Automating rule generation for grammar checkers
Marcin Mi?kowski
Polish Academy of Sciences
1. Introduction
In this paper,I describe several approaches to automatic or semi-automatic development of symbolic rules for grammar checkers from the information contained in corpora.The rules obtained this way are an important addition to manually-created rules that seem to dominate in rule-based checkers. However, the manual process of creation of rules is costly, time-consuming and error-prone.It seems therefore advisable to use machine-learning algorithms to create the rules automatically or semi-automatically. The results obtained seem to corroborate our initial hypothesis that symbolic machine learning algorithms can be useful for acquiring new rules for grammar checking. It turns out, however, that for practical uses, error corpora cannot be the sole source of information used in grammar checking.We suggest therefore that only by using different approaches, grammar-checkers, or more generally, computer-aided proofreading tools, will be able to cover most frequent and severe mistakes and avoid false alarms that seem to distract users.
In what follows, I will show how Transformation-Based Learning (TBL) algorithms may be used to acquire rules. Before doing that, I will discuss the pros and cons of three approaches to creating rules and show the need to make use of them all in a successful grammar-checking tool.The results obtained seem to suggest that the machine-learning approach is actually fruitful, and I will point to some future work related to the reported research.
2. Three approaches to rule-based grammar checking
It could seem that a pure empirical approach is the most advisable way to create rules for grammar checkers. However, it must rely on error corpora,
and these are hard to find and costly. Although it is possible to automatically bootstrap the development of such corpora (see Mi?kowski 2008), they are still not sufficient to cover all variety of important errors in the language on the proper level of generality. Direct use of smaller error corpora does not seem to lead to useful results (see below), and they must be accompanied with a“clean”corpus that contains no or only minor errors.The rules acquired this way, however, can be a useful addition to the existing base of rules.
Pure a priori approach as based on existing normative prescriptions, such as usage dictionaries,style guides,grammar textbooks etc.offers two significant advantages over the pure empirical approach.First,the justification of the rules seems to be more reliable.It is the job of lexicographers to check if the advice offered is accurate and authoritative, and the authors of grammar-checkers can simply rely on it.Moreover,if the grammar checker is to impose style guidelines, then it is doubtful that any empirical corpus would embody them beforehand. For example, if the style guide requires that dates be written only in a recommended format, it is easier to define the standard format and use the corpus only to find out what deviations seems to occur most frequently. This is why for some uses, the a priori approach cannot be replaced with pure empirical one.
For larger-scale projects,however, pure a priori approach has some disadvantages: it lags behind linguistic development, as dictionaries tend to be outdated (especially the printed ones). In other words, some errors listed in the dictionaries may no longer exist in the language, and some existing errors may be missing from the dictionary. Both factors can negatively affect the recall of grammar-checking rules.For example,Polish dictionaries of usage usually advise against using cliches of Soviet propaganda, including especially bureaucratic borrowings from Russian.It turns out that those cliches are almost extinct nowadays; it makes therefore little sense to develop rules detecting them as contemporary speakers are not likely to use the cliches anyway. At the same time, with the wide adoption of computers for word processing, spell-checkers seem to introduce specific kinds of errors into
the text.One of the basic functions in spell-checkers is detecting run-on words but when implemented carelessly, it seems to introduce errors in Polish neologisms(for example,the word“technohumanizm”,which is a correct productive neologism, missing in spelling dictionaries, could be automatically corrected to“techno humanizm”,which is incorrect but composed of two correct dictionary words). This kind of errors is however completely missing from usage dictionaries and similar resources, so by relying completely on them, we would fail to detect it.
There is also another factor that makes a priori approach hard in practical applications.Culturally-transmitted normative prescriptions for linguistic behavior are vague and tend to lack contextual information needed to disambiguate between correct cases and incorrect ones,even if some examples are given. This is true of usage dictionaries, spelling dictionaries, and grammar textbooks. Moreover, as some prescriptions are on the semantic level, which is not fully reducible to the surface level on which most grammar checkers operate,they need to be(sometimes painfully)adjusted to the Procrustean bed of what is expressible on the surface level. As a result, even authors with a background in linguistics need process a corpus with the newly created rules to make sure that they are not resulting in a very high number of false alarms. Yet, as our research shows, the false alarms can also result from automatic acquisition of rules (which is shown by precision levels of 30%), so the step of processing corpora with the rules and refining them seems unavoidable.1
Lastly, minority languages can lack authoritative sources altogether, so in some cases such resources cannot be reused at all. As one of the goals of our research project was to alleviate the process of adding new rules to an open-source proofreading tool, LanguageTool (for a general description, see Naber 2003),and we wanted to include minority languages among the ones supported, using reliable dictionaries could not be a requirement.
1However, some automatically-acquired rules can be discarded automatically if they seem to result in a very high number of matches in a substantial corpus that is considered to be relatively free of errors.
It is possible to use the information from both worlds: machine learning algorithms with some existing language resources,such as authoritative usage guides. Although the quality of some automatically-developed rules may be lower in terms of their precision, we may restrict ourselves to the best ones and refine others manually.The mixed approach uses therefore both corpora to create rules using real-world data and some prescriptions that lead the process of acquisition of rules.
3. Machine learning of rules
One of the machine-learning techniques that was used successfully in the mixed approach is, as we already mentioned, Transformation-Based Learning (see Mangu&Brill1997)but it can also be used in the pure empirical approach, as shown in the section 5. TBL is of course just one of the available machine-learning algorithms that may acquire rules.It is also quite straightforward to use statistical machine translation (SMT), which is already applied in automatic post-editing (Simard et al. 2007) and could also be quite easily adopted to add missing words in the text (like determiners or missing parts of common phrases).It might be used for ESL grammar checking (Gamon et al. 2008). Here, I will focus on TBL, as it creates rules that can be easily translated into LanguageTool symbolic notation (constructs admissible in TBL rules are a subset of what is expressible in LT notation). One of the interesting properties of TBL is that the training corpus can be relatively small to achieve good results, contrary to most SMT-based methods that are data-intensive.
TBL is most well-known for its application in the Brill tagger (Brill 1995), however,it may be used in a wide variety of NLP tasks that involve classification. The algorithm starts with an initial classification, and tries to find transformations from initial classes to the correct ones. In the case of part-of-speech (POS) tagging, it transforms most frequent POS tags, which were applied first,into correct ones,which are taken from a manually-annotated corpus. To constrain the search space of possible transformations, the TBL algorithm uses a finite list of admissible transformation templates
that usually include features (POS tags, surface tokens, lemmas) that precede the feature under consideration or follow it.
The application of TBL to the error-detection task is straightforward: we need to find the transformation from incorrect tokens into the correct ones. Brill used TBL to find commonly confused words in English,by simply substituting POS tags in the POS tagging task with confused words.2The confused words create (at least two-element) confusion sets such as {than, then}, {its, it's}, {there,their}, {advice,advise}. For any member of the confusion set, the original word was replaced with other(s) in a clean corpus, and the original word was moved into the place of the correct feature. In a familiar column format of Brill tagger, this could look like this (assuming that POS tags were used in the clean corpus):3
NN advice advise
Next, the standard learning procedure is run. It results in a ranked list of transformation rules. The original paper of Mangu and Brill used a dictionary-based word list,but TBL can be used with confusion sets created in a different way, using additional corpora or without them.
