Manas Kamal Bhuyan

Hand pose recognition using geometric features

In this paper, a novel approach for hand pose recognition by using key geometrical features of hand is proposed. A skeletal hand model is constructed to analyze the abduction and adduction movements of the fingers and these variations are modeled by multidimensional probabilistic distributions. For recognizing hand poses, proximity measures are computed between input gestures and pre-modeled gesture patterns. The proposed algorithm is more robust to the improper hand segmentation and side movements of fingers. Experimental results show that the proposed method is very much suitable for the applications related to Human Computer Interactions (HCI).

Feature Extraction from 2D Gesture Trajectory in Dynamic Hand Gesture Recognition

Vision-based hand gesture recognition is a popular research topic for human-machine interaction (HMI). We have earlier developed a model-based method for tracking hand motion in complex scene by using Hausdorff tracker. In this paper, we now propose to extract certain features from the gesture trajectory so as to identify the form of the trajectory. Thus, these features can be efficiently used for trajectory guided recognition/classification of hand gestures. Our experimental results show 95% of accuracy in identifying the forms of the gesture trajectories. This indicates that the trajectory features proposed in this paper are appropriate for defining a particular gesture trajectory

A Framework for Hand Gesture Recognition with Applications to Sign Language

Sign language is the most natural and expressive way for the hearing impaired. Because of this, automatic sign language recognition has long attracted vision researchers. It offers enhancement of communication capabilities for the speech and hearing impaired, promising improved social opportunities and integration. This paper describes a gesture recognition system which can recognize wide classes of hand gesture in a vision based setup. Experimental results demonstrate that our proposed recognition system can be used reliably in recognizing some signs of native Indian sign language

Finite state representation of hand gesture using key video object plane

The use of hand gestures has become an important part of human computer interaction (HCI) in recent years. Vision-based hand gesture recognition involves visual analysis of hand shape, position and/or movement. Due to co-articulation that occurs during transition from one gesture to the next, problem is encountered in continuous hand gesture recognition. This may be tackled by identifying the key frames in the gesture video sequence. Key frames are the frames that can represent the salient content of a video shot in an abstracted manner. In this paper, we present an object-based scheme for key frame extraction using Hausdorff distance and subsequent local motion analysis by angular circular local motion descriptor (ACLM) for gesture representation. We propose a finite state machine (FSM) in which gestures are represented by the sequence of key frames and the corresponding key frame duration. Experimental results obtained demonstrate the effectiveness of our proposed scheme for key frame extraction and subsequent gesture representation.

Hand pose identification from monocular image for sign language recognition

In this paper, a novel approach for hand pose recognition is proposed by analyzing the textures and key geometrical features of the hand. A skeletal hand model is constructed to analyze the abduction/adduction movements of the fingers and subsequently, texture analysis is performed to consider some inflexive finger movements. Probabilistic distributions of the geometric features are considered for modelling intra-class abduction/adduction variations. Gestures differing in inflexive positions of fingers are classified based on Homogeneous Texture Descriptors (HTD), where the texture region is characterized using the mean energy and energy deviation from a set of frequency channels. Similarity measures are computed between input gestures and pre-modelled gesture patterns from a database by considering intra class abduction/adduction angle variations and inter class inflexive variations. Experimental results show the efficacy of our proposed hand pose recognition system.

Estimation of 2d motion trajectories from video object planes and its application in hand gesture recognition

Hand gesture recognition from visual images finds applications in areas like human computer interaction, machine vision, virtual reality and so on. Vision-based hand gesture recognition involves visual analysis of hand shape, position and/or movement. In this paper, we present a model-based method for tracking hand motion in a complex scene, thereby estimating the hand motion trajectory. In our proposed technique, we first segment the frames into video object planes (VOPs) with the hand as the video object. This is followed by hand tracking using Hausdorff tracker. In the next step, the centroids of all VOPs are calculated using moments as well as motion information. Finally, the hand trajectory is estimated by joining the VOP centroids. In our experiment, the proposed trajectory estimation algorithm gives about 99% accuracy in finding the actual trajectory.

