For most types of graphics file formats currently available the answer is "no". A virus (or
worm, Trojan horse, and so forth) is fundamentally a collection of code (that is, a program) that
contains instructions which are executed by a CPU. Most graphics files, however, contain only
static data and no executable code. The code that reads, writes, and displays graphics data is
found in translation and display programs and not in the graphics files themselves. If reading or
writing a graphics file caused a system malfunction is it most likely the fault of the program
reading the file and not of the graphics file data itself.
With the introduction of multimedia we have seen new formats appear, and modifications to
older formats made, that allow executable instructions to be stored within a file format. These
instructions are used to direct multimedia applications to play sounds or music, prompt the user
for information, or display other graphics and video information. And such multimedia display
programs may perform these functions by interfacing with their environment via an API, or by
direct interaction with the operating system. One might also imagine a truly object-oriented
graphics file as containing the code required to read, write, and display itself.
Once again, any catastrophes that result from using these multimedia application is most
like the result of unfound bugs in the software and not some sinister instructions in the graphics
file data. Such "logic bombs" are typically exorcised through the use of testing using a wide
variety of different image files for test cases.
If you have a virus scanning program that indicates a specific graphics file is infected by
virus, then it is very possible that the file coincidentally contains a byte pattern that the scanning
programming recognizes as a key byte signature identifying a virus. Contact the author (or even
read the documentation!) of the virus scanning program to discuss the probability of the
mis-identification of a clean file as being infected by a virus. Save the graphics file, as the author
will most likely wish to examine it as well.
If you suspect a graphics file to be at the heart of a virus problem you are experiencing, then
also consider the possibility that the graphics file's transport mechanism (floppy disk, tape or
shell archive file, compressed archive file, and so forth) might be the original source of the virus
and not the graphics file itself.
1, catastrophe [kə'tæstrəfi]
2, sinister ['sinistə]
3, exorcise ['eksɔ:saiz]
4, coincidentally [kəu,insi'dentli]
Continue reading it-e-67 Can Graphics Files Be Infected with a Virus
An image is digitized to convert it to a form that can be stored in a computer's memory or on
some form of storage media such as a hard disk or CD-ROM. This digitization procedure can be
done by a scanner, or by a video camera connected to a frame grabber board in a computer. Once an
image has been digitized, it can be operated upon by various image processing operations.
Image processing operations can be roughly divided into three major categories, Image
Compression, Image Enhancement and Restoration, and Measurement Extraction[提取尺寸]. Image
compression is familiar to most people. It involves reducing the amount of memory needed to
store a digital image.
Image defects which could be caused by the digitization process or by faults in the imaging
set-up (for example, bad lighting) can be corrected using Image Enhancement techniques. Once
the image is in good condition, the Measurement Extraction operations can be used to obtain
useful information from the image.
Some examples of Image Enhancement and Measurement Extraction are given below. The
examples shown all operate on 256 grey-scale images. This means that each pixel in the image is
stored as a number between 0 to 255, where 0 represents a black pixel, 255 represents a white
pixel and values in-between represent shades of grey. These operations can be extended to
operate on colour images.
The examples below represent only a few of the many techniques available for operating on
images. Details about the inner workings of the operations have not been given.
Image Enhancement and Restoration
The image at the left of Figure 1 has been corrupted
by noise during the digitization process. The "clean"
image at the right of Figure 1 was obtained by applying a
median filter [中值滤波器]to the image.
An image with poor contrast, such as the one at
the left of Figure 2, can be improved by adjusting the
image histogram to produce the image shown at the
right of Figure 2.
The image at the top left of Figure 3 has a corrugated effect due to a fault in the acquisition
process. This can be removed by doing a 2-dimensional Fast-Fourier Transform[快速傅里叶变换] on the image
(top right of Figure 3), removing the bright spots (bottom left of Figure 3), and finally doing an
inverse Fast Fourier Transform to return to the original image without the corrugated background
(bottom right of Figure 3).
An image which has been captured in poor lighting conditions, and shows a continuous
change in the background brightness across the image (top left of Figure 4) can be corrected
using the following procedure. First remove the foreground objects by applying a 25 by 25
greyscale dilation operation (top right of Figure 4). Then subtract the original image from the
background image (bottom left of Figure 4). Finally invert the colors and improve the contrast by
adjusting the image histogram (bottom right of Figure 4).
