What is Texture.pdf

Jan 16, 20230 likes37 views

The document discusses texture analysis in computer vision. It begins by asking what texture is and whether objects themselves can be considered textures. It then outlines several statistical and Fourier approaches to texture analysis, citing specific papers on texture energy measures, texton theory, and using textons to model materials. Deep convolutional neural networks are also discussed as being able to recognize and describe texture through learned filter banks. The concept of texels is introduced as low-level features that make up texture at different scales from edges to shapes. The document hypothesizes that CNNs are sensitive to texture because texture repeats across images while object shapes do not, and that CNNs act as texture mappers rather than template matchers. It also questions whether primary visual cortex

Texture Analysis
Structure Analysis…? Not for Real World

Texture Analysis
Statistical Approach!
Law’s Texture Energy Measures (1987)

Texture Analysis
Statistical Approach!
a breif outline of the texton theory of human vision (1980)

Texture Analysis
Statistical Approach!
representing and recognizing the visual appearnace of materials using three-dimensional textons (2000)

Texture Analysis
Statistical Approach!
TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context

Texture Analysis
Statistical Approach!
Deep
fi
lter banks for texture recognition, description, and segmentation (2016)
~ ??… SIFT ? CNN? ??? Texture? ?? “?? ??”?? ?

Texture Analysis
Fourier Approach
https://darkpgmr.tistory.com/171

Texel?? ??? ?????
Texel ~= Low Level Feature
…
Texel Texel…?
??? Edge ???, ?? Shape Matching
~> ??? Constraint? ???, ? Robust? ? ???!
CNN == Statistical Approach + Bag of (Texton) Generator

Why CNN is Texture sensitive?
<CDH ??>
Texture? ? ???/?? ????? ? ??, Object Shape? ??? ??
== Texture? Object ??? ?????, Shape? Object ??? ??
== Shape Extractor? ??? ????? ?? ?? ???, Texture? ?? ???? ?? ??
??

Why CNN is Texture sensitive?
Texture Kernel (Low Level)
Object Kernel (High Level)
Many Activation Middle Activation
One Activation No Activation
Maybe CNN is Texture Mapper… Not Template Matching
??

V1 is Fixed?
plasticity in primary visual cortex

V1 is not Fixed
By age
https://www.dana.org/article/primary-visual-cortex-changes-throughout-life/
Development of Glutamatergic Proteins in Human Visual Cortex across the Lifespan (2017 Neuroscience)
V1 Protein developed through lifespan

V1 is not Fixed
By Seeing Phase
https://www.dana.org/article/primary-visual-cortex-changes-throughout-life/
Development and Plasticity of the Primary Visual Cortex

Also, vision is not robust
Scale and translation-invariance for novel objects in human vision (Nature)

Scale and translation-invariance for novel objects in
human vision (Nature)
Scale Exp
- Target / Test : Same or Not
Also, vision is not robust in size

Even not N-shot
Scale and translation-invariance for novel objects in human vision (Nature)
Non Korean Korean

References
Texture De
fi
nition
- https://courses.cs.washington.edu/courses/cse455/09wi/Lects/lect12.pdf
- https://blogs.unity3d.com/kr/2019/02/14/procedural-stochastic-texturing-in-unity/

This document discusses active learning techniques called Deep Badge Active Learning. It proposes using gradient embeddings to represent samples and k-means++ initialization for sample selection. Specifically, it uses the gradient embedding for feature representation, then performs k-means++ initialization to select samples by finding those with the maximum 2-norm and those farthest from existing samples, adding them to the set iteratively. This aims to select a diverse set of samples, similar to how binary search works. The technique could improve over entropy-based and core-set selection approaches for active learning with convolutional neural networks.

