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In this paper, we aim to improve one of the current state-of-the-art models for facial components detection/localization. The objectives are to increase the amount of landmark points detected and improve the landmark extraction accuracy for frontal faces. The model is following Zhu and Ramanan's approach with a tree-structure. The popular AR dataset is chosen as an alternative training dataset as...
The intent of 3D-model classification is to find categories of similar objects according to their shapes. This task is a challenging and important problem in 3D-mining and shape processing. In this paper, we present a novel method to categorize 3D-objects based on view-based descriptors. The proposed method goes into two stages. The first stage corresponds to the training in which 3D-objects in the...
Roman coins play an important role to understand the Roman empire because they convey rich information about key historical events of the time. Moreover, as large amounts of coins are daily traded over the Internet, it becomes necessary to develop automatic coin recognition systems to prevent illegal trades. In this paper, we propose an automatic recognition method for ancient Roman coins. The proposed...
This paper presents a new approach of Reeb graph extraction adapted to 3D dynamic triangular Meshes. Particularly, we propose a new continuous scalar function, used for Reeb graph construction. This function is based on the heat diffusion properties. The restriction of the heat kernel to temporal domain makes the scalar function intrinsic and stable against perturbations. Due to the presence of neighborhood...
In this paper, we present a novel signature matching method based on supervised topic models. Shape Context features are extracted from signature shape contours which capture the local variations in signature properties. We then use the concept of topic models to learn the shape context features which correspond to individual authors. The approach consists of three primary steps. First, K-means is...
Conventional supervised object recognition methods have been investigated for many years. Despite their successes, there are still two suffering limitations: (1) various information of an object is represented by artificial features only derived from RGB images, (2) lots of manually labeled data is required by supervised learning. To address those limitations, we propose a new semi-supervised learning...
Active contour is a popular technique for vascular segmentation. However, existing active contour segmentation methods require users to set values for various parameters, which requires insights to the method's mathematical formulation. Manual tuning of these parameters to optimize segmentation results is laborious for clinicians who often lack in-depth knowledge of the segmentation algorithms. Moreover,...
To understand the human action in still images, it is effective to detect the human region. However, since appearance of human is much different due to pose and occlusion, the detection is quite difficult. Here we propose robust human detection method to pose and occlusion using Bag-of-Words (BoW). In general, the location information is helpful in classification. When the human has occlusion and...
Cirrhosis liver is a terrible disease which is threatening our lives. Meanwhile, cirrhosis will cause significant hepatic morphological changes. While it is well known that the livers from different subjects have similar global shape structure which means liver shape ensemble should be low-rank. However the deformation which caused by cirrhosis can be considered as sparse compared with the whole liver...
A new approach to recover 3-D shape from a Scanning Electron Microscope (SEM) image is described. With an ideal SEM image, 3-D shape can be recovered using the Fast Marching Method (FMM) applied to the Eikonal equation. However, when the light source direction is oblique, the correct shape cannot be obtained by the usual one-pass FMM. The new approach modifies the intensities in the original SEM image...
We proposed a framework for human action recognition by learning pose dictionary as the human appearance representation. At first, the shape based pose feature is constructed based on the contour points of the human silhouette and invariant to translation and scaling. After the local pose features are extracted from the original videos, the class-specific dictionaries are learned individually on the...
In this paper, we present an approach to retrieve documents based on logo spotting and recognition. A document retrieval system is proposed inspired from our previous method for logo spotting and recognition. First, the key-points from both the query logo images and a given set of document images are extracted and described by SIFT descriptor, and are matched in the SIFT feature space. They are filtered...
The assessment of embryos in vitro is an important tool both in In Vitro Fertilization (IVF) treatment and for research purposes. Traditionally, such assessment has been done manually and demands extensive training and expertise. Interobserver variability limits the use of evaluation, and the manual labor increases the cost of treatment and research. To this end, feature extraction and automatic annotation...
Most literatures have been relying on image processing approaches such as skin detection and depth thresholding for hand detection. These techniques are restricted by strong assumptions and normally possess low robustness in actual applications. In this paper, we focus on an appearance approach and propose a new feature extraction method based on sparse pixel-pair wise intensity comparisons for hand...
Recent works investigated the possibility to design solutions for pattern recognition problems by exploiting the huge amount of work done in bioinformatics. If the pattern recognition problem is cast in biological terms, then a huge range of algorithms, exploitable for classification, detection, visualization, etc. can be effectively borrowed. In this paper, we exploit biological sequence alignment...
Hand orientation is an important control parameter during reach-to-grasp task. In this paper, we presented a study for predicting hand orientation of non-human primate by decoding neural activities from primary motor cortex (M1). A non-human primate subject was guided to do reaching and grasping tasks meanwhile neural activities were acquired by chronically implanted microelectrode arrays. A Support...
Motion recognition based on trajectory is important for motion analysis. Complicated motion recognition is still a challenge in various applications of robot and automation. In this paper, we propose a novel framework with a new model, Scaled Indexing of General Shapes (S-IGS), for complicated motion recognition. The Scaled IGS is a quantified hierarchical model, representing 3D motion trajectories...
Monte Carlo (MC) sequential simulation is capable of providing system chronological information. It is a very useful tool for power system reliability analysis and planning, especially for systems involving time-dependent sources. In this paper, a new sequential simulation approach is proposed for reliability evaluation of composite power systems. The main idea is to apply Latin Hypercube sampling...
Robotic surgical assistant systems can transcend the precision of surgical procedure by integrating the position information of patient and robot in the surgical room. There are two conventional integration methods, the real-time position tracking and physically fixing. In clinical cases, the former has the problem of increasing radiation exposure; so physical fixation is more commonly used in robotic...
Shape context is a classic shape retrieval method with translation invariance, but it has not scalar or rotational invariance, which limits its application. A new shape feature descriptor-centroid distance ratio (CdR) is proposed and an improved shape retrieval algorithm based on CdR and shape context is brought forth. First, the contour points are sampled in order to reduce computation and then,...
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