Titlebook: Computer Vision and Image Processing; 5th International Co Satish Kumar Singh,Partha Roy,P. Nagabhushan Conference proceedings 2021 The Edi

万花筒

Degraded Document Image Binarization Using Active Contour Model,nsely influences the accuracy of OCR. Various degradations like inadequate illumination, complex background, ink bleed, smear, etc. make binarization a challenging exercise. In this paper, we propose a novel method for binarization based on Active Contour Model (ACM) which is different from current

PRE

Accelerated Stereo Vision Using Nvidia Jetson and Intel AVX, algorithms have been a major challenge for their usage in real world applications on small robots. Standard stereo depth estimation algorithms Sum of Absolute Differences (SAD), census transform and an advanced algorithm Semi-Global Matching (SGM) are discussed in this work. This paper presents nov

Ordeal

fertilizer

Capture

Deep Learning Based Image Enhancement and Black Box Filter Parameter Estimation,a critical and often, time consuming part of this process. To simplify the complex and skillful task of retouching, this paper proposes a Deep Learning based automatic photo retouching solution. It builds upon a GAN-based architecture, trained in an unsupervised manner to instantly enhance images. H

炸坏

chandel

Dual Gradient Feature Pair Based Face Recognition for Aging and Pose Changes,ethod, initially segments the rectangular face region into 6 regions which forms 3 pairs which is almost symmetrical to each other. From each pair highly matched HOG features are extracted and the dual gradient features like the positive gradients and negative gradients are extracted from the symmet

STYX

细菌等

Lightweight Photo-Realistic Style Transfer for Mobile Devices,er aims to retain the photorealism in the synthesized image. Many of the state-of-the-art photo-realistic style transfer techniques uses networks like VGG as the backbone effectively making it computationally expensive and infeasible for deployment in systems with limited resources like mobile devic

mediocrity