Neural Radiance FieldDong Heon Cho

Neural Radiance Fields (NeRF) represent scenes as neural networks that map 5D input (3D position and 2D viewing direction) to a 4D output (RGB color and opacity). NeRF uses an MLP that is trained to predict volumetric density and color for a scene from many camera views. Key aspects of NeRF include using positional encodings as inputs to help model view-dependent effects, and training to optimize for integrated color and density values along camera rays. NeRF has enabled novel applications beyond novel view synthesis, including pose estimation, dense descriptors, and self-supervised segmentation.

2020 > Self supervised learningDong Heon Cho

All about that poolingDong Heon Cho

The document discusses various pooling operations used in image processing and convolutional neural networks (CNNs). It provides an overview of common pooling methods like max pooling, average pooling, and spatial pyramid pooling. It also discusses more advanced and trainable pooling techniques like stochastic pooling, mixed/gated pooling, fractional pooling, local importance pooling, and global feature guided local pooling. The document analyzes the tradeoffs of different pooling methods and how they can balance preserving details versus achieving invariance to changes in position or lighting. It references several influential papers that analyzed properties of pooling operations.

Background elimination reviewDong Heon Cho

This document discusses background elimination techniques which involve three main steps: object detection to select the target, segmentation to isolate the target from the background, and refinement to improve the quality of the segmented mask. It provides an overview of approaches that have been used for each step, including early methods based on SVM and more recent deep learning-based techniques like Mask R-CNN that integrate detection and segmentation. The document also notes that segmentation is challenging without object detection cues and discusses types of segmentation as well as refinement methods that use transformations, dimension reduction, and graph-based modeling.

Transparent Latent GANDong Heon Cho

1. TL-GAN matches feature axes in the latent space to generate images without fine-tuning the neural network. 2. It discovers correlations between the latent vector Z and image labels by applying multivariate linear regression and normalizing the coefficients. 3. The vectors are then adjusted to be orthogonal, allowing different properties to be matched while labeling unlabeled data to add descriptions.

Image matting atocDong Heon Cho

Image matting is the process of separating the foreground and background of an image by assigning each pixel an alpha value between 0 and 1 indicating its transparency. Traditionally, matting uses a trimap to classify pixels as foreground, background, or uncertain. Early sampling-based methods calculated alpha values based on feature distances of closest foreground and background pixels. More recent approaches use deep learning, where the first deep learning matting method in 2016 took local and non-local information as input, and the 2017 Deep Image Matting method used an RGB image and trimap as input in a fully deep learning framework.

Multi object Deep reinforcement learningDong Heon Cho

This document discusses multi-objective reinforcement learning and introduces Deep OLS Learning, which combines multi-objective learning with deep Q-networks. It presents Deep OLS Learning with Partial Reuse and Full Reuse to handle multi-objective Markov decision processes by finding a convergence set of policies that optimize multiple conflicting objectives, such as maximizing server performance while minimizing power consumption. The approach is evaluated on multi-objective versions of mountain car and deep sea treasure problems.

Multi agent reinforcement learning for sequential social dilemmasDong Heon Cho

This document summarizes research on multi-agent reinforcement learning in sequential social dilemmas. It discusses how sequential social dilemmas extend traditional matrix games by adding temporal aspects like partial observability. Simulation experiments are described where agents learn cooperative or defective policies for tasks like fruit gathering and wolfpack hunting in a partially observable environment. The agents' learned policies are then used to construct an empirical payoff matrix to analyze whether cooperation or defection is rewarded more, relating the multi-agent reinforcement learning results back to classic social dilemmas.

Multi agent SystemDong Heon Cho

This document discusses multi-agent systems and their applications. It provides examples of multi-agent systems for spacecraft control, manufacturing scheduling, and more. Key points: - Multi-agent systems consist of interacting intelligent agents that can cooperate, coordinate, and negotiate to achieve goals. They offer benefits like robustness, scalability, and reusability. - Challenges include defining global goals from local actions and incentivizing cooperation. Games like the prisoner's dilemma model social dilemmas around cooperation versus defection. - The document outlines architectures like the blackboard model and BDI (belief-desire-intention) model. It also provides a manufacturing example using the JADE platform.

