The richness of natural images makes the quest for optimal representations in image processing and computer vision challenging. The latter observation has not prevented the design of image representations, which trade off between efficiency and complexity, while achieving accurate rendering of smooth regions as well as reproducing faithful contours and textures. The most recent ones, proposed in the past decade, share an hybrid heritage highlighting the multiscale and oriented nature of edges and patterns in images. This paper presents a panorama of the aforementioned literature on decompositions in multiscale, multi-orientation bases or dictionaries. They typically exhibit redundancy to improve sparsity in the transformed domain and sometimes its invariance with respect to simple geometric deformations (translation, rotation). Oriented multiscale dictionaries extend traditional wavelet processing and may offer rotation invariance. Highly redundant dictionaries require specific algorithms to simplify the search for an efficient (sparse) representation. We also discuss the extension of multiscale geometric decompositions to non-Euclidean domains such as the sphere or arbitrary meshed surfaces. The etymology of panorama suggests an overview, based on a choice of partially overlapping “pictures”.

We describe a method for learning steerable deformable part models. Our models exploit the fact that part templates can be written as linear filter banks. We demonstrate that one can enforce steerability and separability during learning by applying rank constraints. These constraints are enforced with a coordinate descent learning algorithm, where each step can be solved with an off-the-shelf structured SVM solver. The resulting models are orders of magnitude smaller than their counterparts, greatly simplifying learning and reducing run-time computation. Limiting the degrees of freedom also reduces overfitting, which is useful for learning large part vocabularies from limited training data. We learn steerable variants of several state-of-the-art models for object detection, human pose estimation, and facial landmark estimation. Our steerable models are smaller, faster, and often improve performance. 1.