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1、1. Translationv Digital image 数字图像 v Gray level 灰度 v Pixel 象素 v Reduce noise 降噪 v Contrast enhancement 对比度增强 v Image sharpening图像锐化v Image smoothing 图像平滑 v Segmentation分割 v Recognition识别 v Angiography 血管造影术 v Computerized tomography 计算机断层成像 v Positron emission tomography 正电子发射断层成像 v Ultrasound imagi
2、ng 超声成像 v First order derivative 一阶微分v Second order derivative 二阶微分v Mean 均值v Random variable 随机变量v Standard deviation 标准差v Variance 方差v Salt-and-pepper noise 椒盐噪声v Median filter 中值滤波v Gradient 梯度v Cutoff frequency 截止频率v Threshold value 阈值v Subjective brightness 主观亮度 v Brightness adaptation level 亮度
3、适应级 v Sampling and quantization 采样和量化v Neutral color 中性色v Hue 色度v Saturation 饱和度v Histogram equalization 直方图均衡化v Histogram matching 直方图匹配v Fluorescence microscopy 荧光显微镜v Dynamic range of an image 图像的动态范围v Probability density function 概率分布函数v Cumulative distribution function 累积分布函数v Smoothing linear
4、filter 平滑线性滤波器v Full color 真彩色v Pseudo color 伪彩色v Electromagnetic spectrum 电磁波谱v Primary colors of light 光的三元色v Primary colors of pigments 颜料三元色v Saturation 饱和度v Chromaticity diagram 色度图v Monochrome image 单色图像v Normal representation of a line 直线标准式v Region growing 区域增长v Region splitting and merging
5、区域分离和合并v Accumulative difference image 累积差分图像2. Problems v Ultrasound imaging (p22 or P44)The ultrasound system transmits high-frequency sound pulse into the body.v Euclidean distance, city-block distance, chessboard distance (p68 or P93)v Example 3.1 and 3.2 (p82-84 or P 135)v Example 3.6 (p105-108
6、 or 165)v Compare the response in image between first- and second-order derivatives. (p127 or P169, P717)v Explain why the discrete histogram equalization technique does not, in general, yield a flat histogram. (p143 or P215 *3.6)v What effect would setting to zero the lower-order bit planes have on
7、 the histogram of an image in general? (P143 3.4 or P215 3.5)v What are the basic steps for filtering in the frequency domain? (P 159 or P285)v Compare ideal lowpass filter, Butterworth lowpass filter and Gaussian lowpass filter. (P 167-176 or P291-298) v Illustrate the use of gray level to color tr
8、ansformations to obtain various degrees of enhancement. (P309 or P442)v Introduce one way to slice a color image and separate objects from their surroundings. ( P 320 or 453)v Explain why the result of smoothing each RGB component image is different from the result of smoothing the intensity of the
9、HIS image and converting to RGB? (p330 or P462 because)v How would you implement the color equivalent of gray scale histogram matching? (P347 6.24 or 481 6.24) v How do you go about eliminating the components in figure that are not part of the line oriented at -450. (P570 or P721)v Explain why the L
10、aplacian generally is not used in its original form for edge detection. There are several reasons: sensitive to noise, produce double edges, and unable to detect edge direction. (p582 the third version has no information)v Compare edges in gradient image and zero-crossing image. (p585 the third vers
11、ion has no information )(1) Edges in the zero-crossing image are thinner than the gradient edges.(2) Edges determined by zero crossings form numerous closed loops. (3) Computation of zero crossings presents a challenge in general, and considerably more sophisticated techniques often are required to
12、obtain acceptable results.(4) Edge-finding techniques based on various implementations of gradient still are used more frequently than zero crossings in the implementation of segmentation algorithms. v Explain edge-linking approach based on the Hough transform. (p590 or P758)v How do we obtain thres
13、hold value automatically for segmentation? (P599 or P764)v Using spatial techniques to monitoring moving object, how can obtain a reference image with only stationary elements? (P629 or P803)v Segment the image. Let P(Ri)=TRUE if all pixels in Ri have the same gray level. Show the quadtree correspon
14、ding to your segmentation. (p641 P815) (answer p81) 1、 The low level processes involve primitive operations such as image preprocessing to reduce noise, contrast enhancement , and image sharpening. both its inputs and outputs are images，mid-level processing on images involves segmentation and classi
15、fication. Its inputs are generally images, but its outputs are attributes extracted from those images. High-level processing involves image analysis and cognitive functions normally associated with vision.2、 Two phenomena clearly demonstrate that perceived brightness is not a simple function of intensity.3、 The principal objective of enhancement is to process an image so that the result is more suitable than the or