Hybrid reward architectureDong Heon Cho

The document discusses Hybrid Reward Architecture (HRA), a reinforcement learning method that decomposes the reward function of an environment into multiple sub-reward functions. In HRA, each sub-reward function is learned by a separate agent using DQN. This allows HRA to learn complex reward functions more quickly and stably compared to using a single reward signal. An experiment is described where HRA learns to eat 5 randomly placed fruits in an environment over 300 steps more effectively than a standard DQN agent.

Use Jupyter notebook guide in 5 minutesDong Heon Cho

AlexNet and so on...Dong Heon Cho

Deep Learning AtoC with Image PerspectiveDong Heon Cho

Deep learning models like CNNs, RNNs, and GANs are widely used for image classification and computer vision tasks. CNNs are commonly used for tasks like classification, detection, segmentation through learning hierarchical image features. Fully convolutional networks with encoder-decoder architectures like SegNet and Mask R-CNN can perform pixel-level semantic segmentation and instance segmentation by combining classification and bounding box detection. Deep learning has achieved state-of-the-art performance on many image applications due to its ability to learn powerful visual representations from large datasets.

LOL win predictionDong Heon Cho

How can we train with few dataDong Heon Cho

The document discusses approaches for using deep learning with small datasets, including transfer learning techniques like fine-tuning pre-trained models, multi-task learning, and metric learning approaches for few-shot and zero-shot learning problems. It also covers domain adaptation techniques when labels are not available, as well as anomaly detection for skewed label distributions. Traditional models like SVM are suggested as initial approaches, with deep learning techniques applied if those are not satisfactory.

Domain adaptation ganDong Heon Cho

The document discusses domain adaptation and transfer learning techniques in deep learning such as feature extraction, fine tuning, and parameter sharing. It specifically describes domain-adversarial neural networks which aim to make the source and target feature distributions indistinguishable and domain separation networks which extract domain-invariant and private features to model each domain separately.

Dense sparse-dense training for dnn and Other ModelsDong Heon Cho

Squeeeze modelsDong Heon Cho

This document discusses various techniques for compressing and speeding up deep neural networks, including singular value decomposition, pruning, and SqueezeNet. Singular value decomposition can be used to compress fully connected layers by minimizing the difference between the original weight matrix and its low-rank approximation. Pruning techniques remove unimportant weights below a threshold. SqueezeNet is highlighted as designing a small CNN architecture from the start that achieves AlexNet-level accuracy with 50x fewer parameters and less than 0.5MB in size.

Anime Colorization - with GANDong Heon Cho

This document discusses using generative adversarial networks (GANs) for colorizing anime sketches and manga. It provides links to research on guided auto colorization with deep convolutional GANs, style transfer techniques, and using a cGAN with a single training image for manga colorization. The document also discusses using an enhanced residual U-net generator and auxiliary classifier GAN for end-to-end anime sketch colorization and modifying the discriminator and training of this model. Finally, it suggests further work on stacking multiple GANs.

Mlc 2017 ?? ??Dong Heon Cho

One shot learning - deep learning ( meta learn )Dong Heon Cho

This document discusses one-shot learning and memory-augmented neural networks. It begins by explaining traditional deep learning models require large datasets for training. One-shot learning aims to give models inference ability after training on just one or few examples. Neural Turing Machines were an early approach to one-shot learning using an external memory component. Memory-Augmented Neural Networks were later developed using only content-based addressing in the external memory to enable rapid learning from small datasets.

False rumors detectionDong Heon Cho

This document discusses detecting false rumors on social media by examining propagation structures. It proposes using a hybrid SVM classifier with a graph kernel and RBF kernel to classify rumors based on 23 features extracted from message propagation trees, including whether the message started from an opinion leader or normal user. An evaluation on a dataset of over 5000 messages from Sina Weibo found the classifier could accurately detect false rumors, especially early in their propagation.