For example, one could use artificially created errors in a clean corpus (Sj?berg & Knuttson 2005). Such approach has been taken in transformation-based learning for punctuation correction in Danish (Hardt 2001). Note that artificial errors should not be just randomly created;otherwise,the rules created will have no recall on normal text,as nobody will actually make mistakes detected by those rules (as Mi?kowski 2008 observes, this was the case of automatically created autocorrection rules for early versions of Polish https://www.doczj.com/doc/a812953629.html,). So, the search space of possible errors should correspond to the space of frequent errors typical of the data-entry method used (OCR, standard keyboard, mobile phone on-screen keyboard...). For example, if the error corpus is to contain frequent typos and the grammar checker will
2The list of words was simply excerpted from a dictionary (Flexner 1983).
3Note that the POS tag used is the one that corresponds to the confused word.
operate in a word processor, then the errors should be selected based on distances between letters in the most popular keyboard layout (c replaced with v more frequently than with p in the case of QWERTY keyboards). If the proofreading tool is to be used in a digitalization project on OCRed text, the typos should be selected based on graphical character similarity in typical typefaces (l replaced with1more frequently than with k). Yet, in all those cases the simulated errors are only mechanical rather than cognitive mistakes that result from insufficient linguistic competence.It is much harder to artificially simulate errors involving confused nouns, non-standard versions of idioms, or false-friends in a translated text without any additional language resources.
An obvious source of confusion set is an error corpus. Approaches based on reusing error corpora were often discarded as non-realistic, as creating such corpora is costly. Yet, there are ways to automate building such corpora by observing the frequency of user revisions to the text (Mi?kowski 2008). Frequent minor edits tend to be related to linguistic mistakes and textual conventions.They have been used to learn transformation rules that automatically correct machine-translated text and improve its quality (Elming 2008).
It can be noted that for some purposes, automating rules for sticking to a linguistic convention may be desired. So, for example, if Wikipedia editors usually correct some non-adherence to the Wikipedia typographical standards, they could use a proofreading tool to scan the texts for non-compliance with those standards. However, in a general proofreading tool those conventional edits should be filtered out; either by averaging on many different revision histories from various sources (which is not yet realistic),or by manually filtering out such frequent revisions.
4. Approaches to learning from the error corpus
It might seem that one could simply use the corpus of errors(or revisions) directly. But rules created from a corpus tend to have high recall with low precision: there are simply not enough examples for members of the
same confusion set to discriminate the cases during learning. Here are the highest-ranked rules created by fnTBL toolkit(Ngai& Florian 2001)on a Holbrook corpus used directly (Holbrook 1964):4
GOOD:21 BAD:0 SCORE:21 RULE: pos_0=Jame word_0=Jame => pos=James
GOOD:20 BAD:0 SCORE:20 RULE: pos_0=dont word_0=dont => pos=don't
GOOD:10 BAD:0 SCORE:10 RULE: pos_0=two pos_1=the
word_0=two word_1=the => pos=to
GOOD:10 BAD:0 SCORE:10 RULE: pos_0=yow word_0=yow =>
pos=you
GOOD:10 BAD:0 SCORE:10 RULE: pos_0=thats word_0=thats => pos=that's
GOOD:10 BAD:0 SCORE:10 RULE: pos_0=oclock word_0=oclock => pos=o'clock
As is easily seen, some of these rules are hardly useful in a normal context. All occurrences of “Jame” are unconditionally transformed into “James” (there are more examples like that, for example “cave” being replaced with “gave”). Moreover, it turns out that the third rule is based on a mistaken tagging5 in the original corpus (the ERR tag contains the suggested correction as the contents of targ attribute):
15 NOVEMBER 1960 The Redex Jack O'Malley: Boss George Green 2
Head John Young2head went
4I left the original names of features from the Brill tagger to stress the connection between both tasks.
5During writing of this paper, the error has been fixed in the edition of Holbrook corpus maintained in computer-readable form by Roger Miton.
This shows that error corpora, which tend to be small, do not offer a sufficient number of examples of features to be learned. Moreover, a slight error in tagging can lead to surprisingly bad results. This is again a question of scale, as in a huge error corpus, a single tagging error could become irrelevant.
However, there is a way to use small corpora as seeds to create artificial errors in a large clean corpus.In other words,we need to extract only confusion sets({two,to},{pictures,pictyres}...)from the error corpus, possibly filter non-dictionary words as they are already flagged by standard spelling checkers, and apply confusion sets to the clean corpus. A bigger corpus will have more examples, and that results with higher precision. Let us look at the result of learning now:
GOOD:4845 BAD:0 SCORE:4845 RULE: pos_0=end word:[-3,-1]=, => pos=and
GOOD:1443 BAD:0 SCORE:1443 RULE: pos_0=end word:[1,3]=PRP => pos=and
GOOD:839 BAD:0 SCORE:839 RULE: pos_0=end pos:[1,3]=. => pos=and
GOOD:681 BAD:0 SCORE:681 RULE: pos_0=end word:[1,3]=DT => pos=and
GOOD:408 BAD:0 SCORE:408 RULE: pos_0=end word:[-3,-1]=DT => pos=and
GOOD:354 BAD:0 SCORE:354 RULE: pos_0=end word:[1,3]=, => pos=and
The rules concerning the confusion of “end” and “and” make now much more sense: “end” most of the time requires a determiner, so it is quite improbable that it will occur straight after a comma or before a determiner. The rule that suggests that “end” is incorrect before a dot (third one) might be however incorrect and result from a too small learning sample. The Table 1 contrasts results achieved using the(1) na?ve learning method and(2)introducing extracted confusion sets to a clean corpus (mixed learning for short). The resulting rules were applied to the Brown corpus (Francis & Kucera 1964).
Corpus type Measure Na?ve
learning
Mixed
learning
Training corpus Recall20.34%99.43% Precision95.11%99.97%
Brown corpus Recall0.00%100.00% Precision0.00%38.00%
The results obtained suggest that rules created directly from Holbrook corpus by naively using it for learning have no practical value for any data sets that were not included in the training. In other words, using the na?ve approach, especially with a small error corpus,makes no sense;only the mixed approach offers some interesting results but precision is also relatively low. It means that only some rules could be usefully used for grammar checking.
Yet,Holbrook corpus seems to be too tiny for almost any machine learning task. This is why we evaluated the strategies on a much larger error corpus, i.e., the one we previously created from the whole Polish Wikipedia revision history(Mi?kowski2008).As a baseline,we used the Frequency Dictionary of Polish corpus, which is comparable to Brown (Kurcz et. al 1974-1977). The results are:
Corpus type Measure Na?ve
learning
Mixed
learning
Training corpus Recall33.60%99.99% Precision80.14%100.00%
Frequency Dictionary Corpus Recall100.00%100.00% Precision0.34%34.65%
situation: the na?ve approach has very low precision, and detects almost any occurrence of a confused word as an error (hence, 100% recall). As before,
the mixed approach enhances precision significantly but the rules still need human evaluation.6
One more thing deserves a mention. The original idea of applying TBL to detect errors focused only on confused words. It is also possible to create rules to detect missing words;the transformation words would simply substitute a missing word for the artificial token “NULL”. We tested the idea by removing the word “si?”, which is required by some reflexive verbs in Polish; the resulting rules cite almost only reflexive verbs. In the case of words that were written separately but sometimes need to be spelled together (“no body” vs. “nobody” in English), the confusion set can simply contain an artificial word “no-body”, so that mapping between subsequent features is retained. As long as the error form can be brought into a form of transforming a single feature into a target one (incorrect word into a correct word), TBL can be used. For more complex structural transformations, it could become less viable.
