方向性勾配ヒストグラム（HOG）
方向性勾配ヒストグラム (HOG)は特徴記述子です コンピュータビジョンで使用 and 画像処理 for オブジェクト検出 and recognition. It captures the structure or shape of objects in an image by analyzing the distribution of gradient orientations within localized portions of the image.
To create a HOG descriptor, an image is first divided into small connected regions called cells. For each cell, the gradient is calculated, which represents the change in intensity or color at each pixel. The gradients are then used to compute a histogram of the orientations of these gradients, usually binned into a fixed number of angles. The histograms from neighboring cells are then combined to form a 特徴ベクトル 画像全体または特定の関心領域を記述します。
HOG is particularly effective because it is robust to changes in illumination and can capture the edges and contours that are crucial for identifying objects. It is commonly used in 歩行者検出 and other applications where distinguishing between different shapes and forms is essential.
HOG descriptors are typically combined with machine learning classifiers, such as サポートベクターマシン (SVM), to improve accuracy in detecting objects within images. Overall, HOG has become a foundational technique in the field of computer vision due to its effectiveness and efficiency.