LOCALIZATION OF OBJECT (SPINE) IN MEDICAL IMAGE USING ACTIVE SHAPE MODELS

Active shape models (ASM), consisting of a shape model and a local gray-level appearance model, can be used to locate the objects in images. In original ASM scheme, the model of object′s gray-level variations is based on the assumption of one-dimensional sampling and searching method. In this work a new way to model the gray-level appearance of the objects is explored, using a two-dimensional sampling and searching technique in a rectangular area around each landmark of object shape. The ASM based on this improvement is compared with the original ASM on an identical medical image set for task of spine localization. Experiments demonstrate that the method produces significantly fast, effective, accurate results for spine localization in medical images.

Optimal shape space and searching in the active shape model

A novel idea,called the optimal shape subspace （OSS） is first proposed for optimizing active shape model （ASM） search.It is constructed from the principal shape subspace and the principal shape variance subspace.It allows the reconstructed shape to vary more than that reconstructed in the standard ASM shape space,hence it is more expressive in representing shapes in real life.Then a cost function is developed,based on a study on the search process.An optimal searching method using the feedback information provided by the evaluation cost is proposed to improve the performance of ASM alignment.Experimental results show that the proposed OSS can offer the maximum shape variation with reserving the principal information and a unique local optimal shape is acquired after optimal searching.The combination of OSS and optimal searching can improve the ASM performance greatly.

Active Shape Model of Combining Pca and Ica: Application to Facial Feature Extraction

Active Shape Model （ASM） is a powerful statistical tool to extract the facial features of a face image under frontal view. It mainly relies on Principle Component Analysis （PCA） to statistically model the variability in the training set of example shapes. Independent Component Analysis （ICA） has been proven to be more efficient to extract face features than PCA. In this paper, we combine the PCA and ICA by the consecutive strategy to form a novel ASM. Firstly, an initial model, which shows the global shape variability in the training set, is generated by the PCA-based ASM. And then, the final shape model, which contains more local characters, is established by the ICA-based ASM. Experimental results verify that the accuracy of facial feature extraction is statistically significantly improved by applying the ICA modes after the PCA modes.

Active shape reconstruction using a novel visuotactile palm sensor

Tactile sensing enables high-precision 3D shape perception when vision is limited.However,tactilebased shape reconstruction remains a challenging problem.In this paper,a novel visuotactile sensor,GelStereo Palm 2.0,is proposed to better capture 3D contact geometry.Leveraging the dense tactile point cloud captured by GelStereo Palm 2.0,an active shape reconstruction pipeline is presented to achieve accurate and efficient 3D shape reconstruction on irregular surfaces.GelStereo Palm 2.0 achieves a spatial resolution of 1.5 mm and a reconstruction accuracy of 0.3 mm.The accuracy of the proposed active shape reconstruction pipeline reaches 2.3 mm within 18 explorations.The proposed method has potential applications in the shape reconstruction of transparent or underwater objects.

Feature Representation for Facial Expression Recognition Based on FACS and LBP

In expression recognition, feature representation is critical for successful recognition since it contains distinctive information of expressions. In this paper, a new approach for representing facial expression features is proposed with its objective to describe features in an effective and efficient way in order to improve the recognition performance. The method combines the facial action coding system(FACS) and 'uniform' local binary patterns(LBP) to represent facial expression features from coarse to fine. The facial feature regions are extracted by active shape models(ASM) based on FACS to obtain the gray-level texture. Then, LBP is used to represent expression features for enhancing the discriminant. A facial expression recognition system is developed based on this feature extraction method by using K nearest neighborhood(K-NN) classifier to recognize facial expressions. Finally, experiments are carried out to evaluate this feature extraction method. The significance of removing the unrelated facial regions and enhancing the discrimination ability of expression features in the recognition process is indicated by the results, in addition to its convenience.

Feature Extraction of Chinese Characters Based on ASM Algorithm

In Chinese Calligraphy education,the computer-based evaluation on Chinese handwriting is one of the problems in the field of computer intelligent education.In this study,the method of feature comparison is first proposed in the process of computer-based evaluation on Chinese handwriting,focusing on automatically and accurately extracting the features of Chinese characters.Then,the key technologies applied in feature extraction of Chinese character were analyzed.It discussed the representation of features,aligns training samples,and reduces dimensions by principal component analysis,established local grayscale model,and converged the gray-scale information of target feature points through statistical analysis.The experimental results show that the accuracy of the algorithm is 93.84%.