5. Adapting the rules to LanguageTool and future work
TBL rules,as noted,can be easily translated into the notation of declarative symbolic rules used in LanguageTool (the exact description of the notation is outside the scope of this paper). Rules are specified in XML files as patterns of tokens; tokens can be tested for multiple attributes (such as POS tags, lemmas and surface forms, and preceding white space) and relations (including conjunction, negation, exceptions, conditional skipping of tokens forward, as well as feature unification). The attributes can be specified as regular expressions. For that reason, the rules can be much more general than the ones created by TBL. So, although it could be possible to convert transformation rules automatically to LT notation, human intervention is still advisable to make them more general.
In all cases we observed the same phenomenon as with the test results
6We accepted all possible candidates for rules from the ranked list; we could apply a threshold to filter out the rules that did not have sufficient examples to learn their features from. This would make recall probably lower but could enhance precision.
above in section 4: we were able to enhance the recall of many existing confused words rules in LT by using TBL, but the precision of some of the automatically-acquired rules was too low. Here are some examples of rules acquired using μ-TBL toolkit (Lager 1999) in its Prolog-style notation: wd:they>the <- tag:'DT'@[5] o
wd:they>the <- tag:'NN'@[-2] o
wd:they>the <- tag:'JJ,NN,RB'@[1] o
wd:they>the <- tag:'NNS,VBZ'@[5] o
wd:there>their <- wd:of@[-1] o
wd:they>the <- tag:'JJ,NN'@[1] o
wd:there>their <- wd:'SENT_START'@[3] o
wd:they>the <- tag:'NN'@[1] o
wd:they>the <- tag:'SENT_START'@[-3] o
As can be seen in the example, we experimented with rule templates quite freely, and tried to see what would be the result of using the feature which is the 5th one after the current one. Contrary to our expectations, the results in most cases are still somewhat useful, although it is of course safer not to use such controversial templates.
The input text was tagged with the internal LanguageTool POS tagger (which disambiguates only to a very limited degree due to specific requirements of grammar checking)to create rules that may be almost directly translated into LT notation with limited manual changes. Polish and English automatically-acquired rules both seem to be useful and are gradually implemented, while human intervention is limited to enhancing rule precision.
In the future,we want to port the current prototype framework (consisting mostly of AWK scripts and Unix tools)to Java,including bootstraping the error corpus from Wikipedia history dump (see Mi?kowski 2008). This would offer more platform-independence. This way, the process of adding a new language to LanguageTool would be greatly facilitated, which is one of the practical goals of the project. We consider also using one of the Java implementations of TBL learning directly in our learning tool, and using automatic translation from TBL rules to LT formalism.
We think that even though automatically-acquired rules not always have a satisfactory level of precision, TBL may actually help with development of rules. If minimizing manual intervention were our goal, we could simply use only the best ranked rules with the best precision and test it on additional corpora to make sure they do not raise an unusually high number of alarms, at the price of lower recall.The best automatically-acquired rules are sometimes simpler than the ones proposed by human linguists,and may become a source of inspiration. Therefore, though we cannot completely rely on the automatically generated results, they seem to alleviate creation of rules in a significant way.
Bibliography
Brill, E. (1995). “Transformation-Based Error-Driven Learning and Natural Language. A Case Study in Part of Speech Tagging.” Computational Linguistics, 21(4):543-565.
Elming, J. (2008). “Transformation-based correction of rule-based MT.” In: Proceedings of EAMT 2006, 219-226.
Flexner S. B., editor (1983). Random House Unabridged Dictionary. Second edition. New York: Random House.
Francis, W., & Kucera, H., (1964). Brown Corpus Manual. Revised 1979. Available at http://www.hit.uib.no/icame/brown/bcm.html.
Gamon, M., Gao, J., Brockett, Ch., Klementiev, A., Dolan, W., Belenko, D. & L. Vanderwende (2008), “Using Contextual Speller Techniques and Language Modeling for ESL Error Correction”. In: Proceedings of IJCNLP 2008, Hyderabad, India: Asian Federation of Natural Language Processing.
Hardt, D. (2001). “Transformation-Based Learning of Danish Grammar Correction.” In: Proceedings of RANLP 2001.
Holbrook, D. (1964). English for the Rejected, Cambridge: Cambridge University Press.
Kurcz, I., Lewicki, A., Sambor, J., & Woronczak, J. (1974). S?ownictwo
wspó?czesnego j?zyka polskiego. Listy frekwencyjne. Tom I. Teksty popularnonaukowe. Warszawa: Uniwersytet Warszawski.
Kurcz, I. Lewicki, A., Sambor, J., Woronczak, J. (1975). S?ownictwo
wspó?czesnego j?zyka polskiego. Listy frekwencyjne. Tom II. Drobne wiadomo?ci prasowe. Warszawa: Uniwersytet Warszawski.
Kurcz, I., Lewicki, A., Sambor, J., Woronczak, J. (1976). S?ownictwo
wspó?czesnego j?zyka polskiego. Listy frekwencyjne. Tom IV. Proza artystyczna. Warszawa: Uniwersytet Warszawski.
Kurcz, I., Lewicki, A., Sambor, J., Woronczak, J. (1977). S?ownictwo
wspó?czesnego j?zyka polskiego. Listy frekwencyjne. Tom V. Dramat artystyczny. Warszawa: Uniwersytet Warszawski.
Lager, T. (1999). “The μ-TBL System: Logic Programming Tools for Transformation-Based Learning.”In: Proceedings of the Third International Workshop on Computational Natural Language Learning (CoNLL-99), Bergen.
Lewicki, A., Mas?owski, W., Sambor, J., Woronczak, J. (1975). S?ownictwo wspó?czesnego j?zyka polskiego. Listy frekwencyjne. Tom III. Publicystyka. Warszawa: Uniwersytet Warszawski.
Mangu, L. & E. Brill. (1997). “Automatic rule acquisition for spelling correction”. In: Proceedings of the 14th International Conference on Machine Learning, 187–194.
Mi?kowski, M. (2008). Automated Building of Error Corpora of Polish, In Corpus Linguistics, Computer Tools, and Applications – State of the Art. PALC 2007, Barbara Lewandowska-Tomaszczyk (ed.), Peter Lang, 631-639.
Naber Daniel (2003). A Rule-Based Style and Grammar Checker, Diplomarbeit, Bielefeld: Universit?t Bielefeld. Available at:
http://www.danielnaber.de/languagetool/download/style_and_grammar_chec ker.pdf
Ngai, G. & R. Florian (2001). “Transformation-Based Learning in the Fast Lane”. In: Proceedings of the Second Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Pittsburgh.
Simard, M., Ueffing, N., Isabelle, P. & R. Kuhn (2007). “Rule-based Translation With Statistical Phrase-based Post-editing.” In: Proceedings of the Second Workshop on Statistical Machine Translation, Prague: Association for Computational Linguistics, 203–206.
Sj?bergh, J. & O. Knutsson. (2005). “Faking errors to avoid making errors: Very weakly supervised learning for error detection in writing”. In: Proceedings of RANLP 2005, Borovets, Bulgaria.
