A novel approach to represent the profile of objects using Gaussian models is presented. The profile is a representation of the object and its surrounding regions. The object profile can be viewed as a comprehensive feature of that object and its surrounding regions. Different algorithms to estimate the profile are described. Geometric descriptors of the model are also proposed. The profile model is empirically shown to be effective and easily applicable to certain object recognition and segmentation tasks. Application experiments include modeling thin and thick objects as straight-lines, curves, and blobs using different primitives such as gray-level intensities, RGB, and HSV color. The datasets used for empirical validation are quite challenging. Datasets include images and videos corresponding to outdoor video, most of them with moving cameras. Some of the typical problems faced with the used datasets are: digital scaling, compression artifacts, camera jitter, weather effects, and cluttered backgrounds. We demonstrate the potential of leveraging the context of objects of interest as a part of an online detection process. Sample applications including detection of wires, sea horizon, street, and vehicles in outdoor videos are considered.