Automatic speech segmentation using probabilistic latent component modeling

Latent variable methods, such as PLCA (Probabilistic Latent Component Analysis) have been successfully used for analysis of non-negative signal representations. In this paper, we formulate PLCS (Probabilistic Latent Component Segmentation), which models each time frame of a spectrogram as a spectral distribution. Given the signal spectrogram, the segmentation boundaries are estimated using a maximum-likelihood approach. For an efficient solution, the algorithm imposes a hard constraint that each segment is modelled by a single latent component. The hard constraint facilitates the solution of ML boundary estimation using dynamic programming. The PLCS framework does not impose a parametric assumption unlike earlier ML segmentation techniques. PLCS can be naturally extended to model coarticulation between successive phones. Experiments on the TIMIT corpus show that the proposed technique is promising compared to most state of the art speech segmentation algorithms.

NESP: Nonlinear enhancement and selection of plane for optimal segmentation and recognition of scene word images

In this paper, we report a breakthrough result on the difficult task of segmentation and recognition of coloured text from the word image dataset of ICDAR robust reading competition challenge 2: reading text in scene images. We split the word image into individual colour, gray and lightness planes and enhance the contrast of each of these planes independently by a power-law transform. The discrimination factor of each plane is computed as the maximum between-class variance used in Otsu thresholding. The plane that has maximum discrimination factor is selected for segmentation. The trial version of Omnipage OCR is then used on the binarized words for recognition. Our recognition results on ICDAR 2011 and ICDAR 2003 word datasets are compared with those reported in the literature. As baseline, the images binarized by simple global and local thresholding techniques were also recognized. The word recognition rate obtained by our non-linear enhancement and selection of plance method is 72.8% and 66.2% for ICDAR 2011 and 2003 word datasets, respectively. We have created ground-truth for each image at the pixel level to benchmark these datasets using a toolkit developed by us. The recognition rate of benchmarked images is 86.7% and 83.9% for ICDAR 2011 and 2003 datasets, respectively.

Image-Segmentation Technique to Analyze Deformation Profiles in Different Direct Shear Tests

Results from interface shear tests on sand-geosynthetic interfaces are examined in light of surface roughness of the interacting geosynthetic material. Three different types of interface shear tests carried out in the frame of direct shear-test setup are compared to understand the effect of parameters like box fixity and symmetry on the interface shear characteristics. Formation of shear bands close to the interface is visualized in the tests and the bands are analyzed using image-segmentation techniques in MATLAB. A woven geotextile with moderate roughness and a geomembrane with minimal roughness are used in the tests. The effect of surface roughness of the geosynthetic material on the formation of shear bands, movement of sand particles, and interface shear parameters are studied and compared through visual observations, image analyses, and image-segmentation techniques.

Crowd Flow Segmentation based on Motion Vectors in H.264 Compressed Domain

In this work, we have explored the prospect of segmenting crowd flow in H. 264 compressed videos by merely using motion vectors. The motion vectors are extracted by partially decoding the corresponding video sequence in the H. 264 compressed domain. The region of interest ie., crowd flow region is extracted and the motion vectors that spans the region of interest is preprocessed and a collective representation of the motion vectors for the entire video is obtained. The obtained motion vectors for the corresponding video is then clustered by using EM algorithm. Finally, the clusters which converges to a single flow are merged together based on the bhattacharya distance measure between the histogram of the of the orientation of the motion vectors at the boundaries of the clusters. We had implemented our proposed approach on the complex crowd flow dataset provided by 1] and compared our results by using Jaccard measure. Since we are performing crowd flow segmentation in the compressed domain using only motion vectors, our proposed approach performs much faster compared to other pixel domain counterparts still retaining better accuracy.

SeamSeg: Video Object Segmentation using Patch Seams

In this paper, we propose a technique for video object segmentation using patch seams across frames. Typically, seams, which are connected paths of low energy, are utilised for retargeting, where the primary aim is to reduce the image size while preserving the salient image contents. Here, we adapt the formulation of seams for temporal label propagation. The energy function associated with the proposed video seams provides temporal linking of patches across frames, to accurately segment the object. The proposed energy function takes into account the similarity of patches along the seam, temporal consistency of motion and spatial coherency of seams. Label propagation is achieved with high fidelity in the critical boundary regions, utilising the proposed patch seams. To achieve this without additional overheads, we curtail the error propagation by formulating boundary regions as rough-sets. The proposed approach out-perform state-of-the-art supervised and unsupervised algorithms, on benchmark datasets.

