I need a complete, publication-ready research paper titled “Advancements in Edge Detection Techniques: Enhancing Image Processing in AI Applications.” The final article must fit within 6–9 IEEE two-column pages and follow every requirement of IEEE Transactions on Image Processing, from section ordering and reference style to figure resolution and LaTeX template usage. Content focus Explain both classic and contemporary methods, then drill down into the newer approaches I specify—Transformers for Edge Detection, Rich Convolutional Features (RCF), Holistically-Nested Edge Detection (HED), the Attention-based Multi-scale Dense Decoder (AMDD), and CannyNet. I expect you to position each technique against traditional operators such as Canny, Sobel, and Prewitt, highlight quantitative improvements on standard benchmarks (BSDS500, NYUD-v2, or similar), and discuss practical implications for current AI pipelines. Research depth Please ground every claim in recent peer-reviewed literature, include up-to-date citations, and provide a rigorous comparison table. All figures must be original or properly licensed, with captions conforming to IEEE style. A brief “Future Work” section should outline open challenges and potential extensions. Deliverables • LaTeX source files and compiled PDF that passes the IEEE submission checker • High-resolution figures (PNG/SVG) in a separate folder • Complete .bib file with IEEE-formatted references • Similarity/plagiarism report (<10 % overlap) I will review a draft first; once feedback is incorporated and the paper aligns with the journal’s template and scope, the project is complete.