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icpr是什么 icpr的翻译

作者: 用户投稿
2023-12-20 07:26:01
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ICPR（International Conference on Pattern Recognition）是一个非常重要的国际性学术会议，它涵盖了模式识别、计算机视觉和图像处理等领域。

1. 历史：ICPR由IAPR（国际模式识别协会）主办，自1970年以来已经举办了20届，每隔2-4年就会举行一次。 icpr是什么 icpr的翻译

2. 会议内容：ICPR会议涵盖了模式识别、计算机视觉、图像处理、机器学习、信号处理、数据挖掘、人工智能等多个领域，旨在推进各领域的前沿研究成果。

3. 投稿形式：ICPR会议接受原创性研究论文、综述文章、应用报告、系统报告、短文介绍、技术报告等多种形式的投稿。

4. 示例代码：是一段Python代码，用于实现图像分类：

python # import the necessary packages from sklearn.neighbors import KNeighborsClassifier from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report from imutils import paths import numpy as np import argp import imutils import cv2 import os # construct the argument p and p the arguments ap = argp.ArgumentPr() ap.add_argument("-d", "--dataset", required=True, help="path to input dataset") args = vars(ap.p_args()) # grab the list of images that we'll be describing print("[INFO] loading images...") imagePaths = list(paths.list_images(args["dataset"])) # initialize the data matrix and labels list data = [] labels = [] # loop over the input images for (i, imagePath) in enumerate(imagePaths): # load the image and extract the class label (assuming that our # path as the format: /path/to/dataset/{class}.{image_num}.jpg image = cv2.imread(imagePath) label = imagePath.split(os.path.sep)[-1].split(".")[0] # construct a feature vector raw pixel intensities, then update # the data matrix and labels list features = image.flatten() data.append(features) labels.append(label) # show an update every 1,000 images if i >0 and i % 1000 == 0: print("[INFO] processed {}/{}".format(i, len(imagePaths))) # encode the labels, converting them from strings to integers le = LabelEncoder() labels = le.fit_transform(labels) # partition the data into training and testing splits, using 75% # of the data for training and the remaining 25% for testing (trainX, testX, trainY, testY) = train_test_split(data, labels, test_size=0.25, random_state=42) # train and evaluate a k-NN classifer on the raw pixel intensities model = KNeighborsClassifier(n_neighbors=3) model.fit(trainX, trainY) print(classification_report(testY, model.predict(testX), target_names=le.cl_))

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