Hand motion tracking and trajectory matching for dynamic hand gesture recognition

Hand gesture recognition finds applications in areas like human computer interaction, machine vision, virtual reality and so on. In this article, we present a vision-based method for recognizing dynamic hand gestures via hand motion tracking and trajectory matching. A model-based approach based on Hausdorff distance is used for tracking hand motion thereby estimating the gesture trajectories. Dynamic Time Warping technique is employed for gesture trajectory time alignment and normalization. Recognition is done by extracting trajectory information like trajectory length, location, orientation and hand velocity from the estimated trajectory. Experimental results confirm the appropriateness of our proposed trajectory features and demonstrate that our proposed trajectory estimator and trajectory matching-based gesture classifier are efficient enough for use in Human Computer Interaction system.

Automatic Detection of Polyp Using Hessian Filter and HOG Features

Abstract An endoscope is a medical instrument that acquires images inside the human body. This paper proposes a new approach for the automatic detection of polyp regions in an endoscope image using a Hessian Filter and machine learning approaches. The approach improves performance of automatic detection of polyp detection with higher accuracy. The approach uses HOG feature as a local feature since the polyp and non-polyp region often have similar color information. The approach also uses Real Adaboost and Random Forests as classifiers which works effciently even when the dimension of feature vector becomes large. It is suggested that Hessian filter can contribute to reducing the computational time in comparison with the case when only HOG features are used to detect the polyp region. K-means++ is introduced to integrate the detection results in the classification. It is shown that polyp detection with high accuracy is performed in the computer experiments with endoscope images.

Hand pose recognition from monocular images by geometrical and texture analysis

One challenging research problem of hand pose recognition is the accurate detection of finger abduction and flexion with a single camera. The detection of flexion movements from a 2D image is difficult, because it involves estimation of finger movements along the optical axis of the camera (z direction). In this paper, a novel approach to hand pose recognition is proposed. We use the concept of object-based video abstraction for segmenting the frames into video object planes (VOPs), as used in MPEG-4, with each VOP corresponding to one semantically meaningful hand position. Subsequently, a particular hand pose is recognized by analyzing the key geometrical features and the textures of the hand. The abduction and adduction movements of the fingers are analyzed by considering a skeletal model. Probabilistic distributions of the geometric features are considered for modeling intra-class abduction and adduction variations. Additionally, gestures differing in flexion positions of the fingers are classified by texture analysis using homogeneous texture descriptors (HTD). Finally, hand poses are classified based on proximity measurement by considering the intra-class abduction and adduction and/or inter-class flexion variations. Experimental results show the efficacy of our proposed hand pose recognition system. The system achieved a 99% recognition rate for one-hand poses and a 97% recognition rate for two-hand poses. HighlightsProposed a novel scheme for hand pose recognition for HCI.Proposed an object-based video abstraction method for hand segmentation.Abduction angle variations are modeled by geometrical features.Flexion angle variations are modeled by analyzing textures of the fingers.Achieved 99% and 97% recognition rate for one-hand and two-hand poses respectively.

Defect Classification of Electronic Board Using Multiple Classifiers and Grid Search of SVM Parameters

This paper proposes a new method to improve the classification accuracy by multiple classes classification using multiple SVM. The proposed approach classifies the true and pseudo defects by adding features to decrease the incorrect classification. This approach consists of two steps. First, the features are extracted from the defect candidate region after extracting the difference between the test image and the reference image. Here, candidate extraction is carefully extracted with high accuracy and the useful combination of features is determined using the feature selection. Second, selected features are learned with multiple SVM and classified into the class. When the result has the multiple same voting counts to the same class, the judgment is treated as the difficult class for the classification. It is shown that the proposed approach gives efficient classification with the higher classification accuracy than the previous approaches through the real experiment.