20 research outputs found
On Using Physical Analogies for Feature and Shape Extraction in Computer Vision
No full textThere is a rich literature of approaches to image feature extraction in computer vision. Many sophisticated approaches exist for low- and high-level feature extraction but can be complex to implement with parameter choice guided by experimentation, but impeded by speed of computation. We have developed new ways to extract features based on notional use of physical paradigms, with parameterisation that is more familiar to a scientifically-trained user, aiming to make best use of computational resource. We describe how analogies based on gravitational force can be used for low-level analysis, whilst analogies of water flow and heat can be deployed to achieve high-level smooth shape detection. These new approaches to arbitrary shape extraction are compared with standard state-of-art approaches by curve evolution. There is no comparator operator to our use of gravitational force. We also aim to show that the implementation is consistent with the original motivations for these techniques and so contend that the exploration of physical paradigms offers a promising new avenue for new approaches to feature extraction in computer vision
On Using Physical Analogies for Feature and Shape Extraction in Computer Vision
No full textThere is a rich literature of approaches to image feature extraction in computer vision. Many sophisticated approaches exist for low- and for high-level feature extraction but can be complex to implement with parameter choice guided by experimentation, but with performance analysis and optimization impeded by speed of computation. We have developed new feature extraction techniques on notional use of physical paradigms, with parametrization aimed to be more familiar to a scientifically trained user, aiming to make best use of computational resource. This paper is the first unified description of these new approaches, outlining the basis and results that can be achieved. We describe how gravitational force can be used for low-level analysis, while analogies of water flow and heat can be deployed to achieve high-level smooth shape detection, by determining features and shapes in a selection of images, comparing results with those by stock approaches from the literature. We also aim to show that the implementation is consistent with the original motivations for these techniques and so contend that the exploration of physical paradigms offers a promising new avenue for new approaches to feature extraction in computer vision
Shape classification via image-based multiscale description
No full textWe introduce a new multiscale Fourier-based object description in 2-D space using a low-pass Gaussian filter (LPGF) and a high-pass Gaussian filter (HPGF), separately. Using the LPGF at different scales (standard deviation) represents the inner and central part of an object more than the boundary. On the other hand using the HPGF at different scales represents the boundary and exterior parts of an object more than the central part. Our algorithms are also organized to achieve size, translation and rotation invariance. Evaluation indicates that representing the boundary and exterior parts more than the central part using the HPGF performs better than the LPGF based multiscale representation, and in comparison to Zernike moments and elliptic Fourier descriptors with respect to increasing noise. Multiscale description using HPGF in 2-D also outperforms Wavelet transform based multiscale contour Fourier descriptors and performs similar to the perimeter descriptors without any noise
Enhanced Facial Feature Extraction Using Region-Based Super-Resolution Aided Video Sequences
No full textFacial feature extraction is a fundamental problem in image processing. Correct extraction of features is essential for the success of many applications. Typical feature extraction algorithms fail for low resolution images which do not contain sufficient facial details. In this paper, a region-based super-resolution aided facial feature extraction method for low resolution video sequences is described. The region based approach makes use of segmented faces as the region of interest whereby a significant reduction in computational complexity of the super-resolution algorithm is achieved. The results indicate that the region-based super-resolution aided facial feature extraction algorithm provides significant performance improvement in terms of correctly detecting the location of the facial feature points. There are 6.4 fold reductions in the computational cost
Shape Extraction Via Heat Flow Analogy
No full textIn this paper, we introduce a novel evolution-based segmentation algorithm by using the heat flow analogy, to gain practical advantage. The proposed algorithm consists of two parts. In the first part, we represent a particular heat conduction problem in the image domain to roughly segment the region of interest. Then we use geometric heat flow to complete the segmentation, by smoothing extracted boundaries and removing possible noise inside the prior segmented region. The proposed algorithm is compared with active contour models and is tested on synthetic and medical images. Experimental results indicate that our approach works well in noisy conditions without pre-processing. It can detect multiple objects simultaneously. It is also computationally more efficient and easier to control and implement in comparison to active contour models
Efficient Face and Facial Feature Tracking using Search Region Estimation
No full textIn this paper an intelligent and efficient combination of several methods are employed for face and facial feature tracking with the motivation for real time applications. Face tracking algorithm is based on color and connected component analysis. It is scale, pose and orientation invariant, and can be implemented in real time in controlled environments. The more challenging problem of facial feature tracking uses intensity based adaptive clustering on facial feature sub-images. New search region estimation for each sub-image is proposed. The technique employs facial expression aware eye sub-image prediction. The simulation results indicate that facial feature tracking is efficient with an average tracking rate of 99% with a three pixel range under different head movements such as translation, rotation, tilt, and scale changes. Furthermore it is robust under varying facial expressions and non-uniform illumination
Skeleton Extraction via Anisotropic Heat Flow
No full textWe introduce a novel skeleton extraction algorithm in binary and gray-scale images,
based on the anisotropic heat diffusion analogy. We propose to diffuse image in the dominance of direction normal to the feature boundaries and also allow tangential diffusion
to contribute slightly. The proposed anisotropic diffusion provides a high quality medial function in the image, since it removes noise and preserves prominent curvatures
of the shape along the level-sets (skeleton locations). Then the skeleton strength map,
which provides the likelihood to be a skeleton point, is obtained by computing the mean
curvature of level-sets. The overall process is completed by non-maxima suppression
and hysteresis thresholding to obtain thin and binary skeleton. Results indicate that this
approach has advantages in handling noise in the image and in obtaining smooth shape
skeleton because of the directional averaging inherent of our new anisotropic heat flow
A novel framework and concept-based semantic search Interface for abnormal crowd behaviour analysis in surveillance videos
No full textMonitoring continuously captured surveillance videos is a challenging and time consuming task. To assist this issue, a new framework is introduced that applies anomaly detection, semantic annotation and provides a concept-based search interface. In particular, novel optical flow based features are used for abnormal crowd behaviour detection. Then, processed surveillance videos are annotated using a new semantic metadata model based on multimedia standards using Semantic Web technologies. In this way, globally inter-operable metadata about abnormal crowd behaviours are generated. Finally, for the first time, based on crowd behaviours, a novel concept-based semantic search interface is proposed. In the proposed interface, along with search results (video segments), statistical data about crowd behaviours are also presented. With extensive user studies, it is demonstrated that the proposed concept-based semantic search interface enables efficient search and analysis of abnormal crowd behaviours. Although there are existing works to achieve (a) crowd anomaly detection, (b) semantic annotation and (c) semantic search interface, none of the existing works combine these three system components in a novel framework like the one proposed in this paper. In each system component, we introduce contributions to the field as well as use the Semantic Web technologies to combine and standardize output of different system components; output of the anomaly detection is automatically annotated with metadata and stored to a semantic database. When continuous surveillance videos are processed, only the semantic database is updated. Finally, the user interface queries the updated database for searching/analyzing surveillance videos without changing any coding. Thus, the framework supports re-usability. This paper explains and evaluates different components of the framework
Region-Based Super-Resolution Aided Facial Feature Extraction from Low-Resolution Video Sequences
No full textA hybrid approach for binary and multi-class classification of voice disorders using a pre-trained model and ensemble classifiers
No full textContains fulltext :
319320.pdf (Publisher’s version ) (Open Access)14 p
