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      <title>Hajun Jang</title>
      <link>https://hajun011103.github.io</link>
      <description>최근 10 건 on Hajun Jang</description>
      <generator>Quartz -- quartz.jzhao.xyz</generator>
      <item>
    <title>maximum-likelihood-estimation</title>
    <link>https://hajun011103.github.io/study/maximum-likelihood-estimation</link>
    <guid>https://hajun011103.github.io/study/maximum-likelihood-estimation</guid>
    <description><![CDATA[ Reference Steve Brunton 교수의 강좌 최대우도법 정리 by 공돌이의 수학정리노트 Definition Maximum Likelihood Estimation 한국말로, 최대우도법 (MLE) 은 모수적인 데이터 밀도 추정 방법으로써 파라미터 \theta=\theta_1, \cdots,\theta_m 으로 구성된 어떤 확률밀도함수 P(x|\theta) 에서 관측된 표본 데이터 집합을 x=(x_1,x_2, \cdots, x_n) 이라 할 때, 이 표본들에서 파라미터 \theta=(\theta_1,\cdots,\theta_m... ]]></description>
    <pubDate>Tue, 28 Jul 2026 19:24:41 GMT</pubDate>
  </item><item>
    <title>droid-slam</title>
    <link>https://hajun011103.github.io/study/droid-slam</link>
    <guid>https://hajun011103.github.io/study/droid-slam</guid>
    <description><![CDATA[ Reference DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras Related works Visual SLAM Deep Learning DROID Abstract Estimate the trajectory of the camera and build a 3D map Differentiable Recurrent Optimization Inspired Design it consists of recurrent iterative update high accurac... ]]></description>
    <pubDate>Tue, 28 Jul 2026 19:24:41 GMT</pubDate>
  </item><item>
    <title>camera-calibration</title>
    <link>https://hajun011103.github.io/study/camera-calibration</link>
    <guid>https://hajun011103.github.io/study/camera-calibration</guid>
    <description><![CDATA[ Reference Stanford CS231A 정확하게 transformation 을 계산하기 위해서는 intrinsic parameters를 정확하게 알아야 하고 이것을 구하기 위해 camera calibration 을 시행한다. ]]></description>
    <pubDate>Tue, 28 Jul 2026 19:24:41 GMT</pubDate>
  </item><item>
    <title>kinetics-of-particles</title>
    <link>https://hajun011103.github.io/study/kinetics-of-particles</link>
    <guid>https://hajun011103.github.io/study/kinetics-of-particles</guid>
    <description><![CDATA[ Kinetics is a branch of classical mechanics which studies the relationship between force and motion. Kinematis 은 Kinetics 문제에 속한다. ]]></description>
    <pubDate>Tue, 28 Jul 2026 19:24:41 GMT</pubDate>
  </item><item>
    <title>likelihood</title>
    <link>https://hajun011103.github.io/study/likelihood</link>
    <guid>https://hajun011103.github.io/study/likelihood</guid>
    <description><![CDATA[ References Probability 와 Likelihood Probability: 주어진 확률분포가 고정된 상태에서, ==관측되는 사건이 변화==될 때, 확률을 표현 Likelihood: 관측된 사건이 고정된 상태에서, ==확률 분포가 변화==될 때 (확률 분포를 모를 때, 가정할 때), 확률을 표현 (가능도 혹은 우도 라고 불림) Likelihood=\mathcal{L}(\theta|x) \quad Probability=P(x|\theta, where \theta is 확률 분포를 나타내는 parameter and x is ... ]]></description>
    <pubDate>Tue, 28 Jul 2026 19:24:41 GMT</pubDate>
  </item><item>
    <title>principle-component-analysis</title>
    <link>https://hajun011103.github.io/study/principle-component-analysis</link>
    <guid>https://hajun011103.github.io/study/principle-component-analysis</guid>
    <description><![CDATA[ Reference PCA main ideas by StatQuest PCA clearly explained by StatQuest 혁펜하임의 주성분 분석 Data-Drive Science and Engineering by Steve Brunton Tldr One of the central applications of the SVD (Singular Value Decomposition) A statistical interpretation of the data-driven, hierarchical coordinate system use... ]]></description>
    <pubDate>Tue, 28 Jul 2026 19:24:41 GMT</pubDate>
  </item><item>
    <title>i-jepa</title>
    <link>https://hajun011103.github.io/study/i-jepa</link>
    <guid>https://hajun011103.github.io/study/i-jepa</guid>
    <description><![CDATA[ Reference Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture Tldr How to improve the semantic level of self-supervised representations without using extra prior knowledge encoded through image transformations. ]]></description>
    <pubDate>Tue, 28 Jul 2026 19:24:41 GMT</pubDate>
  </item><item>
    <title>camera-matrix-model</title>
    <link>https://hajun011103.github.io/study/camera-matrix-model</link>
    <guid>https://hajun011103.github.io/study/camera-matrix-model</guid>
    <description><![CDATA[ Reference Stanford CS231A intrinsic &amp; extrinsic camera parameters, homogenous transformation 3차원에 있는 한 점을 image plane 위에 있는 P&#039; 으로 매핑하는 것을 projective transformation 이라고 한다. ]]></description>
    <pubDate>Tue, 28 Jul 2026 19:24:41 GMT</pubDate>
  </item><item>
    <title>transformations-in-2d</title>
    <link>https://hajun011103.github.io/study/transformations-in-2d</link>
    <guid>https://hajun011103.github.io/study/transformations-in-2d</guid>
    <description><![CDATA[ Reference Stanford CS231A Jinsol Kim’s blog 2차원 공간에서의 변환 종류 이미지에 있는 정보를 알아내기 위해서는 2D 공간에서의 다양한 변환을 알아야 한다. ]]></description>
    <pubDate>Tue, 28 Jul 2026 19:24:41 GMT</pubDate>
  </item><item>
    <title>cameras-and-lens</title>
    <link>https://hajun011103.github.io/study/cameras-and-lens</link>
    <guid>https://hajun011103.github.io/study/cameras-and-lens</guid>
    <description><![CDATA[ Reference Stanford CS231A Pinhole Cameras 의 문제 conflict between crispness and brightness 는 렌즈를 사용하며 완화되었다. Figure 1. A setup of a simple lens model. ]]></description>
    <pubDate>Tue, 28 Jul 2026 19:24:41 GMT</pubDate>
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