Basic Generative Adversarial NetworksDong Heon Cho

The document discusses Generative Adversarial Networks (GANs), a type of generative model proposed by Ian Goodfellow in 2014. GANs use two neural networks, a generator and discriminator, that compete against each other. The generator produces synthetic data to fool the discriminator, while the discriminator learns to distinguish real from synthetic data. GANs have been used successfully to generate realistic images when trained on large datasets. Examples mentioned include Pix2Pix for image-to-image translation and STACKGAN for text-to-image generation.

Deep Learning Optimization - gd to gdDong Heon Cho

The document discusses optimization techniques in machine learning, including gradient descent and its variants. It notes that machine learning aims to optimize an objective function by minimizing a loss function using gradient descent. Batch gradient descent and stochastic gradient descent are introduced as algorithms that use gradients to iteratively update model parameters toward minimizing the loss. Adam is presented as a stochastic optimization method. Deep learning models can now automatically learn their own optimization procedures rather than relying on hand-crafted optimization techniques.

Object C - RIPDong Heon Cho

Data-Models-in-DBMS-An-Overview.pptx.pptxhfebxtveyjxavhx

The truth behind the numbers: spotting statistical misuse.pptxandyprosser3

As a producer of official statistics, being able to define what misinformation means in relation to data and statistics is so important to us. For our sixth webinar, we explored how we handle statistical misuse especially in the media. We were also joined by speakers from the Office for Statistics Regulation (OSR) to explain how they play an important role in investigating and challenging the misuse of statistics across government.

Deep-QPP: A Pairwise Interaction-based Deep Learning Model for Supervised Que...suchanadatta3

Motivated by the recent success of end-to-end deep neural models for ranking tasks, we present here a supervised end-to-end neural approach for query performance prediction (QPP). In contrast to unsupervised approaches that rely on various statistics of document score distributions, our approach is entirely data-driven. Further, in contrast to weakly supervised approaches, our method also does not rely on the outputs from different QPP estimators. In particular, our model leverages information from the semantic interactions between the terms of a query and those in the top-documents retrieved with it. The architecture of the model comprises multiple layers of 2D convolution filters followed by a feed-forward layer of parameters. Experiments on standard test collections demonstrate that our proposed supervised approach outperforms other state-of-the-art supervised and unsupervised approaches.

More Related Content

More from Dong Heon Cho (19)

Multi object Deep reinforcement learningDong Heon Cho

Multi agent reinforcement learning for sequential social dilemmasDong Heon Cho

Multi agent SystemDong Heon Cho

Hybrid reward architectureDong Heon Cho

Use Jupyter notebook guide in 5 minutesDong Heon Cho

AlexNet and so on...Dong Heon Cho

Deep Learning AtoC with Image PerspectiveDong Heon Cho

LOL win predictionDong Heon Cho

How can we train with few dataDong Heon Cho

Domain adaptation ganDong Heon Cho

Dense sparse-dense training for dnn and Other ModelsDong Heon Cho

Squeeeze modelsDong Heon Cho

Anime Colorization - with GANDong Heon Cho

Mlc 2017 ?? ??Dong Heon Cho

One shot learning - deep learning ( meta learn )Dong Heon Cho

False rumors detectionDong Heon Cho

Basic Generative Adversarial NetworksDong Heon Cho

Deep Learning Optimization - gd to gdDong Heon Cho

Object C - RIPDong Heon Cho

Multi object Deep reinforcement learningDong Heon Cho

Multi agent reinforcement learning for sequential social dilemmasDong Heon Cho

Multi agent SystemDong Heon Cho

Hybrid reward architectureDong Heon Cho

Use Jupyter notebook guide in 5 minutesDong Heon Cho

AlexNet and so on...Dong Heon Cho

Deep Learning AtoC with Image PerspectiveDong Heon Cho

LOL win predictionDong Heon Cho

How can we train with few dataDong Heon Cho

Domain adaptation ganDong Heon Cho

Dense sparse-dense training for dnn and Other ModelsDong Heon Cho

Squeeeze modelsDong Heon Cho

Anime Colorization - with GANDong Heon Cho

Mlc 2017 ?? ??Dong Heon Cho

One shot learning - deep learning ( meta learn )Dong Heon Cho

False rumors detectionDong Heon Cho

Basic Generative Adversarial NetworksDong Heon Cho

Deep Learning Optimization - gd to gdDong Heon Cho

Object C - RIPDong Heon Cho

Recently uploaded (20)

