ChromaCut is an interactive cutting-edge toolkit engineered for high-precision image segmentation. It incorporates state-of-the-art superpixel over-segmentation and graph-based segmentation techniques, including the renowned Boykov-Kolmogorov (Min-cut/Max-flow) algorithm for graph partitioning. This toolkit efficiently transforms input images into segmented outputs, starting with superpixel division for in-depth analysis and using graph-based algorithms for accurate segmentation. It utilizes CIDE2000 for color difference evaluation and foreground extraction, improving segmentation precision across various images.
NeuroCluster is an advanced image segmentation toolkit leveraging the Improved Intuitionistic Fuzzy C-Means (IIFCM) algorithm, specifically tailored for magnetic resonance (MR) image analysis. This toolkit enables precise segmentation of MR images into distinct regions, enhancing the ability to identify and analyze various brain structures based on pixel intensity and color values.
This project aims to enhance speech recognition capabilities within banking dialogues by employing a Convolutional, Recurrent, and Dense Neural Network (CRDNN) alongside Language Model (LM) support. Utilizing the HarperValleyBank (HVB) corpus, the project strives to develop an ASR system that can accurately transcribe spoken banking dialogues, improving customer service experiences and operational efficiencies.
This project investigates the use of autoencoders for denoising images to improve handwritten digit recognition accuracy using the MNIST dataset. By introducing noise to the dataset and employing an autoencoder for image reconstruction, the study aims to enhance digit classification performance, demonstrating the practical applications and effectiveness of autoencoders in deep learning.
Image Segmentation
Image Segmentation
Speech Recognition
Image Denoising
