Markerless detection of fingertips of object-manipulating hand

Most reported works on fingertip detection focus on extended fingers where the hand is not occluded by another object. This paper proposes a machine-vision-based technique exploiting the contour of the hand and fingers for detecting the fingertips when the hand is manipulating a ball, which means that the fingers are closed and the hand is partially occluded. The preliminary result of our on-going research is promising where it can be used to generate a more objective performance indicator for monitoring the progress during hand therapy by using a digital webcam. Being markerless and contactless, the proposed technique will require minimal preparation prior to the therapy

Fingertip detection using histogram of gradients and support vector machine

One important application in computer vision is detection of objects. This paper discusses detection of fingertips by using Histogram of Gradients (HOG) as the feature descriptor and Support Vector Machines (SVM) as the classifier. The SVM is trained to produce a classifier that is able to distinguish whether an image contains a fingertip or not. A total of 4200 images were collected by using a commercial-grade webcam, consisting of 2100 fingertip images and 2100 non-fingertip images, were used in the experiment. Our work evaluates the performance of the fingertip detection and the effects of the cell’s size of the HOG and the number of the training data have been studied. It has been found that as expected, the performance of the detection is improved as the number of training data is increased. Additionally, it has also been observed that the 10 x 10 size gives the best results in terms of accuracy in the detection. The highest classification accuracy obtained was less than 90%, which is thought mainly due to the changing orientation of the fingertip and quality of the images

Evaluation of 3D-distance measurement accuracy of stereo-vision systems

Many applications would benefit from a cost effective 3D position or distance measurement systems. Stereo vision systems may offer this functionality with the extra benefits when the images are used for other purposes as well, such as object recognition. In this paper, an accuracy evaluation method of two stereo-vision systems, which use a coordinate measuring machine (CMM) and a reference block, has been presented, and the results have also been presented for systems using infra red cameras and webcams. Following a calibration process, the two systems were used in determining the dimensions of the reference block. The results show that the method could evaluate the two systems. While the evaluation results show that the webcams have a better accuracy and precision, the leap motion controller can be used when shorter measurement distance are required, thanks to their shorter lens distance and wide-angle lenses