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Definition Your ability to see your will made manifest in the world. Explanation If you want to do something, how easy is it for you to do it. To naively put this in informatics terms, you could think of it as the weight associated with a person’s node in a network. Because we live in a world of systems (political, legal, cultural, organisational, social), a lot of that is tied up in how those system are designed. And a lot of that is dependent on who they were designed for, or by. Because, int ...

Challenge of object detectionnot fully observed, scale distraction, illumination changes. Basic conceptsbounding box and class labels, intersection of union (IoU) See more at: Evaluationg Mateics 2D Object Detection Steps (inference)feature extractor, computationally expensive, lower widthe and height, greater depth Prior bounding boxes, or anchor bounding boxes, assume bounding boxes, then guess where and how large they are. centroid location (where), box dimensions (size) Every pixel in ...

Since most material has been covered in previous blogs, I will go through this lecture contents in brief. Brief overview More ContentsActivation function example Task example For classification we have: Softmax output layer Cross-entropy Loss function For regression we have: Linear output layer Mean square error lLoss function Supplement reading LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep learning.” nature 521.7553 (2015):436-444. A Comprehensive Guide to Convolut ...

Image Features: A General Process Step 1 - Feature Detection: identify distinctive points in our images. We call these points features. Step 2 - Feature Description: associate a descriptor for each feature from its neighborhood. Step 3 - Feature Matching: we use these descriptors to match features across two or more images. Feature DetectionFeature Define Features: Points of interest in an image defined by its image pixel coordinates [u, v]. Points of interest should have the following ch ...

Introduction of computer visionComputer vision is a field of artificial intelligence (AI) that enables computers derive meaningful information from digital images, videos and other visual inputs obtained by a camera. Bucket of photons Photons converted to electrons Shift electrons along row for readout The readout on the device will translate the analog signal to either grayscale images or RGB images Image Formation pinhole camera, optical centre, focal length Stereo Cameras Image filter ...

Time domainSound is a wave of pressure travelling through a medium, such as air. We can plot the variation in pressure (captured by microphone) against time to visualise the waveform. Sound sourceAir flow from the lungs is the power source for generating a basic source of sound either using the vocal folds or at a constriction made anywhere in the vocal tract. somehthing about pressure with our vocal folds, the air flow is slow, its only the power source of sound, the pressure change is the ke ...

WaveformThe waveform and a definition of the fundamental period. Fundamental period is the lowest frequency of a vibration object. Types of waveformSimple, complex, periodic, aperiodic, transient, and continuous waveforms. SpectrumThe spectrum, its spectral envelope, and harmonics (Frequency components of a complex periodic sound, peaks in spectrum, $H_1$ has the same frequency value to $F_0$, every $H$ is multiple of $H_1$), and formant. SpectrogramA 3-dimensional figure plotting amount o ...

Introduction to the International Phonetic AlphabetA set of symbols with which any language can be transcribed. Interactive IPA Chart. Vocal anatomyWe use a lot more than just our mouth to produce speech ConsonantsVoice, place, manner which (voice or voiceless) -> where (at voice tract) -> how strong (constriction level) The first consonant chart contains symbols for consonants produced with the pulmonic airstream mechanism. Non-pulmonic consonants includes symbols representing ...

Sensors for robot perception Sensors: Sensor is a device that measures or detects a property of the environment, or changes to a property. Categorization of sensors: Exteroceptive (extero or surroundings), Proprioceptive (proprio or internal). Type of Sensor Feature Weakness Future Trend More words Essential for robot to perceive environment with its rich semantics. ? HD, wide dynamic ranges Comparison Metrics: Resolution, Field of view, Dynamics range This simulates human binocula ...

待办清单： 网络整体：x_net.py 特征融合网络层：x_net_fusion_layers.py 网络配置文件：xnet 开发日志： 日志01使用pycharm的SSH连接docker，不如设置pycharm的编译器为docker中的python。这样做的优势有三： 不需要通过ssh传输图像，pycharm的运行速度更快。 因为docker同步了工作目录，不需要使用pycharm来同步，节省时间。 不需要重新弄配置pycharm。 需要修改的有两个位置，一个是configs中的配置文件，一个是mmdet3d中的models相关文件。参照官方教程：教程 1: 学习配置文件和教程 4: 自定义模型 整个框架的思路是从配置文件中去找对应的模型，程序会在一开始把模型全部注册到一个位置，然后使用配置文件中的type关键字去搜索，然后使用其他的作为参数输入，具体需要什么参数由模型决定。 阅读代码的时候发现，框架对自编码器有一定的支持，这一点在完成主干网络的构建后深入调查一下。 日志02 注意到代码中 fusion layer 本来是分出来的，但是因为代码复用的问题，实际上并没有分出来， ...