Generation gap There always has been generation gap since the dawn of civilization. Old people act like a frog in the well. They are fully convinced with their ideas as ultimate and ideal. They ignore certain vital factors that are no longer valid in modern days. There is a great hue and cry raised by the elders and the young both that the communication between them has broken down and they accuse each other for and bemoan this state of affairs. They attribute it to generation gap. Majority in the society carries along with this moroseness and never pauses to think the why and how of the problem. 为什么？？ The elders are more critical of the younger generation with a big inventory of complaints against the young and the young mostly tend to ignore the grumbling, mumbling and occasional loud protestations of the older generation. But now and then they do protest. They resent the petting attitude of the elders. It is generally observed that the old behave like a frog in the well. They are fully convinced that the ideas they have had throughout their lives are the ultimate and ideal. They ignore certain vital factors that are no longer valid in the case of the modernity. There always has been generation gap since the dawn of civilization. The young have always deviated from the older standards and it was well that they did or there wouldn’t have been any progress today. The younger generation naturally differs in dress, food, habits etc, as all these were not available to the older generation. These changes are indicative of progress. With the passage of time when the society, right from the basic unit of the family through society and the entire humanity, is changing, how can the new generation be asked to stagnate or move bac kward to the ancestors’ way of life? In the olden days, in the Indian society the arranged marriage took place first and love between husband and the wife were thought to be a natural development. The moral standards differed in consonance with the contemporary situation, as human society is essentially a utilitarian one. The elders couldn’t have visualized what was to come in their lifetime i.e. live-in relationship, one night stand, car key pooling and fishing and things that are yet to come out in the open. It will be generally agreed that most fathers want their offspring to adopt the same profession that they themselves followed without giving due weight to the aptitude of the offspring. This causes tension between the elders and the children. The younger generation is more intellectually evolved than the preceding one. But the ego of the older people does not permit them to accept
（部编语文）部编语文阅读理解专项习题及答案解析 一、一年级语文下册阅读理解练习 1．阅读下文，回答问题。 三个小伙伴 小野猪、小象和小袋鼠一同上山栽树。 没有锄头挖树坑怎么办？小野猪说：“不要紧,不要紧,我有硬硬的嘴巴。”不一会儿，小野猪就用嘴巴拱出了一个树坑。 没有箩筐运肥料怎么办？小袋鼠说：“不要紧，不要紧，我有一个皮口袋。”不一会儿，小袋鼠就用皮口袋运来了一堆肥料。 三个小伙伴把小树栽进坑里，施上肥料，培上泥土。 没有水桶浇水怎么办？小象说：“不要紧，不要紧，我有长长的鼻子。”小象跑到小河边，用长鼻子吸足了水，把树坑浇得湿湿的。 小树栽好啦！三个小伙伴手拉着手，围着小树又跳又笑。 （1）照样子，写词语。 又跳又笑又________又________ 又________又________ （2）在横线上填上合适的动词。 ________树坑 ________肥料 ________泥土 ________小手 ________课文 ________手工 （3）读短文选一选，填一填。 ①皮口袋②长鼻子③尖嘴巴 小野猪用________拱出树坑，小袋鼠用________运肥料，小象用________给树浇水。（4）用一句话来夸夸三个小伙伴吧。 【答案】（1）高；大；白；胖 （2）挖；运；培；拉；读；做 （3）③；①；② （4）三个小伙伴团结合作，困难就被克服啦，佩服你们！ 【解析】 2．阅读下文，回答问题。 花和小草 小花，小花，你有伙伴吗？ 小花挨着小草。 小草，小草，你有好朋友吗？ 小草偎着小花。 小花和小草，在风雨中手牵手，从春到夏。 香飘大地，绿满天涯。 （1）小花的伙伴是________，小草的好朋友是________。 （2）小花和小草还会交哪些好朋友？