Improved recognition of aged Kannada documents by effective segmentation of merged characters

In optical character recognition of very old books, the recognition accuracy drops mainly due to the merging or breaking of characters. In this paper, we propose the first algorithm to segment merged Kannada characters by using a hypothesis to select the positions to be cut. This method searches for the best possible positions to segment, by taking into account the support vector machine classifier's recognition score and the validity of the aspect ratio (width to height ratio) of the segments between every pair of cut positions. The hypothesis to select the cut position is based on the fact that a concave surface exists above and below the touching portion. These concave surfaces are noted down by tracing the valleys in the top contour of the image and similarly doing it for the image rotated upside-down. The cut positions are then derived as closely matching valleys of the original and the rotated images. Our proposed segmentation algorithm works well for different font styles, shapes and sizes better than the existing vertical projection profile based segmentation. The proposed algorithm has been tested on 1125 different word images, each containing multiple merged characters, from an old Kannada book and 89.6% correct segmentation is achieved and the character recognition accuracy of merged words is 91.2%. A few points of merge are still missed due to the absence of a matched valley due to the specific shapes of the particular characters meeting at the merges.

CROWD FLOW SEGMENTATION IN COMPRESSED DOMAIN USING CRF

Crowd flow segmentation is an important step in many video surveillance tasks. In this work, we propose an algorithm for segmenting flows in H.264 compressed videos in a completely unsupervised manner. Our algorithm works on motion vectors which can be obtained by partially decoding the compressed video without extracting any additional features. Our approach is based on modelling the motion vector field as a Conditional Random Field (CRF) and obtaining oriented motion segments by finding the optimal labelling which minimises the global energy of CRF. These oriented motion segments are recursively merged based on gradient across their boundaries to obtain the final flow segments. This work in compressed domain can be easily extended to pixel domain by substituting motion vectors with motion based features like optical flow. The proposed algorithm is experimentally evaluated on a standard crowd flow dataset and its superior performance in both accuracy and computational time are demonstrated through quantitative results.

The Effect of Periodic Segmentation Cracks on the Interfacial Debonding: Study on Interfacial Stresses

Hard coatings on relatively soft substrate always face the danger of debonding along the interface. Interfacial stresses are considered to be the initial driving force for the interfacial debonding of the relatively strong bonded coatings. Interfacial stresses due to the mismatch of strain between the coating and substrate are simulated with FEM firstly. The distribution of the interfacial stresses is achieved, which confirms an excessive stresses concentration near the interface end. Subsequently, the redistribution of interfacial stresses is calculated for a coating with periodic segmentation cracks. Results indicate that the distribution of interfacial stresses is altered greatly with the periodic segmentation cracks. To reveal the effect of the spacing of the periodic segmentation cracks on the distribution of interfacial stresses, different crack density is modeled within the coating. It is found that that the peak values of the interfacial stresses decrease with the increase of crack density, i.e. with reduction of spacing of segmentation cracks.

On the Formation of Periodic Segmentation Cracks in a Coating Plated on a Substrate with Periodic Subsurface Inclusions

The mechanism of the formation of periodic segmentation cracks of a coating plated on a substrate with periodic subsurface inclusions (PSI) is investigated. The internal stress in coating and subsequently the strain energy release rate (SERR) of the segmentation cracks are computed with finite element method (FEM). And the effect of the geometrical parameters of the PSI is studied. The results indicate that the ratio of the width of the inclusion to the period of the repeated structure has an optimum value, at which the maximum internal tensile stress and SERR arise. On the other hand, the ratio of the max-thickness of the inclusion to the thickness of the coating has a threshold value, above which the further increase of this ratio should seldom influence the internal stress or the SERR.

On The Interaction Of Channeling/Segmentation Cracks Of Coating: Study On The Critical Spacing

Channeling/segmentation cracks may arise in the coating subjected to in-plane tensile stress. The interaction between these multiple cracks, say the effect of the spacing between two adjacent cracks oil the behaviors of channels themselves and the interface around the interface corners, attracts wide interest. However, if the spacing is greater than a specific magniture,, namely the Critical Spacing (CS), there should be no interaction between such channeling/segmentation cracks. In this study, file mechanism of the effect of the crack spacing oil the interfacial stress around the interface corner will be Interpreted firstly. Then the existence of the CS will be verified and the relationship between the CS and the so-called stress transfer length Ill coating will be established for plane strain condition. Finally, the dependence of the stress transfer length, simultaneously of the CS, on the sensitive parameters will be investigated with finite element method and expressed with a simple empirical formula. (C) 2007 Elsevier Ltd. All rights reserved.