1. Contribution of YOLO YOLO views the object detection task as detecting a spatially separated bounding box and its corresponding class distribution. Further, unlike previous researches, it uses a single neural network architecture to predict bounding box locations and the associated class probabilities simultaneously (One-stage, end-to-end). YOLO archives a real-time evaluation speed (45 frames…

1. Improvement from Fast R-CNN by introducing a Region Proposal Network Introduce a Region Proposal Network (RPN) and replace the external region proposal algorithms (e.g., Selective Search, EdgeBoxes), which costs 2 and 0.2 seconds per image, respectively. Region proposal algorithms are slow by order of magnitude as they are implemented on CPU, while the detection network is on GPU. RPN takes…

1. Dilated Convolution Dilated convolution, which is also often called atrous convolution, was introduced in 2016 ICLR. Its major idea is that when performing convolution, not taking a look at directly adjacent pixels, but further away pixels by a certain distance. This distance is called ‘Dilation rate r,’ and the dilated convolution can…

1. Motivation When we study neural network architectures based on encoder and decoder, it is commonly observed that the network performs downsampling in the encoder and upsampling inside the decoder, as is illustrated in Fig 1. Methods for downsampling are max pooling and strided convolution. …

1. Recap Previously, we have seen how local features are extracted from an image, using scale-invariant local feature descriptors such as Harris-Laplace and SIFT. After extracting local features individually from two images, the local features can be paired by searching the one with the highest similarity. More specifically, the basic matching algorithm…

1. SIFT (Scale Invariant Feature Transform) Another scale-invariant algorithm I want to address is the SIFT. With SIFT, the location of local feature points (interest points) are extracted from an image, and further, the corresponding vector representation of the respective interest point is generated, which could be used later on for feature matching between images. As…

This article is the second part of the topic: scale-invariant local feature extraction. …

1. Motivation: Why scale-invariant? Let’s recall what we studied about the local feature extractors: Harris and Hessian in the last article. Harris and Hessian are strong corner detectors. However, detectors are rotation invariant. However, they are not scale-invariant, which is a crucial drawback in terms of feature detection. …