【部编语文】部编语文阅读理解练习题20篇及解析 一、一年级语文下册阅读理解练习 1．阅读下文，回答问题柳树 春天到了，柳树枝条上爆出了嫩芽，随风飘荡。 小芳和小军一起到公园里去玩。小军看到柳条很美丽，就随手折了一根，玩了起来。这时，小芳看见了急忙说：“公园里的花木是园丁伯伯辛辛苦苦栽培出来的，是给大家欣赏的。我们要爱护它，不能损害它。”小军听了很惭愧地说：“以后我再也不乱摘花木了。” （1）这篇短文有________节。 （2）本文写发生在________和________之间的事情。 （3）________折柳枝，________帮助他。________惭愧地说：“________”。 【答案】（1）2 （2）小芳；小军 （3）小军；小芳；小军；以后我再也不乱摘花木了 【解析】 2．阅读下文，回答问题。 爸爸晚上偏要拉我去散步， 原来花草都像白天一样微笑。 从此再黑再黑的夜晚， 我也能看见小鸟怎样在月光下睡觉…… （1）照样子，填上恰当的词语。 例：（再黑再黑）的夜晚 ________的夜晚 ________的夜晚 （2）用波浪线画出花草具有“人”特征的句子。 （3）你喜欢文中的“我”吗？为什么？ 【答案】（1）很黑很黑；很静很静 （2）原来花草都像白天一样微笑。 （3）喜欢。因为“我”克服了困难，变得勇敢起来。 【解析】 3．阅读短文，完成练习。初秋的夜晚，凉风习习。小树林里树叶“沙沙”地响，好像和谁在说悄悄话。草丛里，蟋蟀不知疲倦地唱着秋夜的歌。池塘边，一群青蛙忙了一夏，更是欢快地“呱呱呱”地叫个不停，好像在说：“好哇，好哇，吃掉害虫，庄稼长得好哇！” （1）短文共________句话，用“／”标出来。 （2）第4句话写的是谁？ （3）短文写的是什么时候的景象？________①初春的夜晚②初夏的夜晚③初秋的夜晚 （4）照样子，连线。树叶________ 呱呱地叫 青蛙________ 叽叽地唱
图像平滑的Matlab实现 20101602310035 黄汉杰 摘要 随着图像处理领域的迅速发展，图像平滑作为图像处理中的重要环节，也逐渐受到人们的关注。图像平滑的目的主要是消除噪声。图像平滑方法按空间域和频率域的分类及各种方法的特点，图像平滑是对图像作低通滤波,可在空间域或频率域实现。空间域图像平滑方法主要用均值滤波、中值滤波等；频率域图像平滑常用的低通滤波器有理想低通滤波器、布特沃斯低通滤波器、低通指数滤波器、低通梯形滤波器等。 关键词：图像平滑；噪声；空间域低通滤波；频域低通滤波 引言： （1）在图像的获取和传输过程中原始图像会受到各种噪声的干扰，使图像质量下降。为了抑制噪声、改善图像质量，要对图像进行平滑处理。抑制或消除这些噪声而改善图像质量的过程称为图像的平滑。图像平滑的目的是为了消除噪声。噪声消除的方法又可以分为空间域或频率域，亦可以分为全局处理或局部处理，亦可以按线性平滑、非线性平滑和自适应平滑来区别。图像的平滑是一种实用的数字图像处理技术，一个较好的平滑处理方法应该既能消除图像噪声，又不使图像边缘轮廓和线条变模糊，这就是研究数字图像平滑处理要追求的目标。一般情况下，减少噪声的方法可以在空间域或频率域进行处理，主要有邻域平均法、中值滤波法、低通滤波法等，邻域平均法即通过提高信噪比，取得较好的平滑效果；空间域低通滤波采用低通滤波的方法去除噪声；以及频域低通滤波法通过除去其高频分量就能去掉噪声，从而使图像得到平滑。 （2）本设计将对图像平滑处理的两大方面即空间域和频率域,以及两种处理
方向里的几种处理方法进行介绍，并对一些常用的简单平滑算法进行分析。 （3）图像平滑主要是为了消除被污染图像中的噪声,这是遥感图像处理研究的最基本内容之一,被广泛应用于图像显示、传输、分析、动画制作、媒体合成等多个方面。该技术是出于人类视觉系统的生理接受特点而设计的一种改善图像质量的方法。处理对象是在图像生成、传输、处理、显示等过程中受到多种因素扰动形成的加噪图像。在图像处理体系中,图像平滑是图像复原技术针对“一幅图像中唯一存在的退化是噪声”时的特例。 1.论文目的 1.1 通过几种图像平滑的方法，实现被噪声污染过的图像的平滑处理，其中包括空间域和频率域； 1.2 在加深对数字图像处理课本知识理解的基础上，学会运用已学的知识对图像 平滑的处理方法的结果进行分析。 2.理论及方案 (1)图像噪声来源及类型 一幅图像在获取和传输等过程中，会受到各种各样噪声的干扰，其主要来源有三：一为在光电、电磁转换过程中引入的人为噪声；二为大气层电（磁）暴、闪电、电压、浪涌等引起的强脉冲性冲激噪声的干扰；三为自然起伏性噪声，由物理量的不连续性或粒子性所引起，这类噪声又可分成热噪声、散粒噪声等。一般在图像处理技术中常见的噪声有：加性噪声、乘性噪声、量化噪声、“盐和胡椒”噪声等。下面介绍两种主要的噪声。 2.1.1、高斯噪声(Gaussian noise) 这种噪声主要来源于电子电路噪声和低照明度或高温带来的传感器噪声,也
Generation gap 父母比我们年龄大，所以无论是生理上还是心理上，都要比我们成熟。 父母的经历和经验也比我们丰富，做事也比我们谨慎、周密。所以他们有能力采取适当的措施同子女进行沟通。如果父母没有做到这些而使自己与子女之间产生“代沟”，那么主要的责任在于父母。 从思想上讲 从现代教育上讲 子女所要接受的教育很多，而家庭教育则是子女接受教育中最为重要的教育内容之 一。"在谈及家庭教育必然要谈到父母与子女的关系问题。我们知道，一个良好的家庭环境必然会形成一个和睦而又温暖的家庭。如果家庭中各成员之间产生隔阂，必然会对子女的成长造成一定的影响。父母的言行和品德对子女有很大的影响。从子女一出生，父母就成了子女的启蒙老师，从此他们就担负着培养和教育子女的责任和义务，同时也是他们承担的社会责任。而父母的失职就可能给家庭蒙上一层阴影，造成家庭冷漠不和，以及子女的悲观、消极思想。 Intermsoffamilyeducation,parentsplayanimportantpartintherelationship between parents and children. We know that a good familyenvironmentisboundtoformaharmoniousandwarmfamily.Whatparents say and behave has a great influence on their children Negligenceoftheparentscancausechildren'spessimisticandnegativevalues.This is also one of the factors causing the generation gap. From the above arguments,we can draw a conclution thatthe generationgap is largely due to parents. 1/ 1
人教部编版一年级语文试题课外阅读训练带答案解析 一、部编版一年级下册语文课外阅读理解 1．阅读下文，回答问题。 荷叶圆圆（节选） 荷叶圆圆的，绿绿的。 小水珠说：“荷叶是我的摇篮。”小水珠躺在荷叶上，眨着亮晶晶的眼睛。 小鱼儿说：“荷叶是我的凉伞。”小鱼儿在荷叶下笑嘻嘻地游来游去，捧起一朵朵很美很美的水花。 （1）荷叶的样子是________，颜色是________。 （2）游来游去、________来________去、________来________去 （3）小水珠把荷叶当作________；小鱼儿把荷叶当作________。 （4）想一想：还有谁把荷叶当作什么？ 解析：（1）圆圆的；绿绿的 （2）走；走；飞；飞 （3）摇篮；凉伞 （4）青蛙把荷叶当作歌台。 【解析】 2．阅读下文，回答问题。 小树谣 小树 在春风里摇， 绿了嫩芽， 绿了树梢。 小树 在春风里摇， 红了花蕊， 红了树梢。 它召唤来 爱唱歌的小鸟， 对它说： “等我长成大树， 狂风来了 也吹不倒。 你就在 我的枝叶间 筑温暖的巢。” （1）“它召唤来爱唱歌的小鸟，对它说”，第一个“它”指的是________，第二个“它”指的是________。 （2）小树对小鸟说了什么？用“________”在文中画出来。
