In this appendix we present the results obtained by applying our method to the problem of boundary detection in natural images. We show that we can outperform state-of-the-art techniques also on this task. Many analogies connect the centerline detection to the contour detection problem. First, in both cases we want to extract one dimensional curves form images. Second, in both situation the exa...

Edge detection is one of the most commonly used operations in image analysis and is also an essential pre-processing step in image segmentation. An edge is the boundary between an object and the background, and indicates the boundary between adjacent parts of image and overlapping objects. Here we are reviewing several techniques for edge detection like Sobel operator technique, Prewitt techniq...

.Various methods have been proposed and developed for video shot boundary detection. Commonly used method for shot detection is Block Matching Algorithm. It is the most commonly used motion estimation technique. Various block matching algorithms have been proposed for shot detection such as Exhaustive Search, Three Step Search, New Three Step Search, Simple and Efficient Search, Four Step Searc...

Acoustic cues related to the voice source, including harmonic structure and spectral tilt, were examined for relevance to prosodic boundary detection. The measurements considered here comprise five categories: duration, pitch, harmonic structure, spectral tilt, and amplitude. Distributions of the measurements and statistical analysis show that the measurements may be used to differentiate betwe...

This paper presents a deep neural network (DNN) approach to sentence boundary detection in broadcast news. We extract prosodic and lexical features at each inter-word position in the transcripts and learn a sequential classifier to label these positions as either boundary or non-boundary. This work is realized by a hybrid DNN-CRF (conditional random field) architecture. The DNN accepts prosodic...

Boundary detection is a fundamental problem in computer vision. However, boundary detection is difficult as it involves integrating multiple cues (intensity, color, texture) as well as trying to incorporate object class or scene level descriptions to mitigate the ambiguity of the local signal. In this paper we investigate incorporating a priori information into boundary detection. We learn a pr...

In echocardiography, the radio-frequency (RF) image is a rich source of information about the investigated tissues. Nevertheless, very few works are dedicated to boundary detection based on the RF image, as opposed to envelope image. In this paper, we investigate the feasibility and limitations of boundary detection in echocardiographic images based on the RF signal. We introduce two types of R...

Robust pattern recognition within the Bayesian framework for scene segmentation/boundary detection is oftentimes hampered by the presence of textures within natural images. In order to improve segmentation/boundary detection on natural images, it is necessary to combine multiple features eeectively. This paper introduces two algorithms for combining both color and texture features to assist bou...

This study presents a database that can be used for boundary detection and image segmentation evaluation. Images in our database are based on image pairs instead of single images, which is a remarkable difference from existing databases. This characteristic will greatly facilitate the algorithms combining low-level cues such as brightness, colour and texture with depth information generated fro...

We extend existing methods for automatic sentence boundary detection by leveraging multiple recognizer hypotheses in order to provide robustness to speech recognition errors. For each hypothesized word sequence, an HMM is used to estimate the posterior probability of a sentence boundary at each word boundary. The hypotheses are combined using confusion networks to determine the overall most lik...