Feature extraction is the main driving force behind the advancement of the image processing techniques infields suchas image quality assessment, objectdetection, and object recognition. In this work, we perform a comprehensive and in-depth study on feature extraction for the following applications: image macro-uniformity assessment, 2.5D printing quality assessment, streak defect detection, and pedestrian detection. Firstly, a set of multi-scale wavelet-based features is proposed, and a quality predictor is trained to predict the perceived macro-uniformity. Secondly, the 2.5D printing quality is characterized by a set of merits that focus on the surface structure.Thirdly, a set of features is proposed to describe the streaks, based on which two detectors are developed: the first one uses Support Vector Machine (SVM) to train a binary classifier to detect the streak; the second one adopts Hidden Markov Model (HMM) to incorporates the row dependency information within a single streak. Finally, a novel set of pixel-difference features is proposed to develop a computationally efficient feature extraction method for pedestrian detection.