This paper presents a computationally efficient framework for part-based modeling and recognition of objects, inspired by the pictorial structure models introduced by Fischler and Elschlager. The core idea is to represent an object as a collection of parts arranged in a deformable configuration, with each part's appearance modeled separately and the configuration represented by spring-like connections between parts. The authors address the challenges of energy minimization, parameterization, and finding multiple good hypotheses for object locations. They provide efficient algorithms for energy minimization, model learning from training examples, and hypothesizing multiple matches. The main contributions include an efficient algorithm for energy minimization, a method for learning model parameters, and techniques for finding multiple good hypotheses. The paper demonstrates these techniques using face and human body recognition, showing the ability to locate these objects in novel images.This paper presents a computationally efficient framework for part-based modeling and recognition of objects, inspired by the pictorial structure models introduced by Fischler and Elschlager. The core idea is to represent an object as a collection of parts arranged in a deformable configuration, with each part's appearance modeled separately and the configuration represented by spring-like connections between parts. The authors address the challenges of energy minimization, parameterization, and finding multiple good hypotheses for object locations. They provide efficient algorithms for energy minimization, model learning from training examples, and hypothesizing multiple matches. The main contributions include an efficient algorithm for energy minimization, a method for learning model parameters, and techniques for finding multiple good hypotheses. The paper demonstrates these techniques using face and human body recognition, showing the ability to locate these objects in novel images.