人工智能(Artificial Intelligence)

人工智能(英語:artificial intelligence,縮寫為AI)亦稱智械、機器智能,指由人製造出來的機器所表現出來的智能。是一個非常廣泛的電腦科學研究領域,主要在創建電腦系統,以執行所需人工智能的任務。

優譯堂Ulatus在人工智能領域具有深厚的專業知識,擁有人工智能和相關學科,如自動編程、機器學習、電腦視覺、自動化技術等相關學科的學科專業翻譯師、雙語校對以及英語母語學科專家編輯,且已翻譯了大量此領域相關的科研論文,並協助諸多學術作者成功在國際知名SCI/EI/SSCI期刊上發表高水準論文。

  • 原始文稿
  • 翻譯後的檔案
  • 雙語核對後的檔案
  • 編修後的檔案
  • 完稿

年齡估計算法實現分層方法(圖 10)。首先,將輸入片段分為三個年齡組: 18 歲以下,18—45 歲,45 歲以上。其次,將這一步的結果細分為七個較小的組,每個小組限制為一個十年。因此,多類別分類的問題被減至一組二進制「一對全部」分類器(BC)。然後,分類器會根據關聯的類別對圖像進行排名,並透過分析這些軼直方圖來取得最終決定。

這些 BC 通過使用兩級方法構建。如前所述,第一次轉移至自適應特徵空間後,使用具有 RBF 核心的支持向量機來分類圖像。

針對亮度特性輸入片段對輸入片段進行預處理,以便對齊並轉換為均勻的比例。此預處理步驟包含顔色-空間轉換和縮放,這兩項作業與性別辨識算法中使用的作業類似。計算每個顏色分量的特徵,並對它們進行組合以形成統一的特徵向量。

訓練和測試要求具備足夠大的顔色圖像數據庫。我們將最先進的 MORPH 和 FG-NET 圖像數據庫與我們自己從不同來源獲得的圖像數據庫結合在一起,包括 10,500 張人臉圖像。利用AadBoost 臉部偵測演算法自動偵測圖像中的臉孔。

共使用 7000 張圖像來訓練和測試第一階段的年齡分類演算法。利用 144 個自適應特徵建立了三個 BC。

第一階段的分類結果顯示,年輕臉孔的準確度為 82%,中年臉孔的準確度為 58%,老年臉孔的準確度為 92%。三個年齡組別的總體年齡分類準確度為 77.3%。

第二階段的 BC 的建構方式與第一階段相同 (如上所述)。圖 11 顯示建議的演算法第一階段的年齡估計視覺範例。

翻譯: 您學科領域的翻譯師翻譯您的原稿

The age estimation algorithm realises hierarchical approach (fig. 10). First of all input fragments are divided for three age groups: smaller than 18 years old, from 18 - 45 years old and bigger than 45 years old. Afterwards the results of this in the first stage are more subdivided to seven newer groups with each limiting to one single decade. Thus the problem of multiclass classification is therefore reduced to a set of binary ‘one-against-all’ classifiers (BC).These classifiers calculate: ranks of each of the analyzed class. The total decision is obtained then by the analysis the previously received histogram of ranks.

These BCs are construction is applied with the transitioning to adaptive feature space, equal to this described earlier, and support vector machines classification of images with RBF kernel.

Input fragments were preprocessed for their luminance characteristics to align and to transform them to uniformal scale. Preprocessing includes color-space transformation and scaling, both similar to that of gender recognition algorithm. Features, calculated for each colour component, are combined to form a uniform featured vector.

Training require a huge enough coloring image database: We used state-of-the-art image databases MORPH and FG-NET with our own image database, gathered from some sources, which comprised of 10500 faces. Faces on the images were detected automatically by AdaBoost face detection algorithms.

A total number of seven thousand images were used for age classification algorithm training and testing on the first stage. 3 binary classifiers were made utilizing 144 adaptive features each of.

Classification results on the first stage are: 82 % accuracy for young age, 58 % accuracy for middle age and 92 % accuracy for senior age. Age classification rate in a three age division problem – 77.3 %.

Binary classifiers of the second stage were constructed equal to the first stage described above. A visual example of age estimation by the proposed algorithm on its first stage is presented in figs. 11.

雙語核對:雙語核對師依照原文檢查譯文是否正確,並修正錯誤

The age estimation algorithm realises hierarchical approach (fig. 10). First of all input fragments are divided into1for three age groups: smaller than 18 years old, from 18 - 45 years old and bigger than 45 years old. Afterwards the results of this in the first stepage are more subdivided to seven smallernewer groups, with each limiteding to one single decade. Thus the problem of multiclass classification is therefore reduced to a set of binary ‘one-against-all’ classifiers (BC).These classifiers calculate: ranks of each of the analyzed class. The total decision is obtained then by the analysis the previously received rank histograms of ranks.

These BCs are constructioned using a two-level approach. After is appliedfirst with the 2transitioning to adaptive feature space, asequal to this3 described earlier, and support vector machines classification of images with RBF kernel.

Input fragments were preprocessed for their luminance characteristics to align and to transform them to uniformal scale. This pPreprocessing step includes color-space transformation and scaling, both operations similar to those used inthat of a gender recognition algorithm. Features, calculated for each colour component, are combined to form a uniform featured4 vector.

Training and testing5 require a sufficientlyhuge largeenough coloring image database: We used state-of-the-art image databases MORPH and FG-NET with our own image database, gathered from some6many sources, which comprised of 10,500 face images. Faces on the images were detected automatically by AdaBoost face detection algorithms.

