Irreversible PrivacyPreserving Images Holding Spatial Information for HOG Feature Extraction In this paper, we propose a generation method of visually protected images and its application to privacypreserving machine learning. Images generated by the proposed method hold the gradient direction information of the original images, but have no the information. HistogramofOrientedGradients (HOG) features are extracted from the protected images, and the features are applied to machine learning algorithms. In addition, the proposed generation method is an irreversible one, so there is no need to manage secret keys, unlike encryption methods. In an experiment, a face classification task is carried out under the use of a support vector machine algorithm with the HOG features to demonstrate the effectiveness of the proposed method.

FPGAbased Implementation of HOG Algorithm: Techniques and Challenges Histogram of Oriented Gradients (HOG) is a method for extracting features from an image, which has many applications in Computer Vision. Due to the complexity and high amount of computations of this algorithm, softwarebased implementations of HOG cannot meet the realtime criterion. Therefore, many researchers have implemented HOG algorithm on hardware platforms such as FPGAs. This paper presents an extensive review of FPGAbased implementations of the HOG algorithm, that have been published from 2010 to 2019. Different techniques for hardware implementation of HOG are classified into three groups: methods which improve a certain stage of the algorithm, methods which optimize the whole algorithm, and methods which make minor simplification on the algorithm. In this paper, these three classes of techniques are reviewed. Finally, the speed and resource utilization of the surveyed papers are compared to each other in order to present a comprehensive conclusion on FPGAbased HOG implementation.