Data-Models-in-DBMS-An-Overview.pptx.pptxhfebxtveyjxavhx

The truth behind the numbers: spotting statistical misuse.pptxandyprosser3

Deep-QPP: A Pairwise Interaction-based Deep Learning Model for Supervised Que...suchanadatta3

iam free indeed.pptxiam free indeed.pptxmuhweziart

Stasiun kernel pabrik kelapa sawit indonesiafikrimanurung1

april 2024 paper 2 ms. english non fictionomokoredeolasunbomi

643663189-Q4W3-Synthesize-Information-1-pptx.pptxrossanthonytan130

2025-03-03-Philly-AAAI-GoodData-Build Secure RAG Apps With Open LLMTimothy Spann

Analyzing Consumer Spending Trends and Purchasing Behavioromololaokeowo1

This project explores consumer spending patterns using Kaggle-sourced data to uncover key trends in purchasing behavior. The analysis involved cleaning and preparing the data, performing exploratory data analysis (EDA), and visualizing insights using ExcelI. Key focus areas included customer demographics, product performance, seasonal trends, and pricing strategies. The project provided actionable insights into consumer preferences, helping businesses optimize sales strategies and improve decision-making.

A Relative Information Gain-based Query Performance Prediction Framework with...suchanadatta3

To improve the QPP estimate for neural models, we propose to use additional information from a set of queries that express a similar information need to the current one (these queries are called variants). The key idea of our proposed method, named Weighted Relative Information Gain (WRIG), is to estimate the performance of these variants, and then to improve the QPP estimate of the original query based on the relative differences with the variants. The hypothesis is that if a query’s estimate is significantly higher than the average QPP score of its variants, then the original query itself is assumed (with a higher confidence) to be one for which a retrieval model works well.

