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Call for papers/Topics

All Abstracts, Reviews, short articles, Full articles, Posters are welcomed related with any of the following research fields:

These represent the foundational building blocks of each distinct field before they intersect or rely on one another.

Kinematics and Dynamics: Forward and inverse kinematics, rigid body dynamics, and trajectory generation.
Actuation and Power Systems: Electric motors (stepper, servo), hydraulic/pneumatic systems, and battery management.
Control Systems: Feedback loops, PID control, adaptive control, and state estimation.
Mechanical Design: End-effectors, link mechanisms, structural materials, and biomimetic design.

Illumination and Lighting: Structural lighting, backlighting, coaxial illumination, and polarization.
Optics and Sensors: Lens selection, focal length, depth of field, CCD/CMOS sensor architecture, and smart cameras.
Industrial Inspections: Metrology, defect detection, pattern matching, and presence/absence verification.
Spatial Calibration: Camera-to-world coordination, lens distortion correction, and hand-eye calibration.

Image Enhancement: Histogram equalization, spatial filtering (smoothing, sharpening), and noise reduction.
Mathematical Morphology: Erosion, dilation, opening, closing, and hit-or-miss transforms.
Frequency Domain Processing: Fourier transforms, wavelet analysis, and bandpass filtering.
Compression and Coding: JPEG/PNG compression standards, lossless vs. lossy coding, and data redundancy reduction.

These subtopics exist at the intersections where two or all three of these primary fields overlap.

Feature Extraction: Edge detection (Canny, Sobel), corner detection (Harris), and scale-invariant features (SIFT, SURF, ORB).
Segmentation: Thresholding, region-growing, watershed algorithms, and semantic/instance segmentation.
Pattern Recognition: Template matching, object classification, and optical character recognition (OCR) via machine learning.
3D Reconstruction: Stereo vision, structured light triangulation, Photometric Stereo, and time-of-flight (ToF) processing.

Visual Servoing: Position-based (PBVS) and image-based (IBVS) visual control loops to guide robotic arms.
Pick-and-Place Automation: Part localization, orientation estimation, and bin-picking in unstructured environments.
Autonomous Navigation: Landmark recognition, obstacle avoidance, and line-following systems.
Safety and Proximity Sensing: Speed and separation monitoring, human detection, and virtual fencing.

Simultaneous Localization and Mapping (SLAM): Visual SLAM (vSLAM), loop closure detection, and feature tracking over time.
Visual Odometry: Estimating the position and orientation of a robot by analyzing sequential camera images.
Scene Understanding: Transforming raw pixel data into semantic maps for robotic path planning.

These complex fields completely merge all three disciplines to power modern, highly autonomous systems.

Embedded Vision and Edge AI: Deploying deep learning image processing models directly onto specialized robotic hardware (e.g., FPGAs, Jetson modules) for real-time machine vision.
Autonomous Driving Architecture: Integrating LiDAR, radar, and cameras (machine vision) with sensor fusion (image processing) to safely steer and accelerate a vehicle (robotics).
Medical Robotics and Computer-Assisted Surgery: Utilizing real-time medical imaging enhancement to guide robotic surgical tools with sub-millimeter precision.
Unmanned Aerial Vehicle (UAV) Remote Sensing: Drones utilizing multispectral imaging and real-time photogrammetry for agricultural monitoring, search and rescue, and autonomous mapping