A total number of seven thousand images were used for age classification algorithm training and testing on the first stage. 3 binary classifiers were made utilizing 144 adaptive features each of.

Classification results on the first stage are: 82 % accuracy for young age, 58 % accuracy for middle age and 92 % accuracy for senior age. Age classification accuracyrate in a three age division problem – 77.3 %.

Binary classifiers of the second stage were constructed equalsame to the first stage described above. A visual example of age estimation by the proposed algorithm on its first stage is presented in figs. 11.  

  1. [準確度]為明確起見,更改了介詞。
  2. [清晰度]提升清晰度和用詞。
  3. [可讀性]修正字面翻譯,提高可讀性。
  4. [術語選擇][雙語學科專家]使用學術界使用的正確術語。
  5. [漏譯]漏譯“and testing” 。
  6. [誤譯]更正誤譯。根據原文將"some"改為 "many"。

編修:英文母語編修師改善文章整體的流暢度與呈現方式

The proposed age estimation algorithm realisesrealizes hierarchical approach (figFig. 10). First of all, the input fragments are divided into1for three age groups: smallerless than 18 years old, from 18 - 45 years old and biggermore than 45 years old. Afterwards2Second, the results of this in the firstfirst stepage are more further subdivided tointo seven smallernewer groups, with each limiteding to onea single decade. ThusThis reduces the problem oforiginal multiclass classification is therefore reduced problem to a set of binary one-against-all3 classifiers (BC). These classifiers calculate:Each classifier then ranks of each of the analyzedthe images based on the associated class. The total decision is, and the final decisions are obtained then by the analysis the previously received analyzing these rank histograms of ranks.       

These BCs are constructioned4 using a two-level approach. After is appliedfirst with the transitioning to an adaptive feature space, asequal to this5 described earlier, and the images are classified using support vector machines classification of images with radial basis function (RBF) kernels.6

The Iinput fragments wereare preprocessed forto align and transform their luminance characteristics to align and to transform them to uniformala uniform scale. This pPreprocessing step includes color-space transformation and scaling, both operations similar to those used inthat of athe 7gender recognition algorithm. Features, are calculated for each colourcolor component, are and combined to form a uniform featured8 vector.

Training and testing9 require a sufficientlyhuge largeenough coloring image database: We used. Here, we combined the state-of-the-art image databases MORPH and FG-NET image databases with our own image database, gathered from some10many sources, which comprised of many sources and comprising 10,500 face images. Faces on. The faces in the images were detected automatically by the AdaBoost face detection algorithms.

A total number of seven thousand7000 images were used for to train and test the first stage of the age classification algorithm training and testing on the first stage. 3 binary classifiers. Three BCs were made utilizingcreated, each with11 144 adaptive features each of.

Classification results on the The first-stage are:classification results showed 82 % accuracy for young agefaces, 58 % accuracy for middle age-aged faces, and 92 % accuracy for seniorelderly faces. The overall age. Age classification accuracyrate in afor the three age division problem –categories was 77.3 %.12

Binary classifiers of tThe second-stage BCs were constructed in the equalsame tomanner as the first stage (described above. A). Fig. 11 shows a visual example of age estimation by the first stage of the proposed algorithm on its first stage is presented in figs. 11..

  1. [準確度]為明確起見,更改了介詞。
  2. [用字遣詞]改善用詞。
  3. [標點符號]改用雙引號。
  4. [清晰度]提升清晰度和用詞。
  5. [可讀性]修正字面翻譯,提高可讀性。
  6. [明確]為維持文意清晰更改寫法。
  7. [語法]修正語法錯誤。
  8. [術語選擇][雙語學科專家]使用學術界使用的正確術語。
  9. [漏譯]漏譯“and testing” 。
  10. [誤譯]更正誤譯。根據原文將"some"改為 "many"。
  11. [一致性][風格]前面用過縮寫了,為維持一致這邊繼續使用
  12. [可讀性]為提高清晰度和可讀性,補充用詞並改寫。

完稿:翻譯完成品準時遞交給客戶

The proposed age estimation algorithm realizes hierarchical approach (Fig. 10). First, the input fragments are divided into three age groups: less than 18 years old, 18–45 years old, and more than 45 years old. Second, the results of this first step are further subdivided into seven smaller groups, each limited to a single decade. This reduces the original multiclass classification problem to a set of binary “one-against-all” classifiers (BC). Each classifier then ranks the images based on the associated class, and the final decisions are obtained by analyzing these rank histograms.

These BCs are constructed using a two-level approach. After first transitioning to an adaptive feature space, as described earlier, the images are classified using support vector machines with radial basis function(RBF) kernels.

The input fragments are preprocessed to align and transform their luminance characteristics to a uniform scale. This preprocessing step includes color-space transformation and scaling, both operations similar to those used in the gender recognition algorithm. Features are calculated for each color component and combined to form a uniform feature vector.

Training and testing require a sufficiently large color image database. Here, we combined the state-of-the-art MORPH and FG-NET image databases with our own image database, gathered from many sources and comprising 10,500 face images. The faces in the images were detected automatically by the AdaBoost face detection algorithms.

A total of 7000 images were used to train and test the first stage of the age classification algorithm. Three BCs were created, each with 144 adaptive features.

The first-stage classification results showed 82% accuracy for young faces, 58% accuracy for middle-aged faces, and 92% accuracy for elderly faces. The overall age classification accuracy for the three age categories was 77.3%.

The second-stage BCs were constructed in the same manner as the first stage (described above). Fig. 11 shows a visual example of age estimation by the first stage of the proposed algorithm.