（3）小鸟听了小树的话，会说什么呢？ 解析：（1）小树；小鸟 （2）“等我长成大树，狂风来了也吹不倒。你就在我的枝叶间筑温暖的巢。” （3）略 【解析】 3．课外阅读我最棒。 大鱼和小鱼 大鱼非常喜欢夸耀自己。一天，它在海底碰到一条小鱼，它说:“我从岸边游到大海深处，谁见了都尊敬我，怕我，躲得远远的。因为我个儿大，力气大，你们小鱼呢，只有被捉的份儿，永远被人瞧不起。 这时候，一张渔网从它们头上撒下来，大鱼被网住了，小鱼从网洞里钻出来，不慌不忙地游走了。 （1）大鱼认为自己________、________,所以在小鱼面前夸耀自己。 （2）小鱼因为________,所以能从网洞里钻出来。 （3）小鱼看到大鱼被网住，会说________，当小鱼长大后,它会________。 解析：（1）个儿大、力气大；略 （2）个儿小 （3）这就是自以为是的下场。；谦虚，多看别人的 【解析】【分析】（1）由选段第三句话可知，大鱼认为自己个儿大，力气大，,所以在小鱼面前夸耀自己。（2）解答时要带着问题快速浏览短文内容，就能找到答案。（3）人物语言的揣摩是要在细致地了解故事情节，理解人物的处境、性格的基础上来完成得。语言的组织要条理。 故答案为：（1）个儿大、力气大（2）个儿小（3）这就是自以为是的下场。谦虚，多看别人的 长处。 【点评】（1）、（2）此题考查在理解课文的基础上筛选相关信息并加以概括的能力。（3）此题主要考查对人物语言的揣摩能力。 4．阅读儿歌，完成练习。 铃(línɡ)声(shēnɡ)响(xiǎnɡ) ，下(xià)课(kè)了(le)。 操(cāo)场(chǎnɡ)上(shànɡ) ，真(zhēn)热(rè)闹(nào)。 跳(tiào)绳(shénɡ)踢(tī)毽(jiàn)丢(diū)沙(shā)包(bāo) ， 天(tiān)天(tiān)锻(duàn)炼(liàn)身(shēn)体(tǐ)好(hǎo)。
数字图像处理作业题目：图像的平滑处理与锐化处理 ：张一凡 学号：4 专业：计算机应用技术
1.1理论背景 现实中的图像由于种种原因都是带噪声的，噪声恶化了图像质量，使图像模糊，甚至淹没和改变特征，给图像分析和识别带来了困难。一般数字图像系统中的常见噪声主要有：高斯噪声、椒盐噪声等。 图像去噪算法根据不通的处理域，可以分为空间域和频域两种处理方法。空间域处理是在图像本身存在的二维空间里对其进行处理。而频域算法是用一组正交函数系来逼近原始信号函数，获得相应的系数，将对原始信号的分析转动了系数空间域。 在图像的识别中常需要突出边缘和轮廓信息，图像锐化就是增强图像的边缘和轮廓。 1.2介绍算法 图像平滑算法：线性滤波（邻域平均法） 对一些图像进行线性滤波可以去除图像中某些类型的噪声。领域平均法就是一种非常适合去除通过扫描得到的图像中的噪声颗粒的线性滤波。 领域平均法是空间域平滑噪声技术。对于给定的图像()j i f,中的每个像素点()n m,，取其领域S。设S含有M个像素，取其平均值作为处理后所得图像像素点()n m,处的灰度。用一像素领域内各像素灰度平均值来代替该像素原来的灰度，即领域平均技术。
领域S 的形状和大小根据图像特点确定。一般取的形状是正方形、矩形及十字形等，S 的形状和大小可以在全图处理过程中保持不变，也可以根据图像的局部统计特性而变化，点(m,n)一般位于S 的中心。如S 为3×3领域，点(m,n)位于S 中心，则 ()()∑∑-=-=++=1111 ,91,i j j n i m f n m f 假设噪声n 是加性噪声，在空间各点互不相关，且期望为0，方差为2σ，图像g 是未受污染的图像，含有噪声图像f 经过加权平均后为 ()()()()∑∑∑+==j i n M j i g M j i f M n m f ,1 ,1 ,1 , 由上式可知，经过平均后，噪声的均值不变，方差221σσM = ，即方差变小，说明噪声强度减弱了，抑制了噪声。 图像锐化算法：拉普拉斯算子 拉普拉斯算子是最简单的各向同性微分算子，具有旋转不变性，比较适用于改善因为光线的漫反射造成的图像模糊。其原理是，在摄像记录图像的过程中，光点将光漫反射到其周围区域，这个过程满足扩散方程: f kV t f 2=?? 经过推导，可以发现当图像的模糊是由光的漫反射造成时，不模糊图像等于模糊图像减去它的拉普拉斯变换的常数倍。另外，人们还发现，即使模糊不是由于光的漫反射造成的，对图像进行拉普拉斯变换也可以使图像更清晰。
（部编语文）一年级上册阅读理解专项练习及解析 一、一年级语文上册阅读理解练习 1．读短文，回答问题。时钟花 小白兔没有钟，不知道时间，它请小山羊帮忙想办法。小山羊送给它三盆花。 太阳出来了，牵牛花开了，张开了小喇叭。中午，午时花开了，张开了笑脸。天黑了，夜来香开了，张开了小嘴轻轻地唱歌。 （1）这篇短文有________段。 （2）小山羊送给小白兔什么花？ （3）读了短文，我会连。牵牛花________ 晚上开 午时花________ 早上开 夜来香________ 中午开 【答案】（1）2 （2）牵牛花，午时花，夜来香。 （3）早上开；中午开；晚上开 【考点】语段阅读 【解析】 2．阅读下文，回答问题要下雨了 有一天，蝴蝶在花草边飞翔，看见很多蚂蚁搬着许多食物往树上爬，觉得很奇怪，就问蚂蚁：“你们在干什么？” 蚂蚁笑着说：“我们全都在搬家啊。” 蝴蝶说：“你们为什么要搬家啊？” 蚂蚁说：“因为马上就要下雨了，我们怕雨会把我们的家给淹没了。” 蝴蝶说：“我只有翅膀没有手，帮不了你们，可是我能帮你们找一个安全的地方。” 蚂蚁们说：“那就先谢谢你了。” 不一会儿，就下起雨来了。蝴蝶的翅膀被雨淋湿了，飞不起来了。但是，它还是帮蚂蚁找到了一个淋不到雨的地方。 （1）你能用一个词语来形容吗？ ________的蚂蚁 ________的蝴蝶 （2）根据故事内容判断对错。 ①蚂蚁搬家是因为要下雨了。________ ②蚂蚁搬家是因为以前的地方不好，重新找到了新住址。________ （3）小蝴蝶主动帮助小蚂蚁找淋不到雨的地方，说明小蝴蝶具有怎样的好品质呢？ 【答案】（1）勤劳；美丽 （2）正确；错误 （3）助人为乐的好品质。 【考点】语段阅读，日月水火 【解析】
目录 1.3 图像噪声 一幅图像在获取和传输等过程中，会受到各种各样噪声的干扰，其主要来源有三：一为在光电、电磁转换过程中引入的人为噪声；二为大气层电（磁）暴、闪电、电压、浪涌等引起的强脉冲性冲激噪声的干扰；三为自然起伏性噪声，由物理量的不连续性或粒子性所引起，这类噪声又可分成热噪声、散粒噪声等。一般在图像处理技术中常见的噪声有：加性噪声、乘性噪声、量化噪声、“盐和胡椒”噪声等。下面介绍两种主要的噪声。 1、高斯噪声 这种噪声主要来源于电子电路噪声和低照明度或高温 带来的传感器噪声,也称为正态噪声,是在实践中经常用到的噪声模型。高斯随机变量z 的概率密度函数(P D F )由下式给出: }2/)(ex p{2/1)(22σμσπ--=z z p 其中, z 表示图像像元的灰度值;μ表示z 的期望;σ表示z 的标准差。 2、椒盐噪声 主要来源于成像过程中的短暂停留和数据传输中产生 的错误。其P D F 为： ?????===其他0)(b z pb a z pa z p 如果b > a , 灰度值b 在图像中显示为一亮点,a 值显
示为一暗点。如果P a和图像均不为零,在图像上的表现类似于随机分布图像上的胡椒和盐粉微粒,因此称为椒盐噪声。当P a为零时,表现为“盐”噪声;当P b为零时,表现为“胡椒”噪声。 图像中的噪声往往是和信号交织在一起的尤其是乘性 噪声，如果平滑不当，就会使图像本身的细节如边缘轮廓‘线条等模糊不清，从而使图像质量降低。
第二章、图像平滑方法 2.1 空域低通滤波 将空间域模板用于图像处理，通常称为空间滤波，而空间域模板称为空间滤波器。空间域滤波按线性和非线性特点有：线性、非线性平滑波器。 线性平滑滤波器包括领域平均法（均值滤波器），非线 性平滑滤波器有中值滤波器。 2.1.1 均值滤波器 对一些图像进行线性滤波可以去除图像中某些类型的噪声，如采用邻域平均法的均值滤波器就非常适用于去除通过扫描得到的图像中的颗粒噪声。邻域平均法是空间域平滑技术。这种方法的基本思想是，在图像空间，假定有一副N ×N 个像素的原始图像f (x ,y ),用领域内几个像素的平均值去代替图像中的每一个像素点值的操作。经过平滑处理后得到一副图像 g (x ,y ), 其表达式如下： ∑∈=s n m n m f M y x g ),(),(/1),( 式中： x ,y =0,1,2,…，N -1；s 为（x ，y ）点领域中点的坐标的集合，但不包括（x ，y ）点；M 为集合内坐标点的总数。 领域平均法有力地抑制了噪声，但随着领域的增大，图像的模糊程度也愈加严重。为了尽可能地减少模糊失真，也可采用阈值法减少由于领域平均而产生的模糊效应。其公式如下： ?????>-=∑∑∈∈其他),(),(/1),(),(/1),(),(),(y x f T n m f M y x f n m f M y x g s n m s n m 式中：T 为规定的非负阈值。
