This paper presents a new approach for multi-view object class detection based on part models. While most existing approaches have in common that they use real images for training, our approach requires only a database of synthetic 3D models to represent both the appearance and the geometry of an object class. We use semantically equivalent object points on 3D models to build part models and encode the local appearance of the parts by a discriminative learning method that applies AdaBoost to histograms of gradients. The geometric configuration of the parts is represented by spatial distributions which are also directly derived from the 3D models. For recognizing an object in an image, our model provides object hypotheses which are re-ranked with global appearance models. The 2D localization is evaluated on the PASCAL 2006 data set for cars and bicycles, showing that its performance can compete with state-of-the-art detection results.

This paper proposes a novel approach to multi-view object class and viewpoint detection for the retrieval of images showing one or several objects from a given viewpoint, a viewpoint range or any viewpoint in image databases. All detectors are trained exclusively on a few synthetic 3D models without any manual bounding-box, viewpoint or part annotation, making object class and viewpoint detection a scalable learning task. Previous work on this topic relies on the detection of object parts for each individual viewpoint, ignoring the responses of part detectors specific to other viewpoints. Instead, we explicitly exploit appearance ambiguities caused by spurious detections of parts under more than one viewpoint by combining all detector responses in a joint spatial pyramid encoding. We achieve state-of-the-art results in multi-view object class detection and viewpoint determination on current benchmarking data sets and demonstrate increased robustness to partial occlusion.

This paper discusses a new framework to enhance image and video quality. Recent advances in high-dynamic-range image fusion and superresolution make it possible to extend the intensity range or to increase the resolution of the image beyond the limitations of the sensor. In this paper, we propose a new way to combine both of these fusion methods in a two-stage scheme. To achieve robust image enhancement in practical application scenarios, we adapt state-of-the-art methods for automatic photometric camera calibration, controlled image acquisition, image fusion and tone mapping. With respect to high-dynamic-range reconstruction, we show that only two input images can sufficiently capture the dynamic range of the scene. The usefulness and performance of this system is demonstrated on images taken with various types of cameras.

This paper presents a 3D approach to multi-view object class detection. Most existing approaches recognize object classes for a particular viewpoint or combine classifiers for a few discrete views. We propose instead to build 3D representations of object classes which allow to handle viewpoint changes and intra-class variability. Our approach extracts a set of pose and class discriminant features from synthetic 3D object models using a filtering procedure, evaluates their suitability for matching to real image data and represents them by their appearance and 3D position. We term these representations 3D Feature Maps. For recognizing an object class in an image we match the synthetic descriptors to the real ones in a 3D voting scheme. Geometric coherence is reinforced by means of a robust pose estimation which yields a 3D bounding box in addition to the 2D localization. The precision of the 3D pose estimation is evaluated on a set of images of a calibrated scene. The 2D localization is evaluated on the PASCAL 2006 dataset for motorbikes and cars, showing that its performance can compete with state-of-the-art 2D object detectors.

The precise alignment of a 3D model to 2D sensor images to recover the pose of an object in a scene is an important topic in computer vision. In this work, we outline a registration scheme to align arbitrary standard 3D models to optical and synthetic aperture radar (SAR) images in order to recover the full 6 degrees of freedom of the object. We propose a novel similarity measure which combines perspective contour matching and an appearance-based Mutual information (MI) measure. Unlike previous work, the resulting similarity measure is optimized using an evolutionary particle swarming strategy, parallelized to exploit the hardware acceleration potential of current generation graphics processors (GPUs). The performance of our registration scheme is systematically evaluated on an object tracking task using synthetic as well as real input images. We show that our approach leads to precise registration results, even for significant image noise, small object dimensions and partial occlusion where other methods would fail.