IFRS Finance Powerpoint ppt Finance D.pptxamantiwari2091

vnptloveeeeeeeeeeeeeeeeeeeeeeeeeeee.pptxdeomom129

Lecture-AI and Alogor Parallel Aglorithms.pptxhumairafatima22

Kaggle & Datathons: A Practical Guide to AI Competitionsrasheedsrq

加拿大成绩单购买原版(鲍颁毕业证书)卡尔加里大学毕业证文凭taqyed

官方办理文凭加拿大文凭购买，加拿大文凭定制，加拿大卡尔加里大学文凭补办【q薇1954292140】专业在线定制加拿大大学文凭可查文凭定购，定做加拿大本科文凭，【q薇1954292140】复制加拿大The University of Calgary completion letter。在线快速补办加拿大本科毕业证、硕士文凭证书，购买加拿大学位证、卡尔加里大学Offer，加拿大大学文凭在线购买。高仿真还原加拿大文凭证书和外壳，定制加拿大卡尔加里大学成绩单和信封。学历认证购买UC毕业证【q薇1954292140】办理加拿大卡尔加里大学毕业证(UC毕业证书)【q薇1954292140】毕业证书电子版卡尔加里大学offer/学位证毕业证样本、留信官方学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作。帮你解决卡尔加里大学学历学位认证难题。特殊原因导致无法毕业，也可以联系我们帮您办理相关材料：１：在卡尔加里大学挂科了，不想读了，成绩不理想怎么办？？？ 2：打算回国了，找工作的时候，需要提供认证《UC成绩单购买办理卡尔加里大学毕业证书范本》【Q/WeChat：1954292140】Buy The University of Calgary Diploma《正式成绩单论文没过》有文凭却得不到认证。又该怎么办？？？加拿大毕业证购买，加拿大文凭购买， 3：回国了找工作没有卡尔加里大学文凭怎么办？有本科却要求硕士又怎么办？帮您解决在加拿大卡尔加里大学未毕业难题（The University of Calgary）文凭购买、毕业证购买、大学文凭购买、大学毕业证购买、买文凭、日韩文凭、英国大学文凭、美国大学文凭、澳洲大学文凭、加拿大大学文凭（q薇1954292140）新加坡大学文凭、新西兰大学文凭、爱尔兰文凭、西班牙文凭、德国文凭、教育部认证，买毕业证，毕业证购买，买大学文凭，购买日韩毕业证、英国大学毕业证、美国大学毕业证、澳洲大学毕业证、加拿大大学毕业证（q薇1954292140）新加坡大学毕业证、新西兰大学毕业证、爱尔兰毕业证、西班牙毕业证、德国毕业证，回国证明，留信网认证，留信认证办理，学历认证。从而完成就业。如果您在英、加、美、澳、欧洲等留学过程中或回国后： 1、在校期间因各种原因未能顺利毕业《UC成绩单工艺详解》【Q/WeChat：1954292140】《Buy The University of Calgary Transcript快速办理卡尔加里大学教育部学历认证书毕业文凭证书》，拿不到官方毕业证； 2、面对父母的压力，希望尽快拿到； 3、不清楚认证流程以及材料该如何准备； 4、回国时间很长，忘记办理； 5、回国马上就要找工作《正式成绩单卡尔加里大学成绩单COPY》【q薇1954292140】《毕业证如何办理UC毕业证如何办理》办给用人单位看；　　 6、企事业单位必须要求办理的； 7、需要报考公务员、购买免税车、落转户口、申请留学生创业基金。加拿大文凭卡尔加里大学成绩单，UC毕业证【q薇1954292140】办理加拿大卡尔加里大学毕业证(UC毕业证书)【q薇1954292140】国外本科offer在线制作卡尔加里大学offer/学位证学历认证证书电子版、留信官方学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作。帮你解决卡尔加里大学学历学位认证难题。【q薇1954292140】办理卡尔加里大学毕业证(UC毕业证书)学位证书成绩单代办服务【q薇1954292140】卡尔加里大学offer/学位证、留信官方学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作加拿大卡尔加里大学毕业证(UC毕业证书)学位证书成绩单代办服务主营项目： 1、真实教育部国外学历学位认证《加拿大毕业文凭证书快速办理卡尔加里大学文凭样本》【q薇1954292140】《论文没过卡尔加里大学正式成绩单》，教育部存档，教育部留服网站100%可查. 2、办理UC毕业证，改成绩单《UC毕业证明办理卡尔加里大学留学生学历学位认证书》【Q/WeChat：1954292140】Buy The University of Calgary Certificates《正式成绩单论文没过》，卡尔加里大学Offer、在读证明、学生卡、信封、证明信等全套材料，从防伪到印刷，从水印到钢印烫金，高精仿度跟学校原版100%相同. 3、真实使馆认证（即留学人员回国证明），使馆存档可通过大使馆查询确认. 4、留信网认证，国家专业人才认证中心颁发入库证书，留信网存档可查. 《卡尔加里大学揭秘加拿大毕业证书办理UC成绩单温感光标》【q薇1954292140】学位证1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。毕业证办理的详细过程加拿大文凭卡尔加里大学成绩单【q薇1954292140】复刻成绩单加拿大卡尔加里大学毕业证(UC毕业证书)毕业证详解细节卡尔加里大学毕业证办理，极速办加拿大卡尔加里大学文凭办理，加拿大卡尔加里大学成绩单办理和真实留信认证、留服认证、卡尔加里大学学历认证。学院文凭定制，卡尔加里大学原版文凭补办，扫描件文凭定做，100%文凭复刻。【q薇1954292140】Buy The University of Calgary Diploma购买美国毕业证，购买英国毕业证，购买澳洲毕业证，购买加拿大毕业证，以及德国毕业证，购买法国毕业证（q薇1954292140）购买荷兰毕业证、购买瑞士毕业证、购买日本毕业证、购买韩国毕业证、购买新西兰毕业证、购买新加坡毕业证、购买西班牙毕业证、购买马来西亚毕业证等。包括了本科毕业证，硕士毕业证。留信认证的作用： 1. 身份认证：留信认证可以证明你的留学经历是真实的，且你获得的学历或学位是正规且经过认证的。这对于一些用人单位来说，尤其是对留学经历有高度要求的公司（如跨国公司或国内高端公司），这是非常重要的一个凭证。专业评定：留信认证不仅认证你的学位证书，还会对你的所学专业进行评定。这有助于展示你的学术背景，特别是对于国内公司而言，能够清楚了解你所学专业的水平和价值。国家人才库入库：认证后，你的信息将被纳入国家人才库，并且可以在国家人才网等平台上展示，供包括500强公司等大型公司挑选和聘用人才。这对于回国找工作特别是进入大公司，具有非常积极的作用。 2. 留信认证对就业的好处提高竞争力：通过留信认证，尤其是对你所学专业的认证，可以大大提高你在国内求职时的竞争力。许多公司对留学生背景和所学专业有很高的要求，认证后的信息能够帮助公司快速识别符合条件的候选人。增强信任度：公司往往会对没有认证的学历背景产生疑虑，而留信认证为你的学历背景提供了第三方权威机构的背书，增强了雇主的信任。对接高端岗位：通过认证后，留学生的个人信息被纳入国家人才库，这也意味着你可以被更多的高端岗位、国有大公司和跨国公司注意到，特别是那些招聘具有国际化背景的人才的公司。