说明 本页面无手机端适配，强制缩放阅读。 使用纯html格式，保存教学用ppt，添加了部分个人笔记。 目录工作正常，可以跳转。 b{color:rgba(0,0,0,0.75)} PaddlePaddle概述 PaddlePaddle概述 PaddlePaddle概述 PaddlePaddle简介 为什么要学PaddlePaddle 什么是PaddlePaddle PaddlePaddle优点 PaddlePaddle缺点 国际竞赛获奖情况 行业应用 课程概览 学习资源 知识讲解 什么是PaddlePaddle Ø PaddlePaddle（Parallel Distributed Deep Learning，中文名飞桨）是百度公司推出的开源、易学习、易使用的分布式深度学习平台 Ø 源于产业实践，在实际中有着优异表现 Ø 支持多种机器学习经典模型 知识讲解 为什么学习Pad ...

说明 本页面无手机端适配，强制缩放阅读。 使用纯html格式，保存教学用ppt，添加了部分个人笔记。 目录工作正常，可以跳转。 b{color:rgba(0,0,0,0.75)} TensorFlow概述 Tensorflow概述 Tensorflow概述 Tensorflow简介 什么是Tensorflow Tensorflow的特点 Tensorflow的发展历史 Tensorflow体系结构 体系结构概述 单机模式与分布式 后端逻辑层次 基本概念 张量 数据流 操作 图和会话 变量和占位符 Tensorflow安装 案例1：快速开始 案例2：张量相加 TensorFlow简介 知识讲解 什么是Tensorflow • TensorFlow由谷歌人工智能团队谷歌大脑（Google Brain）开发和维护的 开源深度学习平台，是目前人工智能领域主流的开发平台，在全世界有着广泛的用户群体。 知识讲解 Tenso ...

注意本教程OpenCV版本过旧。 说明 本页面无手机端适配，强制缩放阅读。 使用纯html格式，保存教学用ppt，添加了部分个人笔记。 目录工作正常，可以跳转。 b{color:rgba(0,0,0,0.75)} 计算机视觉基础 计算机视觉基础 计算机视觉基础 计算机视觉概述 计算机视觉的应用 什么是计算机视觉 计算机视觉相关学科 人眼成像原理 计算机成像原理 数字图像处理基础 灰度级与灰度图像 图像采样与分辨率 彩色图像与色彩空间 颜色空间变化 常用图像处理技术 计算机视觉的应用 什么是计算机视觉 计算机视觉与人工智能 计算机视觉概览 知识讲解 什么是计算机视觉 • 计算机视觉在广义上是和图像相关的技术总称。包括图像的采集获取，图 像的压缩编码，图像的存储和传输，图像的合成，三维图像重建，图像增强，图像修复，图像的分类和识别，目标的检测、跟踪、表达和描述，特征提取，图像的显示和输出等等。 • 随着计算机视觉在各种场景的应用和发展，已有的图像技术也在不断的更 新和扩展。 ...

注意：全部代码为PaddlePaddle1版本的代码 Helloworld1234567891011121314# helloworld示例import paddle.fluid as fluid# 创建两个类型为int64, 形状为1*1张量x = fluid.layers.fill_constant(shape=[1], dtype="int64", value=5)y = fluid.layers.fill_constant(shape=[1], dtype="int64", value=1)z = x + y # z只是一个对象,没有run,所以没有值# 创建执行器place = fluid.CPUPlace() # 指定在CPU上执行exe = fluid.Executor(place) # 创建执行器result = exe.run(fluid.default_main_program(), fetch_list=[z]) #返回哪个结果print(result) # result为多维张量 张量操作 ...

注意：此代码全部为TensorFlow1版本。 查看Tensorflow版本1234567891011from __future__ import absolute_import, division, print_function, unicode_literals# 导入TensorFlow和tf.kerasimport tensorflow as tffrom tensorflow import keras# 导入辅助库import numpy as npimport matplotlib.pyplot as pltprint(tf.__version__) Helloworld程序1234567# tf的helloworld程序import tensorflow as tfhello = tf.constant('Hello, world!') # 定义一个常量sess = tf.Session() # 创建一个sessionprint(sess.run(hello)) # 计算sess.close() 张量相加1234567891011121314 ...