{"product_id":"computer-vision-and-image-processing-9783031937026","title":"Computer Vision and Image Processing","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e.- Do not look so locally to fish skins: Improved YOLOv7 for fish disease detection with Transformers.\u003cbr\u003e.- MDDAMFN: Mixed Dual-Direction Attention Mechanism to Enhance Facial Expression.\u003cbr\u003e.- A brief review of state-of-the-art classification methods on benchmark Peripheral Blood Smears datasets.\u003cbr\u003e.- Detection and Monocular Depth Estimation of Ghost Nets.\u003cbr\u003e.- DiffMamba: Leveraging Mamba for Effective Fusion of Noise and Conditional Features in Diffusion Models for Skin Lesion Segmentation.\u003cbr\u003e.- UDC-Mamba: Deep State Space Model for Under Display Camera Image Restoration.\u003cbr\u003e.- Walking Direction Estimation using Silhouette and Skeletal Representations.\u003cbr\u003e.- Realizing GAN Potential for Image Generation and Image-To-Image Translation Using Pix2Pix.\u003cbr\u003e.- DSFF-Net: Depthwise Separable U-Net with Feature Fusion for Polyp Segmentation towards Hardware Deployment.\u003cbr\u003e.- Cattle Identification through Multi-Biometric Features and Edge Device.\u003cbr\u003e.- Fast sparse SAR Image Reconstruction Using Sparsity Independent Regularized Pursuit.\u003cbr\u003e.- Space Varying Motion Blur Degradation Dataset and Model for Semantic Segmentation.\u003cbr\u003e.- Multi-class classification of Gastrointestinal Disease detection using Vision Transformers.\u003cbr\u003e.- MGC: Music Genre Classification Using a Hybrid CNN-LSTM Model with MFCC Input.\u003cbr\u003e.- DBTC-Net: Dual-Branch Transformer-CNN Network for Brain Tumor Segmentation.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":53195487052119,"sku":"9783031937026","price":75.99,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/computer-vision-and-image-processing-9783031937026","provider":"Book Curl","version":"1.0","type":"link"}