<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>CV | Mohammadhossein (Rastin) Akbari Moafi</title><link>https://r4stin.github.io/tag/cv/</link><atom:link href="https://r4stin.github.io/tag/cv/index.xml" rel="self" type="application/rss+xml"/><description>CV</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Sun, 01 Jun 2025 00:00:00 +0000</lastBuildDate><image><url>https://r4stin.github.io/media/icon_hu2b06ce7dbfc98605c45fb34b724f1004_70123_512x512_fill_lanczos_center_3.png</url><title>CV</title><link>https://r4stin.github.io/tag/cv/</link></image><item><title>3D Explainable AI for Renal Structure Segmentation in CT Scans</title><link>https://r4stin.github.io/project/_xai-renal-ct/</link><pubDate>Sun, 01 Jun 2025 00:00:00 +0000</pubDate><guid>https://r4stin.github.io/project/_xai-renal-ct/</guid><description>&lt;p>An explainable AI project for renal structure segmentation in CT imaging. SegResNet was fine-tuned on multi-phase renal CT scans, reaching a Dice score of 0.78 for organ segmentation. To make the model&amp;rsquo;s decisions transparent, 3D Grad-CAM and Guided Backpropagation were integrated to produce volumetric, class-specific visualizations that show which anatomical regions drive each prediction.&lt;/p></description></item><item><title>Knowledge-Guided Visual Analytics for Scientific Image Annotation</title><link>https://r4stin.github.io/project/_kg_wikidata_annotation/</link><pubDate>Sun, 01 Jun 2025 00:00:00 +0000</pubDate><guid>https://r4stin.github.io/project/_kg_wikidata_annotation/</guid><description>&lt;p>A deep learning pipeline for automatic annotation and retrieval of scientific figures. The system builds on CLIP and BiomedCLIP — including fine-tuned variants — to embed biomedical images and text into a shared semantic space, and integrates Wikidata-based knowledge graphs to enrich that space with structured domain knowledge. The result is stronger semantic alignment between figures and their descriptions, along with interpretable visualisations that make the annotation and retrieval behaviour of the models easier to inspect.&lt;/p></description></item><item><title>Text-Guided Medical Image Denoising with Vision-Language Fusion</title><link>https://r4stin.github.io/project/_vlm-denoising/</link><pubDate>Thu, 01 May 2025 00:00:00 +0000</pubDate><guid>https://r4stin.github.io/project/_vlm-denoising/</guid><description>&lt;p>A medical image denoising system that fuses vision and language: a UNet-based denoiser is conditioned on CLIP text embeddings derived from the VQA-RAD dataset, letting textual context guide the reconstruction of noisy scans. Compared to a vision-only baseline, the vision-language fusion improved reconstruction quality by 12.5% in PSNR and 4.6% in SSIM.&lt;/p></description></item><item><title>Sport Video Analysis for Billiard Matches</title><link>https://r4stin.github.io/project/_biliard/</link><pubDate>Sat, 15 Jun 2024 00:00:00 +0000</pubDate><guid>https://r4stin.github.io/project/_biliard/</guid><description>&lt;p>Built a real-time tracking system in C++ with OpenCV for cue ball and table detection. Visualized ball trajectories across angles using dynamic 2D minimaps.&lt;/p></description></item><item><title>PointNet-SHOT - Enhanced 3D Descriptor Matching</title><link>https://r4stin.github.io/project/_pointnet-shot/</link><pubDate>Sat, 01 Jun 2024 00:00:00 +0000</pubDate><guid>https://r4stin.github.io/project/_pointnet-shot/</guid><description>&lt;p>Enhanced PointNet with SHOT descriptors and custom triplet loss. Achieved 94.12% accuracy for robust 3D object recognition on noisy datasets.&lt;/p></description></item><item><title>Lymphoma – Data Augmentation for Deep Medical Imaging</title><link>https://r4stin.github.io/project/_lymphoma/</link><pubDate>Wed, 01 May 2024 00:00:00 +0000</pubDate><guid>https://r4stin.github.io/project/_lymphoma/</guid><description>&lt;p>Boosted DenseNet121 classification accuracy to 98.67% via PCA, DCT, and noise-based augmentation (baseline - 93.6%).&lt;/p></description></item><item><title>Optimized Point Cloud Alignment</title><link>https://r4stin.github.io/project/_optimized_point_cloud/</link><pubDate>Wed, 01 May 2024 00:00:00 +0000</pubDate><guid>https://r4stin.github.io/project/_optimized_point_cloud/</guid><description>&lt;p>Implemented ICP with KD-Tree and SVD for faster, precise alignment in industrial 3D reconstruction tasks.&lt;/p></description></item><item><title>3D Scene Reconstruction Using Structure from Motion</title><link>https://r4stin.github.io/project/_3d_reconstruction/</link><pubDate>Mon, 15 Apr 2024 00:00:00 +0000</pubDate><guid>https://r4stin.github.io/project/_3d_reconstruction/</guid><description>&lt;p>Built a 3D reconstruction pipeline using ORB matching, RANSAC, and geometric validation from multi-view images.&lt;/p></description></item><item><title>Semi-Global Stereo Matching with Monocular Disparity Guess</title><link>https://r4stin.github.io/project/_sgm/</link><pubDate>Mon, 01 Apr 2024 00:00:00 +0000</pubDate><guid>https://r4stin.github.io/project/_sgm/</guid><description>&lt;p>A stereo depth estimation project that combines classical and learning-based approaches: a monocular disparity prediction is used as the initial guess, which Semi-Global Matching (SGM) then refines into the final disparity map. Seeding SGM with the monocular estimate substantially improved accuracy over the baseline, reducing mean squared error by 75%.&lt;/p></description></item><item><title>TiagoBot Navigation and Pick-Place System</title><link>https://r4stin.github.io/project/_tiago_bot/</link><pubDate>Thu, 01 Feb 2024 00:00:00 +0000</pubDate><guid>https://r4stin.github.io/project/_tiago_bot/</guid><description>&lt;p>Programmed ROS1-based routines for autonomous movement and pick-place using AprilTag recognition.&lt;/p></description></item><item><title>Bone Age Prediction from Hand Radiographs</title><link>https://r4stin.github.io/project/_bone_age/</link><pubDate>Thu, 01 Jun 2023 00:00:00 +0000</pubDate><guid>https://r4stin.github.io/project/_bone_age/</guid><description>&lt;p>Used MediaPipe and CLAHE for preprocessing. Achieved 10.47 MAE using deep regression (ResNet, Inception-v4).&lt;/p></description></item></channel></rss>