#3d image processing

The use of contrasting colors in ancient Indian art: How bright hues like red and yellow are juxtaposed with cooler tones for emphasis. The role of negative space in Indian sculptures and paintings: How empty areas create a sense of depth and enhance the impact of the subject. Understanding the use of scale and size to create visual hierarchy: How central figures and objects are emphasized through their size.

Visual Communication

Purpose: To convey information visually rather than through text alone.

Application: Logos, posters, websites, packaging, ads.

Working Principle: Use of imagery, typography, and layout to quickly attract and inform the viewer.

FIRST PRACTICAL KNOWLEDGE 👈.

Process Workflow

Brief/Objective – Understand the goal.

Research – Study the target audience, competitors, and theme.

Concept Development – Sketch ideas and layouts.

Design Execution – Create digital or physical artwork.

Feedback & Revision – Refine based on input.

Final Output – Deliver in proper formats (print, web, etc.).

Second Practical Knowledge 👈.

Imagine crafting 3D banners that are truly unique to each customer. This video explores how image processing techniques can be used to create personalized 3D banner designs that are both visually stunning and impactful.

Learn about 3D image processing technique 👈.

This is the process of creating the 3D geometry of your object, character, or environment. Polygonal Modeling: Creating objects using polygons. This is the most common method for hard surface objects and characters. Spline Modeling: Creating shapes based on curves and then defining their thickness or depth. NURBS: For smoother surfaces, often used in automotive and industrial design.

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IMPORTANT TOOLS AND SOFTWARE FOR DESIGN

Blender: Free, open-source software known for its versatility in modeling, animation, and rendering.

Autodesk Maya: Used widely in animation and visual effects. It excels at polygonal modeling, rigging, and animation.

3ds Max: Similar to Maya but often preferred for architectural visualization and game asset creation.

ZBrush: Specialized in digital sculpting, ideal for creating highly detailed organic models like characters.

Cinema 4D: Great for motion graphics and 3D modeling with a user-friendly interface.

Image fusion is a process where two or more images are combined to create a single, more informative or visually enhanced image. This technique is used in various fields like photography, remote sensing, medical imaging, and computer vision.

GET PARTICLE IMAGE PROCESSING VIDEO TUTORIAL 🦄

The goal of image fusion is to enhance the overall quality of the final image by combining information from multiple sources while retaining the most relevant details.

Feature-Level Fusion: Instead of combining pixel values, this method focuses on merging the features extracted from images. It's often used in computer vision for object recognition and tracking.

Libraries and Frameworks for Machine Learning Image Processing:

OpenCV-Python is a library of Python bindings designed to solve computer vision problems.

Huge library of image processing algorithms

Open Source + Great Community

Works on both images and videos

Java API Extension

Works with GPUs

Cross-Platform

Supervised Learning: KNN is a supervised learning algorithm, meaning it learns from labeled data to make predictions. Instance-Based Learning: KNN is also considered an instance-based or lazy learning algorithm because it stores the entire training dataset and performs computations only when making predictions. 

Use Java Maven Project For Image Resize

media stands out as the best tool for resizing pictures. With its user-friendly interface and advanced resizing capabilities, Shrink. media allows you to adjust your pictures' dimensions and file sizes while maintaining excellent visual quality.

Boof CV for Java Image Processing

Java CV for Java Image Processing

ImageJ for Java Image Processing

Supervised Learning: KNN is a supervised learning algorithm, meaning it learns from labeled data to make predictions. 

Instance-Based Learning: KNN is also considered an instance-based or lazy learning algorithm because it stores the entire training dataset and performs computations only when making predictions. 

Distance Metric: KNN relies on a distance metric to measure the similarity or distance between data points. 

Finding Nearest Neighbors:

For a new, unlabeled data point, KNN identifies the "k" nearest neighbors in the training data based on the chosen distance metric.

Classification: In classification tasks, the new data point is assigned to the class that is most common among its "k" nearest neighbors.

Regression: In regression tasks, the predicted value for the new data point is the average of the values of its "k" nearest neighbors. 

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