The Role of Christopher Campos Orlando in Sustainability Analyticschristophercamposus1

Introduction Lecture 01 Data Science.pdfmessagetome133

exampleexampleexampleexampleexampleexampleexampleexamplelembiczkat

Optimizing Common Table Expressions in Apache Hive with CalciteStamatis Zampetakis

In many real-world queries, certain expressions may appear multiple times, requiring repeated computations to construct the final result. These recurring computations, known as common table expressions (CTEs), can be explicitly defined in SQL queries using the WITH clause or implicitly derived through transformation rules. Identifying and leveraging CTEs is essential for reducing the cost of executing complex queries and is a critical component of modern data management systems. Apache Hive, a SQL-based data management system, provides powerful mechanisms to detect and exploit CTEs through heuristic and cost-based optimization techniques. This talk delves into the internals of Hive's planner, focusing on its integration with Apache Calcite for CTE optimization. We will begin with a high-level overview of Hive's planner architecture and its reliance on Calcite in various planning phases. The discussion will then shift to the CTE rewriting phase, highlighting key Calcite concepts and demonstrating how they are employed to optimize CTEs effectively.

Introduction to database and analysis software’s suitable for.pptxnabinparajuli9

Data-Models-in-DBMS-An-Overview.pptx.pptxhfebxtveyjxavhx

The truth behind the numbers: spotting statistical misuse.pptxandyprosser3

Deep-QPP: A Pairwise Interaction-based Deep Learning Model for Supervised Que...suchanadatta3

iam free indeed.pptxiam free indeed.pptxmuhweziart

Stasiun kernel pabrik kelapa sawit indonesiafikrimanurung1

april 2024 paper 2 ms. english non fictionomokoredeolasunbomi

643663189-Q4W3-Synthesize-Information-1-pptx.pptxrossanthonytan130

2025-03-03-Philly-AAAI-GoodData-Build Secure RAG Apps With Open LLMTimothy Spann

Analyzing Consumer Spending Trends and Purchasing Behavioromololaokeowo1

A Relative Information Gain-based Query Performance Prediction Framework with...suchanadatta3

