【正文】 。 、法規(guī)。 . 、消耗量定額及有關(guān)規(guī)定。 畢業(yè) 設(shè)計(jì)成果應(yīng)包括： ① 標(biāo)題 ② 摘要 ③ 關(guān)鍵詞 ④ 目錄 ⑤ 正文 ⑥ 參考文獻(xiàn)等部分。 畢業(yè)設(shè)計(jì)成果的要求 畢業(yè)設(shè)計(jì)是學(xué)生在校學(xué)習(xí)的最后階段，是培養(yǎng)學(xué)生綜合運(yùn)用所學(xué)知識(shí)，發(fā)現(xiàn)、提出、分析和解決實(shí)際問題，鍛煉實(shí)踐能力的重要環(huán)節(jié)，是對(duì)學(xué)生實(shí)際工作能力的系統(tǒng)訓(xùn)練和考察過程。 2)參加畢業(yè)答辯，要求學(xué)生應(yīng)該對(duì)畢 業(yè)答辯做好充分的準(zhǔn)備，在答辯時(shí)，首先簡要的陳述畢業(yè)設(shè)計(jì)的課題名稱、設(shè)計(jì)要求、設(shè)計(jì)思路及設(shè)計(jì)過程和設(shè)計(jì)成果。 （ 3） 畢業(yè)答辯階段 畢業(yè)答辯是學(xué)生畢業(yè)設(shè)計(jì)的總結(jié)和回顧，要求學(xué)生做好下列工作： 1)編寫畢業(yè)設(shè)計(jì)說明書，內(nèi)容包括對(duì)畢業(yè)設(shè)計(jì)任務(wù)書的理解、畢業(yè)設(shè)計(jì)的總體思路、在設(shè)計(jì)過程中的參考資料及對(duì)有關(guān)問題的處理意見和畢業(yè)設(shè)計(jì)的體會(huì)。 3)根據(jù)圖紙和相關(guān)資料，編制投標(biāo)文件。 （ 2） 畢業(yè)設(shè)計(jì)階段 1) 根據(jù)畢業(yè)設(shè)計(jì)任務(wù)書的要求，準(zhǔn)備相關(guān)資料。包括設(shè)計(jì)規(guī)范、施工規(guī)范、預(yù)算定額、工程估價(jià)表、標(biāo)準(zhǔn)圖集、相關(guān)的造價(jià)文件和有關(guān)材料的市 場價(jià)格等。 設(shè) 計(jì) 工 作 基 本 要 求 畢業(yè)設(shè)計(jì)程序的要求 （ 1） 設(shè)計(jì)準(zhǔn)備階段 畢業(yè)設(shè)計(jì)題目選定后，應(yīng)由指導(dǎo)教師向?qū)W生下達(dá)畢業(yè)設(shè)計(jì)指導(dǎo)書。 6)報(bào)價(jià)單 （ 2）商務(wù)標(biāo)編制：計(jì)算工程量；確定綜合單價(jià)；進(jìn)行投標(biāo)報(bào)價(jià)；編制投標(biāo)報(bào)價(jià)匯總表，和各類投標(biāo)報(bào)價(jià)單價(jià)表。鼓勵(lì)學(xué)生在完成手工預(yù)算的全部 工作的基礎(chǔ)上，另用工程造價(jià)編制軟件對(duì)手算的結(jié)果進(jìn)行校審復(fù)核。 (3)工程進(jìn)度款，每月末按形象進(jìn)度延遲一個(gè)月?lián)芨叮? （ 4）不足部分通過銀行貸款補(bǔ)足，貸款利率 =12%（單利）； （ 5）不考慮保修金的留設(shè)。施工現(xiàn)場狹小，應(yīng)考慮合理利用現(xiàn) 場空間。 5）本工程結(jié)構(gòu)形式：框架結(jié)構(gòu)，抗震設(shè)防烈度： 6 度 6）本工程建筑等級(jí)：三級(jí)；耐火等級(jí)：為二級(jí)。 line detector. (c) Result of thresholding image. (b) produced the strongest responses in Fig. (b). In order to determine which lines best fit the mask, we simply threshold this image. The result of using a threshold equal to the maximum value in the image is 7 shown in Fig. (c).The maximum value is a good choice for a threshold in applications such as this because the input image is binary and we are looking for the strongest responses. Figure (c) shows in white all points that passed the threshold test. In this case, the procedure extracted the only line segment that was one pixel thick and oriented at 450 (the other ponent of the image oriented in this direction in the top, left quadrant is not one pixel thick). The isolated points shown in Fig. (c) are points that also had similarly strong responses to the mask. In the original image, these points and their immediate neighbors are oriented in such as way that the mask produced a maximum response at those isolated locations. These isolated points can be detected using the mask in Fig. (a) and then deleted, or they could be deleted using morphological erosion, as discussed in the last chapter. Edge Detection Although point and line detection certainly are important in any discussion on segmentation, edge detection is by far the most mon approach for detecting meaningful discontinuities in gray level. In this section we discuss approaches for implementing first and secondorder digital derivatives for the detection of edges in an image. We introduced these derivatives in Section in the context of image enhancement. The focus in this section is on their properties for edge detection. Some of the concepts previously introduced are restated briefly here for the sake continuity in the discussion. Basic formulation Edges were introduced informally in Section . In this section we look at the concept of a digital edge a little closer. Intuitively, an edge is a set of connected pixels that lie on the boundary between two regions. However, we already went through some length in Section to explain the difference between an edge and a boundary. Fundamentally, as we shall see shortly, an edge is a local concept whereas a region boundary, owing to the way it is defined, is a more global idea. A reasonable definition of edge requires the ability to measure graylevel transitions in a meaningful way. We start by modeling an edge intuitively. This will lead us to a formalism to 8 which meaningful transitions in gray levels can be measured. Intuitively, an ideal edge has the properties of the model shown in Figure (a). An ideal edge according to this model is a set of connected pixels (in the vertical direction here), each of which is located at an orthogonal step transition in gray level (as shown by the horizontal profile in the figure). In practice, optics, sampling, and other image acquisition imperfections yield edges that are blurred, with the degree of blurring being determined by factors. such as the quality of the image acquisition system, the sampling rate, and illumination conditions under which the image is acquired. As a result, edges are more closely modeled as having a ramplike profile, such as the one shown in Figure (b). The slope of the ramp is inversely proportional to the degree of blurring in the edge. In this model. we no longer have a thin (one pixel thick) path. Instead, an edge point now is any point contained in the ramp, and an edge would then be a set of such points that arc connected. The thickness of the edge is determined by the length of the ramp. as it transitions from an initial to a final gray level. This length is determined by the slope, which. in turn is determined by the degree of blurring. This makes sense: Blurred edges tend to be thick and sharp edges tend to be thin. Figure (a) shows the image from which the closeup in Fig. (b) was extracted. Figure (b) shows a horizontal graylevel profile of the edge between the two regions. This figure also shows the first and second derivatives of the graylevel profile. The first derivative is positive at the points of transit