（部编语文）部编语文阅读理解专项习题及答案解析及解析 一、一年级语文下册阅读理解练习 1．阅读下文，回答问题 阳光像金子，洒遍田野、高山和小河。 田里的禾苗，因为有了阳光，更绿了。山上的小树，因为有了阳光，更高了。河面闪着阳光，小河就像长长的锦缎了。 早晨，我拉开窗帘，阳光就跳进了我的家。 谁也捉不住阳光，阳光是大家的。 阳光像金子，阳光比金子更宝贵。 （1）数一数，这篇短文一共有________个自然段。 （2）因为有了阳光，________更绿了，________更高了，________更亮了，亮得像长长的锦缎。 （3）阳光像________。我也喜欢阳光，因为________。 【答案】（1）5 （2）田里的禾苗 ；山上的小树 ；河面 （3）金子 ；阳光比金子更宝贵 【解析】 2．阅读短文，完成填空，没学过的字写拼音。 世界多美呀 母鸡蹲（dūn）着孵（fū）小鸡，一蹲蹲了许多天。蛋壳里的小鸡先是睡着的，后来它醒了，看见四周黄乎乎的。小鸡想：整个世界都是黄色的呀！ 小鸡用小尖嘴啄蛋壳儿。它啄呀啄呀，啄了很久才啄出一个小小的洞眼。它看见天空蓝湛（zhàn）湛的，树木绿茵茵的，小河是碧澄（chénɡ）澄的。 原来世界（shìjiè）这么美丽呀！小鸡可高兴了。它用翅膀一撑就把蛋壳儿撑破了。它叽叽叽地叫着，慢慢站了起来。 叽叽，叽叽，小鸡是在说：世界多美呀——蓝湛湛的，绿茵茵的，碧澄澄的。 （1）全文共________段，写出文中表示颜色的词________。 （2）照样子从文中找出词语。 黄乎乎、________、________、________ （3）小鸡是怎样诞生的？ 【答案】（1）4；黄乎乎、黄色、蓝湛湛、绿茵茵、碧澄澄 （2）蓝湛湛；绿茵茵；碧澄澄 （3）小鸡用小尖嘴啄蛋壳儿。它啄呀啄呀，啄了很久才啄出一个小小的洞眼。
1实验目的、要求 实验目的： （1）掌握图像滤波的原理与相关方法。 （2）能使用VC++实现若干种图像滤波技术。 实验要求： A部分： （1）使用VC++设计程序：对一幅256级灰度图像，使用邻域平均平滑算法进行滤波。 （2）使用VC++设计程序：对一幅256级灰度图像，使用中值滤波算法进行滤波。 （3）使用VC++设计程序：对一幅256级灰度图像，使用K近邻均值滤波器（KNNF）进行滤波。 B部分： （1）包括A部分全部要求。 （2）使用VC++设计程序：对一幅256级灰度图像，分别使用K近邻中值滤波器（KNNMF）、最小均方差滤波器进行滤波。 （3）使用VC++设计程序：对一幅24位彩色图像，使用矢量中值滤波算法进行滤波。 2实验原理 图像滤波，即在尽量保留图像细节特征的条件下对目标图像的噪声进行抑制，是图像预处理中不可缺少的操作，其处理效果的好坏将直接影响到后续图像处理和分析的有效性和可靠性。由于成像系统、传输介质和记录设备等的不完善，数字图像在其形成、传输记录过程中往往会受到多种噪声的污染。另外，在图像处理的某些环节当输入的像对象并不如预想时也会在结果图像中引入噪声。这些噪声在图像上常表现为一引起较强视觉效果的孤立象素点或象素块。一般，噪声信号与要研究的对象不相关它以无用的信息形式出现，扰乱图像的可观测信息。对于数字图像信号，噪声表为或大或小的极值，这些极值通过加减作用于图像象素的真实灰度值上，在图像造成亮、暗点干扰，极大降低了图像质量，影响图像复原、分割、特征提取、图识别等后继工作的进行。要构造一种有效抑制噪声的滤波机必须考虑两个基本问题：能有效地去除目标和背景中的噪声，同时能很好地保护图像目标的形状、大小及特定的几何和拓扑结构特征。
形成代沟的原因(The Causes of the Generation Gap) Parents say that children do not show them proper respect and obedience, while children complain that their parents do not understand them. This phenomenon is often referred to as the generation gap. What then are the causes of the generation gap?One important cause of the generation gap is the chance that young people have to choose their way of life. In more traditional societies, when children grow up, they are expected to live in the same area as their parents, to marry people that their parents know and agree to, and to continue the family occupation. In modern society, young people often travel a great distance for their education, move out of the family at an early age, marry or live with people whom their parents have never met, and choose occupations different from those of their parents.in the easily changing society, parents often expect their children to do better than they did: to find better jobs, to make more money; and to do all the things that they were unable to do. Often, however, the strong desire that parents have for their children are another cause of the disagreement between them. Often, they discover that they have very little in common with each other. Finally, the speed at which changes take place in modern society is another cause of the gap between the generations. In a traditional culture, elderly people are valued for their wisdom, but in modern society, the knowledge of a lifetime may become out of date overnight. The young and the old seem to live in two very different worlds, separated by different skills and abilities. No doubt, the generation gap will continue to be a future of our life for some time to come. Its causes are rooted in the changes of our society, and in the rapid speed at which society changes.
小学一年级语文阅读题 分析 集团档案编码：[YTTR-YTPT28-YTNTL98-UYTYNN08]
