## Object Detection with Sliding Window Algorithm

Object detection using sliding window has existed before recent rise of machine learning in computer vision. Like any machine learning algorithm, first requirement of sliding window algorithm is to prepare labeled training set. Imagine we want to build a car detection algorithm using sliding window. Our training images (X) will ... read more

## What is Landmark Detection in Computer Vision?

In image classification with localization, we train neural network to detect object and then localize by predicting coordinates of bounding box around it. Given input images, the output of such an algorithm would be a) probability of finding object, and b) if object exists, coordinates of bounding box ($b_{x}$, \$... read more

## Image Classification with Localization

In image classification, we feed input image to a convolutional neural network and it gives back a feature vector (fully connected layer). The feature vector is then injected to softmax layer to get prediction of a class. Let's say we are building an image classifier for self-driving car application with ... read more

## Understanding Classification, Classification with Localization and Object Detection

Image Classification: In image classification, a supervised machine learning algorithm looks at input image and predicts a class label of that image (binary or multi-class). For example, a binary dog classifier algorithm will predict dog or no-dog class label for a given input image. Image Classification with Localization: In image ... read more

## Why Convolution is Useful in Neural Networks?

You may wonder why convolution layers are so useful when we include them into neural networks. There are essentially two advantages convolution layers have over fully connected layers; 1. Parameter Sharing: Let's say we have 32x32x3 input image and convolve it with six 5x5 filters. This would results into an ... read more