The example below demonstrates how one could go about extracting measurements from an
image. The image at the top left of Figure 5 shows some objects. The aim is to extract information
about the distribution of the sizes (visible areas) of the objects. The first step involves segmenting
the image to separate the objects of interest from the background. This usually involves
thresholding the image, which is done by setting the values of pixels above a certain threshold value
to white, and all the others to black (top right of Figure 5). Because the objects touch, thresholding
at a level which includes the full surface of all the objects does not show separate objects. This
problem is solved by performing a watershed separation on the image (lower left of Figure 5). The
image at the lower right of Figure 5 shows the result of performing a logical AND of the two
images at the left of Figure 5. This shows the effect that the watershed separation has on touching
objects in the original image. Finally, some measurements can be extracted from the image.
frame grabber board 帧中继访问设备
1, corrugated ['kɔrəgeitid]
2, dilation [dai'leiʃən, di-]
3, subtract [səb'trækt]
6, watershed ['wɔ:təʃed, 'wɔ-]
Continue reading it-e-66 Introduction to Digital Image Processing
Why change vectors to bitmaps?
Most of the clip art gallery is vector-based and will need to be converted into bitmap formats
(GIF) prior to putting it on the Web.
Why change bitmaps to vectors?
You will need to change vectors to bitmaps to perform tasks from the Drawing toolbar on a
bitmap picture (such as animate parts of a bitmap picture) you will need to convert it into a vector
format. You can then e.g. ungroup it and apply animations to only parts of it.
Which graphic converter to use?
To change your graphics format, you need to use a graphics converter. A popular graphics
editor you can use for this is Paint Shop Pro. Another graphics editor you can use is Adobe
PhotoShop, which is said to be the best one for this kind of conversion.
How to use your graphic converter?
Open your file in the graphics editor chosen: Select File | Open.
Select File | Save As.
Rename your file and choose a new format. For a bitmap to vectors conversion select the
WMF format. For the opposite conversion, select the GIF format if you have PowerPoint 97,
otherwise select JPEG or TIFF.
Unfortunately, some of the quality may be lost in the switch. MS office also provides
Continue reading it-e-65 Convert a Graphics Format
Continue reading phprpc初步
This course is an introduction to the basic concepts as well as applications of the rapidly
emerging field of digital image processing. It familiarizes the audience with the understanding,
design, and implementation of algorithms in the various subareas of digital image processing
such as image enhancement, image deblurring, image understanding, image security, and image
compression. Over 200 image examples complement the technical descriptions.
This course will enable you to
explain the fundamental concepts and terminologies employed in digital imaging such as
sampling and aliasing, perceptual quantization; filtering, look-up tables, image histogram,
explain the various techniques used in image enhancement for contrast manipulation (e.g.,
histogram equalization), sharpening (e.g., unsharp masking) and noise removal (e.g.,
selective averaging, median filtering);
briefly demonstrate the performance of image deblurring algorithms such as inverse filtering
and Wiener filtering by using image examples;
briefly demonstrate the concepts behind digital signatures for image authentication and
invisible watermarking for image copyright protection;
briefly describe the current research topics in image understanding and demonstrate related
algorithm performances using image examples;
explain the basic technologies that serve the existing JPEG and the emerging JPEG2000
Scientists, engineers, and managers who need to understand and/or apply the fundamental
concepts and techniques employed in digital image processing. Although no particular background is
needed, some prior knowledge of linear system theory (e.g., Fourier transforms) would be helpful.
n. [计] 去模糊
2, histogram ['histəugræm]
3, perceptual [pə'septjuəl]
4, quantization [,kwɔntai'zeiʃən]
5, Fourier ['furiei]
Continue reading it-3-64 Course Description
Continue reading hessian 初步
Continue reading phprpc,xml,json,hessian 协议？
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Boolean.class 和 boolean.class是一样的吗？答案是大大的NO:
Boolean.class.getCanonicalName() -> “java.lang.Boolean”
boolean.class.getCanonicalName() -> “boolean”
Continue reading Boolean.class 和 boolean.class
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