Digital image processing
了解 Rafael C. Gonzalez; Richard E. Woods 的《Digital image processing》（Digital image processing）：2007-02-22–2026-11-01 的 Hacker News 数据中有 13 次提及、13 位讨论者。阅读原文摘录、比较不同观点，将它加入你的书单。
分享到 X社区里的声音
最近 13 条来源记录提及不等于推荐。标签来自源数据的自动分类；请打开原始讨论核对上下文。
此记录保留了作者、时间和来源链接，请打开 HN 阅读完整上下文。
在 Hacker News 阅读原文此记录保留了作者、时间和来源链接，请打开 HN 阅读完整上下文。
在 Hacker News 阅读原文此记录保留了作者、时间和来源链接，请打开 HN 阅读完整上下文。
在 Hacker News 阅读原文此记录保留了作者、时间和来源链接，请打开 HN 阅读完整上下文。
在 Hacker News 阅读原文此记录保留了作者、时间和来源链接，请打开 HN 阅读完整上下文。
在 Hacker News 阅读原文此记录保留了作者、时间和来源链接，请打开 HN 阅读完整上下文。
在 Hacker News 阅读原文此记录保留了作者、时间和来源链接，请打开 HN 阅读完整上下文。
在 Hacker News 阅读原文此记录保留了作者、时间和来源链接，请打开 HN 阅读完整上下文。
在 Hacker News 阅读原文此记录保留了作者、时间和来源链接，请打开 HN 阅读完整上下文。
在 Hacker News 阅读原文Feature Extraction and Image Processing looks pretty good. Do you think a beginner would be able to follow it without reading Digital Image Processing first?
在 Hacker News 阅读原文For that I suggest "Feature Extraction and Image Processing" 2.ed by Mark Nixon and Alberto S Aguado. But "Digital Image Processing" (DIP) by Gonzalez and Woods as suggested by monk_the_dog is pretty much the standard prereq. Actually DIP may get you what you need depending on how deep you need to go.
在 Hacker News 阅读原文Here's my list: For a general introduction: * "Digital Image Processing" by Gonzalez and Woods * "Image Processing, Analysis, and Machine Vision" by Sonka, Hlavac, and Boyle After you learn the basics, some more advanced and specialized books I'd recommend: * "Morphological Image Analysis" by Soille. * "Insight into Images" by Yoo (explains many of the algorithms in the ITK library). I hesitate to recommend this book because it is so dated (1992), but I refer to "Computer and Robot Vision" by Haralick and Shapiro quite often.
在 Hacker News 阅读原文There's a toolbox for Matlab that encapsulates a lot of digital image processing algorithms. Here's the text we used in my computer vision class, probably the classic in the field: http://www.amazon.com/Digital-Image-Processing-Rafael-Gonzal... Learning the specific feature space of digital images is no big deal. You might explore wavelet transformation and different image formats (BMP, JPEG, JPEG2000). Pixel intensity, color, edge detection, high and low pass filtering, connectivity between objects, pattern recognition... there are a lot of topics. And then there are disciplines built on top of image processing, like face detection, watermarking, image retrieval/search, editing/transformation (think Photoshop)... and of course video is another can of worms, adding time-series data. There are strong connections between higher-level image …
在 Hacker News 阅读原文