Hand Detection using HSV Model

Natural Human Computer Interaction (HCI) is the demand of today’s technology oriented world. Detecting and tracking of face and hands are important for gesture recognition. Skin detection is a very popular and useful technique for detecting and tracking human-body parts. It has been much attention mainly because of its vast range of applications such as, face detection and tracking, naked people detection, hand detection and tracking, people retrieval in databases and Internet, etc. Many models and algorithms are being used for detection of face, hand and its gesture. Hand detection using model or classification is to build a decision rule that will discriminate between skin and non-skin pixels. Identifying skin color pixels involves finding the range of values for which most skin pixels would fall in a given color space. All external factors will be eliminated to detect the hand and its color in the image in complex background. Keywords: image segmentation, hand detection, hci, computer vision, RGB, HS

Evolving weighting schemes for the Bag of Visual Words

The Bag of Visual Words (BoVW) is an established representation in computer vision. Taking inspiration from text mining, this representation has proved to be very effective in many domains. However, in most cases, standard term-weighting schemes are adopted (e.g., term-frequency or TF-IDF). It remains open the question of whether alternative weighting schemes could boost the performance of methods based on BoVW. More importantly, it is unknown whether it is possible to automatically learn and determine effective weighting schemes from scratch. This paper brings some light into both of these unknowns. On the one hand, we report an evaluation of the most common weighting schemes used in text mining, but rarely used in computer vision tasks. Besides, we propose an evolutionary algorithm capable of automatically learning weighting schemes for computer vision problems. We report empirical results of an extensive study in several computer vision problems. Results show the usefulness of the proposed method