IFRS Finance Powerpoint ppt Finance D.pptxamantiwari2091

vnptloveeeeeeeeeeeeeeeeeeeeeeeeeeee.pptxdeomom129

Lecture-AI and Alogor Parallel Aglorithms.pptxhumairafatima22

Kaggle & Datathons: A Practical Guide to AI Competitionsrasheedsrq

加拿大成绩单购买原版(鲍颁毕业证书)卡尔加里大学毕业证文凭taqyed

The Role of Christopher Campos Orlando in Sustainability Analyticschristophercamposus1

Introduction Lecture 01 Data Science.pdfmessagetome133

exampleexampleexampleexampleexampleexampleexampleexamplelembiczkat

Optimizing Common Table Expressions in Apache Hive with CalciteStamatis Zampetakis

Introduction to database and analysis software’s suitable for.pptxnabinparajuli9

What is Texture.pdf

1. Texture

2. Texture?

5. Texture is frequency

6. Frequency? About what? : Texel

7. Frequency? About what? : Texel

8. Frequency? About what? : Texel

9. ??… ??/Object? Texture? ????

10. Texture Analysis Structure Analysis

11. Texture Analysis Structure Analysis…? Not for Real World

12. Texture Analysis Statistical Approach! Law’s Texture Energy Measures (1987)

13. Texture Analysis Statistical Approach! a breif outline of the texton theory of human vision (1980)

14. Texture Analysis Statistical Approach! representing and recognizing the visual appearnace of materials using three-dimensional textons (2000)

15. Texture Analysis Statistical Approach! TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context

16. Texture Analysis Statistical Approach! Deep fi lter banks for texture recognition, description, and segmentation (2016) ~ ??… SIFT ? CNN? ??? Texture? ?? “?? ??”?? ?

17. Texture Analysis Fourier Approach https://darkpgmr.tistory.com/171

18. Texel?? ??? ????? Texel ~= Low Level Feature … Texel Texel…? ??? Edge ???, ?? Shape Matching ~> ??? Constraint? ???, ? Robust? ? ???! CNN == Statistical Approach + Bag of (Texton) Generator

19. Why CNN is Texture sensitive? <CDH ??> Texture? ? ???/?? ????? ? ??, Object Shape? ??? ?? == Texture? Object ??? ?????, Shape? Object ??? ?? == Shape Extractor? ??? ????? ?? ?? ???, Texture? ?? ???? ?? ?? ??

20. Why CNN is Texture sensitive? Texture Kernel (Low Level) Object Kernel (High Level) Many Activation Middle Activation One Activation No Activation Maybe CNN is Texture Mapper… Not Template Matching ??

21. V1 is Fixed? plasticity in primary visual cortex

22. V1 is not Fixed By age https://www.dana.org/article/primary-visual-cortex-changes-throughout-life/ Development of Glutamatergic Proteins in Human Visual Cortex across the Lifespan (2017 Neuroscience) V1 Protein developed through lifespan

23. V1 is not Fixed By Seeing Phase https://www.dana.org/article/primary-visual-cortex-changes-throughout-life/ Development and Plasticity of the Primary Visual Cortex

24. Also, vision is not robust Scale and translation-invariance for novel objects in human vision (Nature)

25. Scale and translation-invariance for novel objects in human vision (Nature) Scale Exp - Target / Test : Same or Not Also, vision is not robust in size

26. Even not N-shot Scale and translation-invariance for novel objects in human vision (Nature) Non Korean Korean

27. References Texture De fi nition - https://courses.cs.washington.edu/courses/cse455/09wi/Lects/lect12.pdf - https://blogs.unity3d.com/kr/2019/02/14/procedural-stochastic-texturing-in-unity/

狠狠撸

What is Texture.pdf

Recommended

More Related Content

More from Dong Heon Cho (19)

Recently uploaded (20)

What is Texture.pdf