In this paper, we propose a variational method to segment image objects, which have a given parametric shape based on a level-set formulation of the Mumford-Shah functional, and the shape parameters. We define an ener...
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We study the non-linear behavior of the KIII model for natural image classification. The KIII model is designed to be a dynamic computational model that simulates the sensory cortex. The KIII model has been explored f...
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We study the non-linear behavior of the KIII model for natural image classification. The KIII model is designed to be a dynamic computational model that simulates the sensory cortex. The KIII model has been explored for rudimentary pattern recognition and classification in noisy environment [1-3]. We extend the study of KIII models in understanding whether self-organized neural populations can be exploited into perceptual and memory producing systems such as in natural image classification. Our goal is to obtain a quantitative index on how well the KIII model behaves when it is assigned the task to identify and distinguish one class of natural image from the other based on color and texture features. For twenty training data, twenty validation data and eighty test data set for four image classes, we obtain 80% correct classification using the KIII. We compare a standard non linear neural network tools such as back propagation for the classification of the same set of natural images and obtain 65% correct classification. We conclude that dynamic neural computational models such as KIII may be suitable candidates for natural image classification.
Motivated by the success of free-parts based representations in face recognition, we have attempted to address some of the problems associated with applying such a philosophy to the task of speaker-independent visual ...
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作者：
Lucey, SimonLucey, PatrickAdvanced Multimedia Processing Laboratory
Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA15213 United States Speech
Audio Image and Video Research Laboratory Queensland University of Technology GPO Box 2424 Brisbane4001 Australia
Motivated by the success of free-parts based representations in face recognition [1] we have attempted to address some of the problems associated with applying such a philosophy to the task of speaker-independent auto...
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This paper presents a feature point tracking algorithm using optical flow under the non-prior training active feature model (NPT-AFM) framework. The proposed algorithm mainly focuses on analysis of deformable objects,...
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There has been intense research in feature binding to understand the parallel processing of features in visual information processing. The synchronization of spiking neurons is important for successful feature binding...
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There has been intense research in feature binding to understand the parallel processing of features in visual information processing. The synchronization of spiking neurons is important for successful feature binding. In this work, we propose a novel approach to feature binding in spiking neurons using chaotic synchronization. We exploit each image pixel intensity value as individual neuron to generate chaotic time series. We generate the coupled map lattice series for neighborhood interaction and synchronization in spatiotemporal space. The largest cluster in the time series with similar chaotic synchronization parameter is used to generate segmented image. We obtain proof-of-concept application of our model in MR image clustering and compare our results with the existing Otsu adaptive segmentation technique.
Pseudo-semantic labeling is a novel approach to organize mobile multimedia content such as images and videos. We have developed low-complexity algorithms to derive labels, such as "indoor/outdoor", "fac...
Pseudo-semantic labeling is a novel approach to organize mobile multimedia content such as images and videos. We have developed low-complexity algorithms to derive labels, such as "indoor/outdoor", "face/not face", that can be run on the mobile device. "Indoor/outdoor" classification is done based on the presence of sky in the images. Skin like pixels are detected based on the color information and if present they are matched to a pre-defined "facetemplate" to detect the presence of faces in images. We have developed an initial framework for finding the degree of blurriness in an image, which can be used for labels like "blurry/not blurry".
Motivated by the success of free-parts based representations in face recognition, we have attempted to address some of the problems associated with applying such a philosophy to the task of speaker-independent visual ...
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Motivated by the success of free-parts based representations in face recognition, we have attempted to address some of the problems associated with applying such a philosophy to the task of speaker-independent visual speech recognition. A major problem with canonical area-based approaches in automatic visual speech recognition is the dependence these approaches have on locating and tracking the speaker’s region of interest (ROI) correctly. By employing a free-parts representation,we assume that the position/structure of patches within the mouth image can be relaxed so they can "freely" move to varying extents, hence reducing the influence of the front-end effect. In this paper, we show that by using a free-parts representation we gain some robustness against the problem of ROI localisation and tracking compared to current area-based feature extraction techniques such as the discrete cosine transform (DCT). Also in this paper, we expose the importance of representation for the task of visual speech recognition highlighted by the poor results current representations yield.
We propose a face mosaicing approach to model both the facial appearance and geometry from pose-varying videos, and apply it in face tracking and recognition. The basic idea is that by approximating the human head as ...
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ISBN:
(纸本)0769523722
We propose a face mosaicing approach to model both the facial appearance and geometry from pose-varying videos, and apply it in face tracking and recognition. The basic idea is that by approximating the human head as a 3D ellipsoid, multi-view face images can be back projected onto the surface of the ellipsoid, and the surface texture map is decomposed into an array of local patches. During the online modeling process, the position and pose of the first frame is assumed to be known for a given video sequence. For each frame in the sequence, the algorithm estimates the face-position and pose, and generates a texture map, which is further utilized in updating the mosaic model.
Researchers have been working on human face recognition for decades. Face recognition is hard due to different types of variations in face images, such as pose, illumination and expression, among which pose variation ...
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ISBN:
(纸本)0769523722
Researchers have been working on human face recognition for decades. Face recognition is hard due to different types of variations in face images, such as pose, illumination and expression, among which pose variation is the hardest one to deal with. To improve face recognition under pose variation, this paper presents a geometry assisted probabilistic approach. We approximate a human head with a 3D ellipsoid model, so that any face image is a 2D projection of such a 3D ellipsoid at a certain pose. In this approach, both training and test images are back projected to the surface of the 3D ellipsoid, according to their estimated poses, to form the texture maps. Thus the recognition can be conducted by comparing the texture maps instead of the original images, as done in traditional face recognition. In addition, we represent the texture map as an array of local patches, which enables us to train a probabilistic model for comparing corresponding patches. By conducting experiments on the CMU PIE database, we show that the proposed algorithm provides better performance than the existing algorithms.
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