This paper presents a stereo vision-based scene model for traffic scenarios. Our approach effectively couples bottom-up image segmentation with object-level knowledge in a sound probabilistic fashion. The relevant scene structure, i.e. obstacles and freespace, is encoded using individual Stixels as building blocks that are computed bottom-up from dense disparity images. We present a principled ...

To reduce the gap between performance of traditional speech recognition systems and human speech recognition skills, a new architecture is required. A system that is capable of incremental learning offers one such solution to this problem. This thesis introduces a bottom-up approach for such a speech processing system, consisting of a novel blind speech segmentation algorithm, a segmental featu...

We introduce a segmentation-based detection and top-down figure-ground delineation algorithm. Unlike common methods which use appearance for detection, our method relies primarily on the shape of objects as is reflected by their bottom-up segmentation. Our algorithm receives as input an image, along with its bottom-up hierarchical segmentation. The shape of each segment is then described both b...

A popular trend in semantic segmentation is to use top-down object information to improve bottom-up segmentation. For instance, the classification scores of the Bag of Features (BoF) model for image classification have been used to build a top-down categorization cost in a Conditional Random Field (CRF) model for semantic segmentation. Recent work shows that discriminative sparse dictionary lea...

The process of segmenting an input image refers to the task of determining coherent image regions which are likely to correspond to the existing objects in the image. An image segmentation algorithm follows some basic grouping rules and usually attaches a label to each pixel, denoting which region the pixel belongs to. One such way to model segmentation as a labeling problem is to consider an i...

In this paper, we tackle the problem of unsupervised selection and posterior recognition of visual landmarks in images sequences acquired by an indoor mobile robot. This is a highly valuable perceptual capability for a wide variety of robotic applications, in particular autonomous navigation. Our method combines a bottom-up data driven approach with top-down feedback provided by high level sema...

Given their powerful feature representation for recognition, deep convolutional neural networks (DCNNs) have been driving rapid advances in high-level computer vision tasks. However, their performance in semantic image segmentation is still not satisfactory. Based on the analysis of visual mechanism, we conclude that DCNNs in a bottom-up manner are not enough, because semantic image segmentatio...

This work presents a combined bottom-up and top-down approach to extraction and refinement of building footprints from airborne LIDAR data. Building footprints are interesting for many applications in urban planning. The cadastral maps, however, may be limited for certain areas or not be updated frequently. Airborne laser scanning data is therefore considered by many people in the last decade a...

We develop an object detection method combining top-down recognition with bottom-up image segmentation. There are two main steps in this method: a hypothesis generation step and a verification step. In the top-down hypothesis generation step, we design an improved Shape Context feature, which is more robust to object deformation and background clutter. The improved Shape Context is used to gene...

Despite using different algorithms, most unsupervised automatic phone segmentation methods achieve similar performance in terms of percentage correct boundary detection. Nevertheless, unsupervised segmentation algorithms are not able to perfectly reproduce manually obtained reference transcriptions. This paper investigates fundamental problems for unsupervised segmentation algorithms by compari...