Deep Model for Vision

Neural Networks for CV and NLP — EP03

Before getting into the Deep Model discussion, lets understand the basics.

Computer Vision through Machine Learning

What is Computer Vision ?

Computer vision refers to the ability of machines to identify patterns within visual data and glean meaningful insights. They can then perform tasks like reading written text, recognizing specific faces in images and locating particular objects in a video feed.

The ultimate goal of computer vision is to write computer programs that can interpret images.

Challenges in CV :-

Viewpoint variation. Scale variation. Deformation. Occlusion. Illumination conditions. Background clutter. Intra-class variation, etc.

Classical ML Vision Pipeline :-

(i) Raw Image: start with input pixels from the original image. (ii) Feature Engineering: includes extracting meaningful patterns. (iii) Feature Vector: convert extracted features into a fixed-length numerical representation. (iv) Classifier: feed the vector into models like SVM/ Random Forest. (v) Prediction: output the final class level based on the trained classifier.

Performance limitation in human design, labor intense, difficulty in handling big datasets and complex patterns/shapes all come under the limitations of conventional approaches.

How Deep Learning shines in Vision applications ?

They effectively target and solve the limitations of conventional approach and promotes high scalability and adaptability.

Deep Learning for Computer Vision Tasks and Applications include:

() Image Classification — gives a single label to an entire image (e.g., dog, boy, car) based on its content. () Object Detection — locates and identifies multiple objects in an image using boxes and class labels. () Semantic Segmentation — each pixel of an image is classified into a category, giving a detailed mask of regions & objects. () Pose estimation — detects key joints (of human body) to understand posture & movement. () Depth Estimation — predicts the distance of objects from the camera to understand scene geometry from images. () Optical Flow — finds motion between side-by-side/consecutive frames by tracking pixel level movement. () 3D Reconstruction — 2D images/video sequences converted to 3D model. () Image Super-resolution — enhances low-resolution images to higher resolution with better detail and clarity. () Denoising/Deblurring — removes noise or blur from images to restore visual quality. () Action recognition — detects human (e.g., running, jumping) from video sequences. () Object Tracking in Video — continuously follows a specific object across multiple video frames. () Medical Image Analysis — assists in diagnosis by detecting patterns or anomalies in medical scans (e.g., tumors in MRI). (*) Remote Sensing Applications —analyzes satellite or aerial images for tasks like land use classification, weather monitoring, and disaster assessment.

Feature detection by Hidden layers Neurons

Involves the following key points :-

Hidden Layers — refers to the intermediate layers between i/p & o/p dealing with data processing and decoding patterns.

Feature Visualization — it’s a technique that helps to understand how network extracted info from data.

Activation Function — commons ones include ReLU, Sigmoid, Tanh

Others like functionality and importance collectively work to promote feature detection and learning capabilities in neural networks.

📌 Exploring Underlying Features in Hidden Layers of Neural Network

📌 Layers in Artificial Neural Networks (ANN) — GeeksforGeeks

📌 Application of Deep Learning in Computer Vision

📌 Machine Learning in Computer Vision

📌 The 2026 Guide to Machine Learning