一年级阅读题 阅读方法：1、读短文3遍。2、用序号标段。3、认真读题弄懂题意再做题。4、检查。 建议：孩子每周独立完成2个阅读，对孩子不会认的字家长可以提示,但不要读题。 1.大自然的邮票 春天的树上，长出嫩（nèn）嫩的芽瓣(bàn)。夏天的树上，挂满肥（féi）肥的叶片。秋天的树上，树叶涂（tú）满鲜(xiān)红和金黄。冬天的树下，树叶落地化成土壤（ráng）。落叶是大自然的邮（yóu）票(piào)，把一年四季寄（jì）给你，寄（jì）给我，寄（jì）给大家。 （1）这一段话共有（）句； （2）填空 : a、一年有( )、( ) 、( ) 、( ) 四个季节。 b、春天的树上，芽瓣是( ) ；夏天的树上，叶片是( )；秋天的树叶颜色有( )和( )；冬天的树下，满地是( )。 c、大自然的邮票指 ( )。
2、人有两件宝 人有两件宝，双手和大脑(nǎo)。双手会做工，大脑(nǎo)会思考。用手不用脑(nǎo)，事情做不好。用脑(nǎo)不用手，啥(shá)也做不好。用手又用脑，才能有创造。一切创造靠（kào）劳动，劳动要用手和脑。 （一）这是一首儿歌，一共有（）句话。 （二）填空： （1）人有两件宝是指( )和( )。 做工靠( )，思考靠( )。 （2）做事情要用( )又用( )。这样才能 ( )。 （三）词语搭配： （1）认真地劳动（2）一双手指 辛勤地双手一根手表 勤劳的头脑一只小手 聪明的思考一块手套 3、夏天 初(chū)夏，石榴（liú）花开了。远看，那红色的花朵像一簇（cù）火焰（yàn）。近看，一朵朵石榴（liú）花像一个个小喇叭（lǎba）。
图像平滑处理的空域算法和 频域分析 1 技术要求 对已知图像添加高斯白噪声,并分别用低通滤波器(频域法)和邻域平均法(空域法)对图像进行平滑处理(去噪处理),并分析比较两种方法处理的效果。 2 基本原理 2.1 图像噪声 噪声在理论上可以定义为“不可预测，只能用概率统计方法来认识的随机误差”。实际获得的图像一般都因受到某种干扰而含有噪声。引起噪声的原因有敏感元器件的内部噪声、相片底片上感光材料的颗粒、传输通道的干扰及量化噪声等。噪声产生的原因决定了噪声的分布特性及它和图像信号的关系。 根据噪声和信号的关系可以将其分为两种形式： （1）加性噪声。有的噪声与图像信号g(x,y)无关，在这种情况下，含噪图像f(x,y)可表示为 f(x,y)=g(x,y)+n(x,y) （2）乘性噪声。有的噪声与图像信号有关。这又可以分为两种情况：一种是某像素处的噪声只与该像素的图像信号有关，另一种是某像点处的噪声与该像点及其邻域的图像信号有关，如果噪声与信号成正比，则含噪图像f(x,y)可表示为 f(x,y)=g(x,y)+n(x,y)g(x,y) 另外，还可以根据噪声服从的分布对其进行分类，这时可以分为高斯噪声、泊松噪声和颗粒噪声等。如果一个噪声，它的幅度分布服从高斯分布，而它的功率谱密度又是均匀分布的，则称它为高斯白噪声，一般为加性噪声。
2.2 图像平滑处理技术 平滑技术主要用于平滑图像中的噪声。平滑噪声在空间域中进行，其基本方法是求像素灰度的平均值或中值。为了既平滑噪声又保护图像信号，也有一些改进的技术，比如在频域中运用低通滤波技术。 （1）空域法 在空域中对图像进行平滑处理主要是邻域平均法。这种方法的基本思想是用几个像素灰度的平均值来代替每个像素的灰度。假定有一幅N*N 个像素的图像f(x,y)，平滑处理后得到一幅图像g(x,y)。g(x,y)由下式决定 式中，x,y=0,1,2,…，N-1；S 是(x,y)点邻域中点的坐标的集合，但其中不包括(x,y)点；M 是集合内坐标点的总数。上式说明，平滑化的图像g(x,y)中每个像素的灰度值均由包含在(x,y)的预定邻域中的f(x,y)的几个像素的灰度值的平均值来决定。 （2）频域法 低通滤波法是一种频域处理方法。在分析图像信号的频率特性时，一幅图像的边缘、跳跃部分以及颗粒噪声代表图像信号的高频分量，而大面积的背景区则代表图像信号的低频分量。用滤波的方法滤除其高频部分就能去掉噪声，使图像得到平滑。 由卷积定理可知 其中F(u,v)是含有噪声的图像的傅立叶变换，G(u,v)是平滑处理后的图像的傅立叶变换，H(u,v)是传递函数。选择传递函数H(u,v)，利用H(u,v)使F(u,v)的高频分量得到衰减，得到G(u,v)后再经傅立叶反变换后就可以得到所希望的平滑图像g(x,y)了。根据前面的分析，显然H(u,v)应该具有低通滤波特性，所以这种方法叫低通滤波法平滑化处理。 常用的低通滤波器有如下几种： a.理想低通滤波器 一个理想的二维低通滤波器有一个参数 。它是一个规定的非负的量，叫做理想低通滤波器的截止频率。所谓理想低通滤波器是指以截频 为半径的圆内的所有频率都能无损地通过，而在截频之外的频率分量完全被衰减。理想低通滤波器可以用计算机模拟实 M n m f y x g S n m ∑∈=),(),(),() ,(),(),(G v u F v u H v u ?=0D 0
语文一年级下册试题阅读训练专项训练带答案解析 一、部编版一年级下册语文课外阅读理解 1．我会读短文，按要求答题。 太阳和彩虹 ①刚下过雨，太阳出来了，天上出现了一道七色彩虹，人们都赞美彩虹美丽。彩虹听见了，就骄傲起来，说自己比太阳还美丽。 ②太阳对彩虹说：“你美丽，这是真的。不过，要是没有我，也没有你。”彩虹不相信，反而更加骄傲了。 ③太阳摇摇头，躲进云里去了，彩虹立刻消失了。 （1）短文共有________个自然段，第①自然段有________句话。 （2）“你美丽，这是真的。不过，要是没有我，也没有你。”这句话中的“你”指的是________，“我”指的是________。（填序号） ①太阳②云③彩虹④雨 （3）彩虹不见了，是因为________（填序号） ①彩虹到别的地方去了。 ②人们都赞美彩虹。 ③太阳躲进云里去了。 解析：（1）3；2 （2）③；① （3）③ 【解析】 2．阅读下文，回答问题。 小松鼠，修仓库，一（颗棵）松子儿修一处。挖个坑儿，盖点土，修了多少没记数。想吃松子儿找不到，春天来了怪事出。 一（坐座）仓库一（颗棵）苗，山坡长满小松树。 （1）选择括号内正确的字。 一________（颗棵）松子一________（坐座）仓库 一________（颗棵）苗 （2）想一想，小松鼠埋松子的季节应该是________季，找松子的季节应该是________季。（3）山坡上的小松树是哪里来的？ 解析：（1）颗；座；棵 （2）秋；冬 （3）山坡上的小松树是小松鼠秋天埋下的松子长出来的。 【解析】 3．课外阅读 我问大自然 请你告诉我，大自然，我怎样才能同你交谈？ 我问过小河，可是，派出去的纸船都已沉没（méi mò）。 我问过大山，可是，大山又送回我的呼喊。
江苏科技大学 数字图像处理本科生课程论文 论文题目：图像平滑方法综述与MATLAB实现 完成时间：2018年11月20日 所在专业：信息与计算科学 所在年级：1522105011
图像平滑方法综述与MATLAB 实现 摘要：在图像的生成、传输或变换的过程中，由于多种因素的影响，总要造成图像质量的下降，这就需要进行图像增强。随着图像处理领域的迅速发展，图像平滑作为图像增强的重要环节，也逐渐受到人们的关注。图像平滑的目的为了消除噪声。图像平滑可以在空间域进行，也可以在频率域进行。空间域常用的方法有领域平均法、中值滤波和多图像平均法；在频率域，因为噪声频谱多在高频段，因此可以采用各种形式的低通滤波方法进行平滑处理。 关键词：图像平滑；消除噪声；领域平均法；中值滤波；低通滤波法……… 1 研究背景 总所周知，实际获得的图像在形成、传输接收和处理的过程中，不可避免地存在着外部干扰和内部干扰，如光电转换过程中敏感元件灵敏度的不均匀性、数字化过程中的量化噪声、传输过程中的误差以及人为因素等，均会使图像质量变差，需要进行图像的平滑处理。 图像平滑是一种实用的熟悉图像处理技术，一个较好的平滑处理方法应该既能消除图像噪声，又不使图像边缘轮廓和线条变模糊，这就是研究数字图形平滑处理要追求的目标。 2.主要理论概况 2.1 邻域平均法 邻域平均法就是对含噪声的原始图像(,)f x y 的每一个像素点取一个邻域S ，计算S 中所有像素灰度级的平均值，作为邻域平均处理后的图像(,)g x y 的像素值。即 (,)1 (,)(,)i j S g x y f i j M ?= ∑ 式中，S 是预先确定的邻域（不包括(,)x y ），M 为邻域S 中像素的点数。 图像平滑的直观效果是图像的噪声得以消除或衰减，但同时图像变得比以前模糊了，特别是图像边缘和细节部分，并且所选的邻域半径越大平滑效果越强，图像就越模糊。 为了减轻这种效应，可以采用阈值法，即根据下列准则对图像进行平滑：