromatography condition is Equilibrating buffer is lmol/L(NH_4_2SO_4 in 0.01mol/LNaoAc-HoAc buffer(pH4.5), eluting buffer is 0～0.6mol/L(NH_4)_2SO_4 in 0.01mol/LNaoAc-HoAc buffer (pH4.5) and then be eluted by two steps to acquire the peak of chymotrypsin.

本研究考察了各种纯化方法，将离子交换层析、亲和层析和疏水层析三种分离纯化法相结合，建立了激肽释放酶、胰蛋白酶和糜蛋白酶三酶的联产工艺：胰酶用pH4.5醋酸缓冲溶液提取后，粗提液直接上DEAE-琼脂糖快胶柱吸附平衡缓冲液：0.01mol／LNaoAc-HoAc缓冲液(pH4.5，洗脱缓冲液：0.01mol／LNaoAc-HoAc缓冲液(pH4.5)含0.2～0.35mol／LNaCl分两步洗脱，收集激肽释放酶的洗脱峰；DEAE-琼脂糖快胶的未吸附液上亲和层析柱分批吸附平衡缓冲液：0.01mol／LTris-HCl缓冲液(pH7.5，洗脱液：0.5mol／LNaCl溶液，一次洗脱，收集胰蛋白酶洗脱峰；最后，亲和层析未吸附液用30％～80％硫酸铵分级盐析处理，沉淀溶解后用上苯基—琼脂糖快胶吸附平衡缓冲液：0.01mol／LNaAc-HAc缓冲液(pH4.5含1mol／L(NH_4)_2SO_4，洗脱缓冲液：0.01mol／LNaAc-HAc缓冲液(pH4.5)含0～0.6mol／L(NH_4)_2SO_4，分两步洗脱，收集糜蛋白酶的洗脱峰。

It solves the problem that the unitary contour presentation can not correctly extract face contour in a face image which suffers from scale, rotation etc. The definition of the internal and external energy function is provided. At the same time, the global matching algorithm and local matching algorithm is given. The experiment shows that this presentation and the accompanying matching algorithm can be used to extract the face contour very well. So the image segmentation can be implemented by using it.②By analyzing the recognition principle of PCA method, we can conclude that the face images coming from different surrounding consist of different face image space. This is the essential reason that makes the generality of PCA method worse. Also, we give a measurement means to measure the distance from different face image space, so we can analyze face image space more conveniently.③We also construct various scale models and rotation pose models to detect the scale and rotating angle of face image to be recognized. The experiment results show that the detecting precision is very high. So it is good for face image feature extraction and face image representation.④Similarly, we construct local feature models of face image and utilize them to detect the local feature of face image. At the same time, we put forward a novel face image local feature detection algorithm, locating step by step. The experiment results show that this method can accurately detect the location of local face feature in a image.⑤A novel face image presentation model, dual attribute graph , is put forward. Firstly, it utilizes attribute graph to present the face image, then exact the local principal component coefficient and Gabor transform coefficient of thc pixels which corresponds to the nodes of the graph as the attribute of the nodes. This representation fully makes use of the statistical characteristic of the local face feature and utilizes Gabor transform to present the topographical structure of face image. So DAG has more general property.⑥Based on the DAG presentation, we give a DAG matching function and matching algorithm. During the design of the function and algorithm, the noise factor, e. g., lighting, scale and rotation pose are considered and tried to be eliminated. So the algorithm can give more general property.⑦A general face image recognition system is implemented. The experiment show the system can get better recognition performance under the noise surrounding of lighting, scale and rotation pose.

本文在上述研究的基础上，取得了如下主要研究成果：①构造了一个通用的人脸轮廓模型表示，解决了由于人脸图象尺度、旋转等因素而使得仅用单一轮廓表示无法正确提取人脸轮廓的问题，并给出了模型内、外能函数的定义，同时给出了模型的全局与局部匹配算法，实验表明，使用这种表示形式以及匹配算法，能够较好地提取人脸图象的轮廓，可实际用于人脸图象的分割；②深入分析了PCA方法的识别机制，得出不同成象条件下的人脸图象构成不同的人脸图象空间的结论，同时指出这也是造成PCA方法通用性较差的本质原因，并给出了不同人脸空间距离的一种度量方法，使用该度量方法能够直观地对人脸图象空间进行分析；③构造了各种尺度模板、旋转姿势模板以用于探测待识人脸图象的尺度、旋转角度，实验结果表明，探测精确度很高，从而有利于人脸图象特征提取，以及图象的有效表示；④构造了人脸图象的各局部特征模板，用于人脸图象局部特征的探测；同时提出了一种新的人脸图象局部特征探测法---逐步求精定位法，实验结果表明，使用这种方法能够精确地得到人脸图象各局部特征的位置；⑤提出了一种新的人脸图象表示法---双属性图表示法；利用属性图来表示人脸图象，并提取图节点对应图象位置的局部主成分特征系数以及Gabor变换系数作为图节点的属性，这种表示方法充分利用了人脸图象的局部特征的统计特性，并且使用Gabor变换来反映人脸图象的拓扑结构，从而使得双属性图表示法具有较强的通用性；⑥在双属性图表示的基础上，给出双属性图匹配函数及匹配算法，在函数及算法设计过程中，考虑并解决了光照、尺度、旋转姿势变化等因素对人脸图象识别的影响，使得匹配算法具有较强的通用性；⑦设计并实现了一个通用的人脸图象识别系统，实验结果表明，该系统在图象光照、尺度、旋转姿势情况下，得到了较好的识别效果。

This invention discloses a multi-queue sequence buffer management circuit and a method based on a pipeline applying a pipeline structure including: an arbitration circuit selecting one for process from read, write and distribution buffer requests, a buffer slot state module designing state of the slot requiring operation and queue numbers and assigning idle slots, a buffer slot filter module filtering the slot, a buffer slot filter module filtering the slot states not belonging to the current operation queues nor idle aligned in terms of the head pointer, a queue slot selection module computing continuous idle slot numbers from the slot pointed by the head pointer and refreshing the head pointer and selecting preparing slots, a queue slot prior queuing module refreshing the read pointer and result numbers of the current operation queues with the pointer of the first prepare slots and their numbers which can support multi-queue to share one buffer space, queues can access the buffer in overlap.

本发明公开一种基于流水线的多队列顺序化缓冲管理电路及方法，本发明电路采用流水线结构，包括：仲裁电路，从读、写、分配缓冲请求中选取一路进行处理；缓冲槽口状态设置模块，设置请求操作的槽口状态和队列号，分配空闲槽口；缓冲槽口滤除模块，滤除不属于当前操作队列且非空闲态的槽口状态，按头指针对齐；队列槽口选择模块，计算头指针指向槽口起的连续空闲槽口数并更新头指针，选出预备态的槽口；队列槽口优先排队模块，用第一个预备态槽口的指针和预备态槽口数分别更新当前操作队列的读指针和结果数；本发明可以支持多个队列共享一个缓冲空间，各类指令队列能对缓冲进行交叉访问，并对指令结果的写入读出进行顺序化管理。

与image buffer相关的网络解释 [注：此内容来源于网络，仅供参考]

image buffer：影像缓冲器

image area 映像区 | image buffer 影像缓冲器 | image compression 影像压缩

image buffer：图像缓冲区

1142image area字符显示区 | 1143image buffer图像缓冲区 | 1144image compression图像压缩

page image buffer：页面图像缓冲区

Programmable Interface Adapter 可编程接接口适配器 | Page-Image Buffer 页面图像缓冲区 | Peripheral Interface Channel 外部设备接口通道

Image Buffer Factor：图像缓存因子

Show Progress Image -显示进展图像 | Image Buffer Factor -图像缓存因子 | Image Zoom Factor -图像缩放因子

Image Buffer: Error Bit set：影像缓冲写错误

188 Take-up cassette core motor off failed 收片马达关错误 | 191 Image Buffer: Error Bit set 影像缓冲写错误 | 192 Image Buffer: Buffer